Anaconda (United States)

Abstract This publication is a case study of the seasonal variability of mine water drainage from the Saint Louis Tunnel (SLT) at the inactive Rico-Argentine mine site located in southwestern Colorado. It is an introductory paper for the two passive water treatment system technology evaluations contained in this issue. Mine water chemistry changes from baseflow to a snowmelt runoff event (SMRE) where snowmelt runoff follows preferential migration pathways to flush acidic weathering products from the upper mine workings to the SLT. Baseflow mine drainage is characterized as circumneutral, with Zn, Cd, Mn, and Ni concentrations primarily in the dissolved form. Dissolved Zn, Mn, Fe, and potentially Cd illustrate equilibrium with carbonate minerals. Total concentrations of Fe, Cu, Pb, and As are primarily in the suspended form and suggest sorption to Fe oxides. Mine water chemistry during the SMRE reflects mixing of circumneutral baseflow waters with more acidic waters flushing the upper mine workings. Geothermal activity provides for a consistently warm mine water discharge from the SLT. The two seasons that provide the most challenge to passive water treatment of SLT mine drainage are the SMRE period and the low flow stage of the Dolores River. Mine water flow and chemistry during SMRE are highly correlated with Dolores River flow and this site conceptual model was and will be used to assist in pilot project evaluation, water treatment system design, monitoring system design, a seasonal compliance approach, and water management.

As more analysis-ready datasets are provided on the cloud, we need to consider how researchers access data. To maximize performance and minimize costs, we move the analysis to the data. This notebook demonstrates a Pangeo deployment connected to multiple Dask Gateways to enable analysis, regardless of where the data is stored. Public clouds are partitioned into regions, a geographic location with a cluster of data centers. A dataset like the National Water Model Short-Range Forecast is provided in a single region of some cloud provider (e.g. AWS’s us-east-1). To analyze that dataset efficiently, we do the analysis in the same region as the dataset. That’s especially true for very large datasets. Making local “dark replicas” of the datasets is slow and expensive. In this notebook we demonstrate a few open source tools to compute “close” to cloud data. We use Intake as a data catalog, to discover the datasets we have available and load them as an xarray Dataset. With xarray, we’re able to write the necessary transformations, filtering, and reductions that compose our analysis. To process the large amounts of data in parallel, we use Dask. Behind the scenes, we’ve configured this Pangeo deployment with multiple Dask Gateways, which provide a secure, multi-tenant server for managing Dask clusters. Each Gateway is provisioned with the necessary permissions to access the data. By placing compute (the Dask workers) in the same region as the dataset, we achieve the highest performance: these worker machines are physically close to the machines storing the data and have the highest bandwidth. We minimize cost by avoiding egress costs: fees charged to the data provider when data leaves a cloud region.

This is a bugfix release for 0.54.0. It fixes a regression in structured array type handling, a potential leak on initialization failure in the CUDA target, a regression caused by Numba's vendored cloudpickle module resetting dynamic classes and a few minor testing/infrastructure related problems. Please see details in the release note: https://numba.readthedocs.io/en/0.54.1/release-notes.html#version-0-54-1-7-october-2021

Scientific data have traditionally been distributed via downloads from data server to local computer. This way of working suffers from limitations as scientific datasets grow toward the petabyte scale. A “cloud-native data repository,” as defined in this article, offers several advantages over traditional data repositories-performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access and inclusion. These objectives motivate a set of best practices for cloud-native data repositories: analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with data-proximate computing. The Pangeo Project has developed a prototype implementation of these principles by using open-source scientific Python tools. By providing an ARCO data catalog together with on-demand, scalable distributed computing, Pangeo enables users to process big data at rates exceeding 10 GB/s. Several challenges must be resolved in order to realize cloud computing's full potential for scientific research, such as organizing funding, training users, and enforcing data privacy requirements.

This release focuses on performance improvements, but also adds some new features and contains numerous bug fixes and stability improvements. Please find details in the change log and the announcement post on Discourse.

Scientific data has traditionally been distributed via downloads from data server to local computer. This way of working suffers from limitations as scientific datasets grow towards the petabyte scale. A "cloud-native data repository," as defined in this paper, offers several advantages over traditional data repositories---performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access & inclusion. These objectives motivate a set of best practices for cloud-native data repositories: analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with data-proximate computing. The Pangeo Project has developed a prototype implementation of these principles by using open-source scientific Python tools. By providing an ARCO data catalog together with on-demand, scalable distributed computing, Pangeo enables users to process big data at rates exceeding 10 GB/s. Several challenges must be resolved in order to realize cloud computing's full potential for scientific research, such as organizing funding, training users, and enforcing data privacy requirements.

We're happy to announce the 0.23.2 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-2 You can upgrade with pip as usual: <pre><code>pip install -U scikit-learn </code></pre> The conda-forge builds will be available shortly, which you can then install using: <pre><code>conda install -c conda-forge scikit-learn </code></pre>

The Anaconda Revegetation Treatability Study (ARTS) is being conducted to demonstrate inplace treatments of mill tailings, smelter wastes and soils impacted by smelter emissions at the Anaconda Smelter National Priorities List Site in Anaconda, Montana. A study area of approximately 11,000 acres has been divided into the following five subareas: the Opportunity Tailings Ponds, the Anaconda Tailings Ponds, Smelter Hill, Old Works and Adjacent Areas. Acidic mill tailings that contain elevated metal levels are contained in the Opportunity and Anaconda Tailings Ponds systems. The other three areas have been affected by smelting processes and stack emissions. The soils contain elevated metal concentrations and have pH values ranging from acidic to neutral.