Calculates carbon footprint based on fuel mix and discharge profile at the utility selected. Can create graphs and tabular output for fuel mix based on input file of series of power drawn over a period of time.

Last update: Sep 26, 2022

Overview

carbon-footprint-calculator

Conda distribution

~/anaconda3/bin/conda install anaconda-client conda-build
~/anaconda3/bin/conda config --set anaconda_upload no
~/anaconda3/bin/conda build . --output

~/anaconda3/bin/anaconda login
~/anaconda3/bin/anaconda upload dist/carbonfootprint-1.1.5.tar.gz

PYppi ditribution

1. Generating distribution archives

First install latest version of PyPA’s build then build . This should generate dist directory:

python3 -m pip install --upgrade build
python3 -m build

2. Uploading the distribution archives

python3 -m pip install --upgrade twine
twine check dist/*

    Checking dist/carbon_footprint_calculator-1.1.1-py3-none-any.whl: PASSED
    Checking dist/carbon_footprint_calculator-1.1.1.tar.gz: PASSED

Install twine and upload all of the archives under dist to pypi’s test server

python3 -m twine upload --repository testpypi dist/*

or to pyapi

python3 -m twine upload dist/*

3.Installing the package

From pytest

python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps carbonfootprint

From pyapi

pip3 install carbonfootprint

List of classifiers

https://pypi.org/classifiers/

Test Enviornment

Install and Setup Conda

export PATH=$PATH:/home/altanai/anaconda3/bin

Activate virtual env

 source energycarbon_env/bin/activate

Run unit tests

python tests/unittests.py

Debugging and Help

Issue1 Adding csv for the datasets

solution refer to https://python-packaging.readthedocs.io/en/latest/non-code-files.html Add the following to dynamic setup.py

include_package_data=True
package_data={'': ['dataset/*']},

Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

scc4onnx Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel

16 Dec 22, 2022

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression YOLOv5 with alpha-IoU losses implemented in PyTorch. Example r

147 Dec 5, 2022

A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.

AMAZ3DSim AMAZ3DSim is a lightweight python-based 3D network multi-agent simulator. It uses a cell-based congestion model. It calculates risk, battery

13 Nov 4, 2022

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

33 Dec 7, 2022

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

Advances in Financial Machine Learning Exercises Experimental solutions to selected exercises from the book Advances in Financial Machine Learning by

1.4k Jan 4, 2023

Official implementation for "QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation" (CVPR 2022)

QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation (CVPR2022) https://arxiv.org/abs/2203.08483 Unpaired image-to-image (I2I

50 Dec 16, 2022

Python PID Tuner - Makes a model of the System from a Process Reaction Curve and calculates PID Gains

PythonPID_Tuner_SOPDT Step 1: Takes a Process Reaction Curve in csv format - assumes data at 100ms interval (column names CV and PV) Step 2: Makes a r

1 Jan 18, 2022

Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

shindo.py Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data stored in NumPy array Introduction Japa

3 Sep 23, 2022

Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+

567 Dec 26, 2022

Calculates carbon footprint based on fuel mix and discharge profile at the utility selected. Can create graphs and tabular output for fuel mix based on input file of series of power drawn over a period of time.

Related tags

Overview

carbon-footprint-calculator

Conda distribution

PYppi ditribution

Test Enviornment

Debugging and Help

You might also like...

Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

Official implementation for "QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation" (CVPR 2022)

Python PID Tuner - Makes a model of the System from a Process Reaction Curve and calculates PID Gains

Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+

Releases(1.1.5)

1.1.5(Sep 9, 2021)

Owner

Seattle university Renewable energy research

Official implementation of Sparse Transformer-based Action Recognition

U-2-Net: U Square Net - Modified for paired image training of style transfer

The repo of the preprinting paper "Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection"

MoCap-Solver: A Neural Solver for Optical Motion Capture Data

Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

Training DALL-E with volunteers from all over the Internet using hivemind and dalle-pytorch (NeurIPS 2021 demo)

Deploy recommendation engines with Edge Computing

LibFewShot: A Comprehensive Library for Few-shot Learning.

[Link]deep_portfolo - Use Reforcemet earg ad Supervsed learg to Optmze portfolo allocato []

Code for the paper "Improved Techniques for Training GANs"

Spatial Intention Maps for Multi-Agent Mobile Manipulation (ICRA 2021)

Multiwavelets-based operator model

code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

A reimplementation of DCGAN in PyTorch

Implement face detection, and age and gender classification, and emotion classification.

Implementation for Shape from Polarization for Complex Scenes in the Wild

Unofficial pytorch-lightning implement of Mip-NeRF

A framework for annotating 3D meshes using the predictions of a 2D semantic segmentation model.

Council-GAN - Implementation for our paper Breaking the Cycle - Colleagues are all you need (CVPR 2020)