Centroids (`photutils.centroids`) ================================= Introduction ------------ `photutils.centroids` provides several functions to calculate the centroid of a single source: * :func:`~photutils.centroids.centroid_com`: Calculates the object "center of mass" from 2D image moments. * :func:`~photutils.centroids.centroid_quadratic`: Calculates the centroid by fitting a 2D quadratic polynomial to the data. * :func:`~photutils.centroids.centroid_1dg`: Calculates the centroid by fitting 1D Gaussians to the marginal ``x`` and ``y`` distributions of the data. * :func:`~photutils.centroids.centroid_2dg`: Calculates the centroid by fitting a 2D Gaussian to the 2D distribution of the data. Masks can be input into each of these functions to mask bad pixels. Error arrays can be input into the two Gaussian fitting methods to weight the fits. To calculate the centroids of many sources in an image, use the :func:`~photutils.centroids.centroid_sources` function. This function can be used with any of the above centroiding functions or a custom user-defined centroiding function. Getting Started --------------- Let's extract a single object from a synthetic dataset and find its centroid with each of these methods. For this simple example we will not subtract the background from the data (but in practice, one should subtract the background):: >>> from photutils.datasets import make_4gaussians_image >>> from photutils.centroids import (centroid_1dg, centroid_2dg, ... centroid_com, centroid_quadratic) >>> data = make_4gaussians_image()[43:79, 76:104] >>> x1, y1 = centroid_com(data) >>> print((x1, y1)) # doctest: +FLOAT_CMP (13.93157998341213, 17.051234441067088) >>> x2, y2 = centroid_quadratic(data) >>> print((x2, y2)) # doctest: +FLOAT_CMP (13.948284438186919, 16.98788199435759) .. doctest-requires:: scipy >>> x3, y3 = centroid_1dg(data) >>> print((x3, y3)) # doctest: +FLOAT_CMP (14.040352707371396, 16.962306463644801) .. doctest-requires:: scipy >>> x4, y4 = centroid_2dg(data) >>> print((x4, y4)) # doctest: +FLOAT_CMP (14.002212073733611, 16.996134592982017) Now let's plot the results. Because the centroids are all very similar, we also include an inset plot zoomed in near the centroid: .. plot:: :include-source: import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1.inset_locator import (mark_inset, zoomed_inset_axes) from photutils.centroids import (centroid_1dg, centroid_2dg, centroid_com, centroid_quadratic) from photutils.datasets import make_4gaussians_image data = make_4gaussians_image()[43:79, 76:104] # extract single object xycen1 = centroid_com(data) xycen2 = centroid_quadratic(data) xycen3 = centroid_1dg(data) xycen4 = centroid_2dg(data) xycens = [xycen1, xycen2, xycen3, xycen4] fig, ax = plt.subplots(1, 1, figsize=(4, 5)) ax.imshow(data, origin='lower', interpolation='nearest') marker = '+' ms, mew = 15, 2.0 colors = ('white', 'black', 'red', 'blue') for xycen, color in zip(xycens, colors): plt.plot(*xycen, color=color, marker=marker, ms=ms, mew=mew) ax2 = zoomed_inset_axes(ax, zoom=6, loc=9) ax2.imshow(data, vmin=190, vmax=220, origin='lower', interpolation='nearest') ms, mew = 30, 2.0 for xycen, color in zip(xycens, colors): ax2.plot(*xycen, color=color, marker=marker, ms=ms, mew=mew) ax2.set_xlim(13, 15) ax2.set_ylim(16, 18) mark_inset(ax, ax2, loc1=3, loc2=4, fc='none', ec='0.5') ax2.axes.get_xaxis().set_visible(False) ax2.axes.get_yaxis().set_visible(False) ax.set_xlim(0, data.shape[1] - 1) ax.set_ylim(0, data.shape[0] - 1) Centroiding several sources in an image --------------------------------------- The :func:`~photutils.centroids.centroid_sources` function can be used to calculate the centroids of many sources in a single image given initial guesses for their positions. This function can be used with any of the above centroiding functions or a custom user-defined centroiding function. Here is a simple example using :func:`~photutils.centroids.centroid_com`. A cutout image is made centered at each initial position of size ``box_size``. A centroid is then calculated within the cutout image for each source: .. doctest-requires:: scipy >>> from photutils.centroids import centroid_sources >>> data = make_4gaussians_image() >>> x_init = (25, 91, 151, 160) >>> y_init = (40, 61, 24, 71) >>> x, y = centroid_sources(data, x_init, y_init, box_size=21, ... centroid_func=centroid_com) >>> print(x) # doctest: +FLOAT_CMP [ 24.98911515 90.43056554 150.20332399 159.87234831] >>> print(y) # doctest: +FLOAT_CMP [40.08504359 60.56869612 24.74216925 70.32723054] Let's plot the results: .. plot:: :include-source: import matplotlib.pyplot as plt from photutils.centroids import centroid_com, centroid_sources from photutils.datasets import make_4gaussians_image data = make_4gaussians_image() x_init = (25, 91, 151, 160) y_init = (40, 61, 24, 71) x, y = centroid_sources(data, x_init, y_init, box_size=21, centroid_func=centroid_com) plt.figure(figsize=(8, 4)) plt.imshow(data, origin='lower', interpolation='nearest') plt.scatter(x, y, marker='+', s=80, color='red', label='Centroids') plt.legend() plt.tight_layout() Reference/API ------------- .. automodapi:: photutils.centroids :no-heading: