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3.1 Vis_04 Data Visualization Lecture 3 Visualization Techniques - 1D Scalar Data 2D Scalar Data (part 1)

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3.2 Vis_04 Visualization Techniques - One Dimensional Scalar Data

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3.3 Vis_04 1D Interpolation -The Problem f x Given (x 1,f 1 ), (x 2,f 2 ), (x 3,f 3 ), (x 4,f 4 ) - estimate the value of f at other values of x - say, x*. Suppose x*=

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3.4 Vis_04 Nearest Neighbour f x Take f-value at x* as f-value of nearest data sample. So if x* = 1.75, then f estimated as

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3.5 Vis_04 Linear Interpolation f x Join data points with straight lines- read off f-value corresponding to x*.. in the case that x*=1.75, then f estimated as

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3.6 Vis_04 Linear Interpolation - Doing the Calculation f(x*) = (1-t) f 1 + t f 2 The functions j(t)=1-t and k(t)=t are basis functions. OR, saving a multiplication: f(x*) = f 1 + t ( f 2 - f 1 ) Suppose x* lies between x 1 and x 2. Then apply the transformation: t = (x*-x 1 )/(x 2 -x 1 ) so that t goes from 0 to 1. t=(1.75-1)/(2-1)=0.75 f(1.75)=0.25*1+0.75*3 =2.5 f(1.75)=1+0.75*(3-1) =2.5

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3.7 Vis_04 Nearest Neighbour and Linear Interpolation n Nearest Neighbour – Very fast : no arithmetic involved – Continuity : discontinuous value – Bounds : bounds fixed at data extremes n Linear Interpolation – Fast : one multiply, one divide – Continuity : value only continuous, not slope (C 0 ) – Bounds : bounds fixed at data extremes

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3.8 Vis_04 Drawing a Smooth Curve n Rather than straight line between points, we create a curve piece x1x1 x2x2 f1f1 f2f2 g1g1 g2g2 We estimate the slopes g 1 and g 2 at the data points, and construct curve which has these values and these slopes at end-points

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3.9 Vis_04 Slope Estimation n Slopes estimated as some average of the slopes of adjacent chords - eg to estimate slope at x 2 x1x1 x2x2 x3x3 g2g2 f2f2 f1f1 f3f3 g 2 usually arithmetic mean (ie average) of f 2 -f 1 )/(x 2 -x 1 )

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3.10 Vis_04 Piecewise Cubic Interpolation x1x1 x2x2 f1f1 f2f2 g1g1 g2g2 f(x) = c 1 (x) * f 1 + c 2 (x) * f 2 + h*(d 1 (x) * g 1 - d 2 (x) * g 2 ) c i (x), d i (x) are cubic Hermite basis functions, h = x 2 – x 1. Once the slopes at x 1 and x 2 are known, this is sufficient to define a unique cubic polynomial in the interval [x 1,x 2 ]

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3.11 Vis_04 Cubic Hermite Basis Functions Here they are: Again set t = (x - x 1 )/(x 2 – x 1 ) c 1 (t) = 3(1-t) 2 - 2(1-t) 3 c 2 (t) = 3t 2 - 2t 3 d 1 (t) = (1-t) 2 - (1-t) 3 d 2 (t) = t 2 - t 3 Check the values at x = x 1, x 2 (ie t=0,1)

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3.12 Vis_04 Coal data - cubic interpolation

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3.13 Vis_04 Piecewise Cubic Interpolation n More computation needed than with nearest neighbour or linear interpolation. n Continuity: slope continuity (C 1 ) by construction - and cubic splines will give second derivative continuity (C 2 ) n Bounds: bounds not controlled generally - eg if arithmetic mean used in slope estimation...

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3.14 Vis_04 Shape Control n However special choices for slope estimation do give control over shape n If the harmonic mean is used 1/g 2 = 0.5 ( 1/ 1 + 1/ 2 ) then we find that f(x) lies within the bounds of the data

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3.15 Vis_04 Coal data – keeping within the bounds of the data

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3.16 Vis_04 Rendering Line Graphs n The final rendering step is straightforward n We can assume that the underlying graphics system will be able to draw straight line segments n Thus the linear interpolation case is trivial n For curves, we do an approximation as sequence of small line segments

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3.17 Vis_04 Background Study n Take the coal furnace data from lecture 2 – Investigate how to present this as a graph in Excel (not easy!) – After learning gnuplot in the practical class, create a graph of the coal data using gnuplot n Look for Java applets on the Web that will create line graphs

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3.18 Vis_04 Visualization Techniques Two Dimensional Scalar Data

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3.19 Vis_04 2D Interpolation - Rectangular Grid n Suppose we are given data on rectangular grid: f given at each grid point; data enrichment fills out the empty spaces by interpolating values within each cell

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3.20 Vis_04 Nearest Neighbour Interpolation n Straightforward extension from 1D: take f-value from nearest data sample n No continuity n Bounds fixed at data extremes

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3.21 Vis_04 Bilinear Interpolation n Consider one grid rectangle: – suppose corners are at (0,0), (1,0), (1,1), (0,1)... ie a unit square – values at corners are f 00, f 10, f 11, f 01 f 00 f 10 f 01 f 11 How do we estimate value at a point (x,y) inside the square?

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3.22 Vis_04 Bilinear Interpolation f 00 f 10 f 01 f 11 (i) interpolate in x-direction between f 00,f 10 ; and f 01,f 11 (ii) interpolate in y-direction We carry out three 1D interpolations: Exercise: Show this is equivalent to calculating - f(x,y) = (1-x)(1-y)f 00 +x(1-y)f 10 +(1-x)yf 01 + xyf 11 (x,y)

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3.23 Vis_04 Piecewise Bilinear Interpolation n Apply within each grid rectangle n Fast n Continuity of value, not slope (C 0 ) n Bounds fixed at data extremes

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3.24 Vis_04 Contour Drawing n Contouring is very common technique for 2D scalar data n Isolines join points of equal value – sometimes with shading added n How can we quickly and accurately draw these isolines?

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3.25 Vis_04 An Example n As an example, consider this data: Where does the zero level contour go?

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3.26 Vis_04 Intersections with sides n The bilinear interpolant is linear along any edge - thus we can predict where the contour will cut the edges (just by simple proportions) cross-section view along top edge

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3.27 Vis_04 Simple Approach n A simple approach to get the contour inside the grid rectangle is just to join up the intersection points Question: Does this always work? Try an example where one pair of opposite corners are positive, other pair negative

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