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wradlib Documentation, Release 0.1.1 3.6.4 wradlib.ipol.interpolate wradlib.ipol.interpolate(src, trg, vals, Interpolator, *args, **kwargs) Convenience function to use the interpolation classes in an efficient way ATTENTION: Works only for one- and two-dimensional vals arrays, yet. The interpolation classes in wradlib.ipol are computationally very efficient if they are applied on large multidimensional arrays of which the first dimension must be the locations’ dimension (1d or 2d coordinates) and the following dimensions can be anything (e.g. time or ensembles). This way, the weights need to be computed only once. However, this can only be done with success if all source values for the interpolation are valid numbers. If the source values contain let’s say np.nan types, the result of the interpolation will be np.nan in the vicinity of the corresponding points, too. Imagine that you have a time series of observations at points and in each time step one observation is missing. You would still like to efficiently apply the interpolation classes, but you will need to account for the resulting np.nan values in the interpolation output. In order to still allow for the efficient application, you have to take care of the remaining np.nan in your interpolation result. This is done by this convenience function. Alternatively, you have to make sure that your vals argument does not contain any np.nan values OR you have to post-process missing values in your interpolation result in another way. Parameters src : ndarray of floats, shape (npoints, ndims) Data point coordinates of the source points. trg : ndarray of floats, shape (npoints, ndims) Data point coordinates of the target points. vals : ndarray of float, shape (numsourcepoints, ...) Values at the source points which to interpolate Interpolator : a class which inherits from IpolBase *args : arguments of Interpolator (see class documentation) **kwargs : keyword arguments of Interpolator (see class documentation) Examples >>> # test for 1 dimension in space and two value dimensions >>> src = np.arange(10)[:,None] >>> trg = np.linspace(0,20,40)[:,None] >>> vals = np.hstack((np.sin(src), 10.+np.sin(src))) >>> # here we introduce missing values only in the second dimension >>> vals[3:5,1] = np.nan >>> ipol_result = interpolate(src, trg, vals, Idw, nnearest=2) >>> # plot if you like >>> import pylab as pl >>> pl.plot(trg, ipol_result, ’b+’) >>> pl.plot(src, vals, ’ro’) >>> pl.show() 3.6.5 wradlib.ipol.interpolate_polar wradlib.ipol.interpolate_polar(data, mask=None, Interpolator=) Convenience function to interpolate polar data 50 Chapter 3. Library Reference
wradlib Documentation, Release 0.1.1 Parameters data : 2d-array 2 dimensional array (azimuth, ranges) of floats; if no mask is assigned explicitly polar data should be a masked array mask : array boolean array with pixels to be interpolated set to True; must have the same shape as data Interpolator : a class which inherits from IpolBase Returns filled_data : 2d-array array with interpolated values for the values set to True in the mask Examples >>> import numpy as np >>> import wradlib as wrl >>> # creating a data array and mask some values >>> data = np.arange(12.).reshape(4,3) >>> masked_values = (data==2) | (data==9) >>> # interpolate the masked data based on ’’masked_values’’ >>> filled_a = wrl.ipol.interpolate_polar(data, mask = masked_values, Interpolator = wrl.ipol.Li >>> wrl.vis.polar_plot(filled_a) >>> # the same result can be achieved by using an masked array instead of an explicit mask >>> mdata = np.ma.array(data, mask = masked_values) >>> filled_b = wrl.ipol.interpolate_polar(mdata, Interpolator = wrl.ipol.Linear) >>> wrl.vis.polar_plot(filled_b) 3.7 Data Quality This module will serve two purposes: 1. provide routines to create simple radar data quality related fields. 2. provide routines to decide which radar pixel to choose based on the competing information in different quality fields. Data is supposed to be stored in ‘aligned’ arrays. Aligned here means that all fields are structured such that in each field the data for a certain index is representative for the same physical target. Therefore no assumptions are made on the dimensions or shape of the input fields except that they exhibit the numpy ndarray interface. beam_height_ft beam_height_ft_doviak pulse_volume Calculates the height of a radar beam above the antenna according to Calculates the height of a radar beam above the antenna according to the 4/3 (four-thirds -> ft) effec Calculates the sampling volume of the radar beam per bin depending on range and aperture. 3.7.1 wradlib.qual.beam_height_ft wradlib.qual.beam_height_ft(ranges, elevations, degrees=True, re=6371000) Calculates the height of a radar beam above the antenna according to the 4/3 (four-thirds -> ft) effective Earth 3.7. Data Quality 51
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