| Name | watex JSON |
| Version |
0.3.3
JSON |
| download |
| home_page | https://github.com/WEgeophysics/watex |
| Summary | Machine learning research in water exploration |
| upload_time | 2024-03-14 04:24:52 |
| maintainer | Laurent Kouadio |
| docs_url | None |
| author | Laurent Kouadio |
| requires_python | >=3.9 |
| license | BSD 3-Clause License Copyright (c) 2021-2022 watex developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
exploration
groundwater
machine learning
water
hydro-geophysics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
<img src="docs/_static/logo_wide_rev.svg"><br>
-----------------------------------------------------
# *WATex*: machine learning research in water exploration
### *Life is much better with potable water*
[](https://watex.readthedocs.io/en/latest/?badge=latest)
[](https://coveralls.io/github/WEgeophysics/watex?branch=master)
[](https://doi.org/10.5281/zenodo.7744732)
[](https://badge.fury.io/py/watex)
[](https://anaconda.org/conda-forge/watex)
[](https://anaconda.org/conda-forge/watex)
## Overview
*WATex* is a Python-based library primarily designed for Groundwater Exploration (GWE). It introduces innovative strategies aimed at minimizing losses encountered during hydro-geophysical exploration projects. Integrating methods from Direct-current (DC) resistivity—including Electrical Profiling (ERP) and Vertical Electrical Sounding (VES)—alongside short-period electromagnetic (EM), geology, and hydrogeology, *WATex* leverages Machine Learning techniques to enhance exploration outcomes. Key features include:
- Automating the identification of optimal drilling locations to reduce the incidence of unsuccessful drillings and unsustainable boreholes.
- Predicting well water content, including groundwater flow rates and water inrush levels.
- Restoring EM signal integrity in areas plagued by significant interference noise.
- And more.
## Documentation
For comprehensive information and additional resources, visit the [WATex library website](https://watex.readthedocs.io/en/latest/). To quickly navigate through the software's API reference, access the [API reference page](https://watex.readthedocs.io/en/latest/api_references.html). Explore the [examples section](https://watex.readthedocs.io/en/latest/glr_examples/index.html) for a preview of potential results. Additionally, a detailed [step-by-step guide](https://watex.readthedocs.io/en/latest/glr_examples/applications/index.html#applications-step-by-step-guide) is provided to tackle real-world engineering challenges, such as computing DC parameters and predicting the k-parameter.
## License
*WATex* is distributed under the [BSD-3-Clause License](https://opensource.org/licenses/BSD-3-Clause).
## Installation
*WATex* is best supported on Python 3.9 or later.
### From *pip*
Install *WATex* directly from the Python Package Index (PyPI) with the following command:
```bash
pip install watex
```
### From *conda*
For users who prefer the conda ecosystem, *WATex* can be installed from the conda-forge distribution channel:
```bash
conda install -c conda-forge watex
```
### From Source
To access the most current development version of the code, installation from the source is recommended. Use the following commands to clone the repository and install:
```bash
git clone https://github.com/WEgeophysics/watex.git
```
### Additional Information
For a comprehensive installation guide, including how to manage dependencies effectively,
please refer to our [Installation Guide](https://watex.readthedocs.io/en/latest/installation.html).
## Some Demos
### 1. Drilling Location Auto-detection
In this demonstration, we showcase the process of automatically detecting optimal locations
for drilling by generating 50 stations of synthetic ERP resistivity data. The data is characterized
by minimum and maximum resistivity values set at `10 ohm.m` and `10,000 ohm.m`, respectively:
```python
import watex as wx
data = wx.make_erp(n_stations=50, max_rho=1e4, min_rho=10., as_frame=True, seed=42)
```
#### Naive Auto-detection (NAD)
The NAD method identifies a suitable drilling location without considering any restrictions or
constraints that might be present at the survey site during Groundwater Exploration (GWE). A location
is deemed "suitable" if it is expected to yield a flow rate of at least 1m³/hr:
```python
from watex.methods import ResistivityProfiling
robj = ResistivityProfiling(auto=True).fit(data)
robj.sves_
Out[1]: 'S025'
```
The algorithm proposes station `S25` as the optimal drilling location, which is stored
in the `sves_` attribute.
#### Auto-detection with Constraints (ADC)
In contrast, the ADC method accounts for constraints observed in the survey area during
the Drilling Water Supply Chain (DWSC). These constraints are often encountered in real-world
scenarios. For example, a station near a heritage site may be excluded due to drilling restrictions.
When multiple constraints exist, they should be compiled into a dictionary detailing the reasons for
each and passed to the `constraints` parameter. This ensures that these stations are disregarded during
the automatic detection process:
```python
restrictions = {
'S10': 'Household waste site, avoid contamination',
'S27': 'Municipality site, no authorization for drilling',
'S29': 'Heritage site, drilling prohibited',
'S42': 'Anthropic polluted place, potential future contamination risk',
'S46': 'Marsh zone, likely borehole dry-up during dry season'
}
robj = ResistivityProfiling(constraints=restrictions, auto=True).fit(data)
robj.sves_
# Output: 'S033'
```
This method revises the suitable drilling location to station `S33`, taking into account
the specified constraints. Should a station be near a restricted area, the system raises a warning
to advise against risking drilling operations at that location.
**Important Reminder:** Prior to initiating drilling operations, ensure a DC-sounding (VES) is conducted at the identified location. *WATex* calculates an additional parameter known as `ohmic-area` (ohmS) to evaluate the presence and effectiveness of fracture zones at that site. For further information, refer to the [WATex documentation](https://watex.readthedocs.io/en/latest/).
### 2. EM Tensor Recovery and Analysis
This demonstration outlines the process of recovering and analyzing electromagnetic (EM) tensor data.
We begin by fetching 20 audio-frequency magnetotelluric (AMT) data points stored as EDI objects
from the Huayuan area in Hunan Province, China, known for multiple interference noises:
```python
import watex as wx
e = wx.fetch_data('huayuan', samples=20, key='noised') # Returns an EM object
edi_data = e.data # Retrieve the array of EDI objects
```
Before restoring EM data, it's crucial to assess the data quality and evaluate the confidence
intervals to ensure reliability at each station. Typically, this quality control (QC) analysis
focuses on errors within the resistivity tensor:
```python
from watex.methods import EMAP
po = EMAP().fit(edi_data) # Creates an EM Array Profiling processing object
r = po.qc(tol=0.2, return_ratio=True) # Good data deemed from 80% significance level
r
Out[9]: 0.95
```
To visualize the confidence intervals at the 20 AMT stations:
```python
from watex.utils import plot_confidence_in
plot_confidence_in(edi_data)
```
For a more thorough quality control, we use the `qc` function to filter out invalid data and
interpolate frequencies. To determine the number of frequencies dropped during this analysis:
```python
from watex.utils import qc
QCo = qc(edi_data, tol=.2, return_qco=True) # Returns the quality control object
len(e.emo.freqs_) # Original number of frequencies in noisy data
Out[10]: 56
len(QCo.freqs_) # Number of frequencies in valid data after QC
Out[11]: 53
QCo.invalid_freqs_ # Frequencies discarded based on the tolerance parameter
Out[12]: array([81920.0, 48.53, 5.625]) # 81920.0, 48.53, and 5.625 Hz
```
The `plot_confidence_in` function is crucial for assessing whether tensor values for these
frequencies are recoverable at each station. It's important to note that data is considered
unrecoverable if the confidence level falls below 50%.
Should the initial QC rate of 95% not meet our standards, we can proceed to restore the
impedance tensor `Z`:
```python
Z = po.zrestore() # Returns 3D tensors for XX, XY, YX, and YY components
```
Evaluating the new QC ratio post-restoration confirms the effectiveness of our
recovery efforts:
```python
r, = wx.qc(Z)
r
Out[13]: 1.0
```
As observed, the tensor restoration achieves a 100% success rate across all stations,
significantly improving upon the initial analysis. To visualize this enhancement in
confidence levels:
```python
plot_confidence_in(Z)
```
For further exploration on EM tensor restoration, phase tensor analysis, strike plotting, data filtering, and more, users are encouraged to visit the following links for detailed examples:
- [EM Tensor Restoring](https://watex.readthedocs.io/en/latest/glr_examples/applications/plot_tensor_restoring.html#sphx-glr-glr-examples-applications-plot-tensor-restoring-py)
- [Skewness Analysis Plots](https://watex.readthedocs.io/en/latest/glr_examples/methods/plot_phase_tensors.html#sphx-glr-glr-examples-methods-plot-phase-tensors-py)
- [Strike Plot](https://watex.readthedocs.io/en/latest/glr_examples/utils/plot_strike.html#sphx-glr-glr-examples-utils-plot-strike-py)
- [Filtering Data](https://watex.readthedocs.io/en/latest/methods.html#filtering-tensors-ama-flma-tma)
## Citations
Should you find the [WATex software](https://doi.org/10.1016/j.softx.2023.101367) beneficial
for your research or any published work, we kindly ask you to cite the following article:
> Kouadio, K.L., Liu, J., Liu, R., 2023. watex: machine learning research in water exploration. SoftwareX, 101367(2023). [https://doi.org/10.1016/j.softx.2023.101367](https://doi.org/10.1016/j.softx.2023.101367)
In publications that mention *WATex*, acknowledging [scikit-learn](https://scikit-learn.org/stable/about.html#citing-scikit-learn) may also be relevant due to its integral role in the software's development.
For additional insights and examples, refer to our compilation of [case history papers](https://watex.readthedocs.io/en/latest/citing.html) that utilized *WATex*.
## Contributions
The development and success of *WATex* have been made possible through contributions from the following
institutions:
1. Department of Geophysics, School of Geosciences & Info-physics, [Central South University](https://en.csu.edu.cn/), China.
2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha, Hunan, China.
3. Laboratoire de Geologie, Ressources Minerales et Energetiques, UFR des Sciences de la Terre et des Ressources Minières, [Université Félix Houphouët-Boigny](https://www.univ-fhb.edu.ci/index.php/ufr-strm/), Côte d'Ivoire.
For inquiries, suggestions, or contributions, please reach out to the main developer, [_LKouadio_](https://wegeophysics.github.io/) at <etanoyau@gmail.com>.
Raw data
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"description": "<img src=\"docs/_static/logo_wide_rev.svg\"><br>\r\n\r\n-----------------------------------------------------\r\n\r\n# *WATex*: machine learning research in water exploration\r\n\r\n### *Life is much better with potable water*\r\n\r\n [](https://watex.readthedocs.io/en/latest/?badge=latest)\r\n \r\n \r\n[](https://coveralls.io/github/WEgeophysics/watex?branch=master)\r\n \r\n [](https://doi.org/10.5281/zenodo.7744732)\r\n\r\n [](https://badge.fury.io/py/watex)\r\n[](https://anaconda.org/conda-forge/watex)\r\n[](https://anaconda.org/conda-forge/watex)\r\n\r\n\r\n## Overview\r\n\r\n*WATex* is a Python-based library primarily designed for Groundwater Exploration (GWE). It introduces innovative strategies aimed at minimizing losses encountered during hydro-geophysical exploration projects. Integrating methods from Direct-current (DC) resistivity\u2014including Electrical Profiling (ERP) and Vertical Electrical Sounding (VES)\u2014alongside short-period electromagnetic (EM), geology, and hydrogeology, *WATex* leverages Machine Learning techniques to enhance exploration outcomes. Key features include:\r\n- Automating the identification of optimal drilling locations to reduce the incidence of unsuccessful drillings and unsustainable boreholes.\r\n- Predicting well water content, including groundwater flow rates and water inrush levels.\r\n- Restoring EM signal integrity in areas plagued by significant interference noise.\r\n- And more.\r\n\r\n## Documentation\r\n\r\nFor comprehensive information and additional resources, visit the [WATex library website](https://watex.readthedocs.io/en/latest/). To quickly navigate through the software's API reference, access the [API reference page](https://watex.readthedocs.io/en/latest/api_references.html). Explore the [examples section](https://watex.readthedocs.io/en/latest/glr_examples/index.html) for a preview of potential results. Additionally, a detailed [step-by-step guide](https://watex.readthedocs.io/en/latest/glr_examples/applications/index.html#applications-step-by-step-guide) is provided to tackle real-world engineering challenges, such as computing DC parameters and predicting the k-parameter.\r\n\r\n## License\r\n\r\n*WATex* is distributed under the [BSD-3-Clause License](https://opensource.org/licenses/BSD-3-Clause).\r\n\r\n## Installation\r\n\r\n*WATex* is best supported on Python 3.9 or later.\r\n\r\n### From *pip*\r\n\r\nInstall *WATex* directly from the Python Package Index (PyPI) with the following command:\r\n\r\n```bash\r\npip install watex\r\n```\r\n### From *conda*\r\n\r\nFor users who prefer the conda ecosystem, *WATex* can be installed from the conda-forge distribution channel:\r\n\r\n```bash\r\nconda install -c conda-forge watex\r\n```\r\n\r\n### From Source\r\n\r\nTo access the most current development version of the code, installation from the source is recommended. Use the following commands to clone the repository and install:\r\n```bash\r\ngit clone https://github.com/WEgeophysics/watex.git\r\n```\r\n\r\n### Additional Information\r\n\r\nFor a comprehensive installation guide, including how to manage dependencies effectively, \r\nplease refer to our [Installation Guide](https://watex.readthedocs.io/en/latest/installation.html).\r\n\r\n\r\n## Some Demos\r\n\r\n### 1. Drilling Location Auto-detection\r\n\r\nIn this demonstration, we showcase the process of automatically detecting optimal locations \r\nfor drilling by generating 50 stations of synthetic ERP resistivity data. The data is characterized \r\nby minimum and maximum resistivity values set at `10 ohm.m` and `10,000 ohm.m`, respectively:\r\n\r\n```python\r\nimport watex as wx\r\ndata = wx.make_erp(n_stations=50, max_rho=1e4, min_rho=10., as_frame=True, seed=42)\r\n```\r\n\r\n#### Naive Auto-detection (NAD)\r\n\r\nThe NAD method identifies a suitable drilling location without considering any restrictions or \r\nconstraints that might be present at the survey site during Groundwater Exploration (GWE). A location \r\nis deemed \"suitable\" if it is expected to yield a flow rate of at least 1m\u00b3/hr:\r\n\r\n```python\r\nfrom watex.methods import ResistivityProfiling\r\nrobj = ResistivityProfiling(auto=True).fit(data)\r\nrobj.sves_\r\nOut[1]: 'S025'\r\n```\r\n\r\nThe algorithm proposes station `S25` as the optimal drilling location, which is stored \r\nin the `sves_` attribute.\r\n\r\n#### Auto-detection with Constraints (ADC)\r\n\r\nIn contrast, the ADC method accounts for constraints observed in the survey area during \r\nthe Drilling Water Supply Chain (DWSC). These constraints are often encountered in real-world \r\nscenarios. For example, a station near a heritage site may be excluded due to drilling restrictions. \r\nWhen multiple constraints exist, they should be compiled into a dictionary detailing the reasons for \r\neach and passed to the `constraints` parameter. This ensures that these stations are disregarded during \r\nthe automatic detection process:\r\n\r\n```python\r\nrestrictions = {\r\n 'S10': 'Household waste site, avoid contamination',\r\n\r\n 'S27': 'Municipality site, no authorization for drilling',\r\n 'S29': 'Heritage site, drilling prohibited',\r\n 'S42': 'Anthropic polluted place, potential future contamination risk',\r\n 'S46': 'Marsh zone, likely borehole dry-up during dry season'\r\n}\r\nrobj = ResistivityProfiling(constraints=restrictions, auto=True).fit(data)\r\nrobj.sves_\r\n# Output: 'S033'\r\n```\r\nThis method revises the suitable drilling location to station `S33`, taking into account \r\nthe specified constraints. Should a station be near a restricted area, the system raises a warning \r\nto advise against risking drilling operations at that location.\r\n\r\n**Important Reminder:** Prior to initiating drilling operations, ensure a DC-sounding (VES) is conducted at the identified location. *WATex* calculates an additional parameter known as `ohmic-area` (ohmS) to evaluate the presence and effectiveness of fracture zones at that site. For further information, refer to the [WATex documentation](https://watex.readthedocs.io/en/latest/).\r\n\r\n\r\n### 2. EM Tensor Recovery and Analysis\r\n\r\nThis demonstration outlines the process of recovering and analyzing electromagnetic (EM) tensor data. \r\nWe begin by fetching 20 audio-frequency magnetotelluric (AMT) data points stored as EDI objects \r\nfrom the Huayuan area in Hunan Province, China, known for multiple interference noises:\r\n\r\n```python\r\nimport watex as wx\r\ne = wx.fetch_data('huayuan', samples=20, key='noised') # Returns an EM object\r\nedi_data = e.data # Retrieve the array of EDI objects\r\n```\r\n\r\nBefore restoring EM data, it's crucial to assess the data quality and evaluate the confidence \r\nintervals to ensure reliability at each station. Typically, this quality control (QC) analysis \r\nfocuses on errors within the resistivity tensor:\r\n\r\n```python\r\nfrom watex.methods import EMAP\r\npo = EMAP().fit(edi_data) # Creates an EM Array Profiling processing object\r\nr = po.qc(tol=0.2, return_ratio=True) # Good data deemed from 80% significance level\r\nr\r\nOut[9]: 0.95\r\n```\r\n\r\nTo visualize the confidence intervals at the 20 AMT stations:\r\n\r\n```python\r\nfrom watex.utils import plot_confidence_in\r\nplot_confidence_in(edi_data)\r\n```\r\n\r\nFor a more thorough quality control, we use the `qc` function to filter out invalid data and \r\ninterpolate frequencies. To determine the number of frequencies dropped during this analysis:\r\n\r\n```python\r\nfrom watex.utils import qc\r\nQCo = qc(edi_data, tol=.2, return_qco=True) # Returns the quality control object\r\nlen(e.emo.freqs_) # Original number of frequencies in noisy data\r\nOut[10]: 56\r\nlen(QCo.freqs_) # Number of frequencies in valid data after QC\r\nOut[11]: 53\r\nQCo.invalid_freqs_ # Frequencies discarded based on the tolerance parameter\r\nOut[12]: array([81920.0, 48.53, 5.625]) # 81920.0, 48.53, and 5.625 Hz\r\n```\r\n\r\nThe `plot_confidence_in` function is crucial for assessing whether tensor values for these \r\nfrequencies are recoverable at each station. It's important to note that data is considered \r\nunrecoverable if the confidence level falls below 50%.\r\n\r\nShould the initial QC rate of 95% not meet our standards, we can proceed to restore the \r\nimpedance tensor `Z`:\r\n\r\n```python\r\nZ = po.zrestore() # Returns 3D tensors for XX, XY, YX, and YY components\r\n```\r\n\r\nEvaluating the new QC ratio post-restoration confirms the effectiveness of our \r\nrecovery efforts:\r\n\r\n```python\r\nr, = wx.qc(Z)\r\nr\r\nOut[13]: 1.0\r\n```\r\n\r\nAs observed, the tensor restoration achieves a 100% success rate across all stations, \r\nsignificantly improving upon the initial analysis. To visualize this enhancement in \r\nconfidence levels:\r\n\r\n```python\r\nplot_confidence_in(Z)\r\n```\r\n\r\nFor further exploration on EM tensor restoration, phase tensor analysis, strike plotting, data filtering, and more, users are encouraged to visit the following links for detailed examples:\r\n- [EM Tensor Restoring](https://watex.readthedocs.io/en/latest/glr_examples/applications/plot_tensor_restoring.html#sphx-glr-glr-examples-applications-plot-tensor-restoring-py)\r\n- [Skewness Analysis Plots](https://watex.readthedocs.io/en/latest/glr_examples/methods/plot_phase_tensors.html#sphx-glr-glr-examples-methods-plot-phase-tensors-py)\r\n- [Strike Plot](https://watex.readthedocs.io/en/latest/glr_examples/utils/plot_strike.html#sphx-glr-glr-examples-utils-plot-strike-py)\r\n- [Filtering Data](https://watex.readthedocs.io/en/latest/methods.html#filtering-tensors-ama-flma-tma)\r\n\r\n\r\n## Citations\r\n\r\nShould you find the [WATex software](https://doi.org/10.1016/j.softx.2023.101367) beneficial \r\nfor your research or any published work, we kindly ask you to cite the following article:\r\n\r\n> Kouadio, K.L., Liu, J., Liu, R., 2023. watex: machine learning research in water exploration. SoftwareX, 101367(2023). [https://doi.org/10.1016/j.softx.2023.101367](https://doi.org/10.1016/j.softx.2023.101367)\r\n\r\nIn publications that mention *WATex*, acknowledging [scikit-learn](https://scikit-learn.org/stable/about.html#citing-scikit-learn) may also be relevant due to its integral role in the software's development.\r\n\r\nFor additional insights and examples, refer to our compilation of [case history papers](https://watex.readthedocs.io/en/latest/citing.html) that utilized *WATex*.\r\n\r\n## Contributions\r\n\r\nThe development and success of *WATex* have been made possible through contributions from the following \r\ninstitutions:\r\n\r\n1. Department of Geophysics, School of Geosciences & Info-physics, [Central South University](https://en.csu.edu.cn/), China.\r\n2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha, Hunan, China.\r\n3. Laboratoire de Geologie, Ressources Minerales et Energetiques, UFR des Sciences de la Terre et des Ressources Mini\u00e8res, [Universit\u00e9 F\u00e9lix Houphou\u00ebt-Boigny](https://www.univ-fhb.edu.ci/index.php/ufr-strm/), C\u00f4te d'Ivoire.\r\n\r\nFor inquiries, suggestions, or contributions, please reach out to the main developer, [_LKouadio_](https://wegeophysics.github.io/) at <etanoyau@gmail.com>.\r\n\r\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License Copyright (c) 2021-2022 watex developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.",
"summary": "Machine learning research in water exploration",
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"Homepage": "https://github.com/WEgeophysics/watex",
"Installation Guide": "https://watex.readthedocs.io/en/latest/installation.html",
"Source": "https://github.com/WEgeophysics/watex",
"User Guide": "https://watex.readthedocs.io/en/latest/user_guide.html"
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