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Repository: zjuwss/gnnwr
Branch: main
Commit: 2711bdee9821
Files: 73
Total size: 25.6 MB
Directory structure:
gitextract_bzf1plzq/
├── .github/
│ └── workflows/
│ └── python-package.yml
├── .gitignore
├── LICENSE
├── README.md
├── data/
│ ├── co2_gnnwr.csv
│ ├── co2_gnnwr_pre.csv
│ ├── co2_gtnnwr.csv
│ ├── co2_gtnnwr_predict.csv
│ ├── demo_data_gtnnwr.csv
│ ├── demo_predict_data.csv
│ ├── distances.csv
│ ├── pm25_data.csv
│ ├── pm25_predict_data.csv
│ ├── simulated_data.csv
│ └── simulated_predict_data.csv
├── demo/
│ ├── demo_gnnwr_en_US.ipynb
│ ├── demo_gnnwr_zh_CN.ipynb
│ ├── demo_gtnnwr_en_US.ipynb
│ └── demo_gtnnwr_zh_CN.ipynb
├── demo_result/
│ ├── gnnwr/
│ │ ├── dataset/
│ │ │ ├── test_dataset/
│ │ │ │ ├── dataframe.csv
│ │ │ │ ├── dataset_info.json
│ │ │ │ ├── distances.npy
│ │ │ │ └── scaledDataframe.csv
│ │ │ ├── train_dataset/
│ │ │ │ ├── dataframe.csv
│ │ │ │ ├── dataset_info.json
│ │ │ │ ├── distances.npy
│ │ │ │ └── scaledDataframe.csv
│ │ │ └── val_dataset/
│ │ │ ├── dataframe.csv
│ │ │ ├── dataset_info.json
│ │ │ ├── distances.npy
│ │ │ └── scaledDataframe.csv
│ │ ├── gnnwr_result.csv
│ │ ├── runs/
│ │ │ ├── events.out.tfevents.1768553335.zhjw.19452.0
│ │ │ └── events.out.tfevents.1768553402.zhjw.10108.0
│ │ └── writes/
│ │ ├── events.out.tfevents.1768553370.zhjw.19452.1
│ │ └── events.out.tfevents.1768553438.zhjw.10108.1
│ └── gtnnwr/
│ ├── gtnnwr_result.csv
│ └── runs/
│ ├── events.out.tfevents.1768553480.zhjw.18788.0
│ └── events.out.tfevents.1768553580.zhjw.19340.0
├── doc/
│ ├── Makefile
│ ├── build/
│ │ ├── doctrees/
│ │ │ ├── environment.pickle
│ │ │ └── index.doctree
│ │ └── html/
│ │ ├── .buildinfo
│ │ ├── _sources/
│ │ │ └── index.rst.txt
│ │ ├── _static/
│ │ │ ├── _sphinx_javascript_frameworks_compat.js
│ │ │ ├── basic.css
│ │ │ ├── css/
│ │ │ │ ├── badge_only.css
│ │ │ │ └── theme.css
│ │ │ ├── doctools.js
│ │ │ ├── documentation_options.js
│ │ │ ├── jquery.js
│ │ │ ├── js/
│ │ │ │ ├── badge_only.js
│ │ │ │ └── theme.js
│ │ │ ├── language_data.js
│ │ │ ├── pygments.css
│ │ │ ├── searchtools.js
│ │ │ └── sphinx_highlight.js
│ │ ├── genindex.html
│ │ ├── index.html
│ │ ├── objects.inv
│ │ ├── py-modindex.html
│ │ ├── search.html
│ │ └── searchindex.js
│ ├── make.bat
│ └── source/
│ ├── conf.py
│ └── index.rst
├── requirements.txt
├── src/
│ └── gnnwr/
│ ├── __init__.py
│ ├── datasets.py
│ ├── models.py
│ ├── networks.py
│ └── utils.py
└── tests/
└── test_olr.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/python-package.yml
================================================
# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
name: Python package
on:
push:
branches: [ "main" ]
pull_request:
branches: [ "main" ]
jobs:
build:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.9", "3.10", "3.11"]
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v3
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install flake8 pytest
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- name: Lint with flake8
run: |
# stop the build if there are Python syntax errors or undefined names
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
================================================
FILE: .gitignore
================================================
.idea
*.pkl
*.egg-info
**/__pycache__
*_logs/
*_runs/
*_models/
dist/
*.toml
*.log
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# gnnwr
[](https://pypi.org/project/gnnwr/)
[](https://pepy.tech/project/gnnwr)
A PyTorch implementation of the spatiotemporal intelligent regression (STIR) models and the repository contains:
1. Source code of GNNWR, GTNNWR model, and other derived models
2. Tutorial notebooks of how to use these model
3. Released Python wheels
How to cite:
> Du, Z., Wang, Z., Wu, S., Zhang, F., & Liu, R. (2020). Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. International Journal of Geographical Information Science, 34(7), 1353-1377.
> Wu, S., Wang, Z., Du, Z., Huang, B., Zhang, F., & Liu, R. (2021). Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. International Journal of Geographical Information Science , 35(3), 582-608.
> Yin, Z., Ding, J., Liu, Y., Wang, R., Wang, Y., Chen, Y., Qi, J., Wu, S., and Du, Z. (2024). GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity. Geoscientific Model Development, 17 (22), 8455–8468.
## Table of Contents
- [Introduction](#1-Introduction)
- [Models](#2-Models)
- [Install & Usage](#3-Install-&-Usage)
- [Research Cases](#4-Research-Cases)
- [Reference](#5-Reference)
- [Contributing](#6-Contributing)
- [License](#7-License)
## 1 Introduction
Spatiotemporal non-stationarity is an intrinsic attribute of most geographical processes, and its estimation is a key issue in modeling spatiotemporal non-stationary relationships. To characterize spatiotemporal non-stationary relationships, we converted the expression of geographical proximity and the nonlinear fitting of non-stationary weights into the representation and construction of neural networks, and established a series of **spatiotemporal intelligent regression (STIR)** models, including geographically neural network weighted regression (**GNNWR**), and geographically and temporally neural network weighted regression (**GTNNWR**).
The model performance is significantly better than classical spatiotemporal regression methods such as geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), as well as machine learning methods such as neural networks and random forests. The STIR models have been applied in ecological environment modeling, atmospheric pollutant estimation, urban housing price research and other fields. These researches have been published in journals of *International Journal of Geographical Information Science*, *Environmental Science & Technology*, *Environmental Pollution*, *Science of The Total Environment*, and *International Journal of Applied Earth Observation and Geoinformation*.
## 2 Models
### 2.1 GNNWR
[GNNWR](https://doi.org/10.1080/13658816.2019.1707834) (Geographically neural network weighted regression) is a model to address spatial non-stationarity in various domains with complex geographical processes. A spatially weighted neural network (SWNN) is proposed to represent the nonstationary weight matrix and to achieve local estimation by weighted least squares with these weights. With the superior fitting ability of the neural network, GNNWR has a well-constructed nonstationary weight matrix, which makes it possible to better describe the complex geo-processes in environment and urban research.
<p align="center">
<img title="GNNWR" src="assets/The_estimation_process_of_GNNWR_model.jpeg" alt="GNNWR" width=75%>
</p>
> Du, Z., Wang, Z., Wu, S., Zhang, F., & Liu, R. (2020). Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. *International Journal of Geographical Information Science*, 34(7), 1353-1377.
### 2.2 GTNNWR
[GTNNWR](https://doi.org/10.1080/13658816.2020.1775836) (Geographically and temporally neural network weighted regression), is a model for estimating spatiotemporal non-stationary relationships. Due to the existence of spatiotemporal non-stationary, the spatial relationships of features exhibit significant differences with changes in spatiotemporal structure. The calculation of spatiotemporal distance is an important aspect of solving spatiotemporal non-stationary problems. Therefore, this model introduces spatiotemporal distance into the GNNWR model and proposes a spatiotemporal proximity neural network (STPNN) to accurately calculate spatiotemporal distance. Collaborate with SWNN in the GNNWR model to calculate the spatiotemporal non-stationary weight matrix, thereby achieving accurate modeling of spatiotemporal non-stationary relationships.
<p align="center">
<img title="GTNNWR" src="assets/The_estimation_process_of_GTNNWR_model.jpeg" alt="GTNNWR" width=75%>
</p>
> Wu, S., Wang, Z., Du, Z., Huang, B., Zhang, F., & Liu, R. (2021). Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. *International Journal of Geographical Information Science* , 35(3), 582-608.
## 3 Install & Usage
### 3.1 Install
**⚠ If you want to run gnnwr with your GPU, make sure you have installed *pytorch with CUDA support* beforehead:**
For example, a torch 1.13.1 with cuda 11.7:
``` bash
> pip list | grep torch
torch 1.13.1+cu117
```
You can find install support on [Pytorch's official website](https://pytorch.org/) for installing the right version that suits your environment.
**⚠ If you only want to run gnnwr with your CPU, or you have already installed the correct version of pytorch:**
Using pip to install gnnwr:
```
pip install gnnwr
```
### 3.2 Usage
We provide a series of encapsulated methods and predefined default parameters, users only need to use to load dataset with `pandas` , and call the functions in `gnnwr` package to complete the regression:
```python
from gnnwr import models,datasets
import pandas as pd
data = pd.read_csv('your_data.csv')
train_dataset, val_dataset, test_dataset = datasets.init_dataset(data=data,
test_ratio=0.2, valid_ratio=0.1,
x_column=['x1', 'x2'], y_column=['y'],
spatial_column=['u', 'v'])
gnnwr = models.GNNWR(train_dataset, val_dataset, test_dataset)
gnnwr.run(100)
```
For other uses of customization, the [demos](https://github.com/zjuwss/gnnwr/tree/main/demo) can be referred to.
## 4 Research Cases
### 4.1 Atmospheric Environment
#### 4.1.1 Air pollution of PM2.5
Air pollution, especially the measurement of PM2.5, has recently been a popular research topic in China. Using the data of PM2.5 which is collected by separated stations and considering the complicated terrain variability and wide geographical scope in China, the work of estimating and mapping the PM2.5 distributions across China with high accuracy and reasonable details is challenged by significant spatial nonstationary and complex nonlinearity. GNNWR model can obtain spatially continuous PM2.5 estimates in China with the processed data of AOD, DEM, as well as some climate factors. The estimation of PM2.5 is closer to the ground observation than the results of the traditional regression model, and it is more accurate in the high-value zone and more detailed.
<p align="center">
<img title="PM 2.5" src="assets/figure_PM25.png" alt="PM25" width=75%>
</p>
> Chen, Y., Wu, S., Wang, Y., Zhang, F., Liu, R., & Du, Z. (2021). Satellite-based mapping of high-resolution ground-level pm2. 5 with VIIRS IP AOD in China through spatially neural network weighted regression. *Remote Sensing*, *13*(10), 1979.
#### 4.1.2 Air pollution of NO2
This study utilized TROPOMI remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei region using the GTNNWR model. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), and Convolutional Neural Network (CNN) models in performance metrics, showcasing greater reliability when considering spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin– Hebei region.
<p align="center">
<img title="NO2" src="assets/figure_NO2.png" alt="NO2" width=75%>
</p>
> Liu, C., Wu, S., Dai, Z., Wang, Y., Du, Z., Liu, X., & Qiu, C. (2023). High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data. *Remote Sensing*, *15*(15), 3878.
### 4.2 Coastal and Oceanic Environment
#### 4.2.1 Water quality
The accurate assessment of large-scale and complex coastal waters is a grand challenge due to the spatial non-stationarity and complex nonlinearity involved in integrating remote sensing and in situ data. We developed a water quality assessment method based on a newly proposed GNNWR model to address that challenge and obtained a highly accurate and realistic water quality distribution on the basis of the comprehensive index of Chinese Water Quality Classification Standards. The GNNWR model achieved higher prediction performance (average R2 = 84%) in comparison to the widely used models, and the obtained water quality classification (WQC) maps in May of 2015–2017 and August 2015 can depict intuitively reasonable spatiotemporal patterns of water quality in the ZCS.
<p align="center">
<img title="WaterQuality" src="assets/figure_WaterQuality.jpg" alt="WaterQuality" width=75%>
</p>
> Du, Z., Qi, J., Wu, S., Zhang, F., & Liu, R. (2021). A spatially weighted neural network based water quality assessment method for large-scale coastal areas. *Environmental science & technology*, *55*(4), 2553-2563.
#### 4.2.2 Coastal Environment
Transferring dissolved silicate (DSi) from land to coastal environments is vital for global biogeochemical cycling. Yet pinpointing coastal DSi distribution is complex due to spatiotemporal variability, nonlinear modeling, and low sampling resolution. GTNNWR model, by converting the spatiotemporal relationships between sparse sampling points and unknown points into space-time distances and weights, and using neural networks to determine the nonlinear distances and nonstationary weights, outperforms traditional regression models in both fitting accuracy and generalization ability. This effective data-driven-based method can help explore the fine-scale dynamic changes in coastal seas like surface DSi.
<p align="center">
<img title="DSI" src="assets/figure_DSI.jpg" alt="DSI" width=75%>
</p>
> Qi, J., Du, Z., Wu, S., Chen, Y., & Wang, Y. (2023). A spatiotemporally weighted intelligent method for exploring fine-scale distributions of surface dissolved silicate in coastal seas. _Science of The Total Environment_ , 886, 163981.
#### 4.2.3 Surface Seawater pCO2
The North Pacific Ocean is a key carbon sink, but the spatiotemporal dynamics of carbon dioxide (pCO2) remain poorly understood due to its vast scale and complex influences. Existing machine learning models lack interpretability, limiting insights into underlying mechanisms. To address this, we introduce the gridded spatiotemporal neural network weighted regression (GSTNNWR) model, which accurately predicts surface pCO2 while quantifying environmental influences.
> Liu, Y., Chen, Y., Huang, Z., Liang, H., Qi, J., Wu, S., & Du, Z. (2024). Spatiotemporal weighted neural network reveals surface seawater pCO2 distributions and underlying environmental mechanisms in the North Pacific Ocean. *International Journal of Applied Earth Observation and Geoinformation*, 133, 104120.
### 4.3 Social Science
#### 4.3.1 House price
Housing prices are closely related to the lives of new urban residents, and they also comprise a vital economic index to which the government needs to pay close attention. Compare with traditional regression models, GNNWR and GTNNWR model can improve the accuracy of real estate appraisal with the help of neural networks, which are practical and powerful ways to assess house prices.
An optimized spatial proximity measure was integrated into GNNWR. The optimized spatial proximity fusions multiple distance measures, imporving its ability in modeling spatial-nonstationary process.
<p align="center">
<img title="OSP" src="assets/figure OSP.JPG" alt="OSP" width=75%>
</p>
> Ding, J., Cen, W., Wu, S., Chen, Y., Qi, J., Huang, B., & Du, Z. (2024). A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan. *International Journal of Geographical Information Science*, 1–21.
By using an attention-based architecture to incorporate context similarity into spatial non-stationarity estimation, house prices can be better fitted in complex urban area.
<p align="center">
<img title="OSP" src="assets/figure_CatGWR.jpg" alt="CatGWR" width=75%>
</p>
> Wu, S., Ding, J., Wang, R., Wang, Y., Yin, Z., Huang, B., & Du, Z. (2025). Using an attention-based architecture to incorporate context similarity into spatial non-stationarity estimation. *International Journal of Geographical Information Science*, 1–24
<p align="center">
<img title="HousePrice" src="assets/figure_HousePrice.png" alt="HousePrice" width=75%>
</p>
> Wang, Z., Wang, Y., Wu, S., & Du, Z. (2022). House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China. *ISPRS International Journal of Geo-Information*, *11*(8), 450.
#### 4.3.2 Land Surface Temperature & Nighttime Light
Spatial downscaling is an important approach to obtain high-resolution land surface temperature (LST) for thermal environment research. A high-resolution surface temperature downscaling method based on GNNWR was developed to effectively handle the problem of surface temperature downscaling. The results show that the proposed GNNWR model achieved superior downscaling accuracy compared to widely used methods in four test areas with large differences in topography, landforms, and seasons. The findings suggest that GNNWR is a practical method for surface temperature downscaling considering its high accuracy and model performance.
<p align="center">
<img title="LST" src="assets/figure_LST.png" alt="LST" width=75%>
</p>
> Liang, M., Zhang, L., Wu, S., Zhu, Y., Dai, Z., Wang, Y., ... & Du, Z. (2023). A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression. *Remote Sensing*, *15*(7), 1740.
Downscaling nighttime light (NTL) from satellite imagery is crucial for urban expansion and socio-economic studies but faces challenges due to geographical complexity and factor uncertainties. To address this, we propose the multifactor geographically neural network weighted regression (MF-GNNWR) framework, which integrates surface, socio-economic, and human activity factors to enhance NTL accuracy in heterogeneous urban areas.
> Zhang, L., Wu, S., Liang, M., Jing, H., Shi, S., Zhu, Y., ... & Du, Z. (2024). A Downscaling Framework for Urban Nighttime Light Based on Multi-Factor Geographically Neural Network Weighted Regression. IEEE Transactions on Geoscience and Remote Sensing.
#### 4.3.3 Public health
Accurate prediction of epidemic trends is central to COVID-19 research and mitigation. To address this, GTNNWR model is employed to predict deaths and assess preventive scenarios. Leveraging its ability to capture complex spatiotemporal variations, the GTNNWR model demonstrates superior predictive capacity, outperforming the conventional SEIR model by approximately 9%. Simulation results reveal that while masks are the most effective single measure, combined interventions are significantly more potent, reducing deaths by 27.2% overall and up to 45% during winter. These findings provide critical quantitative insights for optimizing epidemic control strategies in the United States.
<p align="center">
<img title="LST" src="assets/figure_covid_19.png" alt="covid_19" width=75%>
</p>
> Sun, J., Qi, J., Yan, Z., Li, Y., Liang, J., & Wu, S. (2024). Quantitative Study on American COVID-19 Epidemic Predictions and Scenario Simulations. ISPRS International Journal of Geo-Information, 13(1), 31.
### 4.4 Geology
#### 4.4.1 mineral prospectivity
In the field of mineral forecasting, accurate prediction of mineral resources is essential to meet the energy needs of modern society. A geographically neural network-weighted logistic regression is used for mineral prospect mapping. The model combines spatial patterns and neural networks with Shapley's additive theory of interpretation, which effectively handles the anisotropy of variables and nonlinear relationships between variables to achieve accurate predictions and provide explanations of mineralization in a complex spatial environment.
<p align="center">
<img title="mineral prospectivity" src="assets/figure_mine.jpg" alt="mineral prospectivity" width=75%>
</p>
> Wang, L., Yang, J., Wu, S., Hu, L., Ge, Y., & Du, Z. (2024). Enhancing mineral prospectivity mapping with geospatial artificial intelligence: A geographically neural network-weighted logistic regression approach. *International Journal of Applied Earth Observation and Geoinformation*, 128, 103746.
### 4.4.2 heat flow
Surface heat flow (SHF) is crucial for understanding Earth's internal geodynamics. The Tibetan Plateau, a key region for global climate and geodynamic studies, lacks comprehensive SHF data due to sparse measurements. To address this, we develop the geographically neural network weighted regression with enhanced interpretability (EI-GNNWR), which integrates spatial heterogeneity and nonlinear geophysical interactions. Our model accurately predicts SHF across the plateau, revealing that high SHF values are concentrated in the south, northeast, and southeast, influenced by Moho depth, ridges, and topography. These findings enhance understanding of geothermal processes and tectonic activity in the region.
<p align="center">
<img title="DSI" src="assets/figure_SHF.jpg" alt="SHF" width=75%>
</p>
> Zhang, Z., Wu, S., Zhang, B., Du, Z., & Xia, Q. (2024). The distribution of surface heat flow on the Tibetan Plateau revealed by data‐driven methods. *Journal of Geophysical Research: Solid Earth*, 129(10), e2023JB028491.
### 4.5 Planetary Science
#### 4.5.1 Brightness temperature
Brightness temperature (TB) derived from Chang’e microwave radiometers is crucial for understanding lunar subsurface thermal evolution. However, generating high-resolution maps is challenging due to sparse sampling and the neglect of surface factors in conventional methods. To address this, we utilized GNNWR framework effectively captures spatial nonstationarity and heterogeneity. It accurately generates TB maps at $0.0625^\circ$ resolution, achieving superior performance with the highest $R^2$ and lowest error metrics. These findings reveal fine-scale thermal features obscured by traditional interpolation, enhancing microwave emission analysis and geological understanding.
<p align="center">
<img title="mineral prospectivity" src="assets/figure_planetary.png" alt="planetary" width=75%>
</p>
> Zhu, M., Cai, Z., Wu, S., Zhang, Y., & Li, J. (2025). High-resolution lunar brightness temperature model based on Chang’e-2 MRM data and spatially weighted neural network. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-13.
**!!Further, these spatiotemporal intelligent regression models can be applied to other spatiotemporal modeling problems and socioeconomic phenomena.**
## 5 Related Research Papers
### 5.1 Algorithm
1. Du, Z., Wang, Z., Wu, S., Zhang, F., & Liu, R. (2020). Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. *International Journal of Geographical Information Science*, 34(7), 1353-1377.
2. Wu, S., Wang, Z., Du, Z., Huang, B., Zhang, F., & Liu, R. (2021). Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. *International Journal of Geographical Information Science* , 35(3), 582-608.
3. Dai, Z., Wu, S., Wang, Y., Zhou, H., Zhang, F., Huang, B., & Du, Z. (2022). Geographically convolutional neural network weighted regression: A method for modeling spatially non-stationary relationships based on a global spatial proximity grid. *International Journal of Geographical Information Science*, *36*(11), 2248-2269.
### 5.2 Case study demo
1. Chen, Y., Wu, S., Wang, Y., Zhang, F., Liu, R., & Du, Z. (2021). Satellite-based mapping of high-resolution ground-level pm2. 5 with VIIRS IP AOD in China through spatially neural network weighted regression. *Remote Sensing*, *13*(10), 1979.
2. Qi, J., Du, Z., Wu, S., Chen, Y., & Wang, Y. (2023). A spatiotemporally weighted intelligent method for exploring fine-scale distributions of surface dissolved silicate in coastal seas. *Science of The Total Environment* , 886, 163981.
3. Du, Z., Qi, J., Wu, S., Zhang, F., & Liu, R. (2021). A spatially weighted neural network based water quality assessment method for large-scale coastal areas. *Environmental science & technology*, *55*(4), 2553-2563.
4. Liang, M., Zhang, L., Wu, S., Zhu, Y., Dai, Z., Wang, Y., ... & Du, Z. (2023). A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression. *Remote Sensing*, *15*(7), 1740.
5. Liu, C., Wu, S., Dai, Z., Wang, Y., Du, Z., Liu, X., & Qiu, C. (2023). High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data. *Remote Sensing*, *15*(15), 3878.
6. Wang, Z., Wang, Y., Wu, S., & Du, Z. (2022). House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China. *ISPRS International Journal of Geo-Information*, *11*(8), 450.
7. Wu, S., Du, Z., Wang, Y., Lin, T., Zhang, F., & Liu, R. (2020). Modeling spatially anisotropic nonstationary processes in coastal environments based on a directional geographically neural network weighted regression. *Science of the Total Environment*, *709*, 136097.
8. Wang, L., Yang, J., Wu, S., Hu, L., Ge, Y., & Du, Z. (2024). Enhancing mineral prospectivity mapping with geospatial artificial intelligence: A geographically neural network-weighted logistic regression approach. *International Journal of Applied Earth Observation and Geoinformation*, 128, 103746.
9. Ding, J., Cen, W., Wu, S., Chen, Y., Qi, J., Huang, B., & Du, Z. (2024). A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan. *International Journal of Geographical Information Science*, 1–21.
10. Zhu, M., Cai, Z., Wu, S., Zhang, Y., & Li, J. (2025). High-resolution lunar brightness temperature model based on Chang’e-2 MRM data and spatially weighted neural network. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-13.
11. Sun, J., Qi, J., Yan, Z., Li, Y., Liang, J., & Wu, S. (2024). Quantitative Study on American COVID-19 Epidemic Predictions and Scenario Simulations. ISPRS International Journal of Geo-Information, 13(1), 31.
## 6 Group
### Leaders
<table>
<tr>
<td width="25%">
<img src="assets/professor_du.jpg" width="150" alt="Zhenhong Du">
</td>
<td width="75%">
#### Zhenhong Du
- Ph.D
- Professor/ Ph.D Supervisor
- The National Science Fund for Distinguished Young Scholars
- Dean of the School of Earth sciences
- Zhejiang University
</td>
</tr>
<tr>
<td>
<img src="assets/professor_wu.png" width="150" alt="Sensen Wu">
</td>
<td>
#### Sensen Wu
- Ph.D
- Professor/ Ph.D Supervisor
- Zhejiang University
</td>
</tr>
</table>
### Members
- Jin Qi, Postdoctoral fellow, Zhejiang University
- Yijun Chen, Postdoctoral fellow, Zhejiang University
- Jiale Ding, PhD student, Zhejiang University
- Yi Liu, Undergraduate student, Zhejiang University
- Ziyu Yin, Undergraduate student, Zhejiang University
- Yancheng Wu, PhD student, Zhejiang University
- Jingwen Zhong, Graduate student, Zhejiang University
- Yige Wang, Graduate student, Zhejiang University
- Bei Feng, Undergraduate student, Zhejiang University
## 7 License
[GPLv3 license](https://github.com/zjuwss/gnnwr/blob/main/LICENSE)
================================================
FILE: data/co2_gnnwr.csv
================================================
fCO2,Chl,Temp,Salt,lon,lat
386.6210769,0.652422786,10.99645138,33.25679231,235.625,44.625
390.2838551,1.445977211,10.75580597,33.37335944,235.875,44.625
381.7552222,0.85841167,10.98741913,33.24209154,235.625,44.375
386.9113333,0.528669178,11.32999992,32.98632205,235.125,44.125
382.5342,0.648165762,11.15483856,33.05464637,235.375,44.125
370.28275,1.254270673,10.86838722,33.1229707,235.625,44.125
383.957,0.584808469,11.29354763,32.93566072,235.125,43.875
376.3577368,1.465514779,10.69967747,33.00384986,235.625,43.875
378.44708,0.560379326,11.44096756,32.85102713,234.875,43.625
381.147,0.702825904,11.17709637,32.88499939,235.125,43.625
348.8041818,0.991253138,10.84290314,32.88486421,235.375,43.625
357.765,2.935527325,10.58580589,32.88472903,235.625,43.625
375.3675833,0.619520426,11.22290325,32.86219531,234.875,43.375
380.2048462,1.27894485,10.65903187,32.87690896,235.375,43.375
409.1735556,0.923546791,10.89387035,32.91904253,234.875,43.125
399.52175,1.832567334,10.50322533,32.94588953,235.375,43.125
486.7694286,0.692904532,10.65999985,32.97588974,234.875,42.875
427.0227273,0.856589258,10.52290344,33.00970346,235.125,42.875
482.8573846,0.774920166,10.57967758,33.03273696,234.875,42.625
433.9548571,0.813277543,10.60032272,33.04260588,234.875,42.375
433.3903939,0.800426304,10.63806438,33.0054965,234.875,42.125
413.1546774,0.777915537,10.67838669,32.96838713,234.875,41.875
418.4410652,0.800702929,10.74354839,32.93127775,234.875,41.625
431.512,0.762529731,10.84354782,32.91182047,234.875,41.375
422.6084318,0.772762179,10.93870926,32.91001529,234.875,41.125
454.153,0.845942974,11.00290298,32.98121208,235.125,41.125
443.8956667,0.83403188,10.99903202,32.9082101,234.875,40.875
425.52832,0.95161891,11.00354862,32.97584349,235.125,40.875
443.5496129,0.762761235,11.03354836,32.9704749,235.125,40.625
408.4764694,0.794355094,11.10322571,32.94990969,235.125,40.375
428.219,0.843857408,11.26064491,32.91414785,235.125,40.125
422.3205429,0.832743943,11.610322,32.87838602,235.125,39.875
382.5345,0.698340595,11.46096706,32.94010496,235.375,39.875
432.0387619,0.703879774,11.96258068,32.90194178,235.375,39.625
421.9070417,0.641447723,12.50838661,32.88194317,235.375,39.375
451.8331111,0.623298287,12.13806438,32.94189936,235.625,39.375
450.5529459,0.598986745,12.37322521,32.94374388,235.625,39.125
443.9650667,0.700700045,12.3264513,32.94558841,235.625,38.875
446.5770909,0.826242626,11.87483883,33.01290172,235.875,38.875
459.5530566,0.66432482,11.78290272,33.01842481,235.875,38.625
597.085,0.629125059,11.73774147,33.03323042,235.875,38.375
442.3752,0.686998367,11.54903221,33.06640166,236.125,38.375
327.2280826,1.116143703,12.47064495,33.86183542,141.125,38.125
339.7666259,0.638028741,12.41580582,33.89577371,141.375,38.125
344.7441939,0.470972329,12.33612919,33.929712,141.625,38.125
427.0186857,0.700475812,11.73451614,33.09004742,236.125,38.125
419.28,0.694691896,11.52096748,33.09324044,236.375,38.125
333.4655416,1.00600183,12.58870888,33.90694863,141.125,37.875
395.7047561,0.676154852,12.15451622,33.20107251,236.625,37.375
396.9765,0.889159977,11.91258049,33.2223472,236.875,37.375
337.3444133,0.568876088,13.22032261,34.07441145,141.125,37.125
394.5484468,0.812258363,12.0158062,33.25716668,236.875,37.125
409.8105,1.370796323,12.01677418,33.29198617,236.875,36.875
404.1582222,2.620482445,11.80806446,33.30929452,237.125,36.875
401.9309286,3.283119678,11.84774208,33.34726137,237.125,36.625
389.9855094,2.997110605,11.91967678,33.37424803,237.375,36.375
380.0154286,3.103065252,12.10193539,33.38224554,237.375,36.125
413.2549864,1.638160229,12.21935463,33.39459276,237.625,36.125
419.7809714,1.419725776,12.34806442,33.40693998,237.875,36.125
394.7661579,1.174431086,12.42387104,33.4081521,237.625,35.875
395.73136,1.518957615,12.61548328,33.42606115,237.875,35.875
398.29,0.789995015,12.8106451,33.44518232,237.875,35.625
407.0763047,0.753415346,12.99354839,33.47390145,238.125,35.625
354.1616333,0.367518932,19.60258102,34.55290238,139.625,35.125
375.5122692,0.526501238,18.90258026,34.54167524,139.875,35.125
363.46825,0.298557192,20.33419228,34.61707058,139.125,34.875
358.9342619,0.233420581,20.52709579,34.60301417,139.625,34.875
363.8619577,0.334835827,20.33870888,34.59044981,138.625,34.625
363.631,0.269536883,20.97935486,34.61141825,138.875,34.625
374.1395,0.23987788,21.17354774,34.62647542,139.125,34.625
361.0394074,0.215292707,21.44903183,34.6432679,139.625,34.625
353.6753333,0.226841986,21.45645142,34.6508645,139.875,34.625
356.418,0.256305099,21.68774223,34.58561605,138.375,34.375
362.5385,0.268561363,21.61645126,34.605084,138.625,34.375
355.419,0.220162794,22.09709549,34.6707437,139.625,34.375
353.4092034,0.239866778,21.85612869,34.61522329,135.125,33.125
343.935,0.209579766,22.17741966,34.70679539,137.375,33.125
344.204,0.139449701,22.5338707,34.71833974,137.625,33.125
358.0715625,0.151727259,22.88967705,34.77903998,140.125,33.125
354.2584561,0.21209012,22.02387047,34.63010943,135.125,32.875
349.6036667,0.239118323,21.85128975,34.73804837,137.375,32.875
345.6535,0.199910104,22.25387001,34.75057,137.625,32.875
356.1305106,0.148554534,22.90935516,34.79552305,140.125,32.875
354.9177115,0.190119058,22.34225845,34.64499557,135.125,32.625
356.9967956,0.188525692,22.27451515,34.66478312,135.375,32.625
362.3306667,0.217116162,22.15580559,34.68457067,135.625,32.625
349.2573333,0.160922438,22.04903221,34.78280026,137.625,32.625
356.945,0.160553813,22.90580559,34.81200612,140.125,32.625
358.6992667,0.160065264,22.79516029,34.81339967,140.375,32.625
362.3210526,0.174743235,22.58032227,34.71663439,135.625,32.375
348.37175,0.193202153,22.08322525,34.80547982,137.625,32.375
356.2786667,0.161141291,22.90580559,34.8270309,140.125,32.375
356.5392609,0.169143364,22.79129028,34.82862782,140.375,32.375
363.1657143,0.1770643,23.02290344,34.75173748,135.625,32.125
363.4805385,0.177135378,22.86161232,34.76785052,135.875,32.125
350.6966667,0.20159705,22.45096779,34.8186087,137.625,32.125
355.3523333,0.14949432,22.93128967,34.84059739,140.125,32.125
357.3502766,0.166677341,22.81774139,34.84203124,140.375,32.125
359.0745,0.144226834,23.1687088,34.79814756,135.875,31.875
349.8243333,0.175352529,22.95516014,34.83173758,137.625,31.875
352.093,0.185282171,22.97806358,34.83768147,137.875,31.875
355.803,0.166609749,22.95516014,34.85416389,140.125,31.875
357.2792609,0.164751381,22.85741806,34.85543466,140.375,31.875
358.959,0.161427766,22.73064423,34.85670543,140.625,31.875
349.6608,0.158210337,23.30935478,34.84759289,137.875,31.625
350.0115,0.170155644,23.25064468,34.84914464,138.125,31.625
357.642,0.159329191,22.93774223,34.86773038,140.125,31.625
359.3964792,0.150728822,22.7838707,34.86994576,140.625,31.625
358.6545455,0.140397489,23.26322556,34.85100675,136.125,31.375
349.7948,0.163885668,23.34290314,34.86022097,138.125,31.375
356.356,0.151295707,22.8599987,34.87887579,140.125,31.375
359.150449,0.156383485,22.81225777,34.88080817,140.625,31.375
358.9510714,0.145129293,23.26612854,34.86098742,136.125,31.125
360.1028571,0.151361614,23.31258011,34.86522222,136.375,31.125
349.7944,0.162657544,23.37483788,34.87482244,137.625,31.125
351.650125,0.174042374,23.33290291,34.87442774,137.875,31.125
349.8773125,0.159847841,23.26741982,34.87389678,138.125,31.125
354.836,0.161146253,22.79129028,34.88760012,140.125,31.125
358.294,0.153002307,22.82548332,34.88929266,140.625,31.125
358.1408039,0.14689602,23.29354858,34.877527,136.375,30.875
348.327,0.151153341,23.29096794,34.8904168,137.625,30.875
349.3412,0.153882354,23.20645142,34.88757259,138.125,30.875
348.3734,0.153757185,23.09451485,34.88609308,138.375,30.875
354.663,0.172214702,22.75612831,34.89632446,140.125,30.875
357.5614,0.171143934,22.82645035,34.89777714,140.625,30.875
357.3098571,0.179880515,22.8245163,34.89850348,140.875,30.875
360.0473333,0.161677867,23.31838608,34.88983178,136.375,30.625
362.0612609,0.166686282,23.32386971,34.89871478,136.625,30.625
351.0672,0.15953815,23.29516029,34.90601116,137.625,30.625
354.6536667,0.170713082,22.79677391,34.90504879,140.125,30.625
357.4188913,0.166496009,22.81290245,34.90686804,140.875,30.625
362.3230392,0.164716259,23.31903267,34.91402102,136.625,30.375
352.6014,0.151165366,23.30838585,34.92440993,137.375,30.375
355.1753333,0.178886801,22.8848381,34.9143585,140.125,30.375
357.4406957,0.15408501,22.8064518,34.90970862,140.875,30.375
363.1984839,0.141294256,23.32903099,34.9300046,136.625,30.125
358.3723333,0.148876563,23.31193542,34.93782949,136.875,30.125
353.20575,0.147481158,23.29516029,34.93978232,137.375,30.125
356.0073333,0.173176974,22.99258041,34.92425358,140.125,30.125
358.7443333,0.15671742,22.80709648,34.90702522,140.875,30.125
360.4317647,0.144611701,23.35257912,34.95233297,136.875,29.875
351.841,0.151760042,23.31290245,34.95515472,137.375,29.875
357.2266667,0.153832644,23.10806465,34.93414867,140.125,29.875
357.999,0.151245132,23.04322433,34.92421305,140.375,29.875
363.6272571,0.122833639,23.39193535,34.96683645,136.875,29.625
364.3672667,0.138334736,23.34709549,34.96968824,137.125,29.625
353.3693333,0.144111902,23.28870964,34.97052711,137.375,29.625
359.567,0.130869687,23.09032249,34.92991531,140.375,29.625
356.5171724,0.161206141,22.6461277,34.89130133,141.125,29.625
361.7359412,0.12010105,23.34838676,34.97959507,137.125,29.375
351.996,0.130754828,23.22838593,34.97996008,137.375,29.375
354.114,0.130052418,23.1890316,34.9803251,137.625,29.375
358.2473333,0.126403317,23.04612923,34.9277969,140.375,29.375
356.8378298,0.174361944,22.61903191,34.88429451,141.125,29.375
363.1960541,0.099801511,23.3803215,34.98510897,137.125,29.125
363.7778667,0.107250452,23.21967697,34.98345363,137.375,29.125
354.0664,0.125113666,23.12193489,34.98179829,137.625,29.125
358.5466667,0.135929763,23.06386948,34.91785783,140.375,29.125
357.7559167,0.166191235,22.71032143,34.87550926,141.125,29.125
354.7508571,0.133956015,23.05387115,34.83651137,142.875,29.125
364.8944038,0.092764996,23.28741837,34.98694718,137.375,28.875
353.4211429,0.114299841,23.12741852,34.98327148,137.625,28.875
353.144,0.136054337,23.02161217,34.97959578,137.875,28.875
352.0404,0.140970454,22.95967674,34.97539723,138.125,28.875
353.6186667,0.146909952,22.93290329,34.97067583,138.375,28.875
353.7893333,0.137475312,23.15548325,34.90791875,140.375,28.875
358.0438667,0.154482976,22.96935463,34.86672401,141.125,28.875
356.3557105,0.152508795,22.89129066,34.86378098,141.375,28.875
352.807,0.140800908,22.96580505,34.86083794,141.625,28.875
354.05525,0.136538014,23.10645103,34.8578949,141.875,28.875
360.7526667,0.130059719,23.28322601,34.85265708,142.125,28.875
361.0423333,0.114130378,23.43870926,34.84512448,142.375,28.875
359.3336667,0.123743214,23.4909668,34.83759189,142.625,28.875
356.8853333,0.105885409,23.51677322,34.83005929,142.875,28.875
358.8780714,0.098022021,23.4954834,34.82580483,143.125,28.875
365.7810244,0.094572134,23.42258072,34.99044073,137.375,28.625
367.4429,0.104183845,23.20451546,34.98474467,137.625,28.625
353.813,0.153341308,22.88290215,34.96901906,138.375,28.625
352.4802,0.145921156,22.85515976,34.96423137,138.625,28.625
353.295,0.158232957,22.90032196,34.95944369,138.875,28.625
353.5016667,0.139842182,22.94645119,34.95262015,139.125,28.625
352.573,0.14199914,23.0203228,34.94376075,139.375,28.625
353.2435,0.137581825,23.11354828,34.93490136,139.625,28.625
352.3533333,0.128605202,23.21806335,34.92604196,139.875,28.625
357.38108,0.136223957,23.30580521,34.91373473,140.125,28.625
355.9608305,0.124522582,23.29612923,34.89797968,140.375,28.625
363.5166667,0.128262639,23.30580521,34.88222462,140.625,28.625
362.9343333,0.14908962,23.31903267,34.86646956,140.875,28.625
355.7739783,0.160348728,23.25290298,34.85663223,141.375,28.625
361.3593333,0.102441967,23.79096794,34.81907213,143.125,28.625
365.49056,0.109990515,23.34161186,34.97864336,137.625,28.375
358.177375,0.114776261,23.40258026,34.89981282,140.125,28.375
362.0454468,0.136182621,23.53483772,34.84988481,141.375,28.375
361.99875,0.100694351,23.94096756,34.81946272,143.125,28.375
358.8056667,0.109133139,23.4864521,34.88204229,140.125,28.125
362.993625,0.109109402,23.68193436,34.8435387,141.375,28.125
359.9711333,0.093451381,24.00806427,34.82906151,142.875,28.125
361.045,0.098532818,24.01225853,34.8269766,143.125,28.125
366.9013333,0.131202415,23.47870827,34.9447971,137.875,27.875
358.2288333,0.11113026,23.50128937,34.86427176,140.125,27.875
364.378,0.118882261,23.7116127,34.8371926,141.375,27.875
363.7535,0.135508433,23.81161308,34.84003204,141.625,27.875
359.42475,0.098162621,24.00838661,34.8348484,142.875,27.875
361.29344,0.133014023,23.61870956,34.93120593,137.875,27.625
357.1523333,0.10909538,23.40322495,34.85662115,139.875,27.625
362.9412245,0.110437714,23.85967636,34.83501703,141.625,27.625
358.95575,0.114738964,23.99290276,34.8406353,142.875,27.625
361.5905192,0.112575807,23.54161263,34.91353667,138.125,27.375
354.4176667,0.105576814,23.37838745,34.84877634,139.875,27.375
363.5055,0.089670703,24.0158062,34.83962208,141.625,27.375
355.6806667,0.109046035,24.00677299,34.85363084,142.875,27.375
364.9158654,0.106434196,23.66032219,34.90388286,138.125,27.125
354.6406667,0.122366451,23.41032219,34.85162115,139.875,27.125
362.7755778,0.082401142,24.10838699,34.85384721,141.625,27.125
363.3036,0.088900261,24.20290184,34.8605476,141.875,27.125
355.3278,0.089351833,24.02322578,34.87383503,142.875,27.125
356.30625,0.085745431,24.02387047,34.87669915,143.125,27.125
366.6954286,0.112622641,23.81258011,34.89422905,138.125,26.875
366.8579574,0.113440886,23.71322441,34.89184701,138.375,26.875
352.92575,0.114579186,23.48322487,34.85446596,139.875,26.875
353.799,0.11925336,23.5225811,34.84975624,140.125,26.875
364.9384,0.091492176,24.26838684,34.8760156,141.875,26.875
354.6916,0.082634062,24.03741837,34.89403921,142.875,26.875
369.2622424,0.113746062,23.79774094,34.88353074,138.375,26.625
354.102,0.113208659,23.60451508,34.85731077,139.875,26.625
366.9883061,0.097445659,24.34741783,34.8914836,141.875,26.625
356.797,0.078615628,24.12354851,34.9142434,142.875,26.625
368.6676,0.117891796,23.93580627,34.88348246,138.375,26.375
368.7973778,0.117164031,23.86580658,34.88393545,138.625,26.375
358.5416667,0.096713454,23.83354759,34.86607522,139.875,26.375
365.6622,0.088710405,24.38161278,34.90833259,141.875,26.375
361.3676667,0.074713074,24.31935501,34.9350605,142.875,26.375
369.4036909,0.114971533,23.99903107,34.89381266,138.625,26.125
361.1037,0.092461742,24.14548302,34.8807593,139.875,26.125
363.6386667,0.091043435,24.44290352,34.886522,140.625,26.125
368.475,0.083919056,24.48354721,34.88975388,140.875,26.125
366.996,0.077831492,24.47935486,34.89639735,141.125,26.125
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383.2114035,0.034682311,29.06096649,34.2476657,147.875,11.125
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387.0966383,0.040531192,29.61580658,34.06325108,145.875,9.875
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382.5795476,0.056252044,29.41741943,34.03384805,148.875,9.125
399.3832143,0.043553308,29.45741844,34.0344975,149.125,9.125
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382.3059556,0.05945855,29.60161209,34.00810623,149.625,8.125
385.2878235,0.06292998,29.61967659,34.07452697,146.625,7.875
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387.0803265,0.091700606,29.80806351,34.08372861,147.125,7.125
385.9583095,0.075646058,29.7409668,34.04508108,150.125,6.875
388.4023333,0.079074465,29.88516045,34.07732075,147.375,6.625
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386.2930682,0.098188832,30.05451584,34.10077226,150.375,6.125
395.9419434,0.086296201,30.00161171,34.10299391,147.875,5.875
388.3849608,0.088746689,30.11322594,34.12049258,150.375,5.875
393.8272759,0.093124613,30.08419228,34.11622936,147.875,5.625
394.0491538,0.093938835,30.06419373,34.11313486,148.125,5.625
392.651451,0.082887113,30.1258049,34.14021289,150.375,5.625
400.1071481,0.080777682,30.14935493,34.13838029,148.125,5.375
397.2012069,0.071996391,30.11903191,34.183608,150.375,5.375
401.5386522,0.082916282,30.11128998,34.19693428,150.625,5.375
402.4136471,0.078616932,30.21999931,34.17608714,148.125,5.125
403.4616154,0.080100611,30.22096634,34.17319417,148.375,5.125
402.5627692,0.071498722,30.10257912,34.26614732,150.625,5.125
405.6250755,0.075204916,30.28257942,34.21452022,148.375,4.875
409.482,0.075111859,30.23064423,34.21524644,148.625,4.875
398.9173208,0.076344222,30.06064415,34.33536035,150.625,4.875
407.3418148,0.06950485,30.27580643,34.26019168,148.625,4.625
399.5488113,0.080523163,30.00806427,34.40457338,150.625,4.625
402.9952432,0.060762744,30.31580544,34.32873815,148.625,4.375
406.184875,0.060001861,30.25548363,34.33629638,148.875,4.375
401.99,0.077083685,29.98161125,34.4960202,150.625,4.375
401.3553137,0.074374124,29.93387032,34.51762545,150.875,4.375
402.7614038,0.054281145,30.2635479,34.43125051,148.875,4.125
403.1781731,0.08484216,29.91322517,34.63286197,150.875,4.125
399.184087,0.057935782,30.24451637,34.52620465,148.875,3.875
399.5663448,0.055205017,30.16128922,34.54888999,149.125,3.875
405.2121667,0.096449777,29.87193489,34.74809849,150.875,3.875
402.566,0.059147052,30.14161301,34.64692652,149.125,3.625
421.3992766,0.108549833,29.82645035,34.97101951,151.125,3.375
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428.9855882,0.120206155,29.81354713,35.0266602,151.125,3.125
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436.3058571,0.150321111,29.81903076,35.10116506,151.375,2.875
407.2922727,0.118348055,29.98290253,35.00616968,149.625,2.625
412.8896842,0.119008943,29.94483757,35.03495038,149.875,2.625
439.7891458,0.142862275,29.82580566,35.1573751,151.375,2.625
422.1695294,0.118935376,29.94387054,35.07450825,149.875,2.375
446.5010408,0.157119691,29.85774231,35.19057548,151.375,2.375
427.974,0.119985633,29.94967651,35.08868283,149.875,2.125
429.4045588,0.121523082,29.90903091,35.11098588,150.125,2.125
449.2168571,0.157532826,29.89903069,35.20076621,151.375,2.125
431.66682,0.11944402,29.96419334,35.12461364,150.125,1.875
454.6012941,0.129736602,29.92290306,35.21095693,151.375,1.875
453.4999375,0.156125665,29.87838554,35.22935545,151.625,1.875
431.9333333,0.110469751,30.03451538,35.13824141,150.125,1.625
434.5182708,0.122806258,29.99225807,35.15427005,150.375,1.625
449.4601176,0.147608399,29.88903236,35.23901832,151.625,1.625
444.3868718,0.119026713,30.06064415,35.16500348,150.375,1.375
448.8316364,0.126825318,30.01838684,35.1809929,150.625,1.375
441.05826,0.14275682,29.9141922,35.25178397,151.625,1.375
447.8498367,0.126214266,30.08774185,35.19026512,150.625,1.125
439.6799048,0.134915173,29.97774124,35.26765239,151.625,1.125
440.49225,0.139508396,29.90258026,35.28860724,151.875,1.125
448.3563214,0.118740864,30.14258003,35.19953734,150.625,0.875
450.488,0.118854627,30.10387039,35.21688145,150.875,0.875
439.9442333,0.132534117,29.98967743,35.30670774,151.875,0.875
448.7683922,0.115748294,30.11322594,35.22683102,150.875,0.625
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452.3721892,0.129290894,30.10096741,35.26363087,151.125,0.375
437.0067407,0.121058792,30.10903168,35.34422874,151.875,0.375
448.44628,0.111729316,30.14354706,35.28238583,151.125,0.125
================================================
FILE: data/co2_gnnwr_pre.csv
================================================
fCO2,Chl,Temp,Salt,lon,lat
427.0227273,0.856589258,10.52290344,33.00970346,235.125,42.875
482.8573846,0.774920166,10.57967758,33.03273696,234.875,42.625
433.9548571,0.813277543,10.60032272,33.04260588,234.875,42.375
433.3903939,0.800426304,10.63806438,33.0054965,234.875,42.125
413.1546774,0.777915537,10.67838669,32.96838713,234.875,41.875
418.4410652,0.800702929,10.74354839,32.93127775,234.875,41.625
431.512,0.762529731,10.84354782,32.91182047,234.875,41.375
422.6084318,0.772762179,10.93870926,32.91001529,234.875,41.125
454.153,0.845942974,11.00290298,32.98121208,235.125,41.125
443.8956667,0.83403188,10.99903202,32.9082101,234.875,40.875
342.8012058,0.467087626,12.53903198,33.95320386,141.375,37.875
344.6948889,0.383636236,12.38967705,33.99945909,141.625,37.875
407.1016667,0.669980228,11.81645107,33.12304574,236.375,37.875
334.791104,0.737642646,12.71354771,33.95206183,141.125,37.625
343.1821411,0.363651812,12.30128956,34.06920618,141.625,37.625
448.7936667,0.648809254,12.06774139,33.15285105,236.375,37.625
398.4516154,0.817402661,11.91645145,33.16836315,236.625,37.625
340.8363916,0.636316538,12.85290337,34.00788277,141.125,37.375
343.3762396,0.395199537,12.58903217,34.07002074,141.375,37.375
344.1905,0.348403126,12.30709648,34.1321587,141.625,37.375
356.1563111,0.203659728,22.11903191,34.68532681,139.875,34.375
403.7620245,1.215404987,13.84741879,33.58872443,239.125,34.375
346.895102,0.752910435,18.07999992,34.29105127,134.875,34.125
350.4395,0.171294987,22.86128998,34.60141844,138.125,34.125
350.914,0.180465087,22.7048378,34.61788541,138.375,34.125
355.854725,0.191437185,22.54709625,34.70392203,139.875,34.125
358.82,0.183892593,22.67709541,34.71132851,140.125,34.125
345.4965469,0.57190752,19.77290344,34.4065897,134.875,33.875
350.745,0.149422735,23.21645164,34.61241102,137.875,33.875
348.6580909,0.157815263,23.21967697,34.63668877,138.125,33.875
356.8606667,0.172732502,22.92290306,34.72852325,140.125,33.875
348.34304,0.213239476,21.04935455,34.53023609,134.875,33.625
350.8944783,0.24293901,21.25290298,34.53382105,135.125,33.625
348.2677143,0.148660839,23.1506443,34.63910341,137.625,33.625
355.4448333,0.165596977,22.9593544,34.745718,140.125,33.625
350.4318472,0.268164515,21.71225739,34.60033715,135.125,33.375
349.0333333,0.131604359,22.82032204,34.68610948,137.625,33.375
357.4286458,0.159946889,22.9074192,34.76255691,140.125,33.375
================================================
FILE: data/co2_gtnnwr.csv
================================================
lon,lat,year,month,pCO2,Chl,Temp,Salt,pressure,windspeed
152.125,40.625,1997,9,299.3456045,0.132827044,21.91366577,33.73594153,101451.125,1.994543791
218.125,49.875,1997,9,296.0204571,0.564043283,14.85099983,32.35139066,100919.5703,3.616922617
165.125,16.375,1997,9,341.5074969,0.020018393,29.75099945,34.77362049,100902.5859,3.535589933
164.875,27.875,1997,9,353.7347868,0.035843641,29.09766579,35.26010865,101490.0781,5.457360744
235.125,43.875,1997,9,318.7720009,1.12293148,14.08033276,32.37365699,101378.0469,1.959121704
194.625,54.375,1997,9,294.7484784,0.873055875,8.784333229,32.12983567,100349.0156,4.353408813
171.375,48.375,1997,9,352.6901521,0.445019424,10.90866661,32.65240532,101381.1094,2.951976299
171.875,53.125,1997,9,307.6620712,0.870514035,9.116999626,32.86905628,101082.1719,3.003370762
161.625,43.875,1997,9,297.1263348,0.584820151,16.09133339,33.25623,101619.5781,0.887386084
196.625,54.125,1997,9,288.0836745,0.975321651,9.899000168,32.06011388,100320.7266,4.85000515
235.125,39.875,1997,9,340.8726778,0.422971606,16.48533249,32.95083433,101389.7344,5.720654964
236.875,19.875,1997,9,369.1350175,0.059910361,25.83166504,34.68467879,101015.1563,3.748605967
159.125,43.375,1997,9,293.9204393,0.15366967,19.43099976,33.52794886,101600.2969,0.544212818
186.375,54.125,1997,9,300.5439004,1.517566204,9.615333557,32.89163071,100630.2031,4.333070278
165.125,40.375,1997,9,312.2558328,0.131166562,22.0423336,34.02479571,101722.3672,0.833675206
165.125,41.375,1997,9,318.913467,0.312267274,19.01300049,33.77337003,101728.7031,0.438876808
143.625,41.875,1997,9,311.656065,0.701859951,19.57733345,33.15549886,101372.4766,0.105507225
236.125,37.875,1997,9,314.5112309,0.831319094,15.86699963,33.29120809,101332.2813,6.783443451
204.125,54.125,1997,9,285.3392223,0.546082199,12.64900017,32.34167612,100293.8516,4.553453445
162.875,44.125,1997,9,314.914103,0.51051563,15.74433327,33.23151481,101624.7109,1.231621623
237.125,28.625,1997,9,403.1217216,0.060281701,21.59566689,33.80687976,101313.0547,5.967778206
180.125,53.625,1997,9,312.37335,0.886207521,9.197666168,33.19534665,100883.8516,3.927823544
215.125,44.875,1997,9,317.9447501,0.264318019,18.28933334,32.58555073,101364.6094,4.928939819
173.375,49.125,1997,9,349.0099225,0.725725591,10.8876667,32.69440013,101308.4063,3.281776667
154.375,43.375,1997,9,294.9054674,0.391794294,19.15233231,33.16395277,101532.5625,0.809696674
152.625,42.375,1997,9,299.6828125,0.309324652,20.69033241,33.09810555,101487.5625,1.609841943
148.375,38.875,1997,9,313.9008491,0.136976197,24.0826664,33.95412999,101345.5,0.872080028
143.375,36.375,1997,9,327.5949576,0.068273515,27.81933212,33.77823138,101245.9766,2.075202465
165.125,38.375,1997,9,324.5087254,0.10066478,24.9193325,34.3209039,101728.1094,1.410275817
226.625,49.125,1997,9,314.6475691,0.538903832,16.60966682,32.36759073,101020.3984,3.202447653
234.875,40.625,1997,9,343.7706673,0.323495239,16.14866638,32.83426696,101413.3359,4.64056778
151.625,41.625,1997,9,288.0793891,0.216748878,21.13899994,33.28022307,101451.1875,1.700228572
222.875,49.375,1997,9,322.3413365,0.442696899,16.24333382,32.33987927,100992.7656,3.102159023
168.875,46.875,1997,9,353.0453333,0.547766685,12.16866684,32.89715743,101493.9531,2.541702747
174.625,49.875,1997,9,330.0725364,0.707024395,10.64366627,32.80099195,101243.1953,3.421402454
175.375,53.375,1997,9,309.7937816,0.755906105,9.597332954,32.99809116,101014.9922,3.604628563
231.125,20.375,1997,9,359.2285047,0.046043932,24.35066605,34.84976256,101164.375,6.051556587
198.875,54.125,1997,9,282.470389,1.142111182,11.14333344,32.1572026,100288.3906,4.859462738
235.625,44.625,1997,9,308.2374242,5.720064163,11.8409996,32.35617319,101353.25,1.661839724
164.875,19.375,1997,9,345.6033292,0.034865104,29.60866547,35.06007737,100995.3828,6.197584152
182.625,53.875,1997,9,310.9878273,0.784556627,9.376333237,33.03649682,100782.4766,3.962291956
141.875,42.375,1997,9,318.2789958,0.524873674,21.69133377,33.55144052,101348.1719,0.606920898
191.125,54.375,1997,9,287.1571876,1.335819602,9.578000069,32.40486848,100447.8828,4.273795605
164.875,35.375,1997,9,335.3808954,0.090496644,25.41966629,34.47474593,101718.6016,1.622296095
223.625,49.375,1997,9,318.2458551,0.372224957,16.50666618,32.33307207,100992.5469,3.002416372
165.125,40.875,1997,9,305.6924638,0.140210286,20.68266678,33.90026689,101726.5781,0.655759215
148.375,39.125,1997,9,315.1712364,0.134701654,24.11733246,33.93394536,101348.7734,0.980082929
215.125,49.375,1997,9,318.2412037,0.291362613,14.57966614,32.36841995,100942.5156,4.490260124
164.875,36.125,1997,9,341.3030457,0.102134667,25.44933319,34.40846556,101724.0625,1.524119854
215.125,49.125,1997,9,310.8925095,0.299971879,14.75999928,32.37010759,100963.9219,4.483291149
161.875,46.875,1997,9,319.0040226,0.591508985,15.69433308,32.8843233,101518.9688,1.756858945
164.875,18.625,1997,9,344.3487084,0.029374026,29.78433228,34.99148828,100961.1953,5.700772285
165.125,43.375,1997,9,330.4046191,0.879592121,15.62899971,33.2260201,101683.2656,1.384803534
233.125,21.625,1997,9,356.7409338,0.044395696,24.06099892,35.05152917,101126.4688,5.093652725
165.125,36.375,1997,9,334.1444782,0.101253636,24.83866692,34.3941499,101730.8906,1.522912264
158.625,45.875,1997,9,312.0745413,0.908977687,15.29399967,32.89870489,101545.5156,1.076634288
149.625,39.625,1997,9,309.3090177,0.201943249,22.87366676,33.89327466,101376.1406,1.247689843
233.125,43.875,1997,9,314.0567968,0.73167479,16.34866714,32.3458935,101401.2109,3.204950333
147.875,43.875,1997,9,304.2807968,1.702331185,13.49100018,32.99948996,101458.8828,1.174578667
175.625,49.875,1997,9,328.0810709,0.926634133,10.59200001,32.86285508,101225.5469,3.481255293
153.375,46.625,1997,9,319.805567,1.580626369,12.64333344,32.86072272,101487.0156,1.785260916
156.875,45.125,1997,9,293.420467,0.442823648,16.57799911,32.9399842,101549.8281,0.517010272
161.375,43.875,1997,9,301.8207326,0.549035311,16.75300026,33.26451337,101617.1172,0.849467754
202.625,54.125,1997,9,324.9110866,0.884369552,11.96299934,32.30244368,100274.8438,4.430012703
171.125,52.875,1997,9,307.8648763,0.892873824,9.329999924,32.85178715,101103.5859,3.019812584
234.375,41.375,1997,9,355.9957641,0.460610598,15.97166634,32.65374082,101437.2578,4.302416801
146.875,38.375,1997,9,314.8533227,0.127441853,24.58599854,33.83114672,101318.7344,1.038636923
164.875,31.625,1997,9,360.1265349,0.045137323,27.70199966,34.84791821,101660.7656,3.131449223
236.375,37.125,1997,9,321.568788,0.932553411,16.6906662,33.37697381,101343.9688,6.565773964
175.375,49.875,1997,9,325.0520286,0.727090359,10.63066673,32.85134685,101230.1406,3.464847803
164.875,25.625,1997,9,347.1938361,0.034370579,29.65333176,35.37276703,101358,6.418725967
165.125,38.625,1997,9,325.0300857,0.100856781,24.93533325,34.30891436,101726.4688,1.349570036
164.875,27.625,1997,9,356.5713568,0.0346702,29.14866638,35.27529579,101475.5469,5.579859257
231.375,20.125,1997,9,358.6156433,0.049045775,24.58866692,34.85010684,101148.4297,5.91645813
162.875,50.625,1997,9,343.0725925,0.425925344,11.86166668,32.78432363,101259.9063,3.438845158
167.625,51.875,1997,9,334.3301051,0.441671431,10.52699947,32.78038025,101176.5,2.993332863
179.875,53.625,1997,9,311.434657,0.819597542,9.217666626,33.20210689,100884.0156,3.883624792
233.625,44.125,1997,9,310.8369915,0.674403131,15.08766651,32.32506073,101381.9844,3.009859085
144.875,42.375,1997,9,301.0993275,0.545012057,19.13066673,33.07918483,101384.9297,1.195198059
164.625,51.125,1997,9,324.9003645,0.418532938,11.22633362,32.75654596,101224.625,3.235157251
235.375,44.125,1997,9,308.5294756,5.275742531,12.71166611,32.38636565,101369.9688,1.73774004
198.375,54.125,1997,9,297.8161329,1.243775487,10.77366638,32.12465703,100295.2734,4.812888622
232.875,21.625,1997,9,356.6220786,0.04386849,23.86599922,35.04738772,101135.6484,5.264075756
200.875,54.125,1997,9,290.1857691,1.192469716,11.18933296,32.24813217,100270.4766,4.488754272
164.875,28.125,1997,9,355.6709987,0.036821913,29.04100037,35.24492151,101505.0391,5.331676006
165.125,45.375,1997,9,312.0960579,0.564512491,14.00833321,33.15800858,101580.9609,2.052937984
236.625,19.875,1997,9,361.7184573,0.057733785,25.76633263,34.7007153,101018.2188,3.77214241
164.875,17.875,1997,9,344.9437932,0.027798915,29.70399857,34.92086726,100931.4219,5.01584053
236.625,27.375,1997,9,402.1168917,0.057402279,22.39866638,34.05575311,101280.7188,5.694326401
160.625,50.125,1997,9,342.298697,0.523978651,11.91566658,32.74203509,101307.2656,3.901900768
239.375,33.875,1997,10,346.2241919,0.683305681,18.00806427,33.59728646,101453.8594,7.108819008
208.125,53.875,1997,10,304.2345497,0.3742356,10.24354839,32.56291872,100533.4141,2.759591103
157.625,47.125,1997,10,351.1361781,0.863355458,10.67903233,32.86948323,101268.2031,3.83698082
230.125,46.375,1997,10,326.9111046,0.24100624,16.66806412,32.53548646,101429.1719,5.228394985
235.375,46.125,1997,10,319.0727479,1.475952268,14.61580658,32.25643969,101518.8594,3.265786648
158.875,47.875,1997,10,320.0565232,0.971184909,9.636774063,32.85134089,101232.5938,3.47227478
150.125,42.875,1997,10,305.6626991,0.514793932,14.41709614,33.0379287,101369.75,3.407844305
165.125,50.375,1997,10,366.4077065,0.603760719,8.802257538,32.8551476,101202.0625,2.578475237
232.875,41.375,1997,10,319.6072794,0.387253404,16.66806412,32.38684779,101839.1484,4.762591839
232.625,42.625,1997,10,318.093801,0.187864095,17.95096779,32.32317549,101781.6875,4.220050335
211.125,47.875,1997,10,317.1961876,0.261043251,13.08838654,32.46967465,100992.9844,5.656388283
225.875,46.875,1997,10,320.1123394,0.299954385,16.16935349,32.43950093,101344.2969,5.428792477
218.125,47.375,1997,10,321.1277006,0.723123431,14.74290276,32.37046158,101228.9375,6.100135803
205.625,48.375,1997,10,329.7531475,0.395363957,11.26225758,32.39536786,100772.8672,4.034600258
206.875,17.125,1997,10,352.2104975,0.043866154,27.47451591,34.64057404,101358.2734,7.856566906
160.125,10.125,1997,10,349.9247408,0.032368638,29.70419312,34.03628683,101077.9688,3.625798702
180.875,49.875,1997,10,336.0178398,0.398490638,9.31387043,32.81142354,100947.1016,3.927436829
219.375,1.125,1997,10,359.0330739,0.166295916,24.63129044,35.02764356,100981.1875,3.121119022
230.375,45.625,1997,10,324.7602558,0.205152601,17.25548363,32.56759095,101518.4219,5.181357384
211.875,13.625,1997,10,348.1869262,0.080086134,27.79225731,33.88281947,101154.5469,7.745063782
161.625,11.625,1997,10,345.3551016,0.029865284,29.45645142,34.2361576,101111.5625,4.966234207
201.375,48.625,1997,10,331.3806135,0.283483714,10.39354801,32.4598124,100645.9297,1.961458445
145.625,4.875,1997,10,357.4872517,0.071225494,30.66903114,34.09160024,101136.0859,1.867441654
156.625,46.625,1997,10,325.1479137,1.052081585,11.5764513,32.88025993,101289.4531,4.077719212
218.125,52.625,1997,10,314.0153299,0.441638321,11.8129034,32.38015133,100602.8906,4.70919323
234.375,43.375,1997,10,310.9922005,0.473373979,16.52967644,32.5172869,101697.8984,3.067576408
222.875,51.875,1997,10,315.3753234,0.483722508,13.31096745,32.29729974,100722.9453,4.807603836
234.375,48.625,1997,10,324.7196083,1.346669793,12.73483849,31.60109937,101285.0859,2.453017712
183.875,53.875,1997,10,330.4003341,0.527030408,7.990644932,32.93420029,101089.6563,3.769236565
233.125,42.875,1997,10,324.7595385,0.228113875,17.56387138,32.3727693,101753.1719,3.955336094
178.125,53.625,1997,10,323.6873374,0.429148734,7.878709793,33.14595026,101180.9219,1.676390171
144.125,16.375,1997,10,338.6277377,0.035993878,29.97225761,35.03068352,101185.4609,4.406984329
148.625,41.625,1997,10,273.441135,1.582920551,14.25677395,33.11233974,101410.4375,3.12421751
148.625,1.375,1997,10,364.5132543,0.115942664,30.41838646,34.67877179,101156.1875,2.372832775
156.625,0.125,1997,10,362.4966548,0.142139792,29.53516006,35.32506633,101139.7422,4.534590721
235.125,1.625,1997,10,363.4842695,0.152628228,23.95419312,34.70992607,100984.8984,5.414237976
234.875,9.125,1997,10,351.4850621,0.070436403,27.48161316,33.64457917,100966.2188,1.472082138
214.125,11.875,1997,10,347.6041183,0.050999433,28.02709579,33.73482835,101076.2188,7.163446903
234.875,8.125,1997,10,345.8900788,0.067998998,27.48838615,34.08604431,100971.8984,0.21130541
168.125,51.375,1997,10,336.6546437,0.555158675,8.377096176,32.7741034,101212.9375,1.759351969
202.875,20.625,1997,10,343.6439624,0.064694606,27.88354874,34.97964638,101459.7578,4.352082253
163.875,9.625,1997,10,354.4726408,0.033717763,29.62838554,33.98856199,101072.3438,3.540642738
195.125,54.375,1997,10,318.0604658,0.599404633,8.577419281,32.16630834,100932.4609,4.674869537
143.625,18.875,1997,10,343.9496001,0.036057375,30.01451492,34.85308439,101287.2734,4.791641235
214.125,53.375,1997,10,321.734334,0.307652771,11.09967709,32.51660949,100508.1797,4.249663353
150.875,43.125,1997,10,301.252332,0.486824989,14.1870966,33.11681855,101374.9844,3.351185799
144.125,15.875,1997,10,340.462603,0.036726426,30.03096771,35.00644493,101180.7656,4.120388985
234.375,44.875,1997,10,319.7366612,0.615913749,15.44806385,32.26232457,101601.7734,3.559295416
230.125,45.375,1997,10,324.181244,0.213438705,17.33580589,32.58715361,101546.6016,5.114785671
214.875,11.625,1997,10,359.2153542,0.055588245,27.89193535,33.66908157,101067.9219,6.997488976
170.375,52.375,1997,10,354.5560293,0.861361861,7.462580681,32.84643525,101215.6719,0.954429805
229.125,50.375,1997,10,307.4226554,0.389691919,14.2658062,32.25088596,100990.1406,3.731487274
219.625,7.125,1997,10,341.6754687,0.097613744,27.95322609,34.19048935,100982.8203,1.573373795
165.125,5.875,1997,10,346.2318758,0.04664259,30.21193504,34.39687175,101056.1719,2.706587076
229.625,45.875,1997,10,321.0009835,0.203454226,17.06451607,32.57520813,101485.5391,5.242564678
180.125,53.625,1997,10,345.234444,0.369250357,7.865160942,33.12768698,101142.8594,2.581335068
151.375,43.375,1997,10,299.0096595,0.661089897,14.20612907,33.13789457,101373.0234,3.427821636
220.125,52.375,1997,10,313.1435094,0.382972389,12.43870926,32.35768294,100644.7266,4.936696053
207.875,48.125,1997,10,314.4352834,0.316781253,11.44483852,32.46229291,100862.4375,4.845604897
192.875,49.125,1997,10,313.779315,0.3745749,9.990644455,32.60539687,100547.3984,2.400346518
217.875,47.375,1997,10,317.280925,0.742587149,14.68806458,32.36973864,101224.7891,6.10390234
237.875,35.875,1997,10,338.7144821,2.138537884,16.04838753,33.46035188,101591.8281,7.058995247
218.875,8.625,1997,10,346.4546682,0.0596934,28.31225777,33.66964573,100992.4375,4.041669846
203.125,20.375,1997,10,343.4750656,0.065908901,27.72774124,34.95815998,101455.7188,4.149009705
235.125,1.125,1997,10,385.6913259,0.172374412,23.62290192,34.75540262,100986.8672,5.70857048
239.125,34.125,1997,10,351.2260709,0.607727706,18.01967621,33.58095098,101473.3047,7.225286484
199.875,48.625,1997,10,332.1248364,0.263733238,10.58193493,32.49912351,100614.0313,1.319616318
164.875,6.125,1997,10,345.4297044,0.052876148,30.1529026,34.3703602,101054.375,2.264425755
229.875,46.375,1997,10,325.0019001,0.224097267,16.68322563,32.54279858,101427.9688,5.241370201
182.375,49.875,1997,10,338.6433856,0.457931012,9.602257729,32.81337893,100861.6797,3.903858185
144.125,8.375,1997,10,346.680005,0.037307583,30.34516144,33.94792384,101116.6406,0.972408175
235.125,40.375,1997,10,338.8696313,0.589505494,14.16387081,32.82866806,101738.1016,4.573856831
228.625,46.625,1997,10,319.0257333,0.312757492,16.62580681,32.51930076,101390.8281,5.330183983
163.875,13.875,1997,10,346.3855845,0.023130046,29.21129036,34.57556587,101184.3672,6.774831772
153.875,0.125,1997,10,368.4236757,0.15538159,29.72645187,35.33890468,101151.5938,4.253888607
233.125,41.375,1997,10,320.728217,0.438933194,16.50032234,32.41890514,101827.5703,4.76648283
235.625,0.125,1997,10,391.6986875,0.21964556,22.79806328,34.85594451,100993.5313,6.173875332
166.125,50.625,1997,10,354.7572872,0.673990786,8.385806084,32.8461616,101213.0938,2.400587559
212.625,47.625,1997,10,309.3610099,0.309360474,13.60870934,32.43075603,101066.7188,5.857097626
226.125,51.125,1997,10,310.9834511,0.524327338,13.88548374,32.30466974,100869.3203,4.594158649
159.125,0.125,1997,10,362.2684737,0.164636001,29.11645126,35.35427892,101124.3438,4.352081299
234.125,40.375,1997,10,330.7989627,0.601125062,16.17483902,32.57439196,101799.2734,5.685367107
234.625,9.625,1997,10,361.2631955,0.07979624,27.46903229,33.4194237,100967.9688,2.065873623
168.875,51.875,1997,10,334.0827793,0.551302373,8.717096329,32.83722496,101209.9844,1.428897142
142.625,26.875,1997,10,332.2053364,0.054370828,28.62354851,34.84272528,101439.8281,0.344854236
205.875,17.625,1997,10,345.9091529,0.041863982,27.51419258,34.73798496,101379.1406,7.850803375
150.625,0.375,1997,10,362.0162768,0.144133151,29.82903099,35.06467748,101157.2734,2.603222847
234.875,40.875,1997,10,344.8090236,0.719134033,14.90451622,32.74888027,101743.7813,4.605179787
208.125,16.375,1997,10,352.9224094,0.039461397,27.37387085,34.49004257,101311.9609,7.97884655
188.125,49.375,1997,10,330.5964199,0.414356202,9.92870903,32.77281213,100586.2891,3.339587688
233.125,40.875,1997,10,327.1811244,0.425734282,16.46645164,32.38900888,101846.1406,5.154610634
218.125,9.375,1997,10,353.5610623,0.061900932,28.18838692,33.54176086,101006.5234,4.988023758
230.125,46.125,1997,10,319.0529308,0.238836259,16.87677383,32.55224395,101459.2109,5.235258102
165.125,3.875,1997,10,340.6527979,0.141083553,29.59806442,35.00703615,101079.2266,4.974433422
144.125,14.625,1997,10,345.8839006,0.031008273,30.09354782,34.83021706,101171.1563,4.053705215
184.375,49.625,1997,10,342.7245367,0.658587933,9.78064537,32.79689699,100744.8984,3.829699039
229.875,46.125,1997,10,314.9363665,0.224500328,16.89354897,32.55985802,101458.2266,5.240128994
208.625,15.875,1997,10,351.5868122,0.035443798,27.32386971,34.36915195,101283.6641,8.091753006
229.625,45.625,1997,10,321.6906269,0.183958471,17.19290352,32.59224492,101515.6875,5.207366943
202.875,48.375,1997,10,335.6183725,0.246281415,10.33354855,32.44290888,100694.6484,2.81140995
231.125,49.875,1997,10,304.3648856,0.463757396,14.14064503,32.1763877,101094.0313,3.104522467
179.875,53.625,1997,10,348.8377119,0.492937267,7.899354458,33.13865328,101154.3281,2.481375933
164.125,8.875,1997,10,353.442135,0.030759355,29.66322517,33.96046454,101063,2.670571804
165.125,6.125,1997,10,345.6910278,0.045790195,30.19677353,34.35507041,101053.3906,2.225304842
212.125,13.375,1997,10,349.149878,0.07666415,27.80903244,33.87389487,101141.3281,7.696235657
174.875,53.375,1997,10,335.106478,0.567164719,7.808386803,32.83712077,101201.5781,0.401034147
145.125,32.125,1997,11,315.3626973,0.086087093,23.72099876,34.76212639,101973.1875,1.025519848
205.125,5.625,1997,11,356.7313694,0.101595707,27.89333344,34.87873197,101033.8359,2.593375921
233.375,35.375,1997,11,337.8699445,0.159350365,17.29566574,33.01670861,101559.8203,2.992633104
238.375,34.125,1997,11,343.3971557,0.329993993,16.04533386,33.49336213,101530.4375,4.241530418
161.125,36.625,1997,11,298.0367667,0.158866003,19.55233383,34.55828339,102158.125,0.884809732
146.875,34.375,1997,11,311.5266821,0.202921122,23.26866531,34.60004997,101999.3984,0.775605083
171.375,37.375,1997,11,311.5850158,0.155311912,20.64799881,34.49272245,102231.6953,0.807179809
216.625,38.875,1997,11,330.5159481,0.139737859,18.78366661,33.47616386,101160.5547,6.742266655
157.125,36.125,1997,11,297.0083799,0.254036427,22.40999985,34.53709888,102110.6563,0.178082198
247.875,22.375,1997,11,376.6788074,0.105395749,25.64966583,34.42714304,101217.7969,4.176802635
151.875,35.875,1997,11,303.1583677,0.209026754,23.11833191,34.54319566,102063.6875,0.35765177
231.875,35.625,1997,11,344.4113665,0.156039923,17.87366676,32.97453678,101536.4453,3.216549158
192.375,39.125,1997,11,313.3861522,0.195860401,16.05633354,34.27073592,102232.5156,5.648920536
167.125,37.125,1997,11,299.8639893,0.17630592,21.65366554,34.54935378,102210.2813,0.835877836
167.875,37.125,1997,11,305.8957623,0.168245077,21.33499908,34.55559188,102212.4688,0.787421465
211.625,39.375,1997,11,321.6702673,0.123395279,18.89099884,33.67230594,101258.7578,7.846589565
152.375,38.875,1997,11,292.9515137,0.344041944,17.68099976,34.39817482,102052.6563,1.33704114
147.375,38.125,1997,11,290.6380712,0.454015642,18.65066528,34.05945957,101988.4766,1.380787015
165.625,36.875,1997,11,303.1246543,0.177605748,21.79633331,34.53999513,102196.1406,1.022337914
205.125,0.375,1997,11,350.8318657,0.181103602,25.50899887,35.23628151,101029.7969,1.658551216
141.125,34.875,1997,11,317.8488547,0.198664114,24.35466576,34.489223,101954.3438,1.478895664
151.375,35.875,1997,11,302.7562977,0.275482565,23.3876667,34.53339165,102057.6797,0.396486342
178.375,38.125,1997,11,309.8142567,0.239175454,18.74633217,34.36773765,102341.5938,1.031102896
205.125,3.125,1997,11,360.7378479,0.141945124,26.69733238,35.14431548,101026.8438,1.281026125
207.625,39.625,1997,11,316.7408369,0.183099538,18.68400002,33.71783161,101405.7969,7.958404541
218.625,38.625,1997,11,331.5749414,0.104284637,19.18099976,33.52262086,101141.1094,6.646201134
205.125,3.375,1997,11,363.3075154,0.14924708,26.79566574,35.15275311,101026.0781,1.379989505
213.875,39.375,1997,11,328.2399167,0.129532933,19.03499985,33.58346671,101180.2188,7.264211655
151.625,39.625,1997,11,289.3824263,0.486351848,17.90333366,34.29523987,102017.9141,1.72130847
162.125,36.625,1997,11,306.7599291,0.2001376,20.82266617,34.54960161,102165.6563,0.90539062
150.625,39.875,1997,11,310.6440262,0.348351985,18.57233238,34.17921329,101988.5313,2.133389473
147.375,38.375,1997,11,295.7760215,0.503016412,17.44266701,34.02443469,101983.7813,1.65091157
195.375,39.375,1997,11,312.5646456,0.236959949,15.95299911,34.18303025,102084.9297,6.83549881
142.125,33.375,1997,11,314.7160246,0.125007167,23.40833282,34.60131061,101943.3594,0.966019988
244.875,25.375,1997,11,396.0883235,0.096826725,22.52099991,34.17910618,101352.8125,5.535850525
142.625,36.625,1997,11,304.9423886,0.368825018,22.09333229,33.82208663,102003.6094,1.575569749
146.375,39.125,1997,11,277.5661248,0.408751309,18.67666626,33.80864519,101963.8906,2.078311443
141.375,33.375,1997,11,320.8142794,0.09314055,24.15399933,34.5644654,101928.4531,1.113497853
244.125,26.375,1997,11,378.9501526,0.129296988,22.24833298,34.09323895,101396.7266,5.72578001
227.375,36.875,1997,11,343.6266529,0.11804834,18.81933212,33.50621575,101382.4219,5.079958916
152.125,35.875,1997,11,299.653993,0.230761603,23.06866646,34.54477721,102066.5234,0.296778738
244.125,26.125,1997,11,380.9576337,0.133028165,22.43099976,34.10988939,101396.8359,5.669783592
202.875,39.625,1997,11,312.687447,0.138229176,17.31833267,33.7929852,101648.8516,8.180896759
204.625,39.625,1997,11,314.5796304,0.140944928,18.10566711,33.78913867,101554.7969,8.097808838
160.125,36.625,1997,11,302.6157279,0.218024552,21.43299866,34.56578761,102148.125,0.706408501
185.625,38.625,1997,11,315.5270156,0.21021007,16.36266708,34.56656647,102400.1953,2.564788342
182.125,38.375,1997,11,311.7285225,0.223936886,17.66333199,34.52069092,102398.4453,1.642822981
151.625,41.875,1997,11,267.3364183,0.712755263,12.5953331,33.45396578,101923.375,3.055399418
153.125,42.875,1997,11,307.0659779,0.374810666,11.78866673,33.24611127,101878.5781,3.840970516
249.875,20.625,1997,11,374.6955647,0.091505699,27.65866661,34.65707999,101141.6563,3.367796421
153.375,35.875,1997,11,300.8399955,0.218156755,22.64999962,34.54209888,102079.1953,0.273948789
143.625,31.125,1997,11,315.7792347,0.122050583,24.36899948,34.83914977,101946.75,1.347105145
174.875,37.875,1997,11,307.9413902,0.220980719,19.11266708,34.43996179,102276.8125,0.430467129
219.125,38.625,1997,11,330.9714006,0.120778285,19.1566658,33.51632702,101133.5703,6.568056583
246.375,24.125,1997,11,384.5945291,0.12938486,25.24399948,34.31031668,101277.6563,4.864677906
205.125,4.375,1997,11,365.9206011,0.156875789,27.17433357,35.06361198,101028.0469,1.837872982
141.125,33.875,1997,11,320.9401096,0.134919152,24.30699921,34.53556681,101931.3438,1.247055292
146.125,35.375,1997,11,301.0050934,0.217763215,23.18299866,34.50179732,102003.4375,0.991701841
236.625,34.625,1997,11,337.8970456,0.49994424,15.35199928,33.30708104,101566.0469,3.270561218
246.375,23.875,1997,11,386.2295427,0.124340013,25.17933273,34.29792631,101277.5469,4.770085335
216.125,34.375,1997,12,324.6649067,0.087837286,19.13871002,34.17594767,102041.4531,2.641797304
152.625,34.625,1997,12,299.8342836,0.269919157,19.78580666,34.78125423,101842.3672,3.198364496
155.125,34.625,1997,12,301.352178,0.182322353,21.14032173,34.72468245,101832.1016,3.706350803
160.125,0.125,1997,12,368.0806202,0.180227607,28.98386955,35.18258202,100948.7422,2.258672476
130.125,15.375,1997,12,336.0547108,0.034234442,29.22903061,34.60037422,101369.0391,6.912259102
129.875,25.125,1997,12,319.0370167,0.089789361,25.2093544,34.75843459,101972.1406,4.792766571
158.875,34.375,1997,12,303.5065928,0.188841015,21.14999962,34.64045882,101823.5781,4.079008102
129.875,16.875,1997,12,333.1083366,0.037229329,28.97999954,34.64328152,101449.2188,7.439157486
226.375,34.375,1997,12,327.9779408,0.089360557,18.66741943,33.65916753,102075.2109,1.040636063
135.375,24.625,1997,12,324.1747446,0.065310031,26.13419342,34.87114948,101850.5078,3.95619297
130.125,20.125,1997,12,331.9003283,0.056402877,27.33419228,34.76630229,101674.0859,7.390021324
135.125,14.125,1997,12,338.4215226,0.036532391,29.34387016,34.63236576,101257.9375,6.622169495
198.875,34.375,1997,12,302.4123834,0.098383456,18.50806427,34.62370515,102126.9922,4.227140427
144.125,0.125,1997,12,379.6535492,0.084888071,30.3645153,34.94441622,100971.3516,1.754793406
143.125,34.625,1997,12,306.3446946,0.213353142,20.57838631,34.72255933,101852.3125,2.31994915
178.625,0.875,1997,12,363.885095,0.190987736,27.38129044,35.42410856,100875.9844,3.416387558
130.125,15.875,1997,12,333.3531484,0.035639673,29.24064445,34.60943663,101392.7969,6.966792107
173.625,34.375,1997,12,300.4254845,0.192170918,19.85516167,34.73643428,101888.8438,4.312120914
207.875,34.375,1997,12,312.0442688,0.101926297,19.43741798,34.49379301,102021.7969,3.870219707
185.125,34.375,1997,12,310.7967114,0.225467071,17.78257942,34.74900961,101985.3047,4.913475037
127.875,25.875,1997,12,313.0661494,0.095877275,25.03064537,34.80721569,102047.7344,4.349065781
230.125,34.375,1997,12,330.8094976,0.1241953,17.75064468,33.43255812,102047.6797,1.900405407
166.625,0.125,1997,12,359.0045051,0.180075854,28.26516151,35.51290768,100922.7422,1.669592857
134.875,19.125,1997,12,336.9815928,0.037519522,28.60709572,34.70778739,101547.4766,6.805851936
207.375,34.125,1997,12,312.0338296,0.089499608,19.63064384,34.59357309,102043.5313,3.803887129
190.125,5.375,1997,12,371.8194498,0.137383446,28.13838577,35.00012851,100883.3047,6.094239235
194.125,34.375,1997,12,301.345773,0.155491486,17.98064423,34.75315976,102109.9453,4.113876343
137.875,28.375,1997,12,319.8952205,0.121282846,23.12483788,34.97197944,101895.3438,3.250111341
153.875,34.625,1997,12,297.7861243,0.257514775,19.93032265,34.76562941,101837.8906,3.454670906
146.375,34.625,1997,12,315.2163613,0.17129758,20.28741837,34.77495074,101856.6797,2.771981716
227.625,34.375,1997,12,329.1288267,0.092231266,18.33419228,33.61428136,102071.9375,1.26859498
129.875,12.625,1997,12,348.6982917,0.033369157,29.63225746,34.56587821,101251.6563,7.227680683
140.125,34.625,1997,12,312.2453959,0.231427252,21.71774101,34.65841734,101892.5078,2.698728561
190.125,4.375,1997,12,362.5486078,0.146744162,27.71290207,35.1575557,100868.5625,5.35737896
129.875,22.125,1997,12,320.3420544,0.076287203,26.44322586,34.73942214,101809.3281,6.522270203
138.125,29.875,1997,12,315.8153668,0.139212638,23.31354713,34.95264077,101906.875,3.324844599
129.125,27.375,1997,12,311.5623925,0.115542389,23.90612793,34.84580714,102076.8438,3.640623569
129.875,13.375,1997,12,346.3126418,0.028127111,29.6077404,34.57290953,101283.3906,7.206583977
152.375,34.625,1997,12,300.3296384,0.267592818,19.9593544,34.79113394,101843.1328,3.10859108
130.125,11.375,1997,12,342.3139115,0.04608142,29.77548409,34.4358753,101201.2969,7.037822247
144.625,0.125,1997,12,381.5740467,0.081118472,30.47999954,34.99533743,100970.0391,1.696950912
176.875,0.125,1997,12,363.3402492,0.180713058,27.23548317,35.53564823,100879.375,2.883340836
206.125,34.375,1997,12,306.3022167,0.096525632,19.64161301,34.7291401,102050.7422,3.92566061
145.625,0.125,1997,12,372.7488811,0.085574202,30.65161133,34.99479097,100967.4766,1.501533031
177.625,1.875,1997,12,360.4009561,0.179748043,27.74516106,35.34652478,100883.9609,3.792364359
211.875,34.375,1997,12,325.104732,0.1002369,19.220644,34.20585901,102005.2969,3.307215214
149.875,34.625,1997,12,308.874226,0.177348614,20.71774101,34.78772777,101829.2656,3.174161196
177.875,1.625,1997,12,362.3380347,0.169454202,27.63612938,35.35153013,100882.0469,3.661267042
157.375,0.125,1997,12,370.9420388,0.17268528,29.37903214,35.20515007,100957.6953,2.094722986
202.875,34.125,1997,12,306.3708025,0.085123524,19.41483879,34.72830147,102109.2969,3.974182129
213.625,34.375,1997,12,326.3269606,0.09261024,19.17161179,34.19200224,102015.7813,3.071341991
130.375,30.625,1997,12,308.9308595,0.200477153,22.53967667,34.62622035,102160.1406,4.58141613
133.875,24.125,1997,12,331.0939391,0.074934401,24.80483818,34.86249083,101851,4.56054306
130.125,12.625,1997,12,349.164701,0.034635168,29.63161278,34.57965457,101248.7109,7.190691471
179.375,34.375,1997,12,307.4663443,0.247652173,17.40193558,34.80140769,101952.4844,4.40746212
129.875,24.875,1997,12,318.8873501,0.089577787,25.53516006,34.74013442,101960.2344,4.918299675
217.875,34.375,1997,12,323.3990706,0.088518567,19.27516174,34.03135699,102061.3359,2.771473408
131.125,26.875,1997,12,315.0125818,0.112147883,24.50128937,34.85568863,102016.6563,3.685097218
144.375,0.125,1997,12,382.6206217,0.081259675,30.41516113,34.96987683,100970.6953,1.727793336
166.125,34.125,1997,12,302.3706558,0.216923535,19.57386971,34.78470904,101858.1563,3.772908211
135.125,13.875,1997,12,340.2473468,0.035609201,29.38612938,34.63494104,101249.2578,6.639565468
212.625,34.375,1997,12,324.0268879,0.098193049,19.45870972,34.19939452,102008.3594,3.273278713
190.625,34.125,1997,12,302.9446704,0.183422863,18.24451637,34.72476131,102073.5703,4.246184826
168.625,0.125,1997,12,361.4784865,0.194334388,28.04032135,35.37897825,100917.1172,1.219043612
208.375,34.375,1997,12,315.5708527,0.097747989,19.44096756,34.42313242,102014.8047,3.768306255
156.375,0.125,1997,12,366.75264,0.156222373,29.43612862,35.26852131,100959.8281,2.014963388
137.625,27.875,1997,12,319.0329272,0.118898563,23.72903061,34.95129174,101892.7813,3.217167377
135.125,15.375,1997,12,339.3255366,0.035842352,29.25774193,34.74501795,101300.7656,6.706707001
178.125,34.375,1997,12,309.9605901,0.214910835,18.38871002,34.78680587,101941.5625,4.267751694
172.125,0.125,1997,12,355.7346531,0.189031184,27.75322533,35.62844288,100902.4219,1.56907177
204.375,34.375,1997,12,304.9109357,0.107222825,18.62354851,34.74699581,102075.3203,3.985496759
138.625,31.375,1997,12,312.9426952,0.170980215,23.14354706,34.86051112,101914.2969,3.422260046
154.125,47.875,1998,1,386.8685239,0.273422658,2.438386917,33.06492406,100401.9453,6.192867279
208.625,51.375,1998,1,365.0653034,0.316441297,4.553870678,32.86224258,99046.45313,2.466715097
136.625,33.625,1998,1,298.5153256,0.283443332,18.80967712,34.48398983,101854.5,4.873042107
156.375,36.375,1998,1,315.5769082,0.266640872,14.89258003,34.63197827,101151,7.339681625
223.375,45.375,1998,1,320.6699409,0.351062566,9.375805855,32.94791675,99712.15625,5.553726196
147.125,4.875,1998,1,359.7918372,0.09998142,28.27290344,34.57234424,101121.9375,7.358712673
215.125,44.875,1998,1,336.4152368,0.231813252,8.653225899,33.23452854,99376.25,5.343187809
135.375,32.875,1998,1,298.0170489,0.184420794,20.99419403,34.58933526,101862.5781,5.707045555
209.625,42.875,1998,1,327.4785289,0.312292516,8.917097092,33.31918573,99453.14844,5.585385323
162.625,37.375,1998,1,307.6122597,0.357308328,15.39387035,34.47533107,100871.0156,9.167339325
155.875,6.625,1998,1,363.8529541,0.075834528,28.21774101,34.51646519,101151.9219,7.909190655
145.875,0.125,1998,1,370.8313046,0.119870834,28.75838661,34.79124564,101069.5625,4.793756485
237.375,35.625,1998,1,328.0691969,0.36137411,15.11548328,33.29943657,101680.6406,2.873764992
186.375,39.125,1998,1,326.6138134,0.284741402,11.63322544,34.49076766,99745.36719,6.748449326
163.125,37.375,1998,1,307.9514423,0.249544501,15.67064476,34.48800361,100848.5625,9.395528793
163.375,37.375,1998,1,307.7967576,0.252342731,15.73580647,34.49400389,100837.4844,9.472948074
216.875,45.125,1998,1,337.4022742,0.331764966,8.296129227,33.21978533,99419.50781,5.346828461
135.125,31.625,1998,1,299.7636286,0.191257969,20.59451485,34.79422736,101838.1094,4.6628685
147.625,35.875,1998,1,306.2339995,0.289919473,18.39903259,34.74027407,101409.2891,6.392760754
135.875,5.875,1998,1,349.8325243,0.08533068,27.77161217,34.54900026,101173.5,7.060971737
161.625,37.125,1998,1,312.3659427,0.333843261,14.62935448,34.48437816,100938.9688,8.610942841
150.625,36.375,1998,1,309.0660663,0.261421174,16.04741859,34.64586633,101309.7188,6.036464691
174.875,38.875,1998,1,329.2679285,0.266768098,12.28645134,34.35388607,100120.3281,9.439800262
146.875,7.125,1998,1,357.1764239,0.083992288,27.90967751,34.53414464,101173.9844,7.904976845
137.375,29.125,1998,1,296.5488917,0.176310539,20.72129059,34.82525533,101823.1406,3.764240265
135.625,27.375,1998,1,307.2957217,0.164604694,21.56774139,34.88480175,101889.3438,2.600868702
209.875,43.125,1998,1,331.3945623,0.25319171,8.83419323,33.31411505,99427.91406,5.492189407
148.625,44.375,1998,1,406.8920367,0.694079399,3.033548355,33.28810298,101052.8984,7.212738037
182.625,39.375,1998,1,329.7636741,0.254668388,11.74290276,34.21722037,99766.44531,7.253450871
193.625,38.875,1998,1,323.9851106,0.278753996,11.427742,34.33348465,99743.83594,5.028993607
225.375,44.375,1998,1,325.9079347,0.358649582,10.35612869,32.90349877,99951.71875,6.011379719
140.375,23.875,1998,1,318.9396369,0.065206587,24.98032188,34.95101982,101912.6641,1.343918204
139.375,25.875,1998,1,308.4561612,0.09886951,23.3396759,34.93422616,101904.7969,2.380465508
135.125,20.875,1998,1,321.9452591,0.08891736,25.69322586,34.96507174,101846.7344,3.600852489
233.875,38.875,1998,1,323.6806136,0.488288373,13.0990324,32.78479564,101238.6563,5.683565617
164.875,0.375,1998,1,361.783448,0.162648171,28.21096802,34.40556365,101018.375,6.125687599
147.875,4.375,1998,1,367.2418808,0.129991621,28.25516129,34.69469613,101109.375,7.314860344
130.125,21.625,1998,1,319.3573232,0.071529813,25.11645126,34.89437717,101842.5859,3.265776634
129.875,17.125,1998,1,323.0645737,0.045220792,26.14032173,34.72514045,101654.8047,6.06379509
132.875,17.375,1998,1,330.6671451,0.055190194,26.6641922,34.86713791,101678.8906,6.384596825
133.375,17.875,1998,1,330.6995026,0.051598493,26.66806412,34.89122516,101705.0547,6.01335907
219.625,45.875,1998,1,333.294328,0.23231639,8.716773987,33.12917584,99462.21875,5.059787273
171.125,38.375,1998,1,335.0909948,0.275583744,12.4074192,34.46772557,100371.9063,9.951417923
166.875,37.875,1998,1,306.1391154,0.264302071,14.69322586,34.50658953,100637.2422,10.0038147
211.625,43.625,1998,1,332.8487087,0.261017501,8.660644531,33.31089449,99411.53125,5.444564819
139.625,25.125,1998,1,311.2560512,0.070172928,23.77548409,34.93521363,101915.9375,1.896908283
138.875,26.625,1998,1,308.9985339,0.106050506,23.25387001,34.92086762,101891.1406,2.762795448
158.625,50.125,1998,1,370.2311432,0.349583284,2.445161104,33.04139638,100017.5313,4.108365536
151.875,6.875,1998,1,356.7025995,0.097781122,27.9819355,34.58273703,101164.7656,7.825071335
152.875,7.625,1998,1,354.7240797,0.053047493,27.63645172,34.56556696,101185.7344,7.979890823
137.125,28.125,1998,1,298.838473,0.190401524,21.17290306,34.8602556,101862.4141,3.033973694
135.125,31.125,1998,1,300.8787626,0.155933395,20.69967651,34.83601719,101833.6875,4.461550713
146.875,1.625,1998,1,372.2944599,0.117567368,28.58225822,34.64228797,101075.5156,5.888090134
217.375,45.375,1998,1,334.3130068,0.28821215,8.32516098,33.20805073,99412.40625,5.266077995
162.625,51.125,1998,1,386.774167,0.339582385,3.024838686,33.10444808,99814.57031,3.238328934
234.625,38.125,1998,1,322.3176845,0.438671827,13.58354855,33.05866015,101366.5781,5.456466675
149.375,5.875,1998,1,361.2835963,0.102579176,28.21451569,34.67602295,101139.75,7.759735107
134.875,22.125,1998,1,328.1770782,0.091100007,25.30322456,35.03404719,101888.0859,2.502586842
137.125,20.875,1998,1,316.8825273,0.063727453,25.24258041,34.99153548,101851.5938,3.566855907
132.375,16.625,1998,1,328.2399411,0.051158611,26.63806343,34.76323128,101639.8906,6.911308765
174.625,38.875,1998,1,327.4493673,0.229700908,12.52645111,34.35887557,100128.9531,9.538528442
130.125,21.375,1998,1,326.4650212,0.066158704,25.32548332,34.89384496,101833.3594,3.386537075
164.875,6.875,1998,1,361.0430542,0.072603002,27.79032135,34.23059958,101147.6641,8.128067017
188.875,39.125,1998,1,324.5413316,0.268103629,11.60451603,34.47009575,99724.60938,5.949316502
155.875,8.125,1998,1,355.6341679,0.063310891,27.53967667,34.60893595,101200.3125,8.093222618
233.625,34.375,1998,1,331.2786396,0.241657197,14.46999931,33.31917256,101671.2422,4.908166885
135.125,30.375,1998,1,300.2046244,0.157595575,20.98967743,34.87725842,101832.1016,4.014556408
136.125,31.375,1998,1,296.2640344,0.147653237,20.45129013,34.82370055,101800.6953,4.438942909
141.875,21.125,1998,1,324.0920913,0.067061469,25.60419273,35.10625589,101845.7031,3.202651024
156.125,5.125,1998,1,368.1079755,0.073501192,28.57709694,34.49661446,101107.1406,7.694540977
160.875,50.875,1998,1,418.518065,0.323932521,2.934193373,33.05934644,99891.85156,3.767943859
149.625,44.875,1998,1,447.641049,0.345416158,2.722580671,33.29912591,100939.2969,7.328428268
137.875,28.625,1998,1,298.2140792,0.178292722,20.74290276,34.84888309,101839.4688,3.576404333
134.125,18.875,1998,1,331.6795454,0.058018491,26.73354721,34.90441555,101754.9219,5.150638103
135.375,33.125,1998,1,299.1473646,0.194932878,20.66096687,34.51375836,101878.3594,6.236607552
202.125,41.125,1998,1,321.5848364,0.276350021,9.385483742,33.83854955,99562.17188,4.612563133
137.125,31.875,1998,1,298.8411464,0.175583303,20.36741829,34.76086944,101768.6328,4.714304924
137.125,19.375,1998,1,318.7039672,0.069980174,26.33354759,35.00245345,101786.0547,4.792510033
231.875,40.125,1998,1,322.7361463,0.305487216,12.46580601,32.54312629,100952.3438,6.098333359
147.125,5.125,1998,1,360.8969615,0.088027082,28.22935486,34.55830389,101127.5625,7.41220665
133.875,18.625,1998,1,331.5434362,0.059932664,26.65709686,34.90444189,101741.9766,5.381162167
199.375,40.375,1998,1,324.7325039,0.286966652,9.696451187,34.0091117,99615.03906,4.220602036
150.875,45.875,1998,1,397.8224214,0.298552603,2.535161257,33.20229906,100785.3125,6.896332264
135.125,27.625,1998,1,305.1251641,0.1474704,21.48354721,34.86952507,101885.9531,2.720661163
208.875,51.375,1998,1,363.9239288,0.291740042,4.55580616,32.86412394,99037.82813,2.368886471
159.875,0.875,1998,1,363.4894687,0.123672724,28.26064491,34.42779255,101034.875,6.115706921
134.875,24.875,1998,1,315.3180424,0.10203819,23.03935432,35.04731327,101929.6484,0.856561303
211.375,43.625,1998,1,332.5312309,0.28720656,8.617741585,33.30811071,99404.21094,5.412178516
165.125,7.625,1998,1,353.0468567,0.075224489,27.36677361,34.21696961,101173.0078,8.101624489
138.375,27.375,1998,1,305.1986388,0.139386728,22.11806488,34.88975817,101876.6094,3.017898083
235.625,34.375,1998,1,328.4795586,0.264153987,14.50354767,33.28039747,101728.4844,4.014754295
137.125,28.625,1998,1,298.6565739,0.199678451,20.94322586,34.84425074,101844.7188,3.400097609
230.875,40.875,1998,1,322.9936805,0.290049285,12.79354763,32.62965077,100785.7578,6.438989162
212.875,50.125,1998,1,339.4079866,0.254766941,5.421935558,32.98283237,99013.13281,1.289466381
146.875,7.625,1998,1,351.6802483,0.087035619,27.92128944,34.55514956,101188.0313,8.089654922
208.125,42.625,1998,1,323.2797752,0.284981619,8.886451721,33.3295821,99460.03125,5.316469193
159.625,36.875,1998,1,328.9683753,0.247799277,14.46903229,34.51330245,101024.1719,7.759630203
152.125,36.375,1998,1,314.9426442,0.216652562,15.69387054,34.66655356,101269.7969,6.278164864
137.875,0.375,1998,1,345.09124,0.129018962,28.96870995,35.41100979,101108.2813,3.832572937
156.625,49.125,1998,1,402.3298679,0.250484914,2.818064451,33.01773912,100154.6875,4.800408363
135.125,25.125,1998,1,314.8987307,0.09472312,22.95354843,35.0375818,101929.9766,1.000102878
160.625,50.625,1998,1,422.0963904,0.374718154,2.91225791,33.05365491,99895.1875,3.692032576
141.625,22.125,1998,1,323.0364201,0.063949741,25.61645126,35.04953992,101875.2969,2.278868914
137.125,28.375,1998,1,298.615907,0.213568032,21.04322624,34.85399067,101854.1094,3.20778656
233.875,38.625,1998,1,323.6300791,0.474807143,13.2454834,32.82766879,101263.4609,5.703426361
154.875,7.875,1998,1,357.4380626,0.065767936,27.38322449,34.5807355,101193.2656,8.029683113
137.125,3.375,1998,1,360.9419704,0.300797105,27.80387115,34.79216433,101133.25,5.582578659
137.125,34.125,1998,1,299.4454134,0.27256313,17.79161263,34.4152627,101861.1016,6.631349087
232.375,48.125,1998,2,338.2352128,0.30041787,9.645357132,32.12629384,100051.8906,5.860432625
218.375,49.875,1998,2,325.2820999,0.232108369,6.558571339,32.58507669,98671.98438,5.34727335
229.375,48.875,1998,2,346.6382456,0.3285954,8.717857361,32.2414825,99701.78125,5.949467182
224.125,49.375,1998,2,346.512492,0.306351036,7.441071033,32.50278193,99195.23438,6.122861862
223.375,46.375,1998,2,330.9532965,0.272617847,8.282500267,32.67519516,99423.4375,6.504803658
203.875,13.125,1998,2,346.519838,0.082831092,25.1724987,34.55810934,101554.9063,9.567235947
223.125,49.375,1998,2,340.0576147,0.300009221,7.383928299,32.53172463,99108.60938,6.284222126
217.625,22.375,1998,2,335.0848359,0.045806315,22.70749855,35.14425182,102045.8281,2.881526947
226.375,47.125,1998,2,335.258975,0.340491951,8.224285126,32.46246356,99564.89844,5.696117878
201.625,11.625,1998,2,350.4025467,0.077415824,25.58857155,34.53946888,101468.7188,9.821111679
214.875,20.375,1998,2,331.8066387,0.069338508,23.70392799,35.10682482,101969.9063,5.13983345
182.125,0.125,1998,2,354.8904805,0.100191601,28.65392876,34.70540136,101093.4844,5.839221954
229.875,30.125,1998,2,336.8566435,0.083555408,17.51285744,34.71578765,101776.1172,5.456809998
199.625,8.125,1998,2,346.0750388,0.084023274,26.95035553,34.60071468,101280.2813,9.943844795
204.625,13.625,1998,2,340.2470152,0.058932442,25.14749908,34.54848725,101585.3828,9.482903481
201.625,14.375,1998,2,340.7837926,0.04319765,25.0639286,34.66565681,101631.4844,9.274291039
182.875,0.125,1998,2,353.428674,0.113729537,28.73250008,34.76488453,101090.6406,5.848243237
134.125,20.125,1998,2,325.542104,0.062770098,25.82071304,34.93145829,101793.8594,2.539466381
228.375,49.125,1998,2,327.4063087,0.394874662,8.350357056,32.27141249,99593.41406,5.988666058
200.625,11.125,1998,2,346.755983,0.095103458,25.70964241,34.55183452,101438.6797,9.941713333
211.875,18.375,1998,2,335.9057202,0.100555599,24.32642746,34.50309283,101864.4922,6.893054962
219.375,49.625,1998,2,325.5405128,0.280306369,6.816071033,32.58542508,98762.64844,5.807108879
126.375,28.125,1998,2,300.1283176,0.399498329,21.43464279,34.4891206,101977.0625,4.028490067
182.625,0.125,1998,2,353.0725651,0.098963268,28.7142849,34.74505681,101091.7344,5.839142799
220.875,49.625,1998,2,331.1217811,0.28968825,6.808571339,32.56078762,98887.07813,6.286456585
137.125,5.125,1998,2,374.0348294,0.167919248,27.63964272,34.83341956,101284.8672,6.446660519
226.875,47.125,1998,2,337.4081536,0.354150742,8.242856979,32.41670841,99601.71875,5.559462547
225.375,27.375,1998,2,336.5526124,0.08043474,19.60750008,35.44939518,101979.7344,3.802143574
133.625,28.875,1998,2,298.9302787,0.223056724,20.91107178,34.89923573,101799.1094,1.922634602
137.125,4.125,1998,2,356.4391078,0.1574772,27.95321274,34.89388299,101269.1875,6.077847004
136.875,17.875,1998,2,330.5673748,0.036023054,26.62714195,34.85317725,101742.8516,4.550457954
130.125,31.375,1998,2,310.5383369,0.24415338,18.97928429,34.45481825,101914.4688,3.653368473
217.875,49.875,1998,2,321.0612995,0.272020012,6.744999886,32.59277993,98640.85156,5.208584309
135.625,30.625,1998,2,296.7103841,0.263537496,19.9285717,34.84271401,101773.8203,1.473510861
129.625,31.125,1998,2,304.5818296,0.272188544,19.63964272,34.41610366,101922.7656,4.124962807
137.125,4.875,1998,2,375.4356603,0.172827527,27.67749977,34.86685491,101280.6094,6.349546432
196.875,1.125,1998,2,344.3689325,0.180764103,28.55785751,34.84886467,101062.5703,6.775987625
201.625,13.375,1998,2,346.7674814,0.055038527,25.04642868,34.59481353,101570.4219,9.547895432
222.375,49.375,1998,2,335.4381314,0.321886241,7.185357094,32.55055088,99042.96094,6.34968853
136.875,13.625,1998,2,345.7029978,0.035480801,27.1517849,34.56758189,101557.9688,7.047054768
137.125,12.875,1998,2,335.8535836,0.039165083,27.28428459,34.49469227,101527,7.229971886
137.125,4.625,1998,2,359.9577487,0.184119746,27.75714302,34.90029025,101276.8906,6.264093399
236.875,37.625,1998,2,338.8425517,0.698071539,13.58714294,32.72582567,101168.5313,3.688966274
231.625,48.875,1998,2,332.5429138,0.350742698,9.216785431,32.1505132,99953.46094,6.229549408
197.375,1.875,1998,2,355.5623618,0.108284488,28.55392838,34.78280723,101068.0313,7.438421726
136.875,14.625,1998,2,340.2312087,0.035865914,27.20464134,34.69453204,101601.4453,6.623683929
219.875,49.625,1998,2,327.1590758,0.267796755,6.722142696,32.574727,98801.97656,6.018575668
136.875,8.875,1998,2,350.5717604,0.087583624,27.3928566,34.58369559,101379.0859,7.513134956
215.875,21.125,1998,2,328.4438621,0.055247784,23.21035576,35.16430998,102004.5313,4.37726593
183.375,0.125,1998,2,356.2006924,0.1330394,28.89892769,34.79373533,101088.8906,5.854021072
216.375,21.625,1998,2,332.6416919,0.048107035,22.96535683,35.19987077,102022.6641,3.796513796
192.375,0.125,1998,2,353.8379644,0.099776633,28.6442852,34.87808484,101059.8359,6.227700233
199.875,8.875,1998,2,355.0229139,0.081703924,26.49607086,34.55200493,101317.9688,9.95274353
151.375,3.375,1998,2,373.7252831,0.148707494,28.40214157,34.88870817,101224.125,6.542778015
196.625,0.375,1998,2,352.373764,0.069863833,28.61321449,34.90305334,101057.2109,6.213659286
201.375,10.625,1998,2,361.0666588,0.108437538,25.73857117,34.53249985,101410.1641,9.966670036
197.125,8.625,1998,2,347.2537255,0.103737064,26.76892853,34.72793126,101304.9688,10.0368824
136.875,11.875,1998,2,345.4694337,0.061985299,27.47285652,34.46693081,101487.0156,7.340286255
177.125,47.375,1998,3,372.4079107,0.36149618,3.478709698,33.08683681,100423.0313,4.170062542
149.375,11.125,1998,3,354.4819263,0.049982488,27.41903114,34.88633519,101369.7969,7.058477402
137.625,33.625,1998,3,305.651277,0.478428841,19.05580521,34.67203373,101724.2266,4.823055267
149.875,10.125,1998,3,355.9153421,0.068381064,27.54322624,34.86775792,101331.1875,7.281246185
249.875,2.375,1998,3,377.3381504,0.159728348,29.56064415,33.7963208,100896.9609,2.720073223
148.875,12.375,1998,3,352.9476087,0.039498556,27.29322433,34.87795156,101415.625,6.689524651
147.125,17.625,1998,3,339.868981,0.028965073,26.67161179,34.91947854,101639.4063,4.847456455
249.625,9.125,1998,3,343.437637,0.109941155,27.4141922,34.13125825,101021.7109,6.979136467
215.875,47.875,1998,3,338.4433465,0.312813103,6.407742023,32.78976095,100892.9219,2.204241753
178.625,47.625,1998,3,369.9526357,0.375376254,3.496129036,33.07220608,100334.7109,4.356282234
234.375,46.375,1998,3,304.817001,0.539405286,10.44741917,31.56564233,101464.6719,1.914318562
246.625,18.875,1998,3,329.899097,0.137003943,24.68774223,33.86662281,101423.7109,5.217660904
168.125,45.625,1998,3,387.0629615,0.280523896,3.094516039,33.36516893,100737.7969,2.413529396
141.375,35.375,1998,3,297.348729,0.370295316,17.88451576,34.53851157,101610.0156,2.899266005
146.125,37.625,1998,3,343.0776694,0.355609268,11.46096706,34.41950017,101479.9141,5.037311554
172.125,46.375,1998,3,375.9841738,0.301263303,4.291935444,33.256495,100652.6484,2.752307892
243.125,30.375,1998,3,343.6856978,0.212382808,16.11999893,33.82318956,101527.1563,5.999038696
142.125,24.125,1998,3,315.8967172,0.072369173,23.84225845,35.09793097,101856.2969,1.898701668
245.375,23.375,1998,3,340.593504,0.241913483,21.20064545,33.62436974,101532.0781,5.90656662
245.625,22.875,1998,3,339.4224384,0.193401784,21.38967705,33.61581838,101518.9688,5.804878235
144.375,21.875,1998,3,321.5578124,0.063303769,25.64741898,35.06660688,101802.2813,2.886657715
149.875,9.875,1998,3,357.7468782,0.070537329,27.60612869,34.871683,101322.2813,7.323570251
148.125,38.375,1998,3,339.5107142,0.39698032,10.56935501,34.3071261,101342.6563,5.69473362
200.875,47.875,1998,3,339.6674981,0.33042264,5.327419281,32.77731991,100410.0859,4.086090088
160.625,43.625,1998,3,362.3573656,0.307788938,3.886128902,33.36048895,100798.3203,2.264657974
147.375,38.125,1998,3,343.5058605,0.493901017,10.80032253,34.31984234,101392.5156,5.565870285
227.125,47.375,1998,3,328.8151462,0.348214,8.060322762,32.69242942,101259.6328,2.584785938
247.125,17.375,1998,3,327.44489,0.124935858,25.34903145,34.13376617,101367.125,5.357551098
249.375,2.125,1998,3,376.8750652,0.1648563,29.53322601,33.89018548,100899.4766,2.286991596
196.625,47.875,1998,3,337.3077867,0.316034645,5.359354496,32.84398854,100205.9141,4.697331429
230.125,47.125,1998,3,327.5773903,0.296554685,9.287741661,32.26694953,101359.6953,2.323071957
214.375,47.875,1998,3,337.9841043,0.28507486,6.247096539,32.79816425,100864.8438,2.290673494
187.125,47.875,1998,3,346.7319767,0.281837016,4.756128788,33.11588293,100042.1641,4.410054207
211.375,47.875,1998,3,336.9201891,0.267869145,6.01935482,32.78021389,100808.5859,2.362530231
226.875,47.375,1998,3,328.125029,0.331300467,8.004193306,32.71702051,101252.4219,2.616102934
142.125,35.625,1998,3,294.4213756,0.307271093,17.39935493,34.49985784,101606.0313,3.492907047
233.625,46.375,1998,3,321.7046373,0.280876309,9.965161324,31.70953301,101454.2969,1.897519827
150.875,39.625,1998,3,354.3965029,0.324267,9.759354591,34.0520401,101135.6484,5.014902115
232.875,46.625,1998,3,302.8806564,0.753116369,10.02741909,31.91999757,101434.0859,1.968416929
141.625,25.875,1998,3,312.1607335,0.094088525,22.60548401,35.03153247,101858.9219,2.445505381
189.125,47.875,1998,3,331.6447294,0.44548443,4.71935463,33.07937574,100028.8359,4.208040714
249.875,6.125,1998,3,335.8278869,0.070914418,28.7319355,34.62592006,100928.75,6.620294571
149.875,39.125,1998,3,332.8478003,0.207204908,10.14903164,34.20233566,101208.3438,5.527187824
142.625,23.625,1998,3,316.6738839,0.076906748,24.05096817,35.10633284,101848.9766,1.963195801
151.875,40.125,1998,3,348.1019167,0.34777987,8.73870945,34.02504331,101072.3984,4.588917732
188.875,47.875,1998,3,339.4426193,0.453806311,4.726129055,33.08200949,100029.1641,4.238370895
136.125,32.125,1998,3,304.556466,0.266965657,19.62870979,34.80099434,101771.0938,3.256544113
216.625,47.875,1998,3,343.0319269,0.241716251,6.586128712,32.7766645,100905.375,2.278515339
151.375,4.125,1998,3,377.7946303,0.105226874,28.54774094,34.91210723,101159.1875,6.476204872
185.125,47.875,1998,3,365.5372673,0.281376898,4.005806446,33.12099183,100082.3672,4.436099529
228.875,47.125,1998,3,326.6734263,0.285954416,8.862257957,32.44468468,101322.6641,2.458083391
212.125,47.875,1998,3,336.4243417,0.327124715,6.10354805,32.77666771,100824.4297,2.301421881
149.125,38.875,1998,3,346.2334119,0.269135177,10.11935425,34.27766925,101263.5625,5.767593384
249.625,8.125,1998,3,342.0114644,0.069248691,28.30967712,34.13367891,100985.6641,7.156165123
201.875,47.875,1998,3,350.5920411,0.311262399,5.209032059,32.80572623,100456.9453,3.914117336
142.125,27.625,1998,3,303.2091963,0.217191607,20.90870857,35.03491884,101832.5938,2.308529139
182.875,47.875,1998,3,363.5905197,0.36317274,3.989354849,33.0897758,100145.3984,4.447701454
151.125,39.625,1998,3,353.4765346,0.347485721,9.6554842,34.03347868,101125.9766,4.937410355
241.375,29.625,1998,4,343.540374,0.122374721,16.21899986,33.74154162,101780.1563,6.290764809
142.375,32.125,1998,4,307.7595583,0.221230417,20.83333206,34.77844012,101851.2188,3.22678256
170.375,43.875,1998,4,348.1168678,0.364931238,5.938666344,33.18479794,101536.7188,8.052329063
160.375,42.125,1998,4,339.0776508,0.371945232,5.326666355,33.35238671,101615.6406,5.675624847
187.875,44.875,1998,4,339.789067,0.308533341,6.272999763,33.34626251,101905.4531,7.28584671
146.875,37.625,1998,4,299.6717815,0.864746213,13.93799973,34.56708598,101850.6719,1.673977852
141.625,33.375,1998,4,312.6654202,0.300145775,20.02499962,34.75715005,101770.875,3.071452141
234.875,4.875,1998,4,369.7463754,0.154171705,28.71433258,34.55153072,101058.4141,6.871968269
195.625,45.375,1998,4,338.2579501,0.252778888,6.17933321,32.98616928,101899.7734,7.741561413
207.625,46.375,1998,4,345.9398427,0.284301579,6.831999779,32.95750475,101650.0547,7.85389328
165.625,43.125,1998,4,351.7340844,0.355971231,5.33833313,33.2410835,101547.0938,7.106401443
220.625,47.375,1998,4,343.4953618,0.334312677,7.617332935,32.81553078,101620.0156,4.462052345
218.875,52.625,1998,4,345.028706,0.305901796,6.332666397,32.73408425,101043.2344,4.555960178
234.875,4.125,1998,4,375.9234328,0.210966006,28.298666,34.54648995,101044.8672,6.285070419
202.125,54.875,1998,4,377.2139562,6.138123989,5.793333054,31.96875596,100355.1328,3.916701794
235.125,1.125,1998,4,376.0014635,0.207207412,28.50533295,34.84455341,101035.2578,3.217976093
236.125,12.375,1998,4,340.0144369,0.148657103,25.222332,34.44955373,101368.9844,8.341099739
178.625,52.125,1998,4,444.9806248,0.49050799,3.388999939,33.25382811,100336.1875,5.344069481
224.875,50.125,1998,4,339.5155162,0.364930838,7.65599966,32.70743006,101535.2422,3.778627872
221.625,47.375,1998,4,338.1934099,0.345827162,7.642999649,32.83695298,101648.4141,4.229016781
237.125,16.125,1998,4,328.4255996,0.13308163,23.25399971,34.49374104,101573.6953,7.534988403
225.625,49.875,1998,4,335.7349109,0.358057588,7.887000084,32.69156235,101578.2813,3.647988319
233.625,47.125,1998,4,311.834655,0.352845043,10.40566635,31.52032039,101802.2266,2.394170046
238.625,20.375,1998,4,336.8456064,0.083779171,21.22933388,34.22305197,101770,6.33175087
194.375,45.375,1998,4,338.0687724,0.333014399,6.004333019,33.07943207,101897.5859,7.665948868
225.625,47.875,1998,4,334.0346864,0.228350788,8.139332771,32.79442608,101724.5547,3.54638648
144.125,32.125,1998,4,322.872487,0.214035481,20.31399918,34.79583865,101921.5156,3.338442326
190.125,45.125,1998,4,338.8451264,0.316117525,6.116333008,33.29192013,101902.6172,7.543682575
235.125,7.125,1998,4,337.0588279,0.099324867,28.01633263,34.38585865,101122.7578,7.9726367
179.625,51.625,1998,4,473.6154187,0.55190891,4.08166647,33.21799129,100419.1484,6.062355995
181.625,44.625,1998,4,346.8894463,0.388928235,5.878666401,33.4628281,101774.5859,7.340138435
150.125,44.875,1998,4,336.9208187,0.641929865,2.18599987,33.11285937,101468.9844,3.478512287
151.375,39.375,1998,4,345.1990573,0.449582547,10.74399948,34.42953932,101800.5313,3.438926697
146.875,42.875,1998,4,283.6875181,1.157796144,2.125,32.98438615,101647.375,2.37569046
212.125,55.875,1998,4,356.3752443,0.427194715,4.879333019,32.42380702,100544.0078,3.423617601
151.625,39.375,1998,4,349.4550087,0.471736431,10.82866669,34.40343988,101800.9063,3.519236088
191.375,45.125,1998,4,338.3574128,0.330379069,5.924999714,33.24123627,101920.5313,7.487067223
238.125,19.375,1998,4,329.0498541,0.083302587,21.66866684,34.18746322,101733.5156,6.6495924
209.125,46.625,1998,4,348.1778607,0.302143812,6.867666721,32.95417196,101589.1016,7.59847641
236.375,12.875,1998,4,331.303492,0.186786206,24.9046669,34.44295496,101396.7266,8.262047768
222.625,47.625,1998,4,338.0404626,0.331858069,7.606999874,32.85149115,101657.9219,4.046898842
186.625,52.625,1998,4,451.6858335,0.458336681,3.650333166,33.20559216,100429.75,6.262217522
152.625,39.875,1998,4,336.340038,0.415830493,10.75666618,34.12668234,101763.9844,3.846163034
239.625,24.375,1998,4,353.1652015,0.081291132,18.7626667,34.47102547,101862.6328,5.62921524
192.125,53.875,1998,4,431.495837,0.512318671,4.547333241,32.76667857,100359.7266,5.437220573
186.625,44.625,1998,4,342.1783528,0.273859203,6.241333008,33.40727067,101918.4531,7.207177162
137.875,34.375,1998,4,286.6589561,0.613690972,19.95899963,34.40942395,101663.5469,0.531632304
177.625,52.375,1998,4,415.9870875,0.584909022,3.13166666,33.27196902,100298.5,5.006434441
146.125,31.875,1998,4,307.7321595,0.227808058,19.98999977,34.83315891,102015.8438,3.411241531
157.125,49.625,1998,4,263.6301489,0.741469622,1.798999906,33.40779614,100987.0313,3.7417202
240.125,25.875,1998,4,350.0898295,0.082439318,18.19866562,34.28792781,101860.3359,5.731509209
226.375,49.625,1998,4,346.772895,0.273235798,8.026666641,32.63917667,101621.2109,3.536429405
176.875,52.625,1998,4,424.6097292,0.556973696,3.177666664,33.26995552,100271.2969,4.682239532
164.125,51.625,1998,4,396.026149,0.561559141,2.392666578,33.22786236,100646.6406,2.223026752
234.875,4.625,1998,4,371.5312286,0.165722892,28.63500023,34.5657078,101053.3906,6.694004059
166.625,52.125,1998,4,370.2563606,0.343120754,3.136999846,33.08592379,100571.4219,2.612285614
141.375,35.375,1998,4,313.4951336,0.39191258,19.49300003,34.56932408,101739.5156,1.879872084
238.625,21.125,1998,4,341.8941752,0.072960801,20.3623333,34.3237552,101802.9844,6.116109371
175.625,44.625,1998,4,340.8349478,0.318008542,6.140999794,33.25189877,101553.3203,8.033472061
181.125,52.125,1998,4,450.4766845,0.446570426,4.200999737,33.33122343,100389,6.003687382
228.875,48.875,1998,4,335.0547167,0.243189231,8.720000267,32.42576903,101731.2188,2.677820206
187.375,52.875,1998,4,450.0996711,0.425817281,3.698999882,33.1356734,100409.9766,6.230700493
152.375,39.625,1998,4,340.1366158,0.417832345,10.85199928,34.22992975,101783.1563,3.802234173
174.625,52.875,1998,4,392.5316922,0.56952697,2.973333359,33.18861109,100279.2188,3.831522942
180.875,44.625,1998,4,341.5510435,0.329376335,5.978332996,33.4326092,101750.7734,7.494997978
138.375,34.375,1998,4,295.3226156,1.58897078,19.7266655,34.45276469,101669.8281,0.835700154
168.625,52.375,1998,4,375.075744,0.346103728,3.174333334,33.07523578,100499.875,3.047357559
178.375,44.375,1998,4,337.0009679,0.3038221,6.546000004,33.36531264,101693.6406,7.801744461
237.125,15.875,1998,4,329.3943383,0.136634201,23.44633293,34.48708534,101559.8203,7.59162569
147.375,31.875,1998,4,313.063583,0.205306455,19.71933365,34.8711921,102065.5938,3.107611656
211.375,55.875,1998,4,376.9949503,0.374066561,4.904666424,32.39384228,100508.3984,3.185788393
218.125,53.125,1998,4,348.0446337,0.27672565,5.806999683,32.73222578,100973.2109,4.407235146
222.875,50.625,1998,4,343.8870992,0.281091779,7.148999691,32.74456561,101409.8359,4.465582848
202.625,46.125,1998,4,339.5345092,0.340996087,6.358333111,32.90033954,101731.2188,8.163550377
156.375,48.875,1998,4,360.5484608,1.851433396,1.786999941,33.46226102,101056.7188,3.68987751
203.875,46.125,1998,4,346.3926363,0.282835811,6.360333443,32.87733042,101724.8828,8.135858536
220.625,51.625,1998,4,348.6464037,0.272017419,6.759333134,32.77251589,101208.6172,4.841890335
169.625,52.625,1998,4,385.6464858,0.407244176,3.600666523,33.10971016,100455.6875,3.274569511
201.125,45.875,1998,4,336.7103145,0.275935441,6.370999813,32.89887053,101783.1094,8.157832146
209.625,55.875,1998,4,362.1969766,2.215502739,5.339333057,32.37610012,100442.2031,2.657801628
199.625,54.125,1998,4,366.0228872,1.463695884,5.409333229,31.88784415,100411.8281,5.201655388
239.375,23.375,1998,4,348.0244438,0.10774491,19.18533325,34.35220629,101851.4922,5.700963974
158.125,47.625,1998,5,343.4034826,0.616906106,2.693871021,33.01206702,101091.5703,3.307169914
197.625,21.875,1998,5,334.2402318,0.070350222,23.65677261,35.35874772,102029.2188,8.580856323
233.625,47.125,1998,5,308.9625843,0.201798528,12.06838703,31.79633918,101565.9375,4.334711075
148.625,44.125,1998,5,172.8878557,6.259126663,3.163548231,32.53245097,101367.9375,2.957184792
235.125,46.625,1998,5,294.3877636,0.897298217,12.65967751,31.47820926,101544.3125,3.79662466
152.125,31.875,1998,5,322.0871907,0.116582826,22.02193451,34.96108991,101965.9219,2.424890995
142.625,35.875,1998,5,310.9816857,0.400954396,20.98225784,34.476753,101588.3906,2.410390854
195.375,54.375,1998,5,389.2564294,0.679680645,5,32.26878119,100515.2813,4.488536835
181.125,25.875,1998,5,338.4627148,0.050263427,23.16580582,35.36832476,102181.7188,5.983758926
192.625,54.125,1998,5,419.7943024,2.852371855,4.709032059,32.68218768,100407.4609,5.868512154
147.125,40.125,1998,5,313.850794,0.738708913,10.55806446,33.37235487,101590.7344,2.366648674
145.125,41.875,1998,5,262.3268403,1.33510828,6.497096539,32.89908051,101441.4063,3.362842321
142.875,37.875,1998,5,284.9578443,0.545967638,17.34451485,33.64256197,101561.1875,2.330303669
144.125,38.125,1998,5,290.2010913,0.713115215,15.85903168,33.73244947,101587.625,2.397735119
142.875,38.125,1998,5,264.2135935,0.645866573,17.06999969,33.56809276,101554.25,2.44547081
193.625,54.375,1998,5,398.7270826,0.624355972,4.629032135,32.47897917,100406.6953,4.772971153
215.625,52.125,1998,5,347.4580361,0.221051455,6.795806408,32.65789717,101650.7109,5.215363503
147.125,34.625,1998,5,298.9532042,0.293100178,21.49709702,34.82410145,101771.4688,2.152071714
190.125,53.625,1998,5,417.3279592,0.509706378,4.846451283,33.04160476,100370.5391,5.850805283
145.875,41.625,1998,5,292.1423909,1.779432654,5.725806236,32.83409429,101479.3672,3.041074276
198.125,21.875,1998,5,335.162728,0.072120272,23.68032265,35.3566429,102028.5625,8.533309937
210.375,13.375,1998,5,346.0680177,0.069545172,25.03096771,34.85513902,101474.2891,9.373226166
207.125,16.375,1998,5,337.7842778,0.057839252,24.11257935,34.849078,101674.8516,9.118549347
143.625,33.375,1998,5,318.8779878,0.14152655,22.34161186,34.74850661,101663.0469,3.073462009
144.875,41.375,1998,5,289.0691076,1.671693325,6.480000019,32.77117294,101469.9688,3.190316677
223.125,3.375,1998,5,375.8084392,0.1645751,28.20064545,34.97574872,101056.8828,4.829050064
150.375,45.125,1998,5,266.2862815,1.702106714,2.89322567,32.58961457,101314.4688,3.227369308
228.875,48.875,1998,5,319.5862593,0.488418818,10.20258045,32.44133741,101639.6719,3.895542145
194.625,54.375,1998,5,404.2022644,1.947569188,4.849999905,32.38844919,100462.5156,5.07951355
213.625,10.875,1998,5,337.2175815,0.106490187,26.10677338,35.03981334,101324.5234,9.537795067
165.125,29.125,1998,5,339.8380785,0.06384299,22.52774239,35.31183898,102066.5313,2.986386299
213.625,10.625,1998,5,337.8547237,0.092875421,26.26645088,35.050919,101309.3359,9.597484589
212.125,52.875,1998,5,367.3144821,0.596058488,6.025806427,32.53856921,101462.7109,5.860253334
144.375,42.875,1998,5,190.6101702,1.228330374,5.457419395,32.96195221,101347.3594,2.443485022
230.625,48.375,1998,5,290.1555716,0.197198808,11.00129032,32.16278517,101599.3672,4.157608986
199.875,54.125,1998,5,347.8852743,5.310002498,6.172580719,31.89715207,100749.8125,5.933228493
157.375,47.375,1998,5,390.5128677,0.346210718,2.880967617,33.04099393,101115.1641,3.423756838
180.125,51.375,1998,5,467.7115404,0.982770801,4.712903023,33.07009953,100611.4141,4.639090538
142.625,36.125,1998,5,311.188284,0.29535237,20.84096718,34.39999318,101585.2188,2.297199965
209.875,53.125,1998,5,377.8225822,0.509082019,6.042902946,32.57920641,101334.5703,6.157975674
161.625,48.625,1998,5,379.8852923,0.386210561,2.938387156,33.10760951,101004.6172,2.507398605
202.375,53.875,1998,5,243.6185494,2.31192255,6.480322361,32.21184453,100899.5859,6.212860107
150.375,33.625,1998,5,295.0541428,0.14054215,20.53419304,34.85066438,101883.5,2.359823465
148.625,31.875,1998,5,300.278661,0.147052839,21.38709641,34.91238838,101861.0469,2.910097599
207.375,23.625,1998,5,333.4850895,0.095701113,21.9751606,35.4380188,102238.9609,8.589078903
147.875,43.625,1998,5,195.658752,4.884115696,3.830967665,32.4125396,101390.5,2.932347775
140.625,33.875,1998,5,324.6615547,0.136239573,24.0948391,34.6156503,101537.4844,2.600674152
150.125,41.375,1998,5,354.6976562,0.380529225,9.118063927,33.37545836,101566.4297,2.448404312
202.625,53.875,1998,5,246.1078807,2.276112556,6.548064232,32.2465035,100911.2734,6.208810806
193.125,23.125,1998,5,329.4081958,0.062201224,23.77709579,35.32151759,102112.0234,8.228538513
154.625,46.625,1998,5,282.5367418,1.462565899,3.210967541,33.04295599,101186.8828,3.538694382
146.375,32.375,1998,5,313.8652781,0.142172992,22.31354713,34.80703962,101778.0781,2.816383362
218.375,51.625,1998,5,326.6147754,0.40480563,7.500645161,32.78322542,101712.3203,4.728196144
197.875,54.125,1998,5,350.2361605,2.755879641,5.767096519,31.89542395,100652.0469,5.969565868
151.125,41.625,1998,5,380.0105203,0.48615399,8.491290092,33.41347128,101564.3594,2.446888924
144.375,41.375,1998,5,218.6616451,1.62253356,6.394838333,32.71558875,101459.1094,3.2012043
144.125,37.875,1998,5,285.0361383,0.820276856,15.95774174,33.8496843,101595.1563,2.390788555
181.875,52.125,1998,5,459.9514472,1.282320733,3.944515944,33.23280996,100503.4844,4.774675846
143.375,32.625,1998,5,316.2504106,0.115908638,22.39225769,34.77952677,101666.0625,3.129534245
146.125,41.625,1998,5,229.5163532,1.62153554,5.59548378,32.84296328,101485.5391,2.978814363
150.125,45.125,1998,5,255.602284,1.961033821,2.777419329,32.57840782,101312.6719,3.307914495
158.625,30.625,1998,5,329.9123471,0.075351864,22.28935432,35.11503792,102075.8125,1.916461468
209.625,53.125,1998,5,371.3076484,0.519579053,6.07677412,32.58936101,101323.6484,6.172268867
166.625,49.625,1998,5,374.7940702,0.346740007,3.403870821,33.20804,100909.9063,2.280982971
222.125,3.375,1998,5,387.9108087,0.169567615,28.06096649,34.93999875,101054.4844,4.977095604
142.625,33.375,1998,5,317.8579113,0.185200468,22.59806442,34.72089082,101623.0156,3.064310551
235.375,0.375,1998,5,388.9054726,0.173447743,27.16612816,35.10292614,101082.3359,3.157052517
149.625,41.125,1998,5,337.5651991,0.350257337,9.675483704,33.32729274,101574.0234,2.505804062
152.125,41.875,1998,5,371.3345832,0.374197781,8.832258224,33.43730897,101559.6016,2.582306385
193.625,22.875,1998,5,331.6477772,0.066835828,23.84064484,35.33064544,102094.7656,8.319966316
152.875,31.625,1998,5,327.236209,0.096966729,21.50354767,34.98287445,101988.4766,2.356777191
141.625,41.625,1998,5,250.0832089,0.641057551,10.30999947,32.76026485,101386.4531,2.994102001
201.125,53.875,1998,5,260.4036423,2.252162218,6.132903099,32.10282686,100840.0469,6.149973869
219.125,51.625,1998,5,338.6622016,0.303789198,7.409031868,32.76315707,101706.3125,4.51214695
153.875,46.375,1998,5,307.4167511,1.729607105,3.108386993,32.98334527,101210.4219,3.501821518
151.875,34.875,1998,5,293.1042235,0.20793049,21.29903221,34.77999276,101883.3906,2.015338421
148.375,40.875,1998,5,324.8660337,0.59917742,7.615160942,33.22098249,101573.75,2.425868988
150.125,44.875,1998,5,261.4407758,1.886240721,3.045483828,32.57724088,101331.8438,3.213306904
211.125,53.125,1998,5,371.2898215,0.393154889,6.039031982,32.53544563,101388.8672,6.026982307
151.125,32.125,1998,5,331.0460508,0.112925701,21.75419235,34.92626756,101935.2188,2.644490719
174.375,50.625,1998,5,435.8291359,0.768296239,4.545161247,33.04117048,100734.5156,3.415714264
189.375,53.625,1998,5,412.539678,0.395609289,4.891613007,33.10804361,100346.7266,5.609457016
199.375,54.125,1998,5,345.2746825,0.747260153,6.249354839,31.89041293,100725.5625,5.933703423
207.875,15.375,1998,5,343.7656936,0.060701411,24.35225677,34.91545516,101606.6875,9.246676445
234.375,46.625,1998,5,308.9747261,0.199528024,12.51677418,31.6662178,101565.6094,4.042773247
196.875,54.125,1998,5,332.7799588,3.314413309,5.711290359,31.98427871,100604.2031,6.013195992
167.625,49.875,1998,5,373.0989242,0.334105194,3.457741737,33.15218824,100883.4688,2.268979549
144.125,38.625,1998,5,252.1357161,0.589278698,15.08096695,33.47929871,101573.9688,2.322208881
182.875,52.375,1998,5,426.1856604,0.338202596,3.637419224,33.28984356,100459.7891,4.764416695
204.625,18.375,1998,5,334.7232487,0.088441662,23.6958065,34.78813326,101809.4297,10.58081245
143.125,33.375,1998,5,317.5306156,0.165373549,22.55064392,34.73830682,101643.0078,3.059666157
225.625,3.375,1998,5,367.3799083,0.176614597,28.21193504,34.95666748,101061.4766,4.506587982
190.875,53.875,1998,5,417.6590328,0.451700419,4.412258148,32.96898913,100363.3281,5.722838402
188.125,24.125,1998,5,334.8469574,0.059860017,23.48386955,35.43061912,102142.7188,7.17374897
166.875,28.875,1998,5,331.7679753,0.057826269,23.20806313,35.30681908,102080.6172,3.157756805
214.375,9.875,1998,5,341.5661659,0.114737965,26.59967613,35.02957153,101260.8359,9.71139431
150.875,45.375,1998,5,285.9753236,1.525361896,3.039999962,32.61739284,101296.7813,3.223106861
144.125,33.875,1998,5,314.9980672,0.190654576,22.58774185,34.75047499,101674.9063,3.032819271
225.125,50.125,1998,5,334.76464,0.395451903,9.617096901,32.72100294,101686.8672,3.655792236
162.625,48.625,1998,5,375.0445868,0.399490803,3.062903166,33.17269784,101000.7969,2.435852051
149.375,32.375,1998,5,300.5161308,0.160633892,20.79225731,34.89123183,101877.4375,2.850446224
150.375,32.125,1998,5,319.77527,0.123255216,21.03193474,34.91226465,101913.1016,2.782228947
231.875,31.875,1998,6,352.2858112,0.089793555,17.28800011,34.17548543,102086.5703,6.336841106
164.875,45.375,1998,6,341.698259,0.388387222,5.817666531,33.053572,101646.2344,2.046507359
202.375,19.625,1998,6,340.7873336,0.078395359,24.6693325,35.09616995,101726.9609,4.124876976
228.125,31.125,1998,6,348.2718531,0.103446461,17.38533211,34.29111898,102269.5469,6.538399696
165.125,32.125,1998,6,350.8660494,0.089806907,22.42333221,34.87332022,101606.7422,2.020256281
227.375,52.375,1998,6,301.3202303,0.392669708,11.16466618,32.18901038,101991.8594,7.853941917
164.875,47.125,1998,6,346.7030449,0.458681554,4.773666382,33.0568682,101553.4844,2.848840952
204.625,10.125,1998,6,347.5091432,0.107449353,27.42299843,34.9888311,101212.6641,8.892109871
203.375,15.375,1998,6,345.2971911,0.045736615,25.48566628,34.84212148,101506.8438,8.622130394
239.625,33.625,1998,6,311.8251684,0.827383041,15.67866611,33.39247578,101548.5703,6.95817852
224.125,53.375,1998,6,281.3954078,0.735786617,9.840000153,32.48635107,102123.6016,5.761820793
230.125,51.625,1998,6,276.7197783,1.248128295,12.64833355,31.85992819,101843.9531,5.838496208
202.625,18.875,1998,6,339.033607,0.085716747,24.8769989,35.03097123,101702.7031,4.804636955
148.125,15.875,1998,6,371.244627,0.038797867,28.46099854,34.76424301,101281.9219,6.302918911
150.125,41.125,1998,6,311.9918534,0.665696621,13.32999992,33.58373952,101377.8281,1.9202528
202.375,20.375,1998,6,342.4264189,0.067478977,24.55666542,35.16728067,101748.2578,5.288047791
204.375,11.125,1998,6,340.8378211,0.055508845,27.05266571,34.93819749,101262.4766,8.957593918
172.375,46.625,1998,6,365.5323604,0.305509835,5.919332981,33.04028988,101542.7813,3.235453844
165.125,32.375,1998,6,331.0027329,0.106068924,22.07166672,34.86817825,101596.3125,1.895980954
164.625,44.875,1998,6,365.5831923,0.169313237,6.236666679,33.0491311,101666.9844,1.931289315
146.375,39.125,1998,6,288.64198,0.447568953,17.3983326,33.88614655,101262.1406,1.767649412
203.375,0.875,1998,6,399.0777166,0.315245211,26.55966568,35.46036232,101098.1797,5.284402847
205.125,7.125,1998,6,360.4114449,0.108416103,28.19733238,34.91232431,101118.8281,5.843046188
151.625,40.375,1998,6,256.1150137,0.736190259,13.99666595,33.87828827,101385.8594,1.900465012
171.125,46.375,1998,6,375.8931191,0.382059783,5.735333443,33.05025572,101568.6172,2.984134674
148.375,16.125,1998,6,370.7229912,0.031452198,28.52366638,34.75771844,101293.0078,6.285782337
152.375,19.375,1998,6,361.4007531,0.046439342,28.74733353,34.97954887,101443.6484,5.718280792
149.625,17.125,1998,6,363.8997194,0.028200325,28.40766525,34.76180226,101340.0313,6.114798546
143.875,40.125,1998,6,271.7869249,0.462258697,13.62599945,33.08221316,101268.4844,2.324996948
159.875,25.875,1998,6,367.412555,0.038456392,28.19733238,35.32762772,101718.7656,1.573834419
145.375,14.125,1998,6,373.3402661,0.034019638,28.72333336,34.72278565,101205.7266,6.471497059
141.125,35.125,1998,6,268.1881397,0.763600647,21.99300003,34.10715717,101066.6094,0.934224367
203.625,0.625,1998,6,417.6543812,0.359647721,26.33133316,35.47557485,101100.9063,5.243898392
205.125,1.375,1998,6,403.4034911,0.230831146,26.7796669,35.42587769,101110.5234,5.159984589
222.375,56.125,1998,6,284.2286774,1.069543123,9.80566597,32.33124948,101967.7188,3.985288143
221.625,56.625,1998,6,266.1694951,0.654189169,10.03933334,32.28890151,101951.9922,3.552989483
205.125,4.125,1998,6,357.6551487,0.180959389,28.50966644,35.08587849,101092.3906,5.443054199
164.875,43.125,1998,6,343.6423195,0.338248342,7.008999825,32.94706446,101723.9063,1.361417532
163.625,28.875,1998,6,376.1670686,0.056182478,25.37299919,35.2051326,101724.2813,1.999195933
168.125,45.625,1998,6,375.3518584,0.256612092,5.878333092,33.06726295,101630.0625,2.45403862
158.625,46.125,1998,6,366.3389875,0.413925767,5.468999863,32.94245052,101600.6719,1.703063607
219.375,57.625,1998,6,261.9624456,0.809698641,9.639332771,32.26258799,101929.4844,2.675503492
154.625,21.375,1998,6,366.7318859,0.043437466,28.62199974,34.97122234,101541.1406,5.291240692
171.625,46.375,1998,6,370.5389156,0.365506053,5.897999763,33.04848737,101564.9063,3.041090965
162.875,44.375,1998,6,373.1399399,0.42999953,6.440666676,33.02147341,101682.875,1.529401302
203.125,16.625,1998,6,346.4389434,0.055727098,25.01399994,34.85830951,101576.6484,8.518255234
229.625,31.375,1998,6,350.1254856,0.077226885,17.10499954,34.31444269,102201.1641,6.453453541
234.875,32.625,1998,6,358.7052654,0.109819166,16.81433296,33.82124192,101898.5703,6.442013264
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223.625,55.375,1998,6,254.0119794,0.572579682,9.718999863,32.35807914,101980.2813,4.832585335
147.625,15.625,1998,6,373.0767783,0.035056356,28.32433319,34.77024513,101269.625,6.263905525
231.625,50.875,1998,6,406.377431,2.346036673,11.92000008,31.44828698,101817.7344,4.456421852
230.375,51.625,1998,6,290.9102775,1.502196908,12.53166676,31.72908276,101829.2031,5.834415436
223.875,55.375,1998,6,256.7038885,0.762672961,9.785666466,32.34352213,101970.4531,4.941648483
241.625,33.625,1998,6,345.1584201,0.306353539,17.96800041,33.63623673,101446.5391,3.847361088
225.625,52.875,1998,6,301.3390747,0.510479748,10.33799934,32.48053342,102087.2266,6.282875538
215.625,27.125,1998,6,325.2325384,0.149208933,20.50899887,35.47890741,102369.3906,8.17285347
216.875,58.875,1998,6,275.6743706,0.830714881,10.17599964,32.0186452,101862.7422,2.349065304
219.625,57.375,1998,6,263.6818687,0.876700819,9.663666725,32.30808777,101942.375,2.775214434
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232.875,46.875,1998,7,333.969625,0.153265804,14.49741936,32.09277114,101852.5859,4.254965305
164.375,51.625,1998,7,353.2884933,0.41292128,9.257419586,32.85823631,101088.8438,1.785131216
127.375,33.375,1998,7,280.5928033,0.324101835,25.72806358,31.37990224,100594.9688,2.322326422
158.875,37.625,1998,7,352.1237261,0.128105223,20.79645157,33.96194822,101451.4531,3.318909407
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161.875,50.875,1998,7,366.1136925,0.405756176,9.354838371,32.77859521,101075.4531,1.372741342
234.875,46.625,1998,7,267.2342703,0.643906951,14.75322533,31.61433375,101794.4688,3.463793278
244.875,26.875,1998,7,394.2307635,0.172860116,20.72903252,33.56388021,101231.3438,5.995675087
201.625,53.875,1998,7,248.4411825,0.879992902,10.6351614,32.21618932,101300.6016,1.224537373
164.625,51.625,1998,7,354.9918973,0.467621833,9.164838791,32.86987877,101092,1.839400768
137.125,27.875,1998,7,382.4088652,0.02398221,28.62161255,34.5588606,100980.9063,2.980092287
187.375,48.875,1998,7,337.6542815,0.299320104,9.087096214,32.71961087,101798.7266,4.220835686
217.375,49.875,1998,7,357.1852748,0.299481929,11.58967686,32.69810021,101919.1094,4.193139076
233.125,44.875,1998,7,328.1052715,0.146347016,15.39516068,32.22850087,101913.4297,4.242623329
223.375,50.375,1998,7,354.5485332,0.300766945,12.64838696,32.73984396,101962.6953,3.391655445
235.125,40.375,1998,7,348.9417845,0.916716158,12.39903164,32.78507257,101729.3594,7.592503071
164.125,37.875,1998,7,352.3110043,0.132828072,20.7251606,34.20103049,101700.5781,4.057790756
146.375,37.375,1998,7,317.9754454,0.133769825,22.58709717,33.79111594,100946.1172,1.08918786
146.375,43.125,1998,7,272.527028,0.606698632,13.7816124,32.64562351,101082.7734,1.943905354
241.625,33.125,1998,7,378.7692126,0.188291952,19.00967598,33.67741781,101310.3203,3.955278397
142.375,34.875,1998,7,336.8184639,0.059694923,25.75064468,34.10917735,100874.4063,1.844950199
195.875,54.125,1998,7,251.1409237,2.035493851,9.953548431,32.08438641,101315.6719,2.082592964
180.125,9.875,1998,7,348.8510337,0.072938509,27.89451599,34.96043324,101173.3359,7.608465195
147.375,43.625,1998,7,284.624423,1.911149144,13.14128971,32.55660439,101102.3828,2.137710094
184.625,53.875,1998,7,322.7168336,0.596671462,8.451934814,33.20473796,101384.1641,2.858742237
143.875,36.375,1998,7,308.6900321,0.167724952,23.47677422,33.89569753,100916.4609,0.934136569
169.875,37.875,1998,7,362.5284487,0.239121065,19.20387077,34.1765576,102010.0469,3.252916336
139.625,40.875,1998,7,339.7260809,0.179985955,20.71516037,33.02914721,100807.2734,2.698157072
235.375,46.375,1998,7,247.0740097,1.06116116,15.20322514,31.73905909,101791.7344,2.961970806
137.125,13.875,1998,7,393.0344428,0.03587034,29.72129059,34.73032004,101057.1563,2.512107372
151.375,36.875,1998,7,353.0127859,0.156741172,21.96451569,34.22616112,101144.8281,3.572968483
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133.375,37.
gitextract_bzf1plzq/
├── .github/
│ └── workflows/
│ └── python-package.yml
├── .gitignore
├── LICENSE
├── README.md
├── data/
│ ├── co2_gnnwr.csv
│ ├── co2_gnnwr_pre.csv
│ ├── co2_gtnnwr.csv
│ ├── co2_gtnnwr_predict.csv
│ ├── demo_data_gtnnwr.csv
│ ├── demo_predict_data.csv
│ ├── distances.csv
│ ├── pm25_data.csv
│ ├── pm25_predict_data.csv
│ ├── simulated_data.csv
│ └── simulated_predict_data.csv
├── demo/
│ ├── demo_gnnwr_en_US.ipynb
│ ├── demo_gnnwr_zh_CN.ipynb
│ ├── demo_gtnnwr_en_US.ipynb
│ └── demo_gtnnwr_zh_CN.ipynb
├── demo_result/
│ ├── gnnwr/
│ │ ├── dataset/
│ │ │ ├── test_dataset/
│ │ │ │ ├── dataframe.csv
│ │ │ │ ├── dataset_info.json
│ │ │ │ ├── distances.npy
│ │ │ │ └── scaledDataframe.csv
│ │ │ ├── train_dataset/
│ │ │ │ ├── dataframe.csv
│ │ │ │ ├── dataset_info.json
│ │ │ │ ├── distances.npy
│ │ │ │ └── scaledDataframe.csv
│ │ │ └── val_dataset/
│ │ │ ├── dataframe.csv
│ │ │ ├── dataset_info.json
│ │ │ ├── distances.npy
│ │ │ └── scaledDataframe.csv
│ │ ├── gnnwr_result.csv
│ │ ├── runs/
│ │ │ ├── events.out.tfevents.1768553335.zhjw.19452.0
│ │ │ └── events.out.tfevents.1768553402.zhjw.10108.0
│ │ └── writes/
│ │ ├── events.out.tfevents.1768553370.zhjw.19452.1
│ │ └── events.out.tfevents.1768553438.zhjw.10108.1
│ └── gtnnwr/
│ ├── gtnnwr_result.csv
│ └── runs/
│ ├── events.out.tfevents.1768553480.zhjw.18788.0
│ └── events.out.tfevents.1768553580.zhjw.19340.0
├── doc/
│ ├── Makefile
│ ├── build/
│ │ ├── doctrees/
│ │ │ ├── environment.pickle
│ │ │ └── index.doctree
│ │ └── html/
│ │ ├── .buildinfo
│ │ ├── _sources/
│ │ │ └── index.rst.txt
│ │ ├── _static/
│ │ │ ├── _sphinx_javascript_frameworks_compat.js
│ │ │ ├── basic.css
│ │ │ ├── css/
│ │ │ │ ├── badge_only.css
│ │ │ │ └── theme.css
│ │ │ ├── doctools.js
│ │ │ ├── documentation_options.js
│ │ │ ├── jquery.js
│ │ │ ├── js/
│ │ │ │ ├── badge_only.js
│ │ │ │ └── theme.js
│ │ │ ├── language_data.js
│ │ │ ├── pygments.css
│ │ │ ├── searchtools.js
│ │ │ └── sphinx_highlight.js
│ │ ├── genindex.html
│ │ ├── index.html
│ │ ├── objects.inv
│ │ ├── py-modindex.html
│ │ ├── search.html
│ │ └── searchindex.js
│ ├── make.bat
│ └── source/
│ ├── conf.py
│ └── index.rst
├── requirements.txt
├── src/
│ └── gnnwr/
│ ├── __init__.py
│ ├── datasets.py
│ ├── models.py
│ ├── networks.py
│ └── utils.py
└── tests/
└── test_olr.py
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FILE: src/gnnwr/datasets.py
class baseDataset (line 28) | class baseDataset(Dataset):
method __init__ (line 50) | def __init__(self, data=None, x_column: list = None, y_column: list = ...
method __len__ (line 85) | def __len__(self):
method __getitem__ (line 91) | def __getitem__(self, index):
method scale (line 108) | def scale(self, scale_fn, scale_params):
method getScaledDataframe (line 151) | def getScaledDataframe(self):
method rescale (line 159) | def rescale(self, x, y):
method save (line 199) | def save(self, dirname, exist_ok=False):
method read (line 241) | def read(self, dirname):
class predictDataset (line 284) | class predictDataset(Dataset):
method __init__ (line 295) | def __init__(self, data, x_column, process_fn="minmax_scale", scale_in...
method __len__ (line 343) | def __len__(self):
method __getitem__ (line 349) | def __getitem__(self, index):
method rescale (line 361) | def rescale(self, x, y):
method minmax_scaler (line 392) | def minmax_scaler(self, x, min=None, max=None):
method standard_scaler (line 411) | def standard_scaler(self, x, mean=None, std=None):
function BasicDistance (line 431) | def BasicDistance(x, y):
function ManhattanDistance (line 445) | def ManhattanDistance(x, y):
function init_dataset (line 456) | def init_dataset(data,
function init_dataset_split (line 597) | def init_dataset_split(train_data,
function init_dataset_cv (line 881) | def init_dataset_cv(data, test_ratio, k_fold, x_column, y_column, spatia...
function init_predict_dataset (line 929) | def init_predict_dataset(data,
function load_dataset (line 1026) | def load_dataset(directory, use_class=baseDataset):
function _init_gnnwr_distance (line 1034) | def _init_gnnwr_distance(refer_data, train_data, val_data, test_data, sp...
function _init_gnnwr_distance_pred (line 1062) | def _init_gnnwr_distance_pred(refer_data, pred_data, spatial_fun=BasicDi...
function _init_gtnnwr_distance (line 1073) | def _init_gtnnwr_distance(refer_data,
function _init_gtnnwr_distance_pred (line 1102) | def _init_gtnnwr_distance_pred(refer_data,
function _init_gnnwr_spnn_distance (line 1122) | def _init_gnnwr_spnn_distance(
function _init_gnnwr_spnn_distance_pred (line 1151) | def _init_gnnwr_spnn_distance_pred(
function _init_gtnnwr_stpnn_distance (line 1170) | def _init_gtnnwr_stpnn_distance(
function _init_gtnnwr_stpnn_distance_pred (line 1217) | def _init_gtnnwr_stpnn_distance_pred(
FILE: src/gnnwr/models.py
class GNNWR (line 19) | class GNNWR:
method __init__ (line 103) | def __init__(
method init_optimizer (line 198) | def init_optimizer(self, optimizer, optimizer_params=None):
method __train (line 319) | def __train(self):
method __valid (line 361) | def __valid(self):
method __evaluate (line 406) | def __evaluate(self, dataset):
method run (line 442) | def run(self, max_epoch=1, early_stop=-1,**kwargs):
method predict (line 523) | def predict(self, dataset):
method predict_coef (line 564) | def predict_coef(self, dataset):
method load_model (line 597) | def load_model(self, path, use_dict=False, map_location=None):
method gpumodel_to_cpu (line 627) | def gpumodel_to_cpu(self, path, save_path, use_model=True):
method getLoss (line 650) | def getLoss(self):
method add_graph (line 661) | def add_graph(self):
method result (line 676) | def result(self, path=None, use_dict=False, map_location=None):
method reg_result (line 749) | def reg_result(self, filename=None, model_path=None, use_dict=False, o...
method getCoefs (line 857) | def getCoefs(self):
method __str__ (line 874) | def __str__(self) -> str:
method __repr__ (line 879) | def __repr__(self) -> str:
class GTNNWR (line 885) | class GTNNWR(GNNWR):
method __init__ (line 972) | def __init__(self,
FILE: src/gnnwr/networks.py
function default_dense_layer (line 6) | def default_dense_layer(insize, outsize):
class LinearNetwork (line 29) | class LinearNetwork(nn.Module):
method __init__ (line 50) | def __init__(self, insize, outsize, drop_out=0, activate_func=None, ba...
method reset_parameter (line 70) | def reset_parameter(self):
method forward (line 75) | def forward(self, x):
method __str__ (line 83) | def __str__(self) -> str:
method __repr__ (line 89) | def __repr__(self) -> str:
class SWNN (line 92) | class SWNN(nn.Module):
method __init__ (line 113) | def __init__(self, dense_layer=None, insize=-1, outsize=-1, drop_out=0...
method forward (line 140) | def forward(self, x):
class STPNN (line 146) | class STPNN(nn.Module):
method __init__ (line 168) | def __init__(self, dense_layer, insize, outsize, drop_out=0.2, activat...
method forward (line 189) | def forward(self, x):
class STNN_SPNN (line 200) | class STNN_SPNN(nn.Module):
method __init__ (line 221) | def __init__(self, STNN_insize:int, STNN_outsize, SPNN_insize:int, SPN...
method forward (line 232) | def forward(self, input1):
FILE: src/gnnwr/utils.py
class OLS (line 11) | class OLS:
method __init__ (line 20) | def __init__(self, dataset, xName: list, yName: list):
class DIAGNOSIS (line 33) | class DIAGNOSIS:
method __init__ (line 45) | def __init__(self, weight, x_data, y_data, y_pred):
method hat (line 79) | def hat(self):
method F1_Global (line 85) | def F1_Global(self):
method F2_Global (line 99) | def F2_Global(self):
method F3_Local (line 116) | def F3_Local(self):
method AIC (line 146) | def AIC(self):
method AICc (line 152) | def AICc(self):
method R2 (line 160) | def R2(self):
method Adjust_R2 (line 167) | def Adjust_R2(self):
method RMSE (line 174) | def RMSE(self):
class Visualize (line 182) | class Visualize:
method __init__ (line 192) | def __init__(self, data, lon_lat_columns=None, zoom=4):
method display_dataset (line 222) | def display_dataset(self, name="all", y_column=None, colors=None, step...
method coefs_heatmap (line 267) | def coefs_heatmap(self, data_column, colors=None, steps=20, vmin=None,...
method dot_map (line 296) | def dot_map(self, data, lon_column, lat_column, y_column, zoom=4, colo...
FILE: tests/test_olr.py
class OLS_SM (line 8) | class OLS_SM:
method __init__ (line 19) | def __init__(self, dataset, xName: list, yName: list):
class OLS_SKLEARN (line 31) | class OLS_SKLEARN:
method __init__ (line 42) | def __init__(self, dataset, xName: list, yName: list):
class TestOLS (line 55) | class TestOLS(unittest.TestCase):
method test_simulated (line 60) | def test_simulated(self):
method test_pm25 (line 67) | def test_pm25(self):
method test_sio3 (line 76) | def test_sio3(self):
method test_co2_01 (line 85) | def test_co2_01(self):
method test_co2_02 (line 92) | def test_co2_02(self):
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Download .json
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// ... and 12 more files (download for full content)
About this extraction
This page contains the full source code of the zjuwss/gnnwr GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 73 files (25.6 MB), approximately 6.7M tokens, and a symbol index with 172 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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