Simplifying MultiPlot Interface #83

docs/gallery_scripts_template/plot_investigate_spectrum_binning_ms_matplotlib.py

@@ -1,26 +1,40 @@
 """
-Investigate Spctrum Binning ms_matplotlib
+Investigate Spectrum Binning ms_matplotlib
 =======================================
 
-Here we use a dummy spectrum example to investigate spectrum binning. 
+Here we use a dummy spectrum example to investigate spectrum binning.
 """
 
 import pandas as pd
 import matplotlib.pyplot as plt
 import requests
 from io import StringIO
+import sys
+import os
 
+# # Add parent directories to the path (adjust as necessary)
+# sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
+
+# Set the plotting backend to ms_matplotlib
 pd.options.plotting.backend = "ms_matplotlib"
 
-# download the file for example plotting
-url = (
-    "https://github.com/OpenMS/pyopenms_viz/releases/download/v0.1.5/TestSpectrumDf.tsv"
-)
+# Download the file for example plotting
+url = "https://github.com/OpenMS/pyopenms_viz/releases/download/v0.1.5/TestSpectrumDf.tsv"
 response = requests.get(url)
 response.raise_for_status()  # Check for any HTTP errors
 df = pd.read_csv(StringIO(response.text), sep="\t")
 
-# Let's assess the peak binning and create a 4 by 2 subplot to visualize the different methods of binning
+# Add a 'Run' column and duplicate entries for each run group.
+# For example, here we create three run groups (1, 2, and 3).
+runs = [1, 2, 3]
+df_list = []
+for run in runs:
+    df_run = df.copy()
+    df_run["Run"] = run
+    df_list.append(df_run)
+df = pd.concat(df_list, ignore_index=True)
+
+# Update the parameters for binning and visualization.
 params_list = [
     {"title": "Spectrum (Raw)", "bin_peaks": False},
     {
@@ -72,18 +86,26 @@
     },
 ]
 
-# Create a 3-row subplot
+# Create a 4x2 subplot grid to visualize different binning methods.
 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, **params
+    # Here we pass the "Run" column to group the spectrum by run.
+    df.plot(
+        kind="spectrum",
+        x="mz",
+        y="intensity",
+        canvas=axs[i][j],
+        grid=False,
+        show_plot=False,
+        by="Run",
+        **params
     )
     j += 1
-    if j >= 2:  # If we've filled two columns, move to the next row
+    if j >= 2:  # Move to next row when two columns are filled.
         j = 0
         i += 1
 
 fig.tight_layout()
-fig.show()
+plt.show()

docs/gallery_scripts_template/plot_spyogenes_subplots_ms_matplotlib.py

@@ -1,16 +1,20 @@
 """
 Plot Spyogenes subplots ms_matplotlib
-=======================================
+====================================================
 
 Here we show how we can plot multiple chromatograms across runs together
 """
 
 import pandas as pd
 import requests
 import zipfile
-import numpy as np
 import matplotlib.pyplot as plt
+import sys
+import os
+# sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
 
+
+# Set the plotting backend
 pd.options.plotting.backend = "ms_matplotlib"
 
 ###### Load Data #######
@@ -21,76 +25,51 @@
 
 # Download the zip file
 try:
+    # Save the zip file to the current directory
     print(f"Downloading {zip_filename}...")
     response = requests.get(url)
     response.raise_for_status()  # Check for any HTTP errors
-
-    # Save the zip file to the current directory
     with open(zip_filename, "wb") as out:
         out.write(response.content)
     print(f"Downloaded {zip_filename} successfully.")
 except requests.RequestException as e:
     print(f"Error downloading zip file: {e}")
-except IOError as e:
-    print(f"Error writing zip file: {e}")
 
-# Unzipping the file
+# Unzip the file
 try:
     with zipfile.ZipFile(zip_filename, "r") as zip_ref:
-        # Extract all files to the current directory
         zip_ref.extractall()
         print("Unzipped files successfully.")
 except zipfile.BadZipFile as e:
     print(f"Error unzipping file: {e}")
 
-annotation_bounds = pd.read_csv(
-    "spyogenes/AADGQTVSGGSILYR3_manual_annotations.tsv", sep="\t"
-)  # contain annotations across all runs
-chrom_df = pd.read_csv(
-    "spyogenes/chroms_AADGQTVSGGSILYR3.tsv", sep="\t"
-)  # contains chromatogram for precursor across all runs
-
-##### Set Plotting Variables #####
-pd.options.plotting.backend = "ms_matplotlib"
-RUN_NAMES = [
-    "Run #0 Spyogenes 0% human plasma",
-    "Run #1 Spyogenes 0% human plasma",
-    "Run #2 Spyogenes 0% human plasma",
-    "Run #3 Spyogenes 10% human plasma",
-    "Run #4 Spyogenes 10% human plasma",
-    "Run #5 Spyogenes 10% human plasma",
-]
-
-fig, axs = plt.subplots(len(np.unique(chrom_df["run"])), 1, figsize=(10, 15))
-
-# plt.close ### required for running in jupyter notebook setting
-
-# For each run fill in the axs object with the corresponding chromatogram
-plot_list = []
-for i, run in enumerate(RUN_NAMES):
-    run_df = chrom_df[chrom_df["run_name"] == run]
-    current_bounds = annotation_bounds[annotation_bounds["run"] == run]
+# Load the data
+annotation_bounds = pd.read_csv("spyogenes/AADGQTVSGGSILYR3_manual_annotations.tsv", sep="\t")
+chrom_df = pd.read_csv("spyogenes/chroms_AADGQTVSGGSILYR3.tsv", sep="\t")
 
-    run_df.plot(
-        kind="chromatogram",
-        x="rt",
-        y="int",
-        grid=False,
-        by="ion_annotation",
-        title=run_df.iloc[0]["run_name"],
-        title_font_size=16,
-        xaxis_label_font_size=14,
-        yaxis_label_font_size=14,
-        xaxis_tick_font_size=12,
-        yaxis_tick_font_size=12,
-        canvas=axs[i],
-        relative_intensity=True,
-        annotation_data=current_bounds,
-        xlabel="Retention Time (sec)",
-        ylabel="Relative\nIntensity",
-        annotation_legend_config=dict(show=False),
-        legend_config={"show": False},
-    )
+##### Plotting Using Tile By #####
+# Instead of pre-creating subplots and looping over RUN_NAMES,
+# we call the plot method once and provide a facet_column parameter.
+fig = chrom_df.plot(
+    kind="chromatogram",
+    x="rt",
+    y="int",
+    facet_column="run_name",         # Automatically groups data by run_name and creates subplots
+    facet_col_wrap=1,             # Layout: 1 column (one subplot per row)
+    grid=False,
+    by="ion_annotation",
+    title_font_size=16,
+    xaxis_label_font_size=14,
+    yaxis_label_font_size=14,
+    xaxis_tick_font_size=12,
+    yaxis_tick_font_size=12,
+    relative_intensity=True,
+    annotation_data=annotation_bounds,
+    xlabel="Retention Time (sec)",
+    ylabel="Relative\nIntensity",
+    annotation_legend_config={"show": False},
+    legend_config={"show": False},
+)
 
 fig.tight_layout()
 fig

pyopenms_viz/__init__.py

@@ -119,6 +119,7 @@ def _get_call_args(backend_name: str, data: DataFrame, args, kwargs):
                 ("feature_config", None),
                 ("_config", None),
                 ("backend", backend_name),
+                # ("tile_by", None),
             ]
         else:
             raise ValueError(

pyopenms_viz/_config.py

@@ -205,6 +205,9 @@ def default_legend_factory():
     legend_config: LegendConfig | dict = field(default_factory=default_legend_factory)
     opacity: float = 1.0
 
+    facet_column: str | None = None  # Name of the column to tile the plot by.
+    facet_col_wrap: int = 1       # How many columns in the subplot grid.
+
     def __post_init__(self):
         # if legend_config is a dictionary, update it to LegendConfig object
         if isinstance(self.legend_config, dict):