I have data as follows: dat <- structure(list(amount_of_categories = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L ), mun
I have another question, is there a package that interpolates precipitation data taking into account mountains and oceans? I have so far used Numpy and Basemap
I am trying to interpolate 3 values that I have measured, each one is related to a circular area of which I am considering the diameter (40mm, 50mm, and 100mm).
At the Top "right", there is the 2D-density plot of the recorded data (actual), fewer in number. On the top-left is the interpolated data (thin-plate), i.e. la
I have this image as shown below. It is a binary mask I created this image using the below code. Basically I got the x_idx, y_idx for just those white pixels,
Essentially, I have a CSV file full of compass bearings in radians from 0 to 2pi and attached timestamps that looks something like this: time, bearing 0.36,0.01
I have a pair of two-dimensional arrays from a gridded dataset (in GeoTIFF format), both with the exact same resolution and number of rows/columns. Suppose Arra
SciPy interpolation has 3 supported methods: Supported are “linear” and “nearest”, and “splinef2d”. “splinef2d”
I want to perform a (cubic) spline interpolation for population data to "transform" yearly data into quarterly data. I know that there are a fair number of flaw
I have a non-grid-aligned set of input values associated with grid-aligned output values. Given a new input value I want to find the output: &nbs
I have a 6x6 matrix of data(values_master) for a 6x6 set of data points: master_x,master_y=mgrid[950:1450:6j,550:1050:6j] I then try and interpolate the data
I have a pandas DataFrame with time as index (1 min Freq) and several columns worth of data. Sometimes the data contains NaN. If so, I want to interpolate only
I have an array, something like: array = np.arange(0,4,1).reshape(2,2) > [[0 1 2 3]] I want to both upsample this array as well as interpolate the re
continuous-integration