interpolation

I have timestamped sensor data. Because of technical details, I get data from the sensors at approximately one minute intervals. The data may look like this: tstamp val 0 2016-09-01 00:00:00 57 1 2016-09-01 00:01:00 57 2 2016-09-01 00:02:23 57 3 2016-09-01 00:03:04 57 4 2016-09-01 00:03:58 58 5 2016-09-01 00:05:00 60 Now, essentially, I would be extremely happy if I got all data at the exact minute, but I don't. The only way to conserve the distribution and have data at each minute is to interpolate. For example, between row indexes 1 and 2 there are 83 seconds, and the natural way to get a

Here is the graph I currently have : The Dotted Blue line represented the y value that corresponds to the x value I am looking for. I am trying to find the x values of the line's intersections with the blue curve(Upper).Since the interesections do not fall on a point that has already been defined, we need to interpolate a point that falls onto the Upper plot. Here is the information I have: LineValue - The y value of the intersection and the value of the dotted line( y = LineValue) Frequency - an array containing the x value coordinates seen on this plot. The interpolated values of Frequency

I am using PIL to resize my images, my case is to scale up the original image. I am confused about the algorithm used with `resample=ANTIALIAS'. According to the document below, ANTIALIAS seems to be the best while scaling down. I wonder In which case can BICUBIC win?(some of my test case shows bicubic is better choice) An optional resampling filter. This can be one of NEAREST (use nearest neighbour), BILINEAR (linear interpolation in a 2x2 environment), BICUBIC (cubic spline interpolation in a 4x4 environment), or ANTIALIAS (a high-quality downsampling filter). If omitted, or if the image has

I am considering using this method to interpolate some 3D points I have. As an input I have atmospheric concentrations of a gas at various elevations over an area. The data I have appears as values every few feet of vertical elevation for several tens of feet, but horizontally separated by many hundreds of feet (so 'columns' of tightly packed values). The assumption is that values vary in the vertical direction significantly more than in the horizontal direction at any given point in time. I want to perform 3D kriging with that assumption accounted for (as a parameter I can adjust or that is

I have a 3D space (x, y, z) with an additional parameter at each point (energy), giving 4 dimensions of data in total. I would like to find a set of x, y, z points which correspond to an iso-energy surface found by interpolating between the known points. The spacial mesh has constant spacing and surrounds the iso-energy surface entirely, however, it does not occupy a cubic space (the mesh occupies a roughly cylindrical space) Speed is not crucial, I can leave this number crunching for a while. Although I'm coding in Python and NumPy, I can write portions of the code in FORTRAN. I can also wrap

I have some observational data that is relatively complete, but contains some NaN values, in an matrix in matlab and I want to interpolate them to a more evenly spaced grid using interp2 So, to keep things simple lets say I have one complete (no NaN values) matrix, and one that looks something like: A = [ 1 2 3 4; 2 3 2 NaN; 0 2 3 4; 0 NaN 4 5 ] with B and C being complete matrices, interp2 won't accept an input matrix with NaN values. So if I do something like this: [AI,BI] = meshgrid(a,b) %# matrices to interpolate data to, arbitrary CI = interp2(A,B,C,AI,BI) %# interpolation, A has NaN