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.

Overview

carbon-footprint-calculator

Carbonfootprint Latest PY Release Carbonfootprint Latest Anaconda Release

Carbonfootprint Status License Issues

Twitter Stars

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

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/*']},
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.
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

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

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

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.
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

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

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

Official implementation for
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

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

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

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+
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+

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)
Owner
Seattle university Renewable energy research
Seattle university Renewable energy research
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
CondenseNet: Light weighted CNN for mobile devices

CondenseNets This repository contains the code (in PyTorch) for "CondenseNet: An Efficient DenseNet using Learned Group Convolutions" paper by Gao Hua

Shichen Liu 690 Nov 30, 2022
Collection of TensorFlow2 implementations of Generative Adversarial Network varieties presented in research papers.

TensorFlow2-GAN Collection of tf2.0 implementations of Generative Adversarial Network varieties presented in research papers. Model architectures will

41 Apr 28, 2022
Contextual Attention Localization for Offline Handwritten Text Recognition

CALText This repository contains the source code for CALText model introduced in "CALText: Contextual Attention Localization for Offline Handwritten T

0 Feb 17, 2022
​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

PAMA This is the Pytorch implementation of Progressive Attentional Manifold Alignment. Requirements python 3.6 pytorch 1.2.0+ PIL, numpy, matplotlib C

98 Nov 15, 2022
Implementation of FSGNN

FSGNN Implementation of FSGNN. For more details, please refer to our paper Experiments were conducted with following setup: Pytorch: 1.6.0 Python: 3.8

19 Dec 05, 2022
Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Hrishikesh Kamath 31 Nov 20, 2022
Classify the disease status of a plant given an image of a passion fruit

Passion Fruit Disease Detection I tried to create an accurate machine learning models capable of localizing and identifying multiple Passion Fruits in

3 Nov 09, 2021
Data and analysis code for an MS on SK VOC genomes phenotyping/neutralisation assays

Description Summary of phylogenomic methods and analyses used in "Immunogenicity of convalescent and vaccinated sera against clinical isolates of ance

Finlay Maguire 1 Jan 06, 2022
Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets.

Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets.

beringresearch 285 Jan 04, 2023
This code provides various models combining dilated convolutions with residual networks

Overview This code provides various models combining dilated convolutions with residual networks. Our models can achieve better performance with less

Fisher Yu 1.1k Dec 30, 2022
ViDT: An Efficient and Effective Fully Transformer-based Object Detector

ViDT: An Efficient and Effective Fully Transformer-based Object Detector by Hwanjun Song1, Deqing Sun2, Sanghyuk Chun1, Varun Jampani2, Dongyoon Han1,

NAVER AI 262 Dec 27, 2022
Bravia core script for python

Bravia-Core-Script You need to have a mandatory account If this L3 does not work, try another L3. enjoy

5 Dec 26, 2021
Labelbox is the fastest way to annotate data to build and ship artificial intelligence applications

Labelbox Labelbox is the fastest way to annotate data to build and ship artificial intelligence applications. Use this github repository to help you s

labelbox 1.7k Dec 29, 2022
Research shows Google collects 20x more data from Android than Apple collects from iOS. Block this non-consensual telemetry using pihole blocklists.

pihole-antitelemetry Research shows Google collects 20x more data from Android than Apple collects from iOS. Block both using these pihole lists. Proj

Adrian Edwards 290 Jan 09, 2023
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates

Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates Installation Clone the repository: git clone https://github.com/Zengyi-Qi

Zengyi Qin 3 Oct 18, 2022
Music Classification: Beyond Supervised Learning, Towards Real-world Applications

Music Classification: Beyond Supervised Learning, Towards Real-world Applications

104 Dec 15, 2022
Multi-task Multi-agent Soft Actor Critic for SMAC

Multi-task Multi-agent Soft Actor Critic for SMAC Overview The CARE formulti-task: Multi-Task Reinforcement Learning with Context-based Representation

RuanJingqing 8 Sep 30, 2022
iNAS: Integral NAS for Device-Aware Salient Object Detection

iNAS: Integral NAS for Device-Aware Salient Object Detection Introduction Integral search design (jointly consider backbone/head structures, design/de

顾宇超 77 Dec 02, 2022