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Library to apply the Marginal Standard Error Rule for transient regime detection and truncation on Grand Canonical Monte Carlo adsorption simulations
A Python library to apply the Marginal Standard Error Rule (MSER) for transient regime detection and truncation on Grand Canonical Monte Carlo adsorption simulations.
These tips are not mandatory, but they are a sure way of helping you develop the code while maintaining consistency with the current style, structure and formatting choices.
We recommend these tools to ensure code style compatibility.
setup.py
Clone pymser
repository if you haven't done it yet.
Go to pymser
's root folder, there you will find setup.py
file, and run the command below:
python setup.py install
If you intend to use pipenv
, please add the following to your Pipfile
:
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
pymser = "*"
If you intend to use pip
, please run the command below:
pip install pymser
You can run
pip install git+https://github.com/IBM/pymser.git
and then you will be prompted to enter your GitHub username and password/access token.
If you already have a SSH key configured, you can run
pip install git+ssh://git@github.com/IBM/pymser.git
Log into Artifactory and access your user profile. There you will find your API key and username. Then export your credentials as environment variables for later use in the installation process.
export ARTIFACTORY_USERNAME=username@email.com
export ARTIFACTORY_ACCESS_TOKEN=your-access-token
export ARTIFACTORY_URL=your-artifactory-url
If you intend to use pipenv
, please add the following to your Pipfile
:
[[source]]
url = "https://$ARTIFACTORY_USERNAME:$ARTIFACTORY_ACCESS_TOKEN@$ARTIFACTORY_URL"
verify_ssl = true
name = "artifactory"
[packages]
pymser = {version="*", index="artifactory"}
If you intend to use pip
, please run the command below:
pip install pymser --extra-index-url=https://$ARTIFACTORY_USERNAME:$ARTIFACTORY_ACCESS_TOKEN@$ARTIFACTORY_URL
This is a small example of how to use our package:
>>> import pymser
>>> import pandas as pd
>>>
>>> # Reads the example file using pandas
>>> df = pd.read_csv('example_data/Cu-BTT_500165.0_198.000000.csv')
>>>
>>> # Apply the MSER to get the index of the start of equilibrated data
>>> results = pymser.equilibrate(df['mol/kg'], LLM=False, batch_size=1, ADF_test=True, uncertainty='uSD', print_results=True)
pyMSER Equilibration Results
==============================================================================
Start of equilibrated data: 13368 of 48613
Total equilibrated steps: 35245 (72.50%)
Equilibrated: Yes
Average over equilibrated data: 22.4197 ± 0.1905
Number of uncorrelated samples: 22.3
Autocorrelation time: 1579.0
==============================================================================
Augmented Dickey-Fuller Test
==============================================================================
Test statistic for observable: -3.926148246630434
P-value for observable: 0.001850619485090052
The number of lags used: 46
The number of observations used for the ADF regression: 35198
Cutoff Metrics :
1%: -3.430536 | The data is stationary with 99 % confidence
5%: -2.861622 | The data is stationary with 95 % confidence
10%: -2.566814 | The data is stationary with 90 % confidence
You can also access our tutorial.
We have an automated CI/CD pipeline running on TravisCI that takes every single git push
event and executes the build/test/deploy instructions in the .travis.yml
. If you are deploying master
or release
branches, a Python package will be generated and published to a private Pypi registry on Artifactory.
We have an automated CI/CD pipeline running on TravisCI that takes every single git push
event and executes the build/test/deploy instructions in the .travis.yml
. If you are deploying main
branch, a Python package will be generated and published to Pypi.org registry.
FAQs
Library to apply the Marginal Standard Error Rule for transient regime detection and truncation on Grand Canonical Monte Carlo adsorption simulations
We found that pymser demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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