BrownModel.prepare_interp

giant.camera_models.brown_model:

BrownModel.prepare_interp(pixel_bounds=100, temperature_bounds=(-50, 50), temperature_step=5)

This method prepares a SciPy RegularGridInterpolator for converting pixels into undistorted gnomic locations.

This is done by making calls to pixels_to_unit() to compute the transformation at every pixel in the detector plus/minus the pixel bounds and for each temperature in the temperature bounds using the temperature step. (That is: cols = np.arange(-pixel_bounds, self.n_cols+pixel_bounds), rows=np.arange(-pixel_bounds, self.n_rows+pixel_bounds), temps = np.arange(temperature_bounds[0], temperature_bounds[1]+temperature_step, temperature_step).)

This method will likely take a little while to run, but only needs to be run once and then the results are saved for future use, including if the camera model is dumped to a file.

Parameters:

• pixel_bounds (int) – An integer specifying how many pixels to pad when computing the transformation to gnomic locations.

• temperature_bounds (Tuple[int, int]) – A tuple specifying the temperature bounds to compute the transformation over (inclusive). If none of temperature_coefficients are non-zero then this is ignored.

• temperature_step (Real) – An integer specifying the temperature step size to compute the transformation to gnomic locations over. If none of temperature_coefficients are non-zero then this is ignored.