lib5c.plotters.splines module¶
Module for visualizing spline surfaces fit to 5C counts data.

lib5c.plotters.splines.
visualize_spline
(counts_list, primermap, bias_factor, spline, grid_points=10, sample_rate=100, log=True, asymmetric=False)[source]¶ Open an interactive pyplot window showing a visualization of a spline surface, overlayed over representative 5C counts data.
 Parameters
counts_list (List[Dict[str, np.ndarray]]) – A list of counts dicts to use as data points to be compared to the spline surface.
primermap (Dict[str, List[Dict[str, Any]]]) – The primermap corresponding to the counts dicts in
counts_list
.bias_factor (str) – The bias factor being plotted. This string must match metadata keys in
primermap
. That is to say, ifbias_list
is['length']
then we expectprimermap[region][i]['length']
to be a number representing the length of thei
th fragment in the region specified byregion
.spline (scipy.interpolate.BivariateSpline) – The spline object to visualize.
grid_points (int) – The number of grid points to use when constructing the wireframe of the surface represented by
spline
.sample_rate (int) – Only every
sample_rate
th realdata point will be included in the visualization to reduce computational load.log (bool) – Pass True to show counts on a logscale axis.
asymmetric (bool) – Pass True to iterate only over the uppertriangular elements of the counts matrices, which can lead to asymmetric visualizations. By default, the algorithm iterates over all elements of the counts matrices, enforcing symmetry in thevisualizations but incurring some redundancy in the actual counts information.
Notes
The spline will be displayed in an interactive window via
plt.show()
. If your default matplotlib backend is not interactive, this function will try to set your backend to TkAgg. If you prefer to use a different interactive backend, set theMPLBACKEND
environment variable before invoking Python.