HoloPy Tools

Holopy contains a number of tools to help you with common tasks when analyzing holograms. This page provides a summary of the tools available, while full descriptions can be found in the relevant code reference.

General Image Processing Tools

The tools described here are frequently used when analyzing holgrams. They are available from the holopy.core.process namespace.

The normalize() function divides an image by its average, returning an image with a mean pixel value of 1. Note that this is the same normalization convention used by HoloPy when calculating holograms with .calc_holo.

Cropping an image introduces difficulties in keeping track of the relative coordinates of features within an image and maintaining metadata. By using the subimage() function, the image origin is maintained in the cropped image, so coordinate locations of features (such as a scatterer) remain unchanged.

Since holograms of particles usually take the form of concentric rings, the location of a scatterer can usually be found by locating the apparent center(s) of the image. Use center_find() to locate one or more centers in an image.

You can remove isolated dead pixels with zero intensity (e.g. for a background division) by using zero_filter(). This function replaces the dead pixel with the average of its neighbours, and fails if adjacent pixels have zero intensity.

The add_noise() function allows you to add Gaussian-correlated random noise to a calculated image so that it more closely resembles experimental data.

To find gradient values at all points in an image, use image_gradient(). To simply remove a planar intensity gradient from an image, use detrend(). Note that this gives a mean pixel value of zero.

Frequency space analysis provides a powerful tool for working with images. Use fft() and ifft() to perform fourier transforms and inverse fourier transforms, respectively. These make use of scipy.fftpack functions, but are wrapped to correctly interpret HoloPy objects. HoloPy also includes a Hough transform (hough()) to help identify lines and other features in your images.

Math Tools

HoloPy contains implementations of a few mathematical functions related to scattering calculations. These functions are available from the holopy.core.math namespace.

To find the distance between two points, use cartesian_distance().

To rotate a set of points by arbitrary angles about the three coordinate axes, use rotate_points(). You can also calculate a rotation matrix with rotation_matrix() to save and use later.

To convert spherical coordinates into Cartesian coordinates, use to_cartesian(). To convert Cartesian coordinates into spherical coordinates, use to_spherical().

When comparing data to a model, the chi-squared and r-squared values provide measures of goodness-of-fit. You can access these through chisq() and rsq().

If you want want to convert between spherical and cartesian coordinates, use to_cartesian() and to_spherical().