Displaying 1 to 17 from 17 results

holoviews - Stop plotting your data - annotate your data and let it visualize itself.

  •    Python

Stop plotting your data - annotate your data and let it visualize itself. HoloViews is an open-source Python library designed to make data analysis and visualization seamless and simple. With HoloViews, you can usually express what you want to do in very few lines of code, letting you focus on what you are trying to explore and convey, not on the process of plotting.

Bokeh - Interactive Data Visualization in the browser, from Python

  •    Python

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

Chartify - Python library that makes it easier for data scientists to create charts

  •    Python

Chartify is a Python library that aims to make it as easy as possible for data scientists to create charts. Spend less time transforming data to get your charts to work. All plotting functions use a consistent tidy input data format. Chartify is built on top of Bokeh, so if you do need more control you can always fall back on Bokeh's API.

flight_review - web application for flight log analysis & review

  •    Python

This is a web application for flight log analysis. It allows users to upload ULog flight logs, and analyze them through the browser. It uses the bokeh library for plotting and the Tornado Web Server.




geoviews

  •    Python

GeoViews is a Python library that makes it easy to explore and visualize any data that includes geographic locations. It has particularly powerful support for multidimensional meteorological and oceanographic datasets, such as those used in weather, climate, and remote sensing research, but is useful for almost anything that you would want to plot on a map! You can see lots of example notebooks at geo.holoviews.org, and a good overview is in our blog post announcement. GeoViews is built on the HoloViews library for building flexible visualizations of multidimensional data. GeoViews adds a family of geographic plot types based on the Cartopy library, plotted using either the Matplotlib or Bokeh packages. Each of the new GeoElement plot types is a new HoloViews Element that has an associated geographic projection based on cartopy.crs. The GeoElements currently include Feature, WMTS, Tiles, Points, Contours, Image, and Text objects, each of which can easily be overlaid in the same plots. E.g. an object with temperature data can be overlaid with coastline data using an expression like gv.Image(temperature)*gv.Feature(cartopy.feature.COASTLINE). Each GeoElement can also be freely combined in layouts with any other HoloViews Element, making it simple to make even complex multi-figure layouts of overlaid objects.

HexBokehBlur - Hexagonal Bokeh Blur

  •    HLSL

This sample is the companion code to the blog post "Hexagonal Bokeh Blur Revisited".

godot-particle-dof - Bokeh-esque particle depth of field in Godot 3.0

  •    GLSL

This is a shader for particles to create a bokeh-esque depth of field effect. It works by essentially "precomputing" the various amounts of blur, instead of blurring in real time. Since the particles are radial, we can store only a single row of pixels per blur level, and the shader will distort the UV coordinates to turn it into a circle. This gives us a very compact 512x1024 texture that stores 1024 different blur levels, which can be smoothly interpolated. Here's a video of it in action.


Tutorials - Tutorials for creating figures, tables, or other content for AAS Journals.

  •    Jupyter

Tutorials for creating figures, tables, or other content for AAS Journals. These tutorials are provided under a CC-BY-4.0 license except for code snippets or scripts, which are provided under the license specified by the software or an MIT license if none is otherwise designated.

flu-sequence-predictor - An experimental deep learning & genotype network-based system for predicting new influenza protein sequences

  •    Jupyter

An experimental deep learning & genotype network-based system for predicting new influenza protein sequences. Flu Forecaster was first fully developed during my time as an Insight Health Data Fellow. The projected business use case is to predict what future strains of flu will look like, which would thus help inform the pre-emptive development of vaccines. No longer would we have to select currently-circulating strains; instead, we could forecast what strains would look like 6 months down the road, pre-emptively synthesize them (using synthetic biology methods), and rapidly scale up production of the ones used for the flu shot vaccine.

fusion-ekf-python - An extended Kalman Filter implementation in Python for fusing lidar and radar sensor measurements

  •    Jupyter

This is an extended Kalman Filter implementation in C++ for fusing lidar and radar sensor measurements. A Kalman filter can be used anywhere you have uncertain information about some dynamic system, and you want to make an educated guess about what the system is going to do next. This extended kalman filter does just that.

cdsdashboards - JupyterHub extension for ContainDS Dashboards

  •    Python

A Dashboard publishing solution for Data Science teams to share results with decision makers. Run a private on-premise or cloud-based JupyterHub with extensions to instantly publish apps and notebooks as user-friendly interactive dashboards to share with non-technical colleagues.

streamsx

  •    Java

Our goal is to make it easy to create real-time healthcare analytics application using IBM Streams. We want our users to be able to rapidly develop, test and validate healthcare analytics. Researchers and clinicians should focus on the analytics part of an application, while the platform should take care of the necessary plumbing and infrastructure work. As part of our initial work for this platform, we have developed a real-time ECG monitoring sample, using the Physionet Ingest Service, Python and Jupyter notebook.

NYCBuildingEnergyUse - Creating Regression Models Of Building Emissions On Google Cloud

  •    Jupyter

In indentifying outliers I will cover both visual inspection as well a machine learning method called Isolation Forests. Since I will completing this project over multiple days and using Google Cloud, I will go over the basics of using BigQuery for storing the datasets so I won't have to start all over again each time I work on it. At the end of this blogpost I will summarize the findings, and give some specific recommendations to reduce mulitfamily and office building energy usage. In this second post I cover imputations techniques for missing data using Scikit-Learn's impute module using both point estimates (i.e. mean, median) using the SimpleImputer class as well as more complicated regression models (i.e. KNN) using the IterativeImputer class. The later requires that the features in the model are correlated. This is indeed the case for our dataset and in our particular case we also need to transform the feautres in order to discern a more meaningful and predictive relationship between them. As we will see, the transformation of the features also gives us much better results for imputing missing values.

dash-alternative-viz - Dash components & demos to create Altair, Matplotlib, Highcharts , and Bokeh graphs within Dash apps

  •    Javascript

In Dash's built-in dash_core_components library, the dcc.Graph component uses standard Plotly figures. Dash’s component plugin system provides a toolchain to create Dash components from any JavaScript-based library. dash-alternative-viz is a proof-of-concept Dash component library that provides Dash interfaces to Altair, matplotlib (or any compatible system like Seaborn, Pandas.plot, Plotnine and others!), Bokeh (with or without HoloViews), and HighCharts.






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