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.

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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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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Author: Andrew J. FeltonDate: 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 thedirectory, 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 primarilyfor documentation. The main role of this code was to import and wranglethe data needed to calculate ground-based estimates of aboveground water storage.

04_annual_turnover_storage_import.R: This script imports the annual turnover andstorage data for each landcover type. You load in these data from the 01_start.R scriptusing the source() function.

05_minimum_turnover_storage_import.R: This script imports the minimum turnover andstorage data for each landcover type. Minimum is defined as the lowest monthlyestimate.You load in these data from the 01_start.R scriptusing 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 allmaps were produced using Python code found in the "supporting_code"" folder.

In this project, I have done exploratory data analysis on the UCI Automobile dataset available at https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

This dataset consists of data From the 1985 Ward's Automotive Yearbook. Here are the sources

1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

Number of Instances: 398 Number of Attributes: 9 including the class attribute

Attribute Information:

mpg: continuous cylinders: multi-valued discrete displacement: continuous horsepower: continuous weight: continuous acceleration: continuous model year: multi-valued discrete origin: multi-valued discrete car name: string (unique for each instance)

This data set consists of three types of entities:

I - The specification of an auto in terms of various characteristics

II - Tts assigned an insurance risk rating. This corresponds to the degree to which the auto is riskier than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is riskier (or less), this symbol is adjusted by moving it up (or down) the scale. Actuaries call this process "symboling".

III - Its normalized losses in use as compared to other cars. This is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/specialty, etc...), and represents the average loss per car per year.

The analysis is divided into two parts:

Data Wrangling

1. Pre-processing data in python
2. Dealing with missing values
3. Data formatting
4. Data normalization
5. Binning

6. Exploratory Data Analysis

7. Descriptive statistics

8. Groupby

9. Analysis of variance

10. Correlation

11. Correlation stats

Acknowledgment Dataset: UCI Machine Learning Repository Data link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

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.

Data generation algorithm

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.

Network

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.

Time series

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).

Usage

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.

Selecting a particular country

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.

Averaging over time

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()

Source code

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.

Funding

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.

These data belongs to an actual printing company . Each record in Excel file Raw Data/Big_Data present an order from customers. In column "ColorMode" ; 4+0 means the order is one sided and 4+4 means it is two-sided. Files in Instances folder correspond to the instances used for computational tests in the article. Each of these instances has two related file with the same characteristics. One with gdx suffix and one with out any file extension.

Files with gdx suffix can be read by GAMS

Files without suffix are imported by pickle package in Python as objects of class Input (defined in "Input.py" ). You can read the files using the pickle package and Input.py. More information on pickle package at docs.python.org/3/library/pickle

These files are used to import data to the python implementation. The code and relevant description can be found in Read_input.py file.

Data used in the stage one 1NN classification experiment in: "Comparing Clustering Approaches for Smart Meter Time Series: Investigating the Influence of Dataset Properties on Performance"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)

Dataset Card for Python-DPO

This dataset is the larger version of Python-DPO 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 chosen_code:… See the full description on the dataset page: https://huggingface.co/datasets/NextWealth/Python-DPO-Large.

Python Fixings Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Python Script

from datasets import load_dataset import pandas as pd

Load dataset from Hugging Face Hub

dataset = load_dataset( "BeitTigreAI/tigre-data-parallel-multilingual", data_files={ "train": "train.parquet", "validation": "validation.parquet" } )

Print dataset info

print("📊 Dataset Info:") print(dataset)

Convert to pandas for easy analysis

print(" 🔍 Number of samples per target language:")

for split in ["train", "validation"]:… See the full description on the dataset page: https://huggingface.co/datasets/BeitTigreAI/tigre-data-parallel-multilingual.

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.

Llc Python Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This resource contains Jupyter Notebooks with examples for accessing USGS NWIS data via web services and performing subsequent analysis related to drought with particular focus on sites in Utah and the southwestern United States (could be modified to any USGS sites). The code uses the Python DataRetrieval package. The resource is part of set of materials for hydroinformatics and water data science instruction. Complete learning module materials are found in HydroLearn: Jones, A.S., Horsburgh, J.S., Bastidas Pacheco, C.J. (2022). Hydroinformatics and Water Data Science. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:USU+CEE6110+2022/about.

This resources consists of 6 example notebooks: 1. Example 1: Import and plot daily flow data 2. Example 2: Import and plot instantaneous flow data for multiple sites 3. Example 3: Perform analyses with USGS annual statistics data 4. Example 4: Retrieve data and find daily flow percentiles 3. Example 5: Further examination of drought year flows 6. Coding challenge: Assess drought severity

In short

This dataset used to investigate the influence of the unique amount of 3D-Models (Shapes) and Materials (Textures) towards the shape-textures bias, performance and generalization of deep neural network instance segmentation in my bachelor exam.

• one of nine datasets created in Unreal Engine 5 with an NVIDIA RTX A4500
• It uses 160 unique shapes and 80 unique textures
• RGB, depth and solution masks are available
• 20.000 Scenes
• Ready to use Dataloader, training and inference -> see next section

Usage

You can load the images like:

import cv2

image = cv2.imread(img_path)
if image is None:
  raise FileNotFoundError(f"Error during data loading: there is no '{img_path}'")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
depth = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED)
if len(depth.shape) > 2:
  _, depth, _, _ = cv2.split(depth)
      
mask = cv2.imread(mask_path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE)

For easy use I recommend to use my own code. You can directly use it to train Mask R-CNN or just use the dataloader. Both are shown now:

First: Clone my torch github project into your project terminal cd ./path/to/your/project git clone https://github.com/xXAI-botXx/torch-mask-rcnn-instance-segmentation.git Second: Install the anaconda env (optional) terminal cd ./path/to/your/project cd ./torch-mask-rcnn-instance-segmentation conda env create -f conda_env.yml Third: You are ready to use

Using only the dataloader for your custom project: ```python import os import numpy as np import matplotlib.pyplot as plt import cv2 from torch.utils.data import DataLoader

import sys sys.path.append("./torch-mask-rcnn-instance-segmentation")

from maskrcnn_toolkit import DATA_LOADING_MODE, Dual_Dir_Dataset, collate_fn, extract_and_visualize_mask

data_mode = DATA_LOADING_MODE.ALL

dataset = Dual_Dir_Dataset(img_dir="/path/to/rgb-folder", depth_dir="/path/to/depth-folder", mask_dir="/path/to/mask-folder", transform=None, amount=1, start_idx=0, end_idx=0, image_name="...", data_mode=data_mode, use_mask=True, use_depth=False, log_path="./logs", width=1920, height=1080, should_log=True, should_print=True, should_verify=False) data_loader = DataLoader(dataset, batch_size=5, shuffle=True, num_workers=4, collate_fn=collate_fn)

plot

for data in data_loader: for batch_idx in range(len(data[0])): if len(data) == 3: image = data[0][batch_idx].cpu().unsqueeze(0) masks = data[1][batch_idx]["masks"] masks = masks.cpu() name = data[2][batch_idx] else: image = data[0][batch_idx].cpu().unsqueeze(0) name = data[1][batch_idx]

  image = image.cpu().numpy().squeeze(0)
  image = np.transpose(image, (1, 2, 0)) # Convert to HWC

  # Remove 4.th channel if existing
  if image.shape[2] == 4:
    depth = image[:, :, 3]
    image = image[:, :, :3]
  else:
    depth = None

  masks_gt = masks.cpu().numpy()
  masks_gt = np.transpose(masks_gt, (1, 2, 0))
  mask = extract_and_visualize_mask(masks_gt, image=None, ax=None, visualize=False, color_map=None, soft_join=False)

  # plot
  cols = 1
  if depth is not None:
    cols += 1
  if mask is not None:
    cols += 1

  fig, ax = plt.subplots(nrows=1, ncols=cols, figsize=(20, 15*cols))
  fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.05, hspace=0.05)

  plot_idx = 0
  ax[plot_idx].imshow(image)
  ax[plot_idx].set_title("RGB Input Image")
  ax[plot_idx].axis("off")

  if depth is not None:
    plot_idx += 1
    ax[plot_idx].imshow(depth, cmap="gray")
    ax[plot_idx].set_title("Depth Input Image")
    ax[plot_idx].axis("off")

  if mask is not None:
    plot_idx += 1
    ax[plot_idx].imshow(mask)
    ax[plot_idx].set_title("Mask Ground Truth")
    ax[plot_idx].axis("off")

  plt.show()

**Using the whole Mask R-CNN training pipeline:**
```python
import sys
sys.path.append("./torch-mask-rcnn-instance-segmentation")

from maskrcnn_toolkit import DATA_LOADING_MODE, train


# set the vars as you need

WEIGHTS_PATH = None   # Path to the model weights file
USE_DEPTH = False      # Whether to include depth information -> as rgb and depth on green channel
VERIFY_DATA = False     # True is recommended

GROUND_PATH = "D:/3xM"  
DATASET_NAME = "3xM_Dataset_10_10"
IMG_DIR = os.path.join(GR...

Shenzhen Python Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

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

Abstract: Python module for the evaluation of lab experiments. The module implements functions to import meta-data of measurements, filters to search for subsets of them and routines to import and plot data from this meta-data. It works well in its original context but is currently in open alpha since it will be restructured in order to be compatible with new lab environments. TechnicalRemarks: # 🔬️ Experiment Evaluation Python module for the evaluation of lab experiments. The module implements functions to import meta-data of measurements, filters to search for subsets of them and routines to import and plot data from this meta-data. It works well in its original context but is currently in open alpha since it will be restructured in order to be compatible with new lab environments. Examples of its usage in scientific works will soon be published by the author that can be used to reference it. Feel free to use it for your own projects and to ask questions. For now you can cite this repository as source. 💻️ Installation You need a running python3 installation on your OS. The module was written on Debian/GNU-Linux, was tested on Windows and should also run on other OS. It is recommended to work in an virtual environment (see the official python documentation -> from bash: python3 -m venv exp_env source exp_env/bin/activate) or conda installation.