value. This strategy is described in the section \[sec:cheating\].

6.  The fact that each uv-cell contains a number of Taylor terms affords an additional axis available for parallelisation. The strategy is described in section \[sec:parallelisation\].

### Cheating {#sec:cheating}

There are two sources of error when a sample is gridded (see Figure \[fig:error\_pattern\]). The first is from truncating the support; that is, only boxes only within a finite range $R_j = \sqrt{- \delta_R \log \epsilon}$ are updated. Convolutional gridding suffers from the same type of truncation error. The second source of error is from truncation of the Taylor terms; that is, only $q < \infty$ Taylor terms are updated. S91 notes that the equation for calculating the error from truncation of Taylor terms (Equation \[eq:qerror\]) overestimates the error by several orders of magnitude. If this is the case, for a given value of $\epsilon$, the error will be dominated by the truncated support, and the contribution from the truncation of the Taylor terms will be negligible. In principle, therefore, one can reduce the number of Taylor terms without substantially increasing the overall error, as long as it remains less than the error from the truncation of support.

In order to balance 