https://github.com/udaylab/geoanalytics
https://github.com/udaylab/geoanalytics
artificial-intelligence imputation numpy python raster-data statistics
Last synced: 6 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/udaylab/geoanalytics
- Owner: UdayLab
- License: gpl-3.0
- Created: 2022-05-05T05:47:35.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-07-24T05:14:23.000Z (3 months ago)
- Last Synced: 2025-07-24T07:53:51.952Z (3 months ago)
- Topics: artificial-intelligence, imputation, numpy, python, raster-data, statistics
- Language: Jupyter Notebook
- Size: 24.5 MB
- Stars: 5
- Watchers: 3
- Forks: 37
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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[Click here for more information](https://pepy.tech/project/geoAnalytics)
***
# Table of Contents
- [Introduction](#introduction)
- [Recent updates](#recent-updates)
- [Features](#features)
- [Maintenance](#Maintenance)
- [Try your first geoAnalytics program](#try-your-first-geoAnalytics-program)
- [License](#License)
- [Documentation](#Documentation)
- [Getting Help](#Getting-Help)
- [Discussion and Development](#Discussion-and-Development)
- [Contribution to geoAnalytics](#Contribution-to-geoAnalytics)
- [Real-World Case Studies](#real-world-case-studies)
***
# Introduction
geoAnalytics is an open-source Python-based Machine Learning library developed to discover various forms of
useful information hidden in the raster data. The algorithms provided in this library cover a wide-spectrum
of machine learning tasks, such as imputation, image fusion, clustering, classification, one class
classification, and pattern mining. This library being platform independent can run any operating system.
***
# Recent Updates
- **Version 2025.7.2:**
In this latest version, the following updates have been made:
- Included new algorithms in patternMining, **CorrelatedPatternMining**, **CoveragePatternMining**, **FaultTolerantFrequentPatternMining**, **FrequentPatternMining**, **LocalPeriodicPatternMining**, **PartialPeriodicFrequentPatternMining**, **PartialPeriodicPatternMining**, **PartialPeriodicPatternInMultipleTimeSeries**, **PeriodicCorrelatedPatternMining**, **RecurringPatternMining**, **RelativeFrequentPatternMining**, and **PeriodicFrequentPatternMining** for identifying the patterns.
- Included new algorithms in normalization.
- Test cases are implemented using geoanalytics package.
Total number of algorithms: 50+
***
# Features
- ✅ Tested to the best of our possibility
- 🔋 Highly optimized to our best effort, light-weight, and energy-efficient
- 👀 Proper code documentation
- 🍼 Sample examples of using various algorithms at [./tests](https://github.com/UdayLab/geoanalytics/tree/main/tests) folder
- 🤖 Works with AI libraries such as TensorFlow, PyTorch, and sklearn.
- ⚡️ Supports Cuda
- 🖥️ Operating System Independence
- 🔬 Knowledge discovery in static data and streams
- 🐎 Snappy
- 🐻 Ease of use
***
# Maintenance
| **Operation** | **Command** |
|----------------|------------------------------------------|
| Installation | ``pip install geoAnalytics`` |
| Upgradation | ``pip install --upgrade geoAnalytics`` |
| Uninstallation | ``pip uninstall geoAnalytics`` |
| Information | ``pip show geoAnalytics`` |
***
# *Try your first geoAnalytics program*
```shell
$ python
```
### _Install the Necessary Ubuntu Packages_
```shell
$ !apt update
```
```shell
$ !apt install -y nco cdo gdal-bin
```
```shell
$ !which ncrename
```
```
Output: /usr/bin/ncrename
```
```shell
$ !which cdo
```
```
Output: /usr/bin/cdo
```
```shell
$ !which gdal_translate
```
```
Output: /usr/bin/gdal_translate
```
### _Install the geoAnalytics package_
```python
!pip install -U geoanalytics
```
### _Knowing the information of geoAnalytics package_
```python
pip show geoanalytics
```
```
Output:
Name: geoanalytics
Version: 2025.6.10.3
Summary: This software is being developed at the University of Aizu, Aizu-Wakamatsu, Fukushima, Japan
Home-page: https://github.com/UdayLab/geoanalytics
Author:
Author-email: Rage Uday Kiran
License: GPLv3
Location: /usr/local/lib/python3.11/dist-packages
Requires: deprecated, discord.py, fastparquet, matplotlib, mplcursors, networkx, numba, numpy, pandas, Pillow, plotly, psutil, psycopg2-binary, resource, scikit-learn, shapely, sphinx, sphinx-rtd-theme, tqdm, urllib3, validators
Required-by:
```
### _Download any dataset with extension of both `.img` and `.lbl` from the below link_
#### _Link_ --> https://data.darts.isas.jaxa.jp/pub/pds3/sln-l-mi-5-map-v3.0/
#### _Example Direction :_ --> https://data.darts.isas.jaxa.jp/pub/pds3/sln-l-mi-5-map-v3.0/lon042/data/
#### _Upload the `.lbl` file `.img` file_
## **Operatioin 1: Raster to CSV**
### **_Step1: Import RasterToTSV package from geoAnalytics.conversion_**
```python
from geoanalytics.conversion import Raster2CSV
```
### **_Step2: Pass the ``lbl`` file as input and give desired `outputFile` name also specify the `inputBand` value as well as `ouputBand` value_**
```python
converter = Raster2CSV.Raster2CSV(inputFile='MI_MAP_03_S16E035S17E036SC.lbl', outputFile='Moon.csv', startBand=1, endBand=9)
```
### **_Step3: Convert the raster file into a CSV file_**
```python
converter.run()
```
```
Output:
Processing: MI_MAP_03_S16E035S17E036SC.lbl
Done. Output saved to: Moon.csv
```
## **Operation 2 : Clustering the Data**
### **_Step 1.1: Read the obtained CSV into a dataframe_**
```python
import pandas as pd
df = pd.read_csv('Moon.csv', sep='\t')
df
```
| x | y | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|-------------|-------------|------|------|------|------|------|------|------|------|------|
| 1061317.265 | -485173.607 | 1928 | 3446 | 3859 | 3913 | 4026 | 3999 | 4236 | 5245 | 6513 |
| 1061332.071 | -485173.607 | 1924 | 3480 | 3876 | 3930 | 4059 | 3996 | 4243 | 5234 | 6518 |
| 1061346.877 | -485173.607 | 1904 | 3476 | 3834 | 3923 | 4047 | 3992 | 4238 | 5222 | 6523 |
| 1061361.684 | -485173.607 | 1874 | 3452 | 3801 | 3897 | 3959 | 3988 | 4228 | 5210 | 6518 |
| 1061376.490 | -485173.607 | 1907 | 3464 | 3777 | 3868 | 3974 | 3983 | 4218 | 5198 | 6504 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1091566.583 | -515482.151 | 2090 | 3719 | 4007 | 3996 | 4117 | 4056 | 4310 | 5374 | 6633 |
| 1091581.390 | -515482.151 | 2098 | 3734 | 4038 | 4020 | 4177 | 4083 | 4327 | 5389 | 6659 |
| 1091596.196 | -515482.151 | 2114 | 3767 | 4046 | 4040 | 4213 | 4110 | 4332 | 5393 | 6685 |
| 1091611.002 | -515482.151 | 2123 | 3813 | 4019 | 4056 | 4214 | 4136 | 4331 | 5397 | 6711 |
| 1091625.809 | -515482.151 | 2125 | 3784 | 4011 | 4051 | 4184 | 4136 | 4329 | 5402 | 6737 |
`4194304 rows × 11 columns`
### **_Step2: Import any clustering algorithm from geoanalytics.clustering_**
```python
!pip install fuzzy-c-means
from geoanalytics.clustering import FuzzyCMeans
obj = FuzzyCMeans.FuzzyCMeans(dataframe=df)
```
### **_Step3: Execute the run() method to cluster the data_**
```python
labels, centers = obj.run(n_clusters=4)
```
### **_Step4: Print the labels_**
```python
labels
```
| x | y | labels |
|-------------|-------------|--------|
| 1061317.265 | -485173.607 | 2 |
| 1061332.071 | -485173.607 | 2 |
| 1061346.877 | -485173.607 | 2 |
| 1061361.684 | -485173.607 | 2 |
| 1061376.490 | -485173.607 | 2 |
| ... | ... | ... |
| 1091566.583 | -515482.151 | 2 |
| 1091581.390 | -515482.151 | 0 |
| 1091596.196 | -515482.151 | 0 |
| 1091611.002 | -515482.151 | 0 |
| 1091625.809 | -515482.151 | 0 |
4194304 rows × 3 columns
### **_Step5: Print the centers_**
```python
centers
```
```
Output:
array([[2098.51620472, 3762.45416178, 4099.00372289, 4146.23025903,
4295.80971825, 4229.78337628, 4443.6879668 , 5471.85298977,
6790.96720993],
[2274.54605523, 4040.75198398, 4374.89922669, 4395.57058102,
4538.50885801, 4454.45212911, 4677.95182781, 5739.96854105,
7095.06428559],
[1984.82196498, 3578.0340764 , 3908.79548852, 3961.66894715,
4108.90409919, 4049.30456217, 4255.81099407, 5258.24055464,
6549.13133772],
[1896.71176664, 3421.42972113, 3741.56611478, 3802.88625245,
3946.94784255, 3896.3217178 , 4093.18671711, 5066.07543066,
6327.35005223]])
```
### **_Step6: To know the run time and memory consumption of Algorithm, print the statistical values_**
```python
obj.getRuntime()
obj.getMemoryRSS()
obj.getMemoryUSS()
```
```
Output:
Total Execution time of proposed Algorithm: 407.7022657394409 seconds
Memory (RSS) of proposed Algorithm in KB: 1655512.0
Memory (USS) of proposed Algorithm in KB: 1634900.0
```
### **_Step7: Save the labels and centers_**
```python
obj.save(outputFileLabels='FuzzyCMeansLabels.csv', outputFileCenters='FuzzyCMeansCenters.csv')
```
```
Output:
Labels saved to: FuzzyCMeansLabels.csv
Cluster centers saved to: FuzzyCMeansCenters.csv
```
## **Operation3: CSV to Raster**
### **_Step1: Import CSV2Raster from geoanalytics.conversion_**
```python
from geoanalytics.conversion import CSV2Raster as CSV2Raster
```
### **_Step2: Pass the dataFrame and give desired outputFile name_**
```python
process = CSV2Raster.CSV2Raster(dataframe=labels,outputFile='FuzzyCMeans.tiff')
```
### **_Step3: Execute the run() method_**
```python
process.run()
```
```
Output:
(0, '')
(139, 'Segmentation fault (core dumped)')
(0, '')
(0, 'Input file size is 2048, 2048\n0...10...20...30...40...50...60...70...80...90...100 - done.')
(0, '')
```
## **Operation 4: Visualization**
### **_Step1: Import TiffViewer from geoanalytics.visualization_**
```python
pip install rasterio
from geoanalytics.visualization import TiffViewer
```
### **_Step2: Input the RasterFile or pass it into the parameter_**
```python
viewer = TiffViewer.TiffViewer(inputFile='FuzzyCMeans.tiff')
```
### **_Step3: Display the image with desired scaling map_**
###### **_1. gray scale_**
```python
viewer.run(cmap='gray', title='TIFF Image')
```
###### **_2. Jet Scale_**
```python
viewer.run(cmap='jet', title='TIFF Image')
```
***
# License
[](https://github.com/UdayLab/PAMI/blob/main/LICENSE)
***
# Documentation
The official documentation is hosted on [geoAnalytics](https://geoanalytics.readthedocs.io/en/latest/).
***
# Getting Help
For any queries, the best place to go to is Github Issues [Github Issues](https://github.com/orgs/UdayLab/discussions/categories/q-a).
***
# Discussion and Development
In our GitHub repository, the primary platform for discussing development-related matters is the university lab. We encourage our team members and contributors to utilize this platform for a wide range of discussions, including bug reports, feature requests, design decisions, and implementation details.
***
# Contribution to geoAnalytics
We invite and encourage all community members to contribute, report bugs, fix bugs, enhance documentation, propose improvements, and share their creative ideas.
# Real World Case Studies
1. Lunar data analytics 
[Go to Top](#table-of-contents)