contour

> 原文链接： Contour 学习笔记（一）：使用 Contour 接管 Kubernetes 的南北流量 在 Kubernetes 中运行大规模以 Web 为中心的工作负载，最关键的需求之一就是在 L7 层实现高效流畅的入口流量管理。自从第一批 Kubernetes Ingress Controller 开发完成以来， Envoy （由 Matt Klein 和 Lyft 团队开发）已经成为云原生生态系统中的新生力量。Envoy 之所以受到支持，因为它是一个 CNCF 托管的项目，与整个容器圈和云原生架构有着天然的支持。 容器公司 Heptio 开源的项目 Contour 使用 Envoy 作为 Kubernetes 的 Ingress Controller 实现，为大家提供了一条新的 Kubernetes 外部负载均衡实现思路。 据 官方博客 介绍， Heptio Contour 可以为用户提供以下好处： 一种简单的安装机制来快速部署和集成 Envoy。 与 Kubernetes 对象模型的集成。 Ingress 配置的动态更新，而无需重启底层负载均衡器。 项目成熟后，将允许使用 Envoy 一些强大的功能，如熔断器、插件式的处理器链，以及可观测性和可调试性，可以非常方便地对接监控系统。 IngressRoute 之间可以级联，用来做蓝绿部署非常方便。 下面我们就来试用一下。

> 原文链接： Contour 学习笔记（一）：使用 Contour 接管 Kubernetes 的南北流量 在 Kubernetes 中运行大规模以 Web 为中心的工作负载，最关键的需求之一就是在 L7 层实现高效流畅的入口流量管理。自从第一批 Kubernetes Ingress Controller 开发完成以来， Envoy （由 Matt Klein 和 Lyft 团队开发）已经成为云原生生态系统中的新生力量。Envoy 之所以受到支持，因为它是一个 CNCF 托管的项目，与整个容器圈和云原生架构有着天然的支持。 容器公司 Heptio 开源的项目 Contour 使用 Envoy 作为 Kubernetes 的 Ingress Controller 实现，为大家提供了一条新的 Kubernetes 外部负载均衡实现思路。 据 官方博客 介绍， Heptio Contour 可以为用户提供以下好处： 一种简单的安装机制来快速部署和集成 Envoy。 与 Kubernetes 对象模型的集成。 Ingress 配置的动态更新，而无需重启底层负载均衡器。 项目成熟后，将允许使用 Envoy 一些强大的功能，如熔断器、插件式的处理器链，以及可观测性和可调试性，可以非常方便地对接监控系统。 IngressRoute 之间可以级联，用来做蓝绿部署非常方便。 下面我们就来试用一下。

ROI can only be implemented with an rectangle. I however have a contour that I want to set as an ROI. Does anyone have an idea of how I would go about using a contour as an ROI rather than a rectangle? Otherwise if not possible, how could I focus my actions only in pixels in a specific contour? Thanks PS: Sorry for all these OpenCV questions. Just really confused :$ OpenCV supports only rectangular ROIs. However, to make some processing for specific pixels, you can use some helper functions. One of them is pointPolygonTest(), which tells you a given pixel belongs on not to a polygon. So you

I am working on creating a contour plot using Matplotlib. I have all of the data in an array that is multidimensional. It is 12 long about 2000 wide. So it is basically a list of 12 lists that are 2000 in length. I have the contour plot working fine, but I need to smooth the data. I have read a lot of examples. Unfortunately, I don't have the math background to understand what is going on with them. So, how can I smooth this data? I have an example of what my graph looks like and what I want it to look more like. This is my graph: What I want it to look more similar too: What means do I have

I have a 3D array of data (2 spatial dimensions and 1 time dimension) and I'm trying to produce an animated contour plot using matplotlib.animate. I'm using this link as a basis: http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/ And here's my attempt: import numpy as np from matplotlib import pyplot as plt from matplotlib import animation from numpy import array, zeros, linspace, meshgrid from boutdata import collect # First collect data from files n = collect("n") # This is a routine to collect data Nx = n.shape[1] Nz = n.shape[2] Ny = n.shape[3] Nt = n.shape[0] fig =

I have data in X, Y, Z format where all are 1D arrays, and Z is the amplitude of the measurement at coordinate (X,Y). I'd like to show this data as a contour or 'imshow' plot where the contours/color represent the the value Z (amplitude). The grid for measurements and X and Y look are irregularly spaced. Many thanks, len(X)=100 len(Y)=100 len(Z)=100 Does plt.tricontourf(x,y,z) satisfy your requirements? It will plot filled contours for irregularly spaced data (non-rectilinear grid). You might also want to look into plt.tripcolor() . import numpy as np import matplotlib.pyplot as plt x = np

I have a function f(x,y) of two variables, of which I need to know the location of the curves at which it crosses zero. ContourPlot does that very efficiently (that is: it uses clever multi-grid methods, not just a brute force fine-grained scan) but just gives me a plot. I would like to have a set of values {x,y} (with some specified resolution) or perhaps some interpolating function which allows me to get access to the location of these contours. Have thought of extracting this from the FullForm of ContourPlot but this seems to be a bit of a hack. Any better way to do this? If you end up

I have a massive scatterplot (~100,000 points) that I'm generating in matplotlib. Each point has a location in this x/y space, and I'd like to generate contours containing certain percentiles of the total number of points. Is there a function in matplotlib which will do this? I've looked into contour(), but I'd have to write my own function to work in this way. Thanks! Basically, you're wanting a density estimate of some sort. There multiple ways to do this: Use a 2D histogram of some sort (e.g. matplotlib.pyplot.hist2d or matplotlib.pyplot.hexbin ) (You could also display the results as