Investigate Spctrum Binning ms_matplotlib

Here we use a dummy spectrum example to investigate spectrum binning.

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import pandas as pd
import matplotlib.pyplot as plt
from pyopenms_viz.util import download_file

pd.options.plotting.backend = "ms_matplotlib"

url = "https://zenodo.org/records/17904352/files/TestSpectrumDf.tsv?download=1"
local_path = "TestSpectrumDf.tsv"
download_file(url, local_path)
df = pd.read_csv(local_path, sep="\t")

# Let's assess the peak binning and create a 4 by 2 subplot to visualize the different methods of binning
params_list = [
    {"title": "Spectrum (Raw)", "bin_peaks": False},
    {
        "title": "Spectrum (agg: sum | bin: freedman)",
        "bin_peaks": "auto",
        "bin_method": "freedman-diaconis",
        "aggregation_method": "sum",
    },
    {
        "title": "Spectrum (agg: mean | bin: freedman)",
        "bin_peaks": "auto",
        "bin_method": "freedman-diaconis",
        "aggregation_method": "mean",
    },
    {
        "title": "Spectrum (agg: sum | bin: mz-tol-bin=1)",
        "bin_peaks": "auto",
        "bin_method": "mz-tol-bin",
        "mz_tol": 1,
        "aggregation_method": "sum",
    },
    {
        "title": "Spectrum (agg: mean | bin: mz-tol-bin=1)",
        "bin_peaks": "auto",
        "bin_method": "mz-tol-bin",
        "mz_tol": 1,
        "aggregation_method": "mean",
    },
    {
        "title": "Spectrum (agg: max | bin: mz-tol-bin=1)",
        "bin_peaks": "auto",
        "bin_method": "mz-tol-bin",
        "mz_tol": 1,
        "aggregation_method": "max",
    },
    {
        "title": "Spectrum (agg: max | bin: mz-tol-bin=1pct-diff)",
        "bin_peaks": "auto",
        "bin_method": "mz-tol-bin",
        "mz_tol": "1pct-diff",
        "aggregation_method": "max",
    },
    {
        "title": "Spectrum (agg: max | bin: mz-tol-bin=freedman-diaconis)",
        "bin_peaks": "auto",
        "bin_method": "mz-tol-bin",
        "mz_tol": "freedman-diaconis",
        "aggregation_method": "max",
    },
]

# Create a 3-row subplot
fig, axs = plt.subplots(4, 2, figsize=(14, 14))

i = j = 0
for params in params_list:
    p = df.plot(
        kind="spectrum",
        x="mz",
        y="intensity",
        canvas=axs[i][j],
        grid=False,
        show_plot=False,
        **params,
    )
    j += 1
    if j >= 2:  # If we've filled two columns, move to the next row
        j = 0
        i += 1

fig.tight_layout()
plt.show()