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Cant gradient is the amount by which cant is increased or decreased in a given length of track. The change in cant is required in order to connect a tangent track (no cant) to a curved track (with cant) through a transition curve. The rate of change of cant is used to determine the suitable cant gradient for a given design speed.
The Google Chart API allows a variety of graphs to be created. Livegap Charts creates line, bar, spider, polar-area and pie charts, and can export them as images without needing to download any tools. Veusz is a free scientific graphing tool that can produce 2D and 3D plots. Users can use it as a module in Python.
Cant itself refers to the superelevation of the curve, that is, the difference between the elevations of the outside and inside rails. Cant deficiency is present when a rail vehicle's speed on the curve is greater than the speed at which the components of wheel to rail force are normal to the plane of the track.
Line charts — Accepts up to six datasets. (updated 30 August 2023) Vertical bar charts (column charts) — Accepts up to six datasets. Toggle between clustered and stacked charts; user can adjust "Yfloor"—the Y level (usually=0) from which columns rise or fall; user chooses to keep or ignore negative input values. (updated 27 August 2023)
Conjugate gradient, assuming exact arithmetic, converges in at most n steps, where n is the size of the matrix of the system (here n = 2). In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is positive-semidefinite.
Sergeant Chris D. Washington checking his Topographic map during a morning deer hunt in Kilgore, Texas A topographic map of Stowe, Vermont with contour lines Part of the same map in a perspective shaded relief view illustrating how the contour lines follow the terrain Sheet #535 (2013 version; second digital edition) of MTN50 Spanish National Topographic map series, covering Algete town (near ...
A log–log plot of y = x (blue), y = x 2 (green), and y = x 3 (red). Note the logarithmic scale markings on each of the axes, and that the log x and log y axes (where the logarithms are 0) are where x and y themselves are 1.
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a data set. MDS is used to translate distances between each pair of objects in a set into a configuration of points mapped into an abstract Cartesian space.