# pyinterp.Binning2D.push#

Binning2D.push(x: numpy.ndarray, y: numpy.ndarray, z: numpy.ndarray, simple: bool = True) None[source]#

Push new samples into the defined bins.

Parameters
• x – X coordinates of the samples

• y – Y coordinates of the samples

• z – New samples to push into the defined bins.

• simple – If true, a simple binning 2D is used otherwise a linear binning 2d is applied. See the full description of the algorithm below.

The figure below is a graphical presentation of how a sample data point $$x$$ distributes its weight to neighboring grid points.

$$A$$ is the area of the grid cell. $$\alpha$$, $$\beta$$, $$\gamma$$ and $$\delta$$ are the areas of the different sub-rectangles. $$g_{00}$$, $$g_{01}$$, $$g_{10}$$ and $$g_{11}$$ are the grid points identified around point $$x$$. $$w_{00}$$, $$w_{01}$$, $$w_{10}$$ and $$w_{11}$$ are the weights associated with the grid points.

For simple binning, the point $$x$$ gives all its weight to its nearest grid point. In this example, the lower left grid point takes the weight equal to 1, that is $$w_{00}=1$$.

In the case of linear binning, the contribution from $$x$$ is distributed among each of the four surrounding grid points according to the areas of the opposite sub-rectangle induced by the position of the point.