detectorcal.plot#
- detectorcal.plot.plot_pixel_calibrations(volume, dark=None, n_pixels=9, save_path=None, show=True, sigma=50, cutoff=300, coords=None, gpu=False, inch_per_plot=3)#
Plot the calibration a selected number of pixels. If not otherwise specified, n random pixels will be selected. Otherwise, the coords argument can be used to provide (x, y) coordinates for the pixel to be plotted.
- Parameters
- volume: np.ndarray
Detector-response volume to be used for calibration
- dark: np.ndarray
Default None. Dark image to be subtracted for detector response.
- n_pixels: int
Default 9. Number of random pixels for which to plot the calibration
- save_path: None or str
Default None. Optional path to which to save the output (probably .png)
- show: bool
Default True. Should matplotlib display the plot? Perhaps this isnt necessary if you are saving the output.
- sigma: scalar
Default sigma. Standard deviation for the Gaussian Kernel used for smoothing
- cutoff: scalar
Default 300. Minimum value in smoothed image at which to include the value in the regression. This is chosen to elminate values that fall outside of the range of linear response.
- coords: None or list of list
Default None. List of the form [[y_coord, x_coord], …]
- inch_per_plot: scalar
Default 3. How many inches to add to figure size in each dim for each plot