| args | attempted_at | completed_at | duration | duration_sec | |
|---|---|---|---|---|---|
| {"asset_id": 628093331} | 2023-11-07 13:25:16.986917 | 2023-11-07 13:25:17.334414 | 00:00:00.347497 | 0.347497 | |
| {"asset_id": 1704241275} | 2023-11-07 13:25:12.952172 | 2023-11-07 13:25:13.259646 | 00:00:00.307474 | 0.307474 | |
| {"asset_id": 1354622531} | 2023-11-07 13:23:56.980875 | 2023-11-07 13:23:57.338218 | 00:00:00.357343 | 0.357343 | |
| {"asset_id": 246514739} | 2023-11-07 13:23:53.61517 | 2023-11-07 13:23:53.850518 | 00:00:00.235348 | 0.235348 | |
| {"asset_id": 1978384923} | 2023-11-07 13:22:11.900316 | 2023-11-07 13:22:12.20396 | 00:00:00.303644 | 0.303644 | |
| {"asset_id": 522975456} | 2023-11-07 13:22:09.927093 | 2023-11-07 13:22:10.99483 | 00:00:01.067737 | 1.067737 | |
| {"asset_id": 1199683275} | 2023-11-07 10:25:40.979261 | 2023-11-07 10:25:41.20701 | 00:00:00.227749 | 0.227749 | |
| {"asset_id": 1027638506} | 2023-11-07 10:12:09.975943 | 2023-11-07 10:12:10.188947 | 00:00:00.213004 | 0.213004 | |
| {"asset_id": 2089665216} | 2023-11-07 10:11:06.279926 | 2023-11-07 10:11:06.628889 | 00:00:00.348963 | 0.348963 |
| defmodule Context do | |
| @moduledoc false | |
| defmacro __using__(opts) do | |
| repo = Keyword.fetch!(opts, :repo) | |
| quote do | |
| import Context, only: [context: 1, context: 2] | |
| Module.put_attribute(__MODULE__, :__repo__, unquote(repo)) |
Let's look at an example of how to launch a Kubernetes cluster from scratch on DigitalOcean, including kubeadm, an Nginx Ingress controller, and Letsencrypt certificates.
We'll be creating a four-node cluster (k8s-master, k8s-000...k8s-002), load balancer, and ssl certificates.
| // this behavior subject is basically your "give me the next batch" mechanism. | |
| // in this example, we're going to make 5 async requests back to back before requesting more. | |
| const BATCH_SIZE = 5; | |
| const requests = new BehaviorSubject(BATCH_SIZE); // start by requesting five items | |
| // for every request, pump out a stream of events that represent how many you have left to fulfill | |
| requests.flatMap((count) => Observable.range(0, count).map(n => count - n - 1)) | |
| // then concat map that into an observable of what you want to control with backpressure | |
| // you might have some parameterization here you need to handle, this example is simplified | |
| // handle side effects with a `do` block |
Have you ever seen these scatterplots that report a significant correlation between X and Y, but it looks like it's just the one point to the upper-right driving the correlation? Thanks to this interactive tool, you too can do this at home. Click anywhere in the picture, and the red dot will move. The sliding bar displays the resulting correlation coefficient. The grey interval are non-significant values: place the red dot right to get a significant result.
Data are generated from two standard independent gaussian (N=15). Test values are from the asymptotic Fisher transformation test that's on Wikipedia (two-sided, alpha = 5%). Code: R and d3.js.
mask lets you draw an area with multiple colors using just one path.
| scrolling: yes | |
| license: MIT |
| license: mit | |
| scrolling: true | |
| height: 300 |
Cubic Catmull–Rom curves with (b) uniform, (c) chordal and (d) centripetal parameterization. From Yuksel et. al: “Uniform parameterization overshoots and often generates cusps and intersections within short curve segments, while chord-length parameterization exhibits similar behavior for longer curve segments. Centripetal parameterization is the only one that guarantees no intersections within curve segments.”
A re-implementation of Jason Davies’ Phylogenetic Tree of Life, with faded gray lines to connect the leaf nodes of the tree to their corresponding labels inspired by a figure from Nature.
This implementation modifies the depth of interior nodes in a cluster layout to show branch lengths. Toggle the checkbox in the top-left corner to show or hide branch lengths, and mouseover a label to highlight its path to the root.
