Plotting Crosslink Type Distribution
from pyXLMS import __version__
print(f"Installed pyXLMS version: {__version__}")
Installed pyXLMS version: 1.3.0
from pyXLMS import parser
from pyXLMS import plotting
All plotting functionality is available via the plotting
submodule. We also import the parser
submodule here for reading result files.
parser_result = parser.read(
"../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1.pdResult",
engine="MS Annika",
crosslinker="DSS",
)
Reading MS Annika CSMs...: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 826/826 [00:00<00:00, 12047.07it/s]
Reading MS Annika crosslinks...: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 300/300 [00:00<00:00, 21431.22it/s]
We read crosslink-spectrum-matches and crosslinks using the generic parserΒ from a single .pdResult
file.
fig, ax = plotting.plot_crosslink_type_distribution(
parser_result["crosslink-spectrum-matches"],
figsize=(7.0, 4.0),
filename_prefix="crosslink_type_dist_csms",
)
We can plot the crosslink type distribution (intra- and inter-links) for our crosslink-spectrum-matches by passing them as the first argument. The default figure size is 16 by 9 inches and does not need to be set explicitly, we just used a smaller one here for demonstration purposes. The filename_prefix
parameter is also optional, if it is given the plot is saved four times: once without the title in .png
and .svg
format, and once with the title in .png
and .svg
format.
fig, ax = plotting.plot_crosslink_type_distribution(
parser_result["crosslinks"],
plot_type="pie",
title="Crosslink Type Distribution as Pie Chart",
figsize=(7.0, 4.0),
)
We can do the same plot for our crosslinks by passing them as the first argument instead. This time we also specify plot_type="pie"
to draw a pie chart instead and additionally specify a title for our plot via the title
parameter. Since we did not specify a filename_prefix
the plot is not saved to disk. There are also other parameters that can be set to tune your plot like colors
, you can read more about all the possible parameters here: docs.