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Sales skills development brocure no fee
This document provides information about upcoming real estate training events being held by RE/MAX Crest Realty Westside. It includes details on a Sales Associate Orientation, various training sessions on topics like social media, contracts, and comparative market analyses. It also lists upcoming industry speaker events and details on RE/MAX's club level awards and career awards programs.
Getting more out of Matplotlib with GR
Python is well established in software development departments of research and industry, not least because of the proliferation of libraries such as SciPy and Matplotlib. However, when processing large amounts of data, in particular in combination with GUI toolkits (Qt) or three-dimensional visualizations (OpenGL), Python as an interpretative programming language seems to be reaching its limits. In particular, large amounts of data or the visualization of three-dimensional scenes may overwhelm the system.
This presentation shows how visualization applications with special performance requirements can be designed on the basis of Matplotlib and GR, a high-performance visualization library for Linux, OS X and Windows. The lecture focuses on the development of a new graphics backend for Matplotlib based on the GR framework. By combining the power of those libraries the responsiveness of animated visualization applications and their resulting frame rates can be improved significantly. This in turn allows the use of Matplotlib in real-time environments, for example in the area of signal processing.
Using concrete examples, the presentation will demonstrate the benefits of the GR framework as a companion module for Matplotlib, both in Python and Julia. Based on selected applications, the suitability of the GR framework will be highlighted especially in environments where time is critical. The system’s performance capabilities will be illustrated using demanding live applications. In addition, the special abilities of the GR framework are emphasized in terms of interoperability with graphical user interfaces (Qt/PySide) and OpenGL, which opens up new possibilities for existing Matplotlib applications.
The Cool Effect
The document discusses the concept of "cool" and its importance for brands. It describes how today's youth culture, especially millennials, prioritize cool brands and influence trends. These youth heavily use social media and blend ethnic backgrounds, focusing more on shared interests than divisions. Popular culture figures in music and entertainment further drive trends across genres. For brands to stay relevant, the document argues they must understand and acquire cool through authentic alignment with youth tastes and values in both messaging and practices. Maintaining cool status requires consistency while adapting to emerging preferences.
Recommended
Sales skills development brocure no fee
This document provides information about upcoming real estate training events being held by RE/MAX Crest Realty Westside. It includes details on a Sales Associate Orientation, various training sessions on topics like social media, contracts, and comparative market analyses. It also lists upcoming industry speaker events and details on RE/MAX's club level awards and career awards programs.
Getting more out of Matplotlib with GR
Python is well established in software development departments of research and industry, not least because of the proliferation of libraries such as SciPy and Matplotlib. However, when processing large amounts of data, in particular in combination with GUI toolkits (Qt) or three-dimensional visualizations (OpenGL), Python as an interpretative programming language seems to be reaching its limits. In particular, large amounts of data or the visualization of three-dimensional scenes may overwhelm the system.
This presentation shows how visualization applications with special performance requirements can be designed on the basis of Matplotlib and GR, a high-performance visualization library for Linux, OS X and Windows. The lecture focuses on the development of a new graphics backend for Matplotlib based on the GR framework. By combining the power of those libraries the responsiveness of animated visualization applications and their resulting frame rates can be improved significantly. This in turn allows the use of Matplotlib in real-time environments, for example in the area of signal processing.
Using concrete examples, the presentation will demonstrate the benefits of the GR framework as a companion module for Matplotlib, both in Python and Julia. Based on selected applications, the suitability of the GR framework will be highlighted especially in environments where time is critical. The system’s performance capabilities will be illustrated using demanding live applications. In addition, the special abilities of the GR framework are emphasized in terms of interoperability with graphical user interfaces (Qt/PySide) and OpenGL, which opens up new possibilities for existing Matplotlib applications.
The Cool Effect
The document discusses the concept of "cool" and its importance for brands. It describes how today's youth culture, especially millennials, prioritize cool brands and influence trends. These youth heavily use social media and blend ethnic backgrounds, focusing more on shared interests than divisions. Popular culture figures in music and entertainment further drive trends across genres. For brands to stay relevant, the document argues they must understand and acquire cool through authentic alignment with youth tastes and values in both messaging and practices. Maintaining cool status requires consistency while adapting to emerging preferences.
Scientific visualization with_gr
This document summarizes a presentation on using Python for scientific visualization. It discusses using libraries like Matplotlib, Mayavi, VTK, and custom frameworks for 2D and 3D plotting of scientific data. It provides examples of visualizing simulations in real-time, including particle simulations and the Mandelbrot set using just-in-time compilation with Numba for performance. The presentation also demonstrates interactive 3D visualization of MRI data and optimization techniques for Python like vectorization and GPU processing.
GR.jl - Plotting for Julia based on GR
Julia is steadily gaining popularity in the field of data science and visualization. Plotting capabilities are provided by packages such as Gadfly and PyPlot. They are the workhorse plotting utilities of the scientific Julia world. However, depending on the field of application, the software may be reaching its limits. This is the point where the GR framework will help. GR can be used as an alternative graphics module and significantly improve the performance and expand the capabilities of visualization applications.
Julia is an accepted high-level scripting language for scientific computing and visualization, not least because of the proliferation of libraries such as Gadfly and PyPlot. However, when processing large amounts of data, in particular in combination with three-dimensional plotting (OpenGL), the existing graphics packages are too application-specific or seem to be reaching their limits. In particular, large amounts of data or the visualization of three-dimensional scenes may overwhelm the system.
This presentation shows how visualization applications with special performance requirements can be designed on the basis of GR, a high-performance visualization library for Linux, OS X and Windows. The lecture also introduces a new graphics backend for PyPlot based on the GR framework. By combining the power of those libraries the responsiveness of animated visualization applications and their resulting frame rates can be improved significantly. This in turn allows the use of PyPlot in real-time environments, for example in the area of signal processing.
Using concrete examples, the presentation will demonstrate the benefits of the GR framework for high-performance graphics or as a companion module for PyPlot. Based on selected applications, the suitability of the GR framework will be highlighted especially in environments where time is critical. The system’s performance capabilities will be illustrated using demanding live applications. In addition, the special abilities of the GR framework are emphasized in terms of interoperability with OpenGL, which opens up new possibilities for existing applications.
Scientific Visualization
This document discusses scientific visualization with Python. It outlines motivation for easy visualization of 2D and 3D data. It reviews existing visualization software and APIs, noting drawbacks. The document proposes using Python with packages like NumPy, SciPy, Matplotlib, Mayavi2, and a custom GR framework for unified 2D and 3D visualization with high performance. Examples are given for real-time data streaming, interactive 3D visualization, and user interaction.
Getting more out of Matplotlib with GR
Matplotlib is the most popular graphics library for Python. It is the workhorse plotting utility of the scientific Python world. However, depending on the field of application, the software may be reaching its limits. This is the point where the GR framework will help. GR can be used as a backend for Matplotlib applications and significantly improve the performance and expand their capabilities.
การพัฒนาโครงงานคอมพิวเตอร์ประยุกต์ทางด้านธุรกิจ
เป็นประโยชน์แก่นักเรียนที่มีความสนใจที่จะทำงานวิจัยและประกอบอาชีพเกียวกับคอมพิวเตอร์
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Scientific visualization with_gr
This document summarizes a presentation on using Python for scientific visualization. It discusses using libraries like Matplotlib, Mayavi, VTK, and custom frameworks for 2D and 3D plotting of scientific data. It provides examples of visualizing simulations in real-time, including particle simulations and the Mandelbrot set using just-in-time compilation with Numba for performance. The presentation also demonstrates interactive 3D visualization of MRI data and optimization techniques for Python like vectorization and GPU processing.
GR.jl - Plotting for Julia based on GR
Julia is steadily gaining popularity in the field of data science and visualization. Plotting capabilities are provided by packages such as Gadfly and PyPlot. They are the workhorse plotting utilities of the scientific Julia world. However, depending on the field of application, the software may be reaching its limits. This is the point where the GR framework will help. GR can be used as an alternative graphics module and significantly improve the performance and expand the capabilities of visualization applications.
Julia is an accepted high-level scripting language for scientific computing and visualization, not least because of the proliferation of libraries such as Gadfly and PyPlot. However, when processing large amounts of data, in particular in combination with three-dimensional plotting (OpenGL), the existing graphics packages are too application-specific or seem to be reaching their limits. In particular, large amounts of data or the visualization of three-dimensional scenes may overwhelm the system.
This presentation shows how visualization applications with special performance requirements can be designed on the basis of GR, a high-performance visualization library for Linux, OS X and Windows. The lecture also introduces a new graphics backend for PyPlot based on the GR framework. By combining the power of those libraries the responsiveness of animated visualization applications and their resulting frame rates can be improved significantly. This in turn allows the use of PyPlot in real-time environments, for example in the area of signal processing.
Using concrete examples, the presentation will demonstrate the benefits of the GR framework for high-performance graphics or as a companion module for PyPlot. Based on selected applications, the suitability of the GR framework will be highlighted especially in environments where time is critical. The system’s performance capabilities will be illustrated using demanding live applications. In addition, the special abilities of the GR framework are emphasized in terms of interoperability with OpenGL, which opens up new possibilities for existing applications.
Scientific Visualization
This document discusses scientific visualization with Python. It outlines motivation for easy visualization of 2D and 3D data. It reviews existing visualization software and APIs, noting drawbacks. The document proposes using Python with packages like NumPy, SciPy, Matplotlib, Mayavi2, and a custom GR framework for unified 2D and 3D visualization with high performance. Examples are given for real-time data streaming, interactive 3D visualization, and user interaction.
Getting more out of Matplotlib with GR
Matplotlib is the most popular graphics library for Python. It is the workhorse plotting utility of the scientific Python world. However, depending on the field of application, the software may be reaching its limits. This is the point where the GR framework will help. GR can be used as a backend for Matplotlib applications and significantly improve the performance and expand their capabilities.
การพัฒนาโครงงานคอมพิวเตอร์ประยุกต์ทางด้านธุรกิจ
เป็นประโยชน์แก่นักเรียนที่มีความสนใจที่จะทำงานวิจัยและประกอบอาชีพเกียวกับคอมพิวเตอร์