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Author: Andrew J. Felton
Date: 5/5/2024
This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis and figure production for the study entitled:
"Global estimates of the storage and transit time of water through vegetation"
Please note that 'turnover' and 'transit' are used interchangeably in this project.
Data information:
The data folder contains key data sets used for analysis. In particular:
"data/turnover_from_python/updated/annual/multi_year_average/average_annual_turnover.nc" contains a global array summarizing five year (2016-2020) averages of annual transit, storage, canopy transpiration, and number of months of data. This is the core dataset for the analysis; however, each folder has much more data, including a dataset for each year of the analysis. Data are also available is separate .csv files for each land cover type. Oterh data can be found for the minimum, monthly, and seasonal transit time found in their respective folders. These data were produced using the python code found in the "supporting_code" folder given the ease of working with .nc and EASE grid in the xarray python module. R was used primarily for data visualization purposes. The remaining files in the "data" and "data/supporting_data"" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here.
#Code information
Python scripts can be found in the "supporting_code" folder.
Each R script in this project has a particular function:
01_start.R: This script loads the R packages used in the analysis, sets the
directory, and imports custom functions for the project. You can also load in the
main transit time (turnover) datasets here using the `source()` function.
02_functions.R: This script contains the custom function for this analysis,
primarily to work with importing the seasonal transit data. Load this using the
`source()` function in the 01_start.R script.
03_generate_data.R: This script is not necessary to run and is primarily
for documentation. The main role of this code was to import and wrangle
the data needed to calculate ground-based estimates of aboveground water storage.
04_annual_turnover_storage_import.R: This script imports the annual turnover and
storage data for each landcover type. You load in these data from the 01_start.R script
using the `source()` function.
05_minimum_turnover_storage_import.R: This script imports the minimum turnover and
storage data for each landcover type. Minimum is defined as the lowest monthly
estimate.You load in these data from the 01_start.R script
using the `source()` function.
06_figures_tables.R: This is the main workhouse for figure/table production and
supporting analyses. This script generates the key figures and summary statistics
used in the study that then get saved in the manuscript_figures folder. Note that all
maps were produced using Python code found in the "supporting_code"" folder.
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Dataset Card for Python-DPO
This dataset is the smaller version of Python-DPO-Large dataset and has been created using Argilla.
Load with datasets
To load this dataset with datasets, you'll just need to install datasets as pip install datasets --upgrade and then use the following code: from datasets import load_dataset
ds = load_dataset("NextWealth/Python-DPO")
Data Fields
Each data instance contains:
instruction: The problem description/requirements… See the full description on the dataset page: https://huggingface.co/datasets/NextWealth/Python-DPO.
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
To use this dataset:
import tensorflow_datasets as tfds
ds = tfds.load('cifar10', split='train')
for ex in ds.take(4):
print(ex)
See the guide for more informations on tensorflow_datasets.
https://storage.googleapis.com/tfds-data/visualization/fig/cifar10-3.0.2.png" alt="Visualization" width="500px">
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License information was derived automatically
Open Context (https://opencontext.org) publishes free and open access research data for archaeology and related disciplines. An open source (but bespoke) Django (Python) application supports these data publishing services. The software repository is here: https://github.com/ekansa/open-context-py
The Open Context team runs ETL (extract, transform, load) workflows to import data contributed by researchers from various source relational databases and spreadsheets. Open Context uses PostgreSQL (https://www.postgresql.org) relational database to manage these imported data in a graph style schema. The Open Context Python application interacts with the PostgreSQL database via the Django Object-Relational-Model (ORM).
This database dump includes all published structured data organized used by Open Context (table names that start with 'oc_all_'). The binary media files referenced by these structured data records are stored elsewhere. Binary media files for some projects, still in preparation, are not yet archived with long term digital repositories.
These data comprehensively reflect the structured data currently published and publicly available on Open Context. Other data (such as user and group information) used to run the Website are not included.
IMPORTANT
This database dump contains data from roughly 190+ different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump.
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Dataset Card for Census Income (Adult)
This dataset is a precise version of Adult or Census Income. This dataset from UCI somehow happens to occupy two links, but we checked and confirm that they are identical. We used the following python script to create this Hugging Face dataset. import pandas as pd from datasets import Dataset, DatasetDict, Features, Value, ClassLabel
url1 = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data" url2 =… See the full description on the dataset page: https://huggingface.co/datasets/cestwc/census-income.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Multimodal Vision-Audio-Language Dataset is a large-scale dataset for multimodal learning. It contains 2M video clips with corresponding audio and a textual description of the visual and auditory content. The dataset is an ensemble of existing datasets and fills the gap of missing modalities. Details can be found in the attached report. Annotation The annotation files are provided as Parquet files. They can be read using Python and the pandas and pyarrow library. The split into train, validation and test set follows the split of the original datasets. Installation
pip install pandas pyarrow Example
import pandas as pddf = pd.read_parquet('annotation_train.parquet', engine='pyarrow')print(df.iloc[0])
dataset AudioSet filename train/---2_BBVHAA.mp3 captions_visual [a man in a black hat and glasses.] captions_auditory [a man speaks and dishes clank.] tags [Speech] Description The annotation file consists of the following fields:filename: Name of the corresponding file (video or audio file)dataset: Source dataset associated with the data pointcaptions_visual: A list of captions related to the visual content of the video. Can be NaN in case of no visual contentcaptions_auditory: A list of captions related to the auditory content of the videotags: A list of tags, classifying the sound of a file. It can be NaN if no tags are provided Data files The raw data files for most datasets are not released due to licensing issues. They must be downloaded from the source. However, due to missing files, we provide them on request. Please contact us at schaumloeffel@em.uni-frankfurt.de
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Data used in the various stage two experiments in: "Comparing Clustering Approaches for Smart Meter Time Series: Investigating the Influence of Dataset Properties on Performance". This includes datasets with varied characteristics.All datasets are stored in a dict with tuples of (time series array, class labels). To access data in python:import picklefilename = "dataset.txt"with open(filename, 'rb') as f: data = pickle.load(f)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access.
This dataset contains long time series for production, consumption, and line flows, amounting to 20 years of data with a time resolution of one hour, for several thousands of loads and several hundreds of generators of various types representing the ultra-high-voltage transmission grid of continental Europe. The synthetic time series have been statistically validated agains real-world data.
The algorithm is described in a Nature Scientific Data paper. It relies on the PanTaGruEl model of the European transmission network -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated data gathered from the ENTSO-E transparency platform, such as power consumption aggregated at the national level.
The network information is encoded in the file europe_network.json. It is given in PowerModels format, which it itself derived from MatPower and compatible with PandaPower. The network features 7822 power lines and 553 transformers connecting 4097 buses, to which are attached 815 generators of various types.
The time series forming the core of this dataset are given in CSV format. Each CSV file is a table with 8736 rows, one for each hourly time step of a 364-day year. All years are truncated to exactly 52 weeks of 7 days, and start on a Monday (the load profiles are typically different during weekdays and weekends). The number of columns depends on the type of table: there are 4097 columns in load files, 815 for generators, and 8375 for lines (including transformers). Each column is described by a header corresponding to the element identifier in the network file. All values are given in per-unit, both in the model file and in the tables, i.e. they are multiples of a base unit taken to be 100 MW.
There are 20 tables of each type, labeled with a reference year (2016 to 2020) and an index (1 to 4), zipped into archive files arranged by year. This amount to a total of 20 years of synthetic data. When using loads, generators, and lines profiles together, it is important to use the same label: for instance, the files loads_2020_1.csv, gens_2020_1.csv, and lines_2020_1.csv represent a same year of the dataset, whereas gens_2020_2.csv is unrelated (it actually shares some features, such as nuclear profiles, but it is based on a dispatch with distinct loads).
The time series can be used without a reference to the network file, simply using all or a selection of columns of the CSV files, depending on the needs. We show below how to select series from a particular country, or how to aggregate hourly time steps into days or weeks. These examples use Python and the data analyis library pandas, but other frameworks can be used as well (Matlab, Julia). Since all the yearly time series are periodic, it is always possible to define a coherent time window modulo the length of the series.
This example illustrates how to select generation data for Switzerland in Python. This can be done without parsing the network file, but using instead gens_by_country.csv, which contains a list of all generators for any country in the network. We start by importing the pandas library, and read the column of the file corresponding to Switzerland (country code CH):
import pandas as pd
CH_gens = pd.read_csv('gens_by_country.csv', usecols=['CH'], dtype=str)
The object created in this way is Dataframe with some null values (not all countries have the same number of generators). It can be turned into a list with:
CH_gens_list = CH_gens.dropna().squeeze().to_list()
Finally, we can import all the time series of Swiss generators from a given data table with
pd.read_csv('gens_2016_1.csv', usecols=CH_gens_list)
The same procedure can be applied to loads using the list contained in the file loads_by_country.csv.
This second example shows how to change the time resolution of the series. Suppose that we are interested in all the loads from a given table, which are given by default with a one-hour resolution:
hourly_loads = pd.read_csv('loads_2018_3.csv')
To get a daily average of the loads, we can use:
daily_loads = hourly_loads.groupby([t // 24 for t in range(24 * 364)]).mean()
This results in series of length 364. To average further over entire weeks and get series of length 52, we use:
weekly_loads = hourly_loads.groupby([t // (24 * 7) for t in range(24 * 364)]).mean()
The code used to generate the dataset is freely available at https://github.com/GeeeHesso/PowerData. It consists in two packages and several documentation notebooks. The first package, written in Python, provides functions to handle the data and to generate synthetic series based on historical data. The second package, written in Julia, is used to perform the optimal power flow. The documentation in the form of Jupyter notebooks contains numerous examples on how to use both packages. The entire workflow used to create this dataset is also provided, starting from raw ENTSO-E data files and ending with the synthetic dataset given in the repository.
This work was supported by the Cyber-Defence Campus of armasuisse and by an internal research grant of the Engineering and Architecture domain of HES-SO.
Data model and generic query templates for translating and integrating a set of related CSV event logs into a single event graph for as used in https://dx.doi.org/10.1007/s13740-021-00122-1
Provides input data for 5 datasets (BPIC14, BPIC15, BPIC16, BPIC17, BPIC19)
Provides Python scripts to prepare and import each dataset into a Neo4j database instance through Cypher queries, representing behavioral information not globally (as in an event log), but locally per entity and per relation between entities.
Provides Python scripts to retrieve event data from a Neo4j database instance and render it using Graphviz dot.
The data model and queries are described in detail in: Stefan Esser, Dirk Fahland: Multi-Dimensional Event Data in Graph Databases (2020) https://arxiv.org/abs/2005.14552 and https://dx.doi.org/10.1007/s13740-021-00122-1
Fork the query code from Github: https://github.com/multi-dimensional-process-mining/graphdb-eventlogs
To use this dataset:
import tensorflow_datasets as tfds
ds = tfds.load('spoc_robot', split='train')
for ex in ds.take(4):
print(ex)
See the guide for more informations on tensorflow_datasets.
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https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/EULTBChttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/EULTBC
terminal pip install datasets
python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k", data_files=["*.jsonl"])
# Token distribution across PII classes We have taken steps to balance the token distribution across PII classes covered by the dataset. This graph shows the distribution of observations across the different PII classes in this release: Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
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The goal of this task is to train a model that can localize and classify each instance of Person and Car as accurately as possible.
from IPython.display import Markdown, display
display(Markdown("../input/Car-Person-v2-Roboflow/README.roboflow.txt"))
In this Notebook, I have processed the images with RoboFlow because in COCO formatted dataset was having different dimensions of image and Also data set was not splitted into different Format. To train a custom YOLOv7 model we need to recognize the objects in the dataset. To do so I have taken the following steps:
Image Credit - jinfagang
!git clone https://github.com/WongKinYiu/yolov7 # Downloading YOLOv7 repository and installing requirements
%cd yolov7
!pip install -qr requirements.txt
!pip install -q roboflow
!wget "https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"
import os
import glob
import wandb
import torch
from roboflow import Roboflow
from kaggle_secrets import UserSecretsClient
from IPython.display import Image, clear_output, display # to display images
print(f"Setup complete. Using torch {torch._version_} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")
https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67">
I will be integrating W&B for visualizations and logging artifacts and comparisons of different models!
try:
user_secrets = UserSecretsClient()
wandb_api_key = user_secrets.get_secret("wandb_api")
wandb.login(key=wandb_api_key)
anonymous = None
except:
wandb.login(anonymous='must')
print('To use your W&B account,
Go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB.
Get your W&B access token from here: https://wandb.ai/authorize')
wandb.init(project="YOLOvR",name=f"7. YOLOv7-Car-Person-Custom-Run-7")
https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png" alt="">
In order to train our custom model, we need to assemble a dataset of representative images with bounding box annotations around the objects that we want to detect. And we need our dataset to be in YOLOv7 format.
In Roboflow, We can choose between two paths:
https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/Roboflow.PNG" alt="">
user_secrets = UserSecretsClient()
roboflow_api_key = user_secrets.get_secret("roboflow_api")
rf = Roboflow(api_key=roboflow_api_key)
project = rf.workspace("owais-ahmad").project("custom-yolov7-on-kaggle-on-custom-dataset-rakiq")
dataset = project.version(2).download("yolov7")
Here, I am able to pass a number of arguments: - img: define input image size - batch: determine
Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
To use this dataset:
import tensorflow_datasets as tfds
ds = tfds.load('fashion_mnist', split='train')
for ex in ds.take(4):
print(ex)
See the guide for more informations on tensorflow_datasets.
https://storage.googleapis.com/tfds-data/visualization/fig/fashion_mnist-3.0.1.png" alt="Visualization" width="500px">
Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.