Plotting/pyopenms viz

[singjc]

User description

I updated all the plotting to use pyopenms_viz. This PR is related and depends on these to PRs for pyopenms_viz to be merged first: pyopenms_viz PR#19 and pyopenms_viz PR#20

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PR Type

enhancement, other

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Description

• Refactored the plotting logic to utilize the ms_plotly backend, enhancing the visualization capabilities.
• Added new UI options for spectrum plotting, including peak binning and number of bins selection.
• Removed the BasePlotter and MSExperimentPlotter classes, along with their associated methods and configurations.
• Updated the view module to integrate the new plotting logic, replacing old plotting functions with the new API.

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Changes walkthrough 📝

	Relevant files
Enhancement	common.py Add spectrum plotting and binning options                                src/common.py Added spectrum plotting options in the UI. Introduced binning options for spectrum peaks. +6/-0      view.py Update view plotting to use ms_plotly backend                        src/view.py Updated plotting methods to use ms_plotly backend. Integrated new plotting logic for chromatograms and spectra. Removed old plotting functions and replaced with new API. +112/-88
Other	BasePlotter.py Remove BasePlotter abstract class and utilities                    src/plotting/BasePlotter.py Removed the BasePlotter abstract class. Removed associated configuration and utility methods. +0/-58    MSExperimentPlotter.py Remove MSExperimentPlotter class and API                                  src/plotting/MSExperimentPlotter.py Removed MSExperimentPlotter class and its methods. Removed functional API for plotting MS experiments. +0/-221

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PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Key issues to review Performance Concern The plot_ms_spectrum function is decorated with @st.cache_resource, which may not be the most appropriate caching mechanism for this use case. Consider using @st.cache_data instead for better performance and memory management. Code Duplication The plotting logic for TIC, BPC, and XIC is very similar and could be refactored into a single function to reduce code duplication and improve maintainability.

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PR Code Suggestions ✨

Category	Suggestion	Score
Maintainability	Refactor repetitive plotting code into a separate function to reduce duplication The code for plotting TIC, BPC, and EIC is very similar and repetitive. Consider refactoring this into a separate function to reduce code duplication and improve maintainability. src/view.py [71-103] -if st.session_state.view_tic: - df = st.session_state.view_ms1.groupby("RT").sum().reset_index() - df["type"] = "TIC" - if df["inty"].max() > max_int: - max_int = df["inty"].max() +def plot_chromatogram(df, plot_type, color): + df["type"] = plot_type fig = df.plot( backend="ms_plotly", kind="chromatogram", fig=fig, x="RT", y="inty", by="type", - line_color="#f24c5c", + line_color=color, show_plot=False, grid=False, ) - fig = fig.fig + return fig.fig, df["inty"].max() + +if st.session_state.view_tic: + df = st.session_state.view_ms1.groupby("RT").sum().reset_index() + fig, max_tic = plot_chromatogram(df, "TIC", "#f24c5c") + max_int = max(max_int, max_tic) + if st.session_state.view_bpc: df = st.session_state.view_ms1.groupby("RT").max().reset_index() - df["type"] = "BPC" - if df["inty"].max() > max_int: - max_int = df["inty"].max() - fig = df.plot( - backend="ms_plotly", - kind="chromatogram", - fig=fig, - x="RT", - y="inty", - by="type", - line_color="#2d3a9d", - show_plot=False, - grid=False, - ) - fig = fig.fig + fig, max_bpc = plot_chromatogram(df, "BPC", "#2d3a9d") + max_int = max(max_int, max_bpc) Apply this suggestion Suggestion importance[1-10]: 9 Why: Refactoring repetitive code into a function significantly improves code maintainability and reduces the risk of errors, making future modifications easier.	9
	Use constants for color values to improve maintainability and consistency Consider using a constant or configuration variable for the color values ("#f24c5c", "#2d3a9d", "#f6bf26") instead of hardcoding them in multiple places. This would improve maintainability and consistency across the codebase. src/view.py [76-87] +COLOR_TIC = "#f24c5c" +COLOR_BPC = "#2d3a9d" +COLOR_XIC = "#f6bf26" + fig = df.plot( backend="ms_plotly", kind="chromatogram", fig=fig, x="RT", y="inty", by="type", - line_color="#f24c5c", + line_color=COLOR_TIC, show_plot=False, grid=False, ) Apply this suggestion Suggestion importance[1-10]: 7 Why: Using constants for color values improves maintainability and consistency across the codebase, making it easier to update and manage color schemes in the future.	7
Enhancement	Simplify the logic for finding the maximum intensity across different plot types The max_int variable is being updated in multiple if statements. Consider using the max() function with a list comprehension to simplify the logic and reduce code duplication. src/view.py [70-92] -max_int = 0 +intensity_maxes = [] if st.session_state.view_tic: df = st.session_state.view_ms1.groupby("RT").sum().reset_index() df["type"] = "TIC" - if df["inty"].max() > max_int: - max_int = df["inty"].max() + intensity_maxes.append(df["inty"].max()) fig = df.plot( backend="ms_plotly", kind="chromatogram", fig=fig, x="RT", y="inty", by="type", line_color="#f24c5c", show_plot=False, grid=False, ) fig = fig.fig if st.session_state.view_bpc: df = st.session_state.view_ms1.groupby("RT").max().reset_index() df["type"] = "BPC" - if df["inty"].max() > max_int: - max_int = df["inty"].max() + intensity_maxes.append(df["inty"].max()) +max_int = max(intensity_maxes) if intensity_maxes else 0 + Apply this suggestion Suggestion importance[1-10]: 8 Why: This suggestion reduces code duplication and simplifies the logic for determining the maximum intensity, which enhances code readability and maintainability.	8
Error handling	Improve error handling for EIC plotting to handle invalid inputs more gracefully The error handling for the EIC plotting could be improved. Currently, it catches a ValueError and displays an error message, but the function continues execution. Consider returning from the function or raising a custom exception to handle this case more gracefully. src/view.py [109-135] try: target_value = float(target_value) ppm_tolerance = st.session_state.view_eic_ppm tolerance = (target_value * ppm_tolerance) / 1e6 # Filter the DataFrame df_eic = df[ (df["mz"] >= target_value - tolerance) & (df["mz"] <= target_value + tolerance) ] if not df_eic.empty: df_eic["type"] = "XIC" if df_eic["inty"].max() > max_int: max_int = df_eic["inty"].max() fig = df_eic.plot( backend="ms_plotly", kind="chromatogram", fig=fig, x="RT", y="inty", by="type", line_color="#f6bf26", show_plot=False, grid=False, ) fig = fig.fig + else: + st.warning("No data found for the specified m/z value and tolerance.") except ValueError: st.error("Invalid m/z value for XIC provided. Please enter a valid number.") + return None # or raise a custom exception Apply this suggestion Suggestion importance[1-10]: 8 Why: Enhancing error handling by providing warnings and potentially stopping execution on invalid input improves user experience and prevents further errors in the code execution.	8

[axelwalter]

Requires pyopenms_viz and pandas needs to be installed together with pyopenms_viz (not pyopenms) in order to set the backends.