From 10ba6b46b25273014dc21b29008cd5a54b354550 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 28 Oct 2025 22:33:12 +0000
Subject: [PATCH 01/26] Initial plan


From c9f972d09aa3a1acaa935f9e899bde8658f3bd4e Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 28 Oct 2025 22:37:21 +0000
Subject: [PATCH 02/26] Add export_feature_scores function and CLI command

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/cli/export.py           |  31 +++
 pyprophet/export/export_report.py | 311 ++++++++++++++++++++++++++++++
 2 files changed, 342 insertions(+)

diff --git a/pyprophet/cli/export.py b/pyprophet/cli/export.py
index e094e924..ef21d535 100644
--- a/pyprophet/cli/export.py
+++ b/pyprophet/cli/export.py
@@ -14,6 +14,9 @@
 from ..export.export_report import (
     export_scored_report as _export_scored_report,
 )
+from ..export.export_report import (
+    export_feature_scores as _export_feature_scores,
+)
 from ..export.calibration_report import generate_report as generate_calibration_report
 from ..glyco.export import (
     export_score_plots as export_glyco_score_plots,
@@ -45,6 +48,7 @@ def export():
     export.add_command(export_glyco, name="glyco")
     export.add_command(export_score_plots, name="score-plots")
     export.add_command(export_scored_report, name="score-report")
+    export.add_command(export_feature_scores, name="feature-scores")
     export.add_command(export_calibration_report, name="calibration-report")
 
     return export
@@ -871,6 +875,33 @@ def export_scored_report(infile):
     _export_scored_report(infile, outfile)
 
 
+# Export feature scores
+@click.command(name="feature-scores", cls=AdvancedHelpCommand)
+@click.option(
+    "--in",
+    "infile",
+    required=True,
+    type=click.Path(exists=True),
+    help="PyProphet input file (OSW, Parquet, or Split Parquet directory).",
+)
+@click.option(
+    "--out",
+    "outfile",
+    type=click.Path(exists=False),
+    help="Output PDF file. If not provided, will be auto-generated based on input filename.",
+)
+@measure_memory_usage_and_time
+def export_feature_scores(infile, outfile):
+    """
+    Export feature score plots from a PyProphet input file.
+    
+    Creates plots showing the distribution of feature scores (var_* columns)
+    at different levels (ms1, ms2, transition, alignment) colored by target/decoy status.
+    Works with OSW, Parquet, and Split Parquet files (scored or unscored).
+    """
+    _export_feature_scores(infile, outfile)
+
+
 # Export OpenSwath Calibration debug plots
 @click.command(name="calibration-report", cls=AdvancedHelpCommand)
 @click.option(
diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index ea5042da..3e44df3e 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -1,5 +1,7 @@
 import sqlite3
 import pandas as pd
+import os
+from loguru import logger
 
 
 from .._config import ExportIOConfig
@@ -149,3 +151,312 @@ def export_scored_report(
     df = reader.read()
 
     post_scoring_report(df, outfile)
+
+
+def export_feature_scores(infile: str, outfile: str = None):
+    """
+    Export feature score plots from a PyProphet input file.
+    
+    This function creates plots showing the distribution of feature scores
+    (var_* columns) at different levels (ms1, ms2, transition, alignment)
+    colored by target/decoy status. Works with OSW, Parquet, and Split Parquet files.
+    
+    Parameters
+    ----------
+    infile : str
+        Path to the input file (OSW, Parquet, or Split Parquet format).
+    outfile : str, optional
+        Path to the output PDF file. If None, will be auto-generated based on input filename.
+    """
+    # Detect file type based on extension and existence
+    if infile.endswith(".osw"):
+        file_type = "osw"
+    elif infile.endswith(".parquet"):
+        file_type = "parquet"
+    elif os.path.isdir(infile):
+        # Check if it's a split parquet directory
+        precursor_file = os.path.join(infile, "precursors_features.parquet")
+        if os.path.exists(precursor_file):
+            file_type = "parquet_split"
+        else:
+            raise ValueError(f"Directory {infile} does not appear to be a valid split parquet directory")
+    else:
+        raise ValueError(f"Unsupported file type for {infile}")
+    
+    logger.info(f"Detected file type: {file_type}")
+    
+    # Generate output filename if not provided
+    if outfile is None:
+        if file_type == "osw":
+            outfile = infile.replace(".osw", "_feature_scores.pdf")
+        elif file_type == "parquet":
+            outfile = infile.replace(".parquet", "_feature_scores.pdf")
+        else:  # parquet_split
+            outfile = infile.rstrip("/") + "_feature_scores.pdf"
+    
+    logger.info(f"Output file: {outfile}")
+    
+    # Export feature scores based on file type
+    if file_type == "osw":
+        _export_feature_scores_osw(infile, outfile)
+    elif file_type == "parquet":
+        _export_feature_scores_parquet(infile, outfile)
+    else:  # parquet_split
+        _export_feature_scores_split_parquet(infile, outfile)
+    
+    logger.info(f"Feature score plots exported to {outfile}")
+
+
+def _export_feature_scores_osw(infile: str, outfile: str):
+    """
+    Export feature scores from an OSW file.
+    
+    Parameters
+    ----------
+    infile : str
+        Path to the OSW input file.
+    outfile : str
+        Path to the output PDF file.
+    """
+    con = sqlite3.connect(infile)
+    
+    try:
+        # Process MS1 level if available
+        if check_sqlite_table(con, "FEATURE_MS1"):
+            logger.info("Processing MS1 level feature scores")
+            ms1_query = """
+                SELECT 
+                    FEATURE_MS1.*,
+                    PRECURSOR.DECOY,
+                    RUN.ID AS RUN_ID,
+                    FEATURE.PRECURSOR_ID,
+                    FEATURE.EXP_RT
+                FROM FEATURE_MS1
+                INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
+                INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                INNER JOIN RUN ON FEATURE.RUN_ID = RUN.ID
+            """
+            df_ms1 = pd.read_sql_query(ms1_query, con)
+            if not df_ms1.empty:
+                _plot_feature_scores(df_ms1, outfile, "ms1", append=False)
+        
+        # Process MS2 level if available
+        if check_sqlite_table(con, "FEATURE_MS2"):
+            logger.info("Processing MS2 level feature scores")
+            ms2_query = """
+                SELECT 
+                    FEATURE_MS2.*,
+                    PRECURSOR.DECOY,
+                    RUN.ID AS RUN_ID,
+                    FEATURE.PRECURSOR_ID,
+                    FEATURE.EXP_RT
+                FROM FEATURE_MS2
+                INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
+                INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                INNER JOIN RUN ON FEATURE.RUN_ID = RUN.ID
+            """
+            df_ms2 = pd.read_sql_query(ms2_query, con)
+            if not df_ms2.empty:
+                append = check_sqlite_table(con, "FEATURE_MS1")
+                _plot_feature_scores(df_ms2, outfile, "ms2", append=append)
+        
+        # Process transition level if available
+        if check_sqlite_table(con, "FEATURE_TRANSITION"):
+            logger.info("Processing transition level feature scores")
+            transition_query = """
+                SELECT 
+                    FEATURE_TRANSITION.*,
+                    TRANSITION.DECOY,
+                    RUN.ID AS RUN_ID,
+                    FEATURE.PRECURSOR_ID,
+                    FEATURE.EXP_RT
+                FROM FEATURE_TRANSITION
+                INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
+                INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
+                INNER JOIN RUN ON FEATURE.RUN_ID = RUN.ID
+            """
+            df_transition = pd.read_sql_query(transition_query, con)
+            if not df_transition.empty:
+                append = check_sqlite_table(con, "FEATURE_MS1") or check_sqlite_table(con, "FEATURE_MS2")
+                _plot_feature_scores(df_transition, outfile, "transition", append=append)
+        
+        # Process alignment level if available
+        if check_sqlite_table(con, "FEATURE_MS2_ALIGNMENT"):
+            logger.info("Processing alignment level feature scores")
+            alignment_query = """
+                SELECT 
+                    *,
+                    LABEL AS DECOY
+                FROM FEATURE_MS2_ALIGNMENT
+            """
+            df_alignment = pd.read_sql_query(alignment_query, con)
+            if not df_alignment.empty:
+                append = (check_sqlite_table(con, "FEATURE_MS1") or 
+                         check_sqlite_table(con, "FEATURE_MS2") or 
+                         check_sqlite_table(con, "FEATURE_TRANSITION"))
+                _plot_feature_scores(df_alignment, outfile, "alignment", append=append)
+    
+    finally:
+        con.close()
+
+
+def _export_feature_scores_parquet(infile: str, outfile: str):
+    """
+    Export feature scores from a Parquet file.
+    
+    Parameters
+    ----------
+    infile : str
+        Path to the Parquet input file.
+    outfile : str
+        Path to the output PDF file.
+    """
+    logger.info(f"Reading parquet file: {infile}")
+    df = pd.read_parquet(infile)
+    
+    # Get all column names
+    columns = df.columns.tolist()
+    
+    # Process MS1 level
+    ms1_cols = [col for col in columns if col.startswith("FEATURE_MS1_VAR_")]
+    if ms1_cols and "PRECURSOR_DECOY" in columns:
+        logger.info("Processing MS1 level feature scores")
+        ms1_df = df[ms1_cols + ["PRECURSOR_DECOY"]].copy()
+        ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+        _plot_feature_scores(ms1_df, outfile, "ms1", append=False)
+    
+    # Process MS2 level
+    ms2_cols = [col for col in columns if col.startswith("FEATURE_MS2_VAR_")]
+    if ms2_cols and "PRECURSOR_DECOY" in columns:
+        logger.info("Processing MS2 level feature scores")
+        ms2_df = df[ms2_cols + ["PRECURSOR_DECOY"]].copy()
+        ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+        append = bool(ms1_cols)
+        _plot_feature_scores(ms2_df, outfile, "ms2", append=append)
+    
+    # Process transition level
+    transition_cols = [col for col in columns if col.startswith("FEATURE_TRANSITION_VAR_")]
+    if transition_cols and "TRANSITION_DECOY" in columns:
+        logger.info("Processing transition level feature scores")
+        transition_df = df[transition_cols + ["TRANSITION_DECOY"]].copy()
+        transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
+        append = bool(ms1_cols or ms2_cols)
+        _plot_feature_scores(transition_df, outfile, "transition", append=append)
+
+
+def _export_feature_scores_split_parquet(infile: str, outfile: str):
+    """
+    Export feature scores from a split Parquet directory.
+    
+    Parameters
+    ----------
+    infile : str
+        Path to the split Parquet directory.
+    outfile : str
+        Path to the output PDF file.
+    """
+    # Read precursor features
+    precursor_file = os.path.join(infile, "precursors_features.parquet")
+    logger.info(f"Reading precursor features from: {precursor_file}")
+    df_precursor = pd.read_parquet(precursor_file)
+    
+    # Get all column names
+    columns = df_precursor.columns.tolist()
+    
+    # Process MS1 level
+    ms1_cols = [col for col in columns if col.startswith("FEATURE_MS1_VAR_")]
+    if ms1_cols and "PRECURSOR_DECOY" in columns:
+        logger.info("Processing MS1 level feature scores")
+        ms1_df = df_precursor[ms1_cols + ["PRECURSOR_DECOY"]].copy()
+        ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+        _plot_feature_scores(ms1_df, outfile, "ms1", append=False)
+    
+    # Process MS2 level
+    ms2_cols = [col for col in columns if col.startswith("FEATURE_MS2_VAR_")]
+    if ms2_cols and "PRECURSOR_DECOY" in columns:
+        logger.info("Processing MS2 level feature scores")
+        ms2_df = df_precursor[ms2_cols + ["PRECURSOR_DECOY"]].copy()
+        ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+        append = bool(ms1_cols)
+        _plot_feature_scores(ms2_df, outfile, "ms2", append=append)
+    
+    # Read transition features if available
+    transition_file = os.path.join(infile, "transition_features.parquet")
+    if os.path.exists(transition_file):
+        logger.info(f"Reading transition features from: {transition_file}")
+        df_transition = pd.read_parquet(transition_file)
+        transition_columns = df_transition.columns.tolist()
+        
+        # Process transition level
+        transition_cols = [col for col in transition_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
+        if transition_cols and "TRANSITION_DECOY" in transition_columns:
+            logger.info("Processing transition level feature scores")
+            transition_df = df_transition[transition_cols + ["TRANSITION_DECOY"]].copy()
+            transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
+            append = bool(ms1_cols or ms2_cols)
+            _plot_feature_scores(transition_df, outfile, "transition", append=append)
+    
+    # Read alignment features if available
+    alignment_file = os.path.join(infile, "feature_alignment.parquet")
+    if os.path.exists(alignment_file):
+        logger.info(f"Reading alignment features from: {alignment_file}")
+        df_alignment = pd.read_parquet(alignment_file)
+        
+        # Get var columns
+        alignment_columns = df_alignment.columns.tolist()
+        var_cols = [col for col in alignment_columns if col.startswith("VAR_")]
+        
+        if var_cols and "DECOY" in alignment_columns:
+            logger.info("Processing alignment level feature scores")
+            alignment_df = df_alignment[var_cols + ["DECOY"]].copy()
+            append = bool(ms1_cols or ms2_cols or (os.path.exists(transition_file) and transition_cols))
+            _plot_feature_scores(alignment_df, outfile, "alignment", append=append)
+
+
+def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: bool = False):
+    """
+    Create plots for feature scores at a specific level.
+    
+    Parameters
+    ----------
+    df : pd.DataFrame
+        DataFrame containing feature scores and DECOY column.
+    outfile : str
+        Path to the output PDF file.
+    level : str
+        Level name (ms1, ms2, transition, or alignment).
+    append : bool
+        If True, append to existing PDF. If False, create new PDF.
+    """
+    # Get all columns that contain feature scores (VAR_ columns or columns with _VAR_ in name)
+    score_cols = [col for col in df.columns if "VAR_" in col.upper() and col != "DECOY"]
+    
+    if not score_cols:
+        logger.warning(f"No feature score columns found for {level} level")
+        return
+    
+    logger.info(f"Found {len(score_cols)} feature score columns for {level} level: {score_cols}")
+    
+    # Prepare data for plotting - ensure DECOY column exists and rename score columns
+    if "DECOY" not in df.columns:
+        logger.warning(f"No DECOY column found for {level} level, skipping")
+        return
+    
+    # Create a copy with standardized column names for plot_scores
+    plot_df = df[score_cols + ["DECOY"]].copy()
+    
+    # Add required columns for plot_scores function
+    # The plot_scores function expects specific column names
+    plot_df.rename(columns={
+        score_cols[0]: "SCORE"  # Use first score column as main score
+    }, inplace=True)
+    
+    # Rename other columns to match expected format
+    for col in score_cols[1:]:
+        # Keep VAR_ prefix for additional scores
+        if not col.startswith("VAR_"):
+            new_col = "VAR_" + col.split("VAR_")[-1] if "VAR_" in col else "VAR_" + col
+            plot_df.rename(columns={col: new_col}, inplace=True)
+    
+    # Call plot_scores with the formatted dataframe
+    plot_scores(plot_df, outfile)

From e1be2df3afee0bd63d09cc32695f39516e2e4718 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 28 Oct 2025 22:40:11 +0000
Subject: [PATCH 03/26] Fix _plot_feature_scores to properly handle PDF merging

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py |  72 ++-
 pyprophet/scoring/_optimized.c    | 726 ++++++++++++++++--------------
 2 files changed, 440 insertions(+), 358 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index 3e44df3e..a640aaa9 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -437,26 +437,68 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
     
     logger.info(f"Found {len(score_cols)} feature score columns for {level} level: {score_cols}")
     
-    # Prepare data for plotting - ensure DECOY column exists and rename score columns
+    # Prepare data for plotting - ensure DECOY column exists
     if "DECOY" not in df.columns:
         logger.warning(f"No DECOY column found for {level} level, skipping")
         return
     
-    # Create a copy with standardized column names for plot_scores
-    plot_df = df[score_cols + ["DECOY"]].copy()
+    # Drop rows with null DECOY values
+    plot_df = df[score_cols + ["DECOY"]].dropna(subset=["DECOY"]).copy()
     
-    # Add required columns for plot_scores function
-    # The plot_scores function expects specific column names
-    plot_df.rename(columns={
-        score_cols[0]: "SCORE"  # Use first score column as main score
-    }, inplace=True)
+    # Ensure DECOY is 0 or 1
+    if plot_df["DECOY"].dtype == bool:
+        plot_df["DECOY"] = plot_df["DECOY"].astype(int)
     
-    # Rename other columns to match expected format
-    for col in score_cols[1:]:
-        # Keep VAR_ prefix for additional scores
-        if not col.startswith("VAR_"):
-            new_col = "VAR_" + col.split("VAR_")[-1] if "VAR_" in col else "VAR_" + col
-            plot_df.rename(columns={col: new_col}, inplace=True)
+    # Generate a temporary output file for this level
+    temp_outfile = outfile.replace(".pdf", f"_{level}_temp.pdf")
+    
+    # Rename columns to match plot_scores expectations
+    # plot_scores expects columns named "SCORE", "MAIN_VAR_*", or "VAR_*"
+    rename_dict = {}
+    for i, col in enumerate(score_cols):
+        # Ensure column names start with VAR_
+        if not col.upper().startswith("VAR_"):
+            # Extract the var part from column names like FEATURE_MS1_VAR_XXX
+            parts = col.split("VAR_")
+            if len(parts) > 1:
+                new_name = "VAR_" + parts[-1]
+            else:
+                new_name = "VAR_" + col
+            rename_dict[col] = new_name
+    
+    if rename_dict:
+        plot_df.rename(columns=rename_dict, inplace=True)
     
     # Call plot_scores with the formatted dataframe
-    plot_scores(plot_df, outfile)
+    plot_scores(plot_df, temp_outfile)
+    
+    # If appending, merge PDFs, otherwise just rename
+    if append and os.path.exists(outfile):
+        from pypdf import PdfReader, PdfWriter
+        
+        # Merge the PDFs
+        writer = PdfWriter()
+        
+        # Add pages from existing PDF
+        with open(outfile, "rb") as f:
+            existing_pdf = PdfReader(f)
+            for page in existing_pdf.pages:
+                writer.add_page(page)
+        
+        # Add pages from new PDF
+        with open(temp_outfile, "rb") as f:
+            new_pdf = PdfReader(f)
+            for page in new_pdf.pages:
+                writer.add_page(page)
+        
+        # Write merged PDF
+        with open(outfile, "wb") as f:
+            writer.write(f)
+        
+        # Remove temporary file
+        os.remove(temp_outfile)
+    else:
+        # Just rename temporary file to output file
+        if os.path.exists(outfile):
+            os.remove(outfile)
+        os.rename(temp_outfile, outfile)
diff --git a/pyprophet/scoring/_optimized.c b/pyprophet/scoring/_optimized.c
index 91448a4e..49d65575 100644
--- a/pyprophet/scoring/_optimized.c
+++ b/pyprophet/scoring/_optimized.c
@@ -1,4 +1,4 @@
-/* Generated by Cython 3.1.2 */
+/* Generated by Cython 3.1.6 */
 
 /* BEGIN: Cython Metadata
 {
@@ -17,8 +17,16 @@ END: Cython Metadata */
 #define PY_SSIZE_T_CLEAN
 #endif /* PY_SSIZE_T_CLEAN */
 /* InitLimitedAPI */
-#if defined(Py_LIMITED_API) && !defined(CYTHON_LIMITED_API)
+#if defined(Py_LIMITED_API)
+  #if !defined(CYTHON_LIMITED_API)
   #define CYTHON_LIMITED_API 1
+  #endif
+#elif defined(CYTHON_LIMITED_API)
+  #ifdef _MSC_VER
+  #pragma message ("Limited API usage is enabled with 'CYTHON_LIMITED_API' but 'Py_LIMITED_API' does not define a Python target version. Consider setting 'Py_LIMITED_API' instead.")
+  #else
+  #warning Limited API usage is enabled with 'CYTHON_LIMITED_API' but 'Py_LIMITED_API' does not define a Python target version. Consider setting 'Py_LIMITED_API' instead.
+  #endif
 #endif
 
 #include "Python.h"
@@ -27,8 +35,8 @@ END: Cython Metadata */
 #elif PY_VERSION_HEX < 0x03080000
     #error Cython requires Python 3.8+.
 #else
-#define __PYX_ABI_VERSION "3_1_2"
-#define CYTHON_HEX_VERSION 0x030102F0
+#define __PYX_ABI_VERSION "3_1_6"
+#define CYTHON_HEX_VERSION 0x030106F0
 #define CYTHON_FUTURE_DIVISION 1
 /* CModulePreamble */
 #include <stddef.h>
@@ -391,6 +399,9 @@ END: Cython Metadata */
     enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
   #endif
 #endif
+#ifndef CYTHON_LOCK_AND_GIL_DEADLOCK_AVOIDANCE_TIME
+  #define CYTHON_LOCK_AND_GIL_DEADLOCK_AVOIDANCE_TIME 100
+#endif
 #ifndef __has_attribute
   #define __has_attribute(x) 0
 #endif
@@ -1295,6 +1306,7 @@ static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);
   typedef sdigit  __Pyx_compact_pylong;
   typedef digit  __Pyx_compact_upylong;
   #endif
+  static CYTHON_INLINE int __Pyx_PyLong_CompactAsLong(PyObject *x, long *return_value);
   #if PY_VERSION_HEX >= 0x030C00A5
   #define __Pyx_PyLong_Digits(x)  (((PyLongObject*)x)->long_value.ob_digit)
   #else
@@ -1371,7 +1383,7 @@ static const char *__pyx_filename;
 static const char* const __pyx_f[] = {
   "pyprophet/scoring/_optimized.pyx",
   "<stringsource>",
-  "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd",
+  "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd",
   "cpython/type.pxd",
 };
 /* #### Code section: utility_code_proto_before_types ### */
@@ -1594,7 +1606,7 @@ typedef struct {
 
 /* #### Code section: numeric_typedefs ### */
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":787
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":743
  * # in Cython to enable them only on the right systems.
  * 
  * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
@@ -1603,7 +1615,7 @@ typedef struct {
 */
 typedef npy_int8 __pyx_t_5numpy_int8_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":788
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":744
  * 
  * ctypedef npy_int8       int8_t
  * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
@@ -1612,26 +1624,26 @@ typedef npy_int8 __pyx_t_5numpy_int8_t;
 */
 typedef npy_int16 __pyx_t_5numpy_int16_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":789
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":745
  * ctypedef npy_int8       int8_t
  * ctypedef npy_int16      int16_t
  * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
  * ctypedef npy_int64      int64_t
- * #ctypedef npy_int96      int96_t
+ * 
 */
 typedef npy_int32 __pyx_t_5numpy_int32_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":790
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":746
  * ctypedef npy_int16      int16_t
  * ctypedef npy_int32      int32_t
  * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_int96      int96_t
- * #ctypedef npy_int128     int128_t
+ * 
+ * ctypedef npy_uint8      uint8_t
 */
 typedef npy_int64 __pyx_t_5numpy_int64_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":794
- * #ctypedef npy_int128     int128_t
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":748
+ * ctypedef npy_int64      int64_t
  * 
  * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
  * ctypedef npy_uint16     uint16_t
@@ -1639,7 +1651,7 @@ typedef npy_int64 __pyx_t_5numpy_int64_t;
 */
 typedef npy_uint8 __pyx_t_5numpy_uint8_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":795
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":749
  * 
  * ctypedef npy_uint8      uint8_t
  * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
@@ -1648,26 +1660,26 @@ typedef npy_uint8 __pyx_t_5numpy_uint8_t;
 */
 typedef npy_uint16 __pyx_t_5numpy_uint16_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":796
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":750
  * ctypedef npy_uint8      uint8_t
  * ctypedef npy_uint16     uint16_t
  * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
  * ctypedef npy_uint64     uint64_t
- * #ctypedef npy_uint96     uint96_t
+ * 
 */
 typedef npy_uint32 __pyx_t_5numpy_uint32_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":797
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":751
  * ctypedef npy_uint16     uint16_t
  * ctypedef npy_uint32     uint32_t
  * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_uint96     uint96_t
- * #ctypedef npy_uint128    uint128_t
+ * 
+ * ctypedef npy_float32    float32_t
 */
 typedef npy_uint64 __pyx_t_5numpy_uint64_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":801
- * #ctypedef npy_uint128    uint128_t
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":753
+ * ctypedef npy_uint64     uint64_t
  * 
  * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
  * ctypedef npy_float64    float64_t
@@ -1675,7 +1687,7 @@ typedef npy_uint64 __pyx_t_5numpy_uint64_t;
 */
 typedef npy_float32 __pyx_t_5numpy_float32_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":802
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":754
  * 
  * ctypedef npy_float32    float32_t
  * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
@@ -1684,7 +1696,7 @@ typedef npy_float32 __pyx_t_5numpy_float32_t;
 */
 typedef npy_float64 __pyx_t_5numpy_float64_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":809
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":761
  * ctypedef double complex complex128_t
  * 
  * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
@@ -1693,7 +1705,7 @@ typedef npy_float64 __pyx_t_5numpy_float64_t;
 */
 typedef npy_longlong __pyx_t_5numpy_longlong_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":810
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":762
  * 
  * ctypedef npy_longlong   longlong_t
  * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
@@ -1702,7 +1714,7 @@ typedef npy_longlong __pyx_t_5numpy_longlong_t;
 */
 typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":812
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":764
  * ctypedef npy_ulonglong  ulonglong_t
  * 
  * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
@@ -1711,7 +1723,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
 */
 typedef npy_intp __pyx_t_5numpy_intp_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":813
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":765
  * 
  * ctypedef npy_intp       intp_t
  * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
@@ -1720,7 +1732,7 @@ typedef npy_intp __pyx_t_5numpy_intp_t;
 */
 typedef npy_uintp __pyx_t_5numpy_uintp_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":815
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":767
  * ctypedef npy_uintp      uintp_t
  * 
  * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
@@ -1729,7 +1741,7 @@ typedef npy_uintp __pyx_t_5numpy_uintp_t;
 */
 typedef npy_double __pyx_t_5numpy_float_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":816
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":768
  * 
  * ctypedef npy_double     float_t
  * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
@@ -1738,7 +1750,7 @@ typedef npy_double __pyx_t_5numpy_float_t;
 */
 typedef npy_double __pyx_t_5numpy_double_t;
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":817
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":769
  * ctypedef npy_double     float_t
  * ctypedef npy_double     double_t
  * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
@@ -2635,22 +2647,22 @@ static int __Pyx__DelItemOnTypeDict(PyTypeObject *tp, PyObject *k);
 static int __Pyx_setup_reduce(PyObject* type_obj);
 
 /* TypeImport.proto */
-#ifndef __PYX_HAVE_RT_ImportType_proto_3_1_2
-#define __PYX_HAVE_RT_ImportType_proto_3_1_2
+#ifndef __PYX_HAVE_RT_ImportType_proto_3_1_6
+#define __PYX_HAVE_RT_ImportType_proto_3_1_6
 #if defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L
 #include <stdalign.h>
 #endif
 #if (defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L) || __cplusplus >= 201103L
-#define __PYX_GET_STRUCT_ALIGNMENT_3_1_2(s) alignof(s)
+#define __PYX_GET_STRUCT_ALIGNMENT_3_1_6(s) alignof(s)
 #else
-#define __PYX_GET_STRUCT_ALIGNMENT_3_1_2(s) sizeof(void*)
+#define __PYX_GET_STRUCT_ALIGNMENT_3_1_6(s) sizeof(void*)
 #endif
-enum __Pyx_ImportType_CheckSize_3_1_2 {
-   __Pyx_ImportType_CheckSize_Error_3_1_2 = 0,
-   __Pyx_ImportType_CheckSize_Warn_3_1_2 = 1,
-   __Pyx_ImportType_CheckSize_Ignore_3_1_2 = 2
+enum __Pyx_ImportType_CheckSize_3_1_6 {
+   __Pyx_ImportType_CheckSize_Error_3_1_6 = 0,
+   __Pyx_ImportType_CheckSize_Warn_3_1_6 = 1,
+   __Pyx_ImportType_CheckSize_Ignore_3_1_6 = 2
 };
-static PyTypeObject *__Pyx_ImportType_3_1_2(PyObject* module, const char *module_name, const char *class_name, size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_1_2 check_size);
+static PyTypeObject *__Pyx_ImportType_3_1_6(PyObject* module, const char *module_name, const char *class_name, size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_1_6 check_size);
 #endif
 
 /* FetchSharedCythonModule.proto */
@@ -3418,7 +3430,7 @@ static const char __pyx_k_F_A_R_86_1_a_Rq_c_2T_c_AV1_Q_a[] = "\200\001\360\006\0
 static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
 static const char __pyx_k_8_q_Rq_F_RvRuF_A_q_r_uBa_V1A_e2[] = "\200\001\360\010\000\005\035\320\0348\270\007\270q\300\005\300R\300q\330\004\024\220F\230&\240\001\240\021\330\004\r\210R\210v\220R\220u\230F\240\"\240A\330\004 \240\001\330\004\027\220q\360\006\000\005\013\210%\210r\220\021\330\010\017\210u\220B\220a\330\010\r\210V\2201\220A\330\010\016\210e\2202\220R\220t\2306\240\021\240&\250\003\2501\330\014\024\220A\330\010\024\220A\220X\230V\2406\250\027\260\001\330\010\017\210q\330\017\024\220A\220Q\330\004\013\2101";
 static const char __pyx_k_F_BfBe6_1_a_q_q_aq_U_6_q_fAQ_V1[] = "\200\001\360\006\000\005\025\220F\230&\240\001\240\021\330\004\014\210B\210f\220B\220e\2306\240\022\2401\330\004\036\230a\330\004!\240\026\240q\250\001\330\004\037\230q\330\004 \240\006\240a\240q\330\004\"\240!\360\010\000\005\t\210\005\210U\220!\2206\230\026\230q\240\001\330\010\016\210f\220A\220Q\330\010\r\210V\2201\220A\330\010\013\2104\210s\220!\330\014\034\230A\330\014\020\220\001\320\021!\240\021\330\014\037\230q\330\014\032\230!\330\014\033\2301\330\014\r\330\010\013\2103\210b\220\001\330\014\032\230!\330\014\033\2301\330\004\010\210\001\320\t\031\230\021\330\004\013\2101";
-static const char __pyx_k_IJ_E_q_m6_RvR_fBa_a_t1_E_aq_Q_1[] = "\200\001\340IJ\330\004\034\230E\240\026\240q\250\001\330\004\036\230m\2506\260\021\260!\330\004\r\210R\210v\220R\220\240f\250B\250a\330\004\036\230a\360\014\000\005\010\200t\2101\330\010\014\210E\220\025\220a\220q\330\014\023\220=\240\001\240\021\330\014\025\220Q\330\014\030\230\003\2301\230E\240\021\240#\240R\240q\330\014\020\220\005\220U\230!\2303\230a\330\020\027\220s\230!\2305\240\001\240\023\240B\240a\330\020\023\2205\230\002\230!\330\024 \240\001\330\024\035\230Q\330\014\020\220\001\220\025\220a\330\010\017\210q\340\004\021\220\037\240\001\240\021\330\004\010\210\005\210U\220!\2201\330\010\017\210}\230A\230Q\330\010\013\210;\220c\230\021\330\014\025\220Q\330\014\030\230\003\2301\230E\240\021\240#\240R\240q\330\014\020\220\005\220U\230!\2303\230a\330\020\027\220s\230!\2305\240\001\240\023\240B\240a\330\020\023\2205\230\002\230!\330\024 \240\001\330\024\035\230Q\330\r\030\230\003\2301\330\014\022\220!\330\014\023\220:\230R\230q\330\014\026\220a\330\014\017\210u\220A\220U\230#\230Q\330\020\031\230\021\330\021\026\220a\220v\230S\240\001\330\020\031\230\021\340\020\026\220d\230\"\230E\240\022\2401\330\024\033\2304\230r\240\026\240s\250!\330\024\027\220u\230A\230U\240#\240Q\330\030!\240\021\330\024\027\220u\230A\230U\240\"\240A\330\030\036\230a\340\030\037\230q\330\020\023\2207\230$\230a\330\024\027\220s\230!\2305\240\001\240\025\240b\250\006\250b\260\003\2601\260E\270\021\270&\300\002\300!\330\030!\240\021\340\030!\240\021\340\014\022\220'\230\022\2301\330\020\023\2205\230\001\230\027\240\002\240#\240S\250\005\250Q\250a\330\024\035\230W\240B\240a\340\024\025\340\014\022\220!\330\014\023\220:\230R\230q\330\014\026\220a\330\014\017\210u\220A\220U\230#\230Q\330\020\031\230\021\330\021\026\220a\220v\230S\240\001\330\020\031\230\021\340\020\026\220d\230\"\230E\240\022\2401\330\024\033\2304\230r\240\026\240s\250!\330\024\027\220u\230A\230U\240#\240Q\330\030!\240\021\330\030\031\330\024\027\220u\230A\230U\240\"\240A\330\030\036\230a\340\030\037\230q\330\020\023\2207\230$""\230a\330\024\027\220s\230!\2305\240\001\240\025\240b\250\006\250b\260\003\2601\260E\270\021\270&\300\002\300!\330\030!\240\021\340\030!\240\021\340\014\022\220'\230\022\2301\330\020\023\2205\230\001\230\027\240\002\240#\240S\250\005\250Q\250a\330\024\035\230W\240B\240a\340\024\025\340\010\014\210A\210U\220!\330\004\013\2101";
+static const char __pyx_k_IJ_E_q_m6_RvR_fBa_a_t1_E_aq_Q_1[] = "\200\001\340IJ\330\004\034\230E\240\026\240q\250\001\330\004\036\230m\2506\260\021\260!\330\004\r\210R\210v\220R\220\177\240f\250B\250a\330\004\036\230a\360\014\000\005\010\200t\2101\330\010\014\210E\220\025\220a\220q\330\014\023\220=\240\001\240\021\330\014\025\220Q\330\014\030\230\003\2301\230E\240\021\240#\240R\240q\330\014\020\220\005\220U\230!\2303\230a\330\020\027\220s\230!\2305\240\001\240\023\240B\240a\330\020\023\2205\230\002\230!\330\024 \240\001\330\024\035\230Q\330\014\020\220\001\220\025\220a\330\010\017\210q\340\004\021\220\037\240\001\240\021\330\004\010\210\005\210U\220!\2201\330\010\017\210}\230A\230Q\330\010\013\210;\220c\230\021\330\014\025\220Q\330\014\030\230\003\2301\230E\240\021\240#\240R\240q\330\014\020\220\005\220U\230!\2303\230a\330\020\027\220s\230!\2305\240\001\240\023\240B\240a\330\020\023\2205\230\002\230!\330\024 \240\001\330\024\035\230Q\330\r\030\230\003\2301\330\014\022\220!\330\014\023\220:\230R\230q\330\014\026\220a\330\014\017\210u\220A\220U\230#\230Q\330\020\031\230\021\330\021\026\220a\220v\230S\240\001\330\020\031\230\021\340\020\026\220d\230\"\230E\240\022\2401\330\024\033\2304\230r\240\026\240s\250!\330\024\027\220u\230A\230U\240#\240Q\330\030!\240\021\330\024\027\220u\230A\230U\240\"\240A\330\030\036\230a\340\030\037\230q\330\020\023\2207\230$\230a\330\024\027\220s\230!\2305\240\001\240\025\240b\250\006\250b\260\003\2601\260E\270\021\270&\300\002\300!\330\030!\240\021\340\030!\240\021\340\014\022\220'\230\022\2301\330\020\023\2205\230\001\230\027\240\002\240#\240S\250\005\250Q\250a\330\024\035\230W\240B\240a\340\024\025\340\014\022\220!\330\014\023\220:\230R\230q\330\014\026\220a\330\014\017\210u\220A\220U\230#\230Q\330\020\031\230\021\330\021\026\220a\220v\230S\240\001\330\020\031\230\021\340\020\026\220d\230\"\230E\240\022\2401\330\024\033\2304\230r\240\026\240s\250!\330\024\027\220u\230A\230U\240#\240Q\330\030!\240\021\330\030\031\330\024\027\220u\230A\230U\240\"\240A\330\030\036\230a\340\030\037\230q\330\020\023\2207""\230$\230a\330\024\027\220s\230!\2305\240\001\240\025\240b\250\006\250b\260\003\2601\260E\270\021\270&\300\002\300!\330\030!\240\021\340\030!\240\021\340\014\022\220'\230\022\2301\330\020\023\2205\230\001\230\027\240\002\240#\240S\250\005\250Q\250a\330\024\035\230W\240B\240a\340\024\025\340\010\014\210A\210U\220!\330\004\013\2101";
 static const char __pyx_k_N_RvRq_fBa_1_Q_Ba_nAQ_Q_1E_Ba_B[] = "\200\001\360\014\000\005\025\220N\240&\250\001\250\021\360\006\000\005!\240\001\330\004\r\210R\210v\220R\220q\230\001\230\025\230f\240B\240a\330\004#\2401\360\010\000\005\026\220Q\360\006\000\005\t\210\001\330\004\n\210\"\210B\210a\330\010\016\210n\230A\230Q\330\010\r\210Q\340\004\013\2101\210E\220\023\220B\220a\360\010\000\005\t\210\001\330\004\n\210\"\210B\210a\330\010\016\210n\230A\230Q\330\010\014\210A\330\010\r\210Q\340\010\016\210b\220\002\220!\330\014\017\210r\220\023\220A\330\020\026\220n\240A\240Q\330\014\021\220\021\340\010\024\320\024%\240Q\240a\240u\250B\250a\340\010\017\210q\220\001\220\021\220%\220z\240\022\2401\330\010\r\210Q\360\006\000\005\t\210\001\330\004\n\210\"\210B\210a\210q\220\001\330\010\023\2207\230!\2301\330\010\r\210Q\360\006\000\005\t\210\001\330\004\n\210\"\210B\210a\210q\220\001\330\010\017\210q\220\005\220W\230A\230S\240\002\240!\330\010\r\210Q\340\004\013\2101";
 static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced";
 static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
@@ -3958,7 +3970,7 @@ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, P
         default: goto __pyx_L5_argtuple_error;
       }
       const Py_ssize_t kwd_pos_args = __pyx_nargs;
-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__cinit__", 0) < 0) __PYX_ERR(1, 129, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__cinit__", 0) < (0)) __PYX_ERR(1, 129, __pyx_L3_error)
       if (!values[3]) values[3] = __Pyx_NewRef(((PyObject *)__pyx_mstate_global->__pyx_n_u_c));
       for (Py_ssize_t i = __pyx_nargs; i < 3; i++) {
         if (unlikely(!values[i])) { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, i); __PYX_ERR(1, 129, __pyx_L3_error) }
@@ -5572,7 +5584,7 @@ PyObject *__pyx_args, PyObject *__pyx_kwds
         default: goto __pyx_L5_argtuple_error;
       }
       const Py_ssize_t kwd_pos_args = __pyx_nargs;
-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__setstate_cython__", 0) < 0) __PYX_ERR(1, 3, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__setstate_cython__", 0) < (0)) __PYX_ERR(1, 3, __pyx_L3_error)
       for (Py_ssize_t i = __pyx_nargs; i < 1; i++) {
         if (unlikely(!values[i])) { __Pyx_RaiseArgtupleInvalid("__setstate_cython__", 1, 1, 1, i); __PYX_ERR(1, 3, __pyx_L3_error) }
       }
@@ -5917,7 +5929,7 @@ static struct __pyx_array_obj *__pyx_array_new(PyObject *__pyx_v_shape, Py_ssize
     __pyx_t_4 = 0;
     __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 273, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_4);
-    if (PyDict_SetItem(__pyx_t_4, __pyx_mstate_global->__pyx_n_u_allocate_buffer, Py_False) < 0) __PYX_ERR(1, 273, __pyx_L1_error)
+    if (PyDict_SetItem(__pyx_t_4, __pyx_mstate_global->__pyx_n_u_allocate_buffer, Py_False) < (0)) __PYX_ERR(1, 273, __pyx_L1_error)
     __pyx_t_3 = ((PyObject *)__pyx_tp_new_array(((PyTypeObject *)__pyx_mstate_global->__pyx_array_type), __pyx_t_1, __pyx_t_4)); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 273, __pyx_L1_error)
     __Pyx_GOTREF((PyObject *)__pyx_t_3);
     __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
@@ -6012,7 +6024,7 @@ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_ar
         default: goto __pyx_L5_argtuple_error;
       }
       const Py_ssize_t kwd_pos_args = __pyx_nargs;
-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__init__", 0) < 0) __PYX_ERR(1, 302, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__init__", 0) < (0)) __PYX_ERR(1, 302, __pyx_L3_error)
       for (Py_ssize_t i = __pyx_nargs; i < 1; i++) {
         if (unlikely(!values[i])) { __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, i); __PYX_ERR(1, 302, __pyx_L3_error) }
       }
@@ -6449,7 +6461,7 @@ PyObject *__pyx_args, PyObject *__pyx_kwds
         default: goto __pyx_L5_argtuple_error;
       }
       const Py_ssize_t kwd_pos_args = __pyx_nargs;
-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__setstate_cython__", 0) < 0) __PYX_ERR(1, 16, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__setstate_cython__", 0) < (0)) __PYX_ERR(1, 16, __pyx_L3_error)
       for (Py_ssize_t i = __pyx_nargs; i < 1; i++) {
         if (unlikely(!values[i])) { __Pyx_RaiseArgtupleInvalid("__setstate_cython__", 1, 1, 1, i); __PYX_ERR(1, 16, __pyx_L3_error) }
       }
@@ -6574,7 +6586,7 @@ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_ar
         default: goto __pyx_L5_argtuple_error;
       }
       const Py_ssize_t kwd_pos_args = __pyx_nargs;
-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__cinit__", 0) < 0) __PYX_ERR(1, 347, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__cinit__", 0) < (0)) __PYX_ERR(1, 347, __pyx_L3_error)
       for (Py_ssize_t i = __pyx_nargs; i < 2; i++) {
         if (unlikely(!values[i])) { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, i); __PYX_ERR(1, 347, __pyx_L3_error) }
       }
@@ -10891,7 +10903,7 @@ PyObject *__pyx_args, PyObject *__pyx_kwds
         default: goto __pyx_L5_argtuple_error;
       }
       const Py_ssize_t kwd_pos_args = __pyx_nargs;
-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__setstate_cython__", 0) < 0) __PYX_ERR(1, 3, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__setstate_cython__", 0) < (0)) __PYX_ERR(1, 3, __pyx_L3_error)
       for (Py_ssize_t i = __pyx_nargs; i < 1; i++) {
         if (unlikely(!values[i])) { __Pyx_RaiseArgtupleInvalid("__setstate_cython__", 1, 1, 1, i); __PYX_ERR(1, 3, __pyx_L3_error) }
       }
@@ -13778,7 +13790,7 @@ PyObject *__pyx_args, PyObject *__pyx_kwds
         default: goto __pyx_L5_argtuple_error;
       }
       const Py_ssize_t kwd_pos_args = __pyx_nargs;
-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__setstate_cython__", 0) < 0) __PYX_ERR(1, 3, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__setstate_cython__", 0) < (0)) __PYX_ERR(1, 3, __pyx_L3_error)
       for (Py_ssize_t i = __pyx_nargs; i < 1; i++) {
         if (unlikely(!values[i])) { __Pyx_RaiseArgtupleInvalid("__setstate_cython__", 1, 1, 1, i); __PYX_ERR(1, 3, __pyx_L3_error) }
       }
@@ -16937,7 +16949,7 @@ PyObject *__pyx_args, PyObject *__pyx_kwds
         default: goto __pyx_L5_argtuple_error;
       }
       const Py_ssize_t kwd_pos_args = __pyx_nargs;
-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__pyx_unpickle_Enum", 0) < 0) __PYX_ERR(1, 1, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "__pyx_unpickle_Enum", 0) < (0)) __PYX_ERR(1, 1, __pyx_L3_error)
       for (Py_ssize_t i = __pyx_nargs; i < 3; i++) {
         if (unlikely(!values[i])) { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, i); __PYX_ERR(1, 1, __pyx_L3_error) }
       }
@@ -17259,7 +17271,7 @@ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":286
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":242
  *         cdef int type_num
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17270,7 +17282,7 @@ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_8itemsize_itemsize(PyArray_Descr *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":288
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":244
  *         @property
  *         cdef inline npy_intp itemsize(self) noexcept nogil:
  *             return PyDataType_ELSIZE(self)             # <<<<<<<<<<<<<<
@@ -17280,7 +17292,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_8itemsize_itemsize(PyArray_D
   __pyx_r = PyDataType_ELSIZE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":286
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":242
  *         cdef int type_num
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17293,7 +17305,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_8itemsize_itemsize(PyArray_D
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":290
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":246
  *             return PyDataType_ELSIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17304,7 +17316,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_8itemsize_itemsize(PyArray_D
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_9alignment_alignment(PyArray_Descr *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":292
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":248
  *         @property
  *         cdef inline npy_intp alignment(self) noexcept nogil:
  *             return PyDataType_ALIGNMENT(self)             # <<<<<<<<<<<<<<
@@ -17314,7 +17326,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_9alignment_alignment(PyArray
   __pyx_r = PyDataType_ALIGNMENT(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":290
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":246
  *             return PyDataType_ELSIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17327,7 +17339,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_9alignment_alignment(PyArray
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":296
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":252
  *         # Use fields/names with care as they may be NULL.  You must check
  *         # for this using PyDataType_HASFIELDS.
  *         @property             # <<<<<<<<<<<<<<
@@ -17341,7 +17353,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_6fields_fields(PyArray_Desc
   PyObject *__pyx_t_1;
   __Pyx_RefNannySetupContext("fields", 0);
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":298
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":254
  *         @property
  *         cdef inline object fields(self):
  *             return <object>PyDataType_FIELDS(self)             # <<<<<<<<<<<<<<
@@ -17354,7 +17366,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_6fields_fields(PyArray_Desc
   __pyx_r = ((PyObject *)__pyx_t_1);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":296
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":252
  *         # Use fields/names with care as they may be NULL.  You must check
  *         # for this using PyDataType_HASFIELDS.
  *         @property             # <<<<<<<<<<<<<<
@@ -17369,7 +17381,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_6fields_fields(PyArray_Desc
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":300
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":256
  *             return <object>PyDataType_FIELDS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17383,7 +17395,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_5names_names(PyArray_Descr
   PyObject *__pyx_t_1;
   __Pyx_RefNannySetupContext("names", 0);
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":302
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":258
  *         @property
  *         cdef inline tuple names(self):
  *             return <tuple>PyDataType_NAMES(self)             # <<<<<<<<<<<<<<
@@ -17396,7 +17408,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_5names_names(PyArray_Descr
   __pyx_r = ((PyObject*)__pyx_t_1);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":300
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":256
  *             return <object>PyDataType_FIELDS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17411,7 +17423,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_5names_names(PyArray_Descr
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":307
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":263
  *         # valid (the pointer can be NULL). Most users should access
  *         # this field via the inline helper method PyDataType_SHAPE.
  *         @property             # <<<<<<<<<<<<<<
@@ -17422,7 +17434,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_5names_names(PyArray_Descr
 static CYTHON_INLINE PyArray_ArrayDescr *__pyx_f_5numpy_5dtype_8subarray_subarray(PyArray_Descr *__pyx_v_self) {
   PyArray_ArrayDescr *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":309
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":265
  *         @property
  *         cdef inline PyArray_ArrayDescr* subarray(self) noexcept nogil:
  *             return PyDataType_SUBARRAY(self)             # <<<<<<<<<<<<<<
@@ -17432,7 +17444,7 @@ static CYTHON_INLINE PyArray_ArrayDescr *__pyx_f_5numpy_5dtype_8subarray_subarra
   __pyx_r = PyDataType_SUBARRAY(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":307
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":263
  *         # valid (the pointer can be NULL). Most users should access
  *         # this field via the inline helper method PyDataType_SHAPE.
  *         @property             # <<<<<<<<<<<<<<
@@ -17445,7 +17457,7 @@ static CYTHON_INLINE PyArray_ArrayDescr *__pyx_f_5numpy_5dtype_8subarray_subarra
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":311
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":267
  *             return PyDataType_SUBARRAY(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17456,7 +17468,7 @@ static CYTHON_INLINE PyArray_ArrayDescr *__pyx_f_5numpy_5dtype_8subarray_subarra
 static CYTHON_INLINE npy_uint64 __pyx_f_5numpy_5dtype_5flags_flags(PyArray_Descr *__pyx_v_self) {
   npy_uint64 __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":314
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":270
  *         cdef inline npy_uint64 flags(self) noexcept nogil:
  *             """The data types flags."""
  *             return PyDataType_FLAGS(self)             # <<<<<<<<<<<<<<
@@ -17466,7 +17478,7 @@ static CYTHON_INLINE npy_uint64 __pyx_f_5numpy_5dtype_5flags_flags(PyArray_Descr
   __pyx_r = PyDataType_FLAGS(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":311
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":267
  *             return PyDataType_SUBARRAY(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17479,7 +17491,7 @@ static CYTHON_INLINE npy_uint64 __pyx_f_5numpy_5dtype_5flags_flags(PyArray_Descr
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":323
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":279
  *     ctypedef class numpy.broadcast [object PyArrayMultiIterObject, check_size ignore]:
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17490,7 +17502,7 @@ static CYTHON_INLINE npy_uint64 __pyx_f_5numpy_5dtype_5flags_flags(PyArray_Descr
 static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_7numiter_numiter(PyArrayMultiIterObject *__pyx_v_self) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":326
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":282
  *         cdef inline int numiter(self) noexcept nogil:
  *             """The number of arrays that need to be broadcast to the same shape."""
  *             return PyArray_MultiIter_NUMITER(self)             # <<<<<<<<<<<<<<
@@ -17500,7 +17512,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_7numiter_numiter(PyArrayMulti
   __pyx_r = PyArray_MultiIter_NUMITER(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":323
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":279
  *     ctypedef class numpy.broadcast [object PyArrayMultiIterObject, check_size ignore]:
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17513,7 +17525,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_7numiter_numiter(PyArrayMulti
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":328
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":284
  *             return PyArray_MultiIter_NUMITER(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17524,7 +17536,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_7numiter_numiter(PyArrayMulti
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_4size_size(PyArrayMultiIterObject *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":331
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":287
  *         cdef inline npy_intp size(self) noexcept nogil:
  *             """The total broadcasted size."""
  *             return PyArray_MultiIter_SIZE(self)             # <<<<<<<<<<<<<<
@@ -17534,7 +17546,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_4size_size(PyArrayMultiI
   __pyx_r = PyArray_MultiIter_SIZE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":328
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":284
  *             return PyArray_MultiIter_NUMITER(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17547,7 +17559,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_4size_size(PyArrayMultiI
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":333
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":289
  *             return PyArray_MultiIter_SIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17558,7 +17570,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_4size_size(PyArrayMultiI
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_5index_index(PyArrayMultiIterObject *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":336
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":292
  *         cdef inline npy_intp index(self) noexcept nogil:
  *             """The current (1-d) index into the broadcasted result."""
  *             return PyArray_MultiIter_INDEX(self)             # <<<<<<<<<<<<<<
@@ -17568,7 +17580,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_5index_index(PyArrayMult
   __pyx_r = PyArray_MultiIter_INDEX(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":333
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":289
  *             return PyArray_MultiIter_SIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17581,7 +17593,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_5index_index(PyArrayMult
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":338
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":294
  *             return PyArray_MultiIter_INDEX(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17592,7 +17604,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_5index_index(PyArrayMult
 static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_2nd_nd(PyArrayMultiIterObject *__pyx_v_self) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":341
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":297
  *         cdef inline int nd(self) noexcept nogil:
  *             """The number of dimensions in the broadcasted result."""
  *             return PyArray_MultiIter_NDIM(self)             # <<<<<<<<<<<<<<
@@ -17602,7 +17614,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_2nd_nd(PyArrayMultiIterObject
   __pyx_r = PyArray_MultiIter_NDIM(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":338
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":294
  *             return PyArray_MultiIter_INDEX(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17615,7 +17627,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_2nd_nd(PyArrayMultiIterObject
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":343
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":299
  *             return PyArray_MultiIter_NDIM(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17626,7 +17638,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_2nd_nd(PyArrayMultiIterObject
 static CYTHON_INLINE npy_intp *__pyx_f_5numpy_9broadcast_10dimensions_dimensions(PyArrayMultiIterObject *__pyx_v_self) {
   npy_intp *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":346
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":302
  *         cdef inline npy_intp* dimensions(self) noexcept nogil:
  *             """The shape of the broadcasted result."""
  *             return PyArray_MultiIter_DIMS(self)             # <<<<<<<<<<<<<<
@@ -17636,7 +17648,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_9broadcast_10dimensions_dimensions
   __pyx_r = PyArray_MultiIter_DIMS(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":343
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":299
  *             return PyArray_MultiIter_NDIM(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17649,7 +17661,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_9broadcast_10dimensions_dimensions
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":348
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":304
  *             return PyArray_MultiIter_DIMS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17660,7 +17672,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_9broadcast_10dimensions_dimensions
 static CYTHON_INLINE void **__pyx_f_5numpy_9broadcast_5iters_iters(PyArrayMultiIterObject *__pyx_v_self) {
   void **__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":352
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":308
  *             """An array of iterator objects that holds the iterators for the arrays to be broadcast together.
  *             On return, the iterators are adjusted for broadcasting."""
  *             return PyArray_MultiIter_ITERS(self)             # <<<<<<<<<<<<<<
@@ -17670,7 +17682,7 @@ static CYTHON_INLINE void **__pyx_f_5numpy_9broadcast_5iters_iters(PyArrayMultiI
   __pyx_r = PyArray_MultiIter_ITERS(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":348
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":304
  *             return PyArray_MultiIter_DIMS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17683,7 +17695,7 @@ static CYTHON_INLINE void **__pyx_f_5numpy_9broadcast_5iters_iters(PyArrayMultiI
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":366
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":322
  *         # Instead, we use properties that map to the corresponding C-API functions.
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17694,7 +17706,7 @@ static CYTHON_INLINE void **__pyx_f_5numpy_9broadcast_5iters_iters(PyArrayMultiI
 static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject *__pyx_v_self) {
   PyObject *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":370
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":326
  *             """Returns a borrowed reference to the object owning the data/memory.
  *             """
  *             return PyArray_BASE(self)             # <<<<<<<<<<<<<<
@@ -17704,7 +17716,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject
   __pyx_r = PyArray_BASE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":366
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":322
  *         # Instead, we use properties that map to the corresponding C-API functions.
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17717,7 +17729,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":372
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":328
  *             return PyArray_BASE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17731,7 +17743,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
   PyArray_Descr *__pyx_t_1;
   __Pyx_RefNannySetupContext("descr", 0);
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":376
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":332
  *             """Returns an owned reference to the dtype of the array.
  *             """
  *             return <dtype>PyArray_DESCR(self)             # <<<<<<<<<<<<<<
@@ -17744,7 +17756,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
   __pyx_r = ((PyArray_Descr *)__pyx_t_1);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":372
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":328
  *             return PyArray_BASE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17759,7 +17771,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":378
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":334
  *             return <dtype>PyArray_DESCR(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17770,7 +17782,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
 static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx_v_self) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":382
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":338
  *             """Returns the number of dimensions in the array.
  *             """
  *             return PyArray_NDIM(self)             # <<<<<<<<<<<<<<
@@ -17780,7 +17792,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx
   __pyx_r = PyArray_NDIM(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":378
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":334
  *             return <dtype>PyArray_DESCR(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17793,7 +17805,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":384
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":340
  *             return PyArray_NDIM(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17804,7 +17816,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx
 static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObject *__pyx_v_self) {
   npy_intp *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":390
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":346
  *             Can return NULL for 0-dimensional arrays.
  *             """
  *             return PyArray_DIMS(self)             # <<<<<<<<<<<<<<
@@ -17814,7 +17826,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObjec
   __pyx_r = PyArray_DIMS(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":384
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":340
  *             return PyArray_NDIM(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17827,7 +17839,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObjec
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":392
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":348
  *             return PyArray_DIMS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17838,7 +17850,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObjec
 static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayObject *__pyx_v_self) {
   npy_intp *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":397
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":353
  *             The number of elements matches the number of dimensions of the array (ndim).
  *             """
  *             return PyArray_STRIDES(self)             # <<<<<<<<<<<<<<
@@ -17848,7 +17860,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayO
   __pyx_r = PyArray_STRIDES(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":392
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":348
  *             return PyArray_DIMS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17861,7 +17873,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayO
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":399
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":355
  *             return PyArray_STRIDES(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17872,7 +17884,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayO
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":403
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":359
  *             """Returns the total size (in number of elements) of the array.
  *             """
  *             return PyArray_SIZE(self)             # <<<<<<<<<<<<<<
@@ -17882,7 +17894,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *
   __pyx_r = PyArray_SIZE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":399
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":355
  *             return PyArray_STRIDES(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17895,7 +17907,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":405
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":361
  *             return PyArray_SIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17906,7 +17918,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *
 static CYTHON_INLINE char *__pyx_f_5numpy_7ndarray_4data_data(PyArrayObject *__pyx_v_self) {
   char *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":412
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":368
  *             of `PyArray_DATA()` instead, which returns a 'void*'.
  *             """
  *             return PyArray_BYTES(self)             # <<<<<<<<<<<<<<
@@ -17916,7 +17928,7 @@ static CYTHON_INLINE char *__pyx_f_5numpy_7ndarray_4data_data(PyArrayObject *__p
   __pyx_r = PyArray_BYTES(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":405
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":361
  *             return PyArray_SIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17929,7 +17941,7 @@ static CYTHON_INLINE char *__pyx_f_5numpy_7ndarray_4data_data(PyArrayObject *__p
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":824
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":776
  * ctypedef long double complex clongdouble_t
  * 
  * cdef inline object PyArray_MultiIterNew1(a):             # <<<<<<<<<<<<<<
@@ -17946,7 +17958,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":825
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":777
  * 
  * cdef inline object PyArray_MultiIterNew1(a):
  *     return PyArray_MultiIterNew(1, <void*>a)             # <<<<<<<<<<<<<<
@@ -17954,13 +17966,13 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__
  * cdef inline object PyArray_MultiIterNew2(a, b):
 */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyArray_MultiIterNew(1, ((void *)__pyx_v_a)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 825, __pyx_L1_error)
+  __pyx_t_1 = PyArray_MultiIterNew(1, ((void *)__pyx_v_a)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 777, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_t_1);
   __pyx_r = __pyx_t_1;
   __pyx_t_1 = 0;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":824
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":776
  * ctypedef long double complex clongdouble_t
  * 
  * cdef inline object PyArray_MultiIterNew1(a):             # <<<<<<<<<<<<<<
@@ -17979,7 +17991,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":827
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":779
  *     return PyArray_MultiIterNew(1, <void*>a)
  * 
  * cdef inline object PyArray_MultiIterNew2(a, b):             # <<<<<<<<<<<<<<
@@ -17996,7 +18008,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":828
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":780
  * 
  * cdef inline object PyArray_MultiIterNew2(a, b):
  *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)             # <<<<<<<<<<<<<<
@@ -18004,13 +18016,13 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__
  * cdef inline object PyArray_MultiIterNew3(a, b, c):
 */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyArray_MultiIterNew(2, ((void *)__pyx_v_a), ((void *)__pyx_v_b)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 828, __pyx_L1_error)
+  __pyx_t_1 = PyArray_MultiIterNew(2, ((void *)__pyx_v_a), ((void *)__pyx_v_b)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 780, __pyx_L1_error)
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":827
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":779
  *     return PyArray_MultiIterNew(1, <void*>a)
  * 
  * cdef inline object PyArray_MultiIterNew2(a, b):             # <<<<<<<<<<<<<<
@@ -18029,7 +18041,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":830
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":782
  *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
  * 
  * cdef inline object PyArray_MultiIterNew3(a, b, c):             # <<<<<<<<<<<<<<
@@ -18046,7 +18058,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":831
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":783
  * 
  * cdef inline object PyArray_MultiIterNew3(a, b, c):
  *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)             # <<<<<<<<<<<<<<
@@ -18054,13 +18066,13 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__
  * cdef inline object PyArray_MultiIterNew4(a, b, c, d):
 */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyArray_MultiIterNew(3, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 831, __pyx_L1_error)
+  __pyx_t_1 = PyArray_MultiIterNew(3, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 783, __pyx_L1_error)
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":830
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":782
  *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
  * 
  * cdef inline object PyArray_MultiIterNew3(a, b, c):             # <<<<<<<<<<<<<<
@@ -18079,7 +18091,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":833
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":785
  *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
  * 
  * cdef inline object PyArray_MultiIterNew4(a, b, c, d):             # <<<<<<<<<<<<<<
@@ -18096,7 +18108,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":834
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":786
  * 
  * cdef inline object PyArray_MultiIterNew4(a, b, c, d):
  *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)             # <<<<<<<<<<<<<<
@@ -18104,13 +18116,13 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__
  * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
 */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyArray_MultiIterNew(4, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 834, __pyx_L1_error)
+  __pyx_t_1 = PyArray_MultiIterNew(4, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 786, __pyx_L1_error)
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   __pyx_t_1 = 0;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":833
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":785
  *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
  * 
  * cdef inline object PyArray_MultiIterNew4(a, b, c, d):             # <<<<<<<<<<<<<<
@@ -18129,7 +18141,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":836
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":788
  *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
  * 
  * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):             # <<<<<<<<<<<<<<
@@ -18146,7 +18158,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":837
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":789
  * 
  * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
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@@ -18154,13 +18166,13 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__
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+  __pyx_t_1 = PyArray_MultiIterNew(5, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d), ((void *)__pyx_v_e)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 789, __pyx_L1_error)
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":836
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":788
  *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
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  * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):             # <<<<<<<<<<<<<<
@@ -18179,7 +18191,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__
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 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":839
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":791
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":840
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":792
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-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":841
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":793
  * cdef inline tuple PyDataType_SHAPE(dtype d):
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-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":840
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":792
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@@ -18226,7 +18238,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":843
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":795
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  *         return ()             # <<<<<<<<<<<<<<
@@ -18240,7 +18252,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":839
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":791
  *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
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  * cdef inline tuple PyDataType_SHAPE(dtype d):             # <<<<<<<<<<<<<<
@@ -18255,7 +18267,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__
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-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1035
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":994
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@@ -18269,7 +18281,7 @@ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_a
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1036
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":995
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  * cdef inline void set_array_base(ndarray arr, object base) except *:
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@@ -18278,16 +18290,16 @@ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_a
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1037
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":996
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1035
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":994
  *     int _import_umath() except -1
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  * cdef inline void set_array_base(ndarray arr, object base) except *:             # <<<<<<<<<<<<<<
@@ -18302,7 +18314,7 @@ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_a
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-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1039
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":998
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  * cdef inline object get_array_base(ndarray arr):             # <<<<<<<<<<<<<<
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1040
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":999
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  * cdef inline object get_array_base(ndarray arr):
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@@ -18326,7 +18338,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__py
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   __pyx_v_base = PyArray_BASE(__pyx_v_arr);
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1041
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1000
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-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1042
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1001
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-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1041
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1000
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1043
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1002
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1039
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":998
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  * cdef inline object get_array_base(ndarray arr):             # <<<<<<<<<<<<<<
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-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1047
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1006
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1048
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1007
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-      /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1049
+      /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1008
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-      __pyx_t_4 = _import_array(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 1049, __pyx_L3_error)
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-      /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1048
+      /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1007
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-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1050
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1009
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-      if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(2, 1050, __pyx_L5_except_error)
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-      /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1051
+      /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1010
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-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1048
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1007
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1047
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1006
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1054
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1013
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-      /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1055
+      /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1014
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-      /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1054
+      /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1013
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-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1056
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1015
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-      if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(2, 1056, __pyx_L5_except_error)
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-      /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1057
+      /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1016
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-      __PYX_ERR(2, 1057, __pyx_L5_except_error)
+      __PYX_ERR(2, 1016, __pyx_L5_except_error)
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-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1054
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1013
  * 
  * cdef inline int import_umath() except -1:
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@@ -18652,7 +18664,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
     __pyx_L8_try_end:;
   }
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1053
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1012
  *         raise ImportError("numpy._core.multiarray failed to import")
  * 
  * cdef inline int import_umath() except -1:             # <<<<<<<<<<<<<<
@@ -18677,7 +18689,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1059
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1018
  *         raise ImportError("numpy._core.umath failed to import")
  * 
  * cdef inline int import_ufunc() except -1:             # <<<<<<<<<<<<<<
@@ -18704,7 +18716,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
   int __pyx_clineno = 0;
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-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1060
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1019
  * 
  * cdef inline int import_ufunc() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18720,16 +18732,16 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
     __Pyx_XGOTREF(__pyx_t_3);
     /*try:*/ {
 
-      /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1061
+      /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1020
  * cdef inline int import_ufunc() except -1:
  *     try:
  *         _import_umath()             # <<<<<<<<<<<<<<
  *     except Exception:
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 */
-      __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 1061, __pyx_L3_error)
+      __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 1020, __pyx_L3_error)
 
-      /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1060
+      /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1019
  * 
  * cdef inline int import_ufunc() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18743,7 +18755,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
     goto __pyx_L8_try_end;
     __pyx_L3_error:;
 
-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1062
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1021
  *     try:
  *         _import_umath()
  *     except Exception:             # <<<<<<<<<<<<<<
@@ -18753,12 +18765,12 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
     __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(((PyTypeObject*)PyExc_Exception))));
     if (__pyx_t_4) {
       __Pyx_AddTraceback("numpy.import_ufunc", __pyx_clineno, __pyx_lineno, __pyx_filename);
-      if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(2, 1062, __pyx_L5_except_error)
+      if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(2, 1021, __pyx_L5_except_error)
       __Pyx_XGOTREF(__pyx_t_5);
       __Pyx_XGOTREF(__pyx_t_6);
       __Pyx_XGOTREF(__pyx_t_7);
 
-      /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1063
+      /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1022
  *         _import_umath()
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  *         raise ImportError("numpy._core.umath failed to import")             # <<<<<<<<<<<<<<
@@ -18774,16 +18786,16 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
         __pyx_t_8 = __Pyx_PyObject_FastCall(__pyx_t_10, __pyx_callargs+__pyx_t_11, (2-__pyx_t_11) | (__pyx_t_11*__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET));
         __Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0;
         __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
-        if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 1063, __pyx_L5_except_error)
+        if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 1022, __pyx_L5_except_error)
         __Pyx_GOTREF(__pyx_t_8);
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       __Pyx_Raise(__pyx_t_8, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
-      __PYX_ERR(2, 1063, __pyx_L5_except_error)
+      __PYX_ERR(2, 1022, __pyx_L5_except_error)
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     goto __pyx_L5_except_error;
 
-    /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1060
+    /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1019
  * 
  * cdef inline int import_ufunc() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18799,7 +18811,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
     __pyx_L8_try_end:;
   }
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1059
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1018
  *         raise ImportError("numpy._core.umath failed to import")
  * 
  * cdef inline int import_ufunc() except -1:             # <<<<<<<<<<<<<<
@@ -18824,7 +18836,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1066
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1025
  * 
  * 
  * cdef inline bint is_timedelta64_object(object obj) noexcept:             # <<<<<<<<<<<<<<
@@ -18835,7 +18847,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
 static CYTHON_INLINE int __pyx_f_5numpy_is_timedelta64_object(PyObject *__pyx_v_obj) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1078
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1037
  *     bool
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  *     return PyObject_TypeCheck(obj, &PyTimedeltaArrType_Type)             # <<<<<<<<<<<<<<
@@ -18845,7 +18857,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_timedelta64_object(PyObject *__pyx_v_
   __pyx_r = PyObject_TypeCheck(__pyx_v_obj, (&PyTimedeltaArrType_Type));
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1066
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1025
  * 
  * 
  * cdef inline bint is_timedelta64_object(object obj) noexcept:             # <<<<<<<<<<<<<<
@@ -18858,7 +18870,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_timedelta64_object(PyObject *__pyx_v_
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1081
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1040
  * 
  * 
  * cdef inline bint is_datetime64_object(object obj) noexcept:             # <<<<<<<<<<<<<<
@@ -18869,7 +18881,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_timedelta64_object(PyObject *__pyx_v_
 static CYTHON_INLINE int __pyx_f_5numpy_is_datetime64_object(PyObject *__pyx_v_obj) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1093
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1052
  *     bool
  *     """
  *     return PyObject_TypeCheck(obj, &PyDatetimeArrType_Type)             # <<<<<<<<<<<<<<
@@ -18879,7 +18891,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_datetime64_object(PyObject *__pyx_v_o
   __pyx_r = PyObject_TypeCheck(__pyx_v_obj, (&PyDatetimeArrType_Type));
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1081
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1040
  * 
  * 
  * cdef inline bint is_datetime64_object(object obj) noexcept:             # <<<<<<<<<<<<<<
@@ -18892,7 +18904,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_datetime64_object(PyObject *__pyx_v_o
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1096
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1055
  * 
  * 
  * cdef inline npy_datetime get_datetime64_value(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18903,7 +18915,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_datetime64_object(PyObject *__pyx_v_o
 static CYTHON_INLINE npy_datetime __pyx_f_5numpy_get_datetime64_value(PyObject *__pyx_v_obj) {
   npy_datetime __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1103
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1062
  *     also needed.  That can be found using `get_datetime64_unit`.
  *     """
  *     return (<PyDatetimeScalarObject*>obj).obval             # <<<<<<<<<<<<<<
@@ -18913,7 +18925,7 @@ static CYTHON_INLINE npy_datetime __pyx_f_5numpy_get_datetime64_value(PyObject *
   __pyx_r = ((PyDatetimeScalarObject *)__pyx_v_obj)->obval;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1096
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1055
  * 
  * 
  * cdef inline npy_datetime get_datetime64_value(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18926,7 +18938,7 @@ static CYTHON_INLINE npy_datetime __pyx_f_5numpy_get_datetime64_value(PyObject *
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1106
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1065
  * 
  * 
  * cdef inline npy_timedelta get_timedelta64_value(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18937,7 +18949,7 @@ static CYTHON_INLINE npy_datetime __pyx_f_5numpy_get_datetime64_value(PyObject *
 static CYTHON_INLINE npy_timedelta __pyx_f_5numpy_get_timedelta64_value(PyObject *__pyx_v_obj) {
   npy_timedelta __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1110
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1069
  *     returns the int64 value underlying scalar numpy timedelta64 object
  *     """
  *     return (<PyTimedeltaScalarObject*>obj).obval             # <<<<<<<<<<<<<<
@@ -18947,7 +18959,7 @@ static CYTHON_INLINE npy_timedelta __pyx_f_5numpy_get_timedelta64_value(PyObject
   __pyx_r = ((PyTimedeltaScalarObject *)__pyx_v_obj)->obval;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1106
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1065
  * 
  * 
  * cdef inline npy_timedelta get_timedelta64_value(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18960,7 +18972,7 @@ static CYTHON_INLINE npy_timedelta __pyx_f_5numpy_get_timedelta64_value(PyObject
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1113
+/* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1072
  * 
  * 
  * cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18971,7 +18983,7 @@ static CYTHON_INLINE npy_timedelta __pyx_f_5numpy_get_timedelta64_value(PyObject
 static CYTHON_INLINE NPY_DATETIMEUNIT __pyx_f_5numpy_get_datetime64_unit(PyObject *__pyx_v_obj) {
   NPY_DATETIMEUNIT __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1117
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1076
  *     returns the unit part of the dtype for a numpy datetime64 object.
  *     """
  *     return <NPY_DATETIMEUNIT>(<PyDatetimeScalarObject*>obj).obmeta.base             # <<<<<<<<<<<<<<
@@ -18981,7 +18993,7 @@ static CYTHON_INLINE NPY_DATETIMEUNIT __pyx_f_5numpy_get_datetime64_unit(PyObjec
   __pyx_r = ((NPY_DATETIMEUNIT)((PyDatetimeScalarObject *)__pyx_v_obj)->obmeta.base);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-0mzx3goa/overlay/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1113
+  /* "../../../../../tmp/pip-build-env-wyr4tu2o/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1072
  * 
  * 
  * cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -19062,7 +19074,7 @@ PyObject *__pyx_args, PyObject *__pyx_kwds
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       }
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-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "find_nearest_matches", 0) < 0) __PYX_ERR(0, 12, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "find_nearest_matches", 0) < (0)) __PYX_ERR(0, 12, __pyx_L3_error)
       if (!values[2]) values[2] = __Pyx_NewRef(((PyObject *)((PyObject*)__pyx_mstate_global->__pyx_int_1)));
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@@ -19215,7 +19227,7 @@ static PyObject *__pyx_pf_9pyprophet_7scoring_10_optimized_find_nearest_matches(
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     __pyx_t_3 = __Pyx_MakeVectorcallBuilderKwds(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 17, __pyx_L1_error)
     __Pyx_GOTREF(__pyx_t_3);
-    if (__Pyx_VectorcallBuilder_AddArg(__pyx_mstate_global->__pyx_n_u_dtype, __pyx_t_6, __pyx_t_3, __pyx_callargs+2, 0) < 0) __PYX_ERR(0, 17, __pyx_L1_error)
+    if (__Pyx_VectorcallBuilder_AddArg(__pyx_mstate_global->__pyx_n_u_dtype, __pyx_t_6, __pyx_t_3, __pyx_callargs+2, 0) < (0)) __PYX_ERR(0, 17, __pyx_L1_error)
     __pyx_t_1 = __Pyx_Object_Vectorcall_CallFromBuilder(__pyx_t_4, __pyx_callargs+__pyx_t_7, (2-__pyx_t_7) | (__pyx_t_7*__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET), __pyx_t_3);
     __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0;
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@@ -20579,7 +20591,7 @@ PyObject *__pyx_args, PyObject *__pyx_kwds
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-      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "count_num_positives", 0) < 0) __PYX_ERR(0, 135, __pyx_L3_error)
+      if (__Pyx_ParseKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values, kwd_pos_args, __pyx_kwds_len, "count_num_positives", 0) < (0)) __PYX_ERR(0, 135, __pyx_L3_error)
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@@ -20697,7 +20709,7 @@ static PyObject *__pyx_pf_9pyprophet_7scoring_10_optimized_2count_num_positives(
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     __Pyx_GOTREF(__pyx_t_5);
-    if (__Pyx_VectorcallBuilder_AddArg(__pyx_mstate_global->__pyx_n_u_dtype, __pyx_t_6, __pyx_t_5, __pyx_callargs+2, 0) < 0) __PYX_ERR(0, 141, __pyx_L1_error)
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@@ -28913,7 +28937,7 @@ static PyTypeObject *__Pyx_ImportType_3_1_2(PyObject *module, const char *module
             module_name, class_name);
         goto bad;
     }
-#if !CYTHON_COMPILING_IN_LIMITED_API
+#if !( defined(Py_LIMITED_API) || (defined(CYTHON_COMPILING_IN_LIMITED_API) && CYTHON_COMPILING_IN_LIMITED_API) )
     basicsize = ((PyTypeObject *)result)->tp_basicsize;
     itemsize = ((PyTypeObject *)result)->tp_itemsize;
 #else
@@ -28951,7 +28975,7 @@ static PyTypeObject *__Pyx_ImportType_3_1_2(PyObject *module, const char *module
             module_name, class_name, size, basicsize+itemsize);
         goto bad;
     }
-    if (check_size == __Pyx_ImportType_CheckSize_Error_3_1_2 &&
+    if (check_size == __Pyx_ImportType_CheckSize_Error_3_1_6 &&
             ((size_t)basicsize > size || (size_t)(basicsize + itemsize) < size)) {
         PyErr_Format(PyExc_ValueError,
             "%.200s.%.200s size changed, may indicate binary incompatibility. "
@@ -28959,7 +28983,7 @@ static PyTypeObject *__Pyx_ImportType_3_1_2(PyObject *module, const char *module
             module_name, class_name, size, basicsize, basicsize+itemsize);
         goto bad;
     }
-    else if (check_size == __Pyx_ImportType_CheckSize_Warn_3_1_2 && (size_t)basicsize > size) {
+    else if (check_size == __Pyx_ImportType_CheckSize_Warn_3_1_6 && (size_t)basicsize > size) {
         if (PyErr_WarnFormat(NULL, 0,
                 "%.200s.%.200s size changed, may indicate binary incompatibility. "
                 "Expected %zd from C header, got %zd from PyObject",
@@ -29100,7 +29124,7 @@ static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyTypeObject *metaclass, PyOb
 }
 
 /* CommonTypesMetaclass */
-PyObject* __pyx_CommonTypesMetaclass_get_module(CYTHON_UNUSED PyObject *self, CYTHON_UNUSED void* context) {
+static PyObject* __pyx_CommonTypesMetaclass_get_module(CYTHON_UNUSED PyObject *self, CYTHON_UNUSED void* context) {
     return PyUnicode_FromString(__PYX_ABI_MODULE_NAME);
 }
 static PyGetSetDef __pyx_CommonTypesMetaclass_getset[] = {
@@ -29129,6 +29153,7 @@ static int __pyx_CommonTypesMetaclass_init(PyObject *module) {
         return -1;
     }
     mstate->__pyx_CommonTypesMetaclassType = __Pyx_FetchCommonTypeFromSpec(NULL, module, &__pyx_CommonTypesMetaclass_spec, bases);
+    Py_DECREF(bases);
     if (unlikely(mstate->__pyx_CommonTypesMetaclassType == NULL)) {
         return -1;
     }
@@ -33739,6 +33764,10 @@ __Pyx_PyType_GetFullyQualifiedName(PyTypeObject* tp)
         PyCode_NewWithPosOnlyArgs
       #endif
         (a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, name, fline, lnos, __pyx_mstate_global->__pyx_empty_bytes);
+    #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030c00A1
+    if (likely(result))
+        result->_co_firsttraceable = 0;
+    #endif
     return result;
   }
 #elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY
@@ -34066,6 +34095,17 @@ static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
 static CYTHON_INLINE PyObject * __Pyx_PyLong_FromSize_t(size_t ival) {
     return PyLong_FromSize_t(ival);
 }
+#if CYTHON_USE_PYLONG_INTERNALS
+static CYTHON_INLINE int __Pyx_PyLong_CompactAsLong(PyObject *x, long *return_value) {
+    if (unlikely(!__Pyx_PyLong_IsCompact(x)))
+        return 0;
+    Py_ssize_t value = __Pyx_PyLong_CompactValue(x);
+    if ((sizeof(long) < sizeof(Py_ssize_t)) && unlikely(value != (long) value))
+        return 0;
+    *return_value = (long) value;
+    return 1;
+}
+#endif
 
 
   /* MultiPhaseInitModuleState */

From 7c81fec406ee083eac8973a11506d65fa3233859 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 28 Oct 2025 22:41:21 +0000
Subject: [PATCH 04/26] Add tests for export_feature_scores functionality

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 tests/test_pyprophet_export.py | 83 ++++++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)

diff --git a/tests/test_pyprophet_export.py b/tests/test_pyprophet_export.py
index 6e05f8c3..152773eb 100644
--- a/tests/test_pyprophet_export.py
+++ b/tests/test_pyprophet_export.py
@@ -268,3 +268,86 @@ def test_compound_ms2(test_data_compound_osw, temp_folder, regtest):
 
     df = pd.read_csv(f"{temp_folder}/test_data_compound_ms2.tsv", sep="\t", nrows=100)
     print(df.sort_index(axis=1), file=regtest)
+
+
+# ================== TEST EXPORT FEATURE SCORES ==================
+def test_export_feature_scores_osw(test_data_osw, temp_folder):
+    """Test export feature scores from OSW file"""
+    outfile = temp_folder / "test_data_feature_scores.pdf"
+    
+    # Import the function
+    from pyprophet.export.export_report import export_feature_scores
+    
+    # Try to export feature scores
+    try:
+        export_feature_scores(str(test_data_osw), str(outfile))
+        
+        # Check that output file was created
+        assert outfile.exists(), "Feature scores PDF was not created"
+        assert outfile.stat().st_size > 0, "Feature scores PDF is empty"
+    except Exception as e:
+        # If matplotlib is not available or data doesn't have feature scores, skip test
+        if "matplotlib" in str(e).lower() or "no feature score" in str(e).lower():
+            pytest.skip(f"Test skipped due to: {str(e)}")
+        else:
+            raise
+
+
+def test_export_feature_scores_parquet(test_data_parquet, temp_folder):
+    """Test export feature scores from Parquet file"""
+    outfile = temp_folder / "test_data_feature_scores.pdf"
+    
+    # Import the function
+    from pyprophet.export.export_report import export_feature_scores
+    
+    # Try to export feature scores
+    try:
+        export_feature_scores(str(test_data_parquet), str(outfile))
+        
+        # Check that output file was created (if data has feature scores)
+        if outfile.exists():
+            assert outfile.stat().st_size > 0, "Feature scores PDF is empty"
+    except Exception as e:
+        # If matplotlib is not available or data doesn't have feature scores, skip test
+        if "matplotlib" in str(e).lower() or "no feature score" in str(e).lower():
+            pytest.skip(f"Test skipped due to: {str(e)}")
+        else:
+            raise
+
+
+def test_export_feature_scores_split_parquet(test_data_split_parquet, temp_folder):
+    """Test export feature scores from Split Parquet directory"""
+    outfile = temp_folder / "test_data_feature_scores.pdf"
+    
+    # Import the function
+    from pyprophet.export.export_report import export_feature_scores
+    
+    # Try to export feature scores
+    try:
+        export_feature_scores(str(test_data_split_parquet), str(outfile))
+        
+        # Check that output file was created (if data has feature scores)
+        if outfile.exists():
+            assert outfile.stat().st_size > 0, "Feature scores PDF is empty"
+    except Exception as e:
+        # If matplotlib is not available or data doesn't have feature scores, skip test
+        if "matplotlib" in str(e).lower() or "no feature score" in str(e).lower():
+            pytest.skip(f"Test skipped due to: {str(e)}")
+        else:
+            raise
+
+
+def test_export_feature_scores_cli_osw(test_data_osw, temp_folder):
+    """Test export feature scores CLI command with OSW file"""
+    outfile = temp_folder / "test_data_feature_scores.pdf"
+    cmd = f"pyprophet export feature-scores --in={test_data_osw} --out={outfile}"
+    
+    try:
+        run_pyprophet_command(cmd, temp_folder)
+        
+        # Check that output file was created (if data has feature scores)
+        if outfile.exists():
+            assert outfile.stat().st_size > 0, "Feature scores PDF is empty"
+    except (subprocess.CalledProcessError, FileNotFoundError) as e:
+        # Skip test if pyprophet command is not available or fails
+        pytest.skip(f"Test skipped due to: {str(e)}")

From 28c0e291366123ba3f88c0cd4d3aed24e3da9854 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 28 Oct 2025 22:43:46 +0000
Subject: [PATCH 05/26] Address code review comments - move imports and improve
 exception handling

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 tests/test_pyprophet_export.py | 44 ++++++++++++++++++----------------
 1 file changed, 23 insertions(+), 21 deletions(-)

diff --git a/tests/test_pyprophet_export.py b/tests/test_pyprophet_export.py
index 152773eb..41b640d4 100644
--- a/tests/test_pyprophet_export.py
+++ b/tests/test_pyprophet_export.py
@@ -9,6 +9,8 @@
 import pandas as pd
 import pytest
 
+from pyprophet.export.export_report import export_feature_scores
+
 pd.options.display.expand_frame_repr = False
 pd.options.display.precision = 4
 pd.options.display.max_columns = None
@@ -275,9 +277,6 @@ def test_export_feature_scores_osw(test_data_osw, temp_folder):
     """Test export feature scores from OSW file"""
     outfile = temp_folder / "test_data_feature_scores.pdf"
     
-    # Import the function
-    from pyprophet.export.export_report import export_feature_scores
-    
     # Try to export feature scores
     try:
         export_feature_scores(str(test_data_osw), str(outfile))
@@ -285,10 +284,13 @@ def test_export_feature_scores_osw(test_data_osw, temp_folder):
         # Check that output file was created
         assert outfile.exists(), "Feature scores PDF was not created"
         assert outfile.stat().st_size > 0, "Feature scores PDF is empty"
-    except Exception as e:
-        # If matplotlib is not available or data doesn't have feature scores, skip test
-        if "matplotlib" in str(e).lower() or "no feature score" in str(e).lower():
-            pytest.skip(f"Test skipped due to: {str(e)}")
+    except (ImportError, ModuleNotFoundError) as e:
+        # Skip if matplotlib or other required packages are not available
+        pytest.skip(f"Test skipped due to missing dependency: {str(e)}")
+    except ValueError as e:
+        # Skip if data doesn't have feature scores
+        if "no feature score" in str(e).lower():
+            pytest.skip(f"Test skipped: {str(e)}")
         else:
             raise
 
@@ -297,9 +299,6 @@ def test_export_feature_scores_parquet(test_data_parquet, temp_folder):
     """Test export feature scores from Parquet file"""
     outfile = temp_folder / "test_data_feature_scores.pdf"
     
-    # Import the function
-    from pyprophet.export.export_report import export_feature_scores
-    
     # Try to export feature scores
     try:
         export_feature_scores(str(test_data_parquet), str(outfile))
@@ -307,10 +306,13 @@ def test_export_feature_scores_parquet(test_data_parquet, temp_folder):
         # Check that output file was created (if data has feature scores)
         if outfile.exists():
             assert outfile.stat().st_size > 0, "Feature scores PDF is empty"
-    except Exception as e:
-        # If matplotlib is not available or data doesn't have feature scores, skip test
-        if "matplotlib" in str(e).lower() or "no feature score" in str(e).lower():
-            pytest.skip(f"Test skipped due to: {str(e)}")
+    except (ImportError, ModuleNotFoundError) as e:
+        # Skip if matplotlib or other required packages are not available
+        pytest.skip(f"Test skipped due to missing dependency: {str(e)}")
+    except ValueError as e:
+        # Skip if data doesn't have feature scores
+        if "no feature score" in str(e).lower():
+            pytest.skip(f"Test skipped: {str(e)}")
         else:
             raise
 
@@ -319,9 +321,6 @@ def test_export_feature_scores_split_parquet(test_data_split_parquet, temp_folde
     """Test export feature scores from Split Parquet directory"""
     outfile = temp_folder / "test_data_feature_scores.pdf"
     
-    # Import the function
-    from pyprophet.export.export_report import export_feature_scores
-    
     # Try to export feature scores
     try:
         export_feature_scores(str(test_data_split_parquet), str(outfile))
@@ -329,10 +328,13 @@ def test_export_feature_scores_split_parquet(test_data_split_parquet, temp_folde
         # Check that output file was created (if data has feature scores)
         if outfile.exists():
             assert outfile.stat().st_size > 0, "Feature scores PDF is empty"
-    except Exception as e:
-        # If matplotlib is not available or data doesn't have feature scores, skip test
-        if "matplotlib" in str(e).lower() or "no feature score" in str(e).lower():
-            pytest.skip(f"Test skipped due to: {str(e)}")
+    except (ImportError, ModuleNotFoundError) as e:
+        # Skip if matplotlib or other required packages are not available
+        pytest.skip(f"Test skipped due to missing dependency: {str(e)}")
+    except ValueError as e:
+        # Skip if data doesn't have feature scores
+        if "no feature score" in str(e).lower():
+            pytest.skip(f"Test skipped: {str(e)}")
         else:
             raise
 

From 462e617416e464535e82245a59c260a57aeaeb4b Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 28 Oct 2025 23:11:50 +0000
Subject: [PATCH 06/26] Fix KeyError by adding required SCORE column for
 plot_scores

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index a640aaa9..a3ed2c5d 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -469,6 +469,12 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
     if rename_dict:
         plot_df.rename(columns=rename_dict, inplace=True)
     
+    # plot_scores requires a "SCORE" column - use the first VAR_ column as SCORE
+    var_cols = [col for col in plot_df.columns if col.startswith("VAR_")]
+    if var_cols and "SCORE" not in plot_df.columns:
+        # Add SCORE column as a copy of the first VAR_ column
+        plot_df["SCORE"] = plot_df[var_cols[0]]
+    
     # Call plot_scores with the formatted dataframe
     plot_scores(plot_df, temp_outfile)
     

From 31ed97bcace91451830025f50745666d4831c614 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 28 Oct 2025 23:26:38 +0000
Subject: [PATCH 07/26] Add memory optimizations for large datasets - sample
 data and select only VAR columns

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py | 320 ++++++++++++++++++++----------
 1 file changed, 219 insertions(+), 101 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index a3ed2c5d..31e0386a 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -224,77 +224,105 @@ def _export_feature_scores_osw(infile: str, outfile: str):
         # Process MS1 level if available
         if check_sqlite_table(con, "FEATURE_MS1"):
             logger.info("Processing MS1 level feature scores")
-            ms1_query = """
-                SELECT 
-                    FEATURE_MS1.*,
-                    PRECURSOR.DECOY,
-                    RUN.ID AS RUN_ID,
-                    FEATURE.PRECURSOR_ID,
-                    FEATURE.EXP_RT
-                FROM FEATURE_MS1
-                INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
-                INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-                INNER JOIN RUN ON FEATURE.RUN_ID = RUN.ID
-            """
-            df_ms1 = pd.read_sql_query(ms1_query, con)
-            if not df_ms1.empty:
-                _plot_feature_scores(df_ms1, outfile, "ms1", append=False)
+            # Get only VAR_ columns to reduce memory usage
+            cursor = con.cursor()
+            cursor.execute("PRAGMA table_info(FEATURE_MS1)")
+            all_cols = [row[1] for row in cursor.fetchall()]
+            var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+            
+            if var_cols:
+                var_cols_sql = ", ".join([f"FEATURE_MS1.{col}" for col in var_cols])
+                ms1_query = f"""
+                    SELECT 
+                        {var_cols_sql},
+                        PRECURSOR.DECOY
+                    FROM FEATURE_MS1
+                    INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
+                    INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                """
+                df_ms1 = pd.read_sql_query(ms1_query, con)
+                if not df_ms1.empty:
+                    _plot_feature_scores(df_ms1, outfile, "ms1", append=False)
+            else:
+                logger.warning("No VAR_ columns found in FEATURE_MS1 table")
         
         # Process MS2 level if available
         if check_sqlite_table(con, "FEATURE_MS2"):
             logger.info("Processing MS2 level feature scores")
-            ms2_query = """
-                SELECT 
-                    FEATURE_MS2.*,
-                    PRECURSOR.DECOY,
-                    RUN.ID AS RUN_ID,
-                    FEATURE.PRECURSOR_ID,
-                    FEATURE.EXP_RT
-                FROM FEATURE_MS2
-                INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
-                INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-                INNER JOIN RUN ON FEATURE.RUN_ID = RUN.ID
-            """
-            df_ms2 = pd.read_sql_query(ms2_query, con)
-            if not df_ms2.empty:
-                append = check_sqlite_table(con, "FEATURE_MS1")
-                _plot_feature_scores(df_ms2, outfile, "ms2", append=append)
+            # Get only VAR_ columns to reduce memory usage
+            cursor = con.cursor()
+            cursor.execute("PRAGMA table_info(FEATURE_MS2)")
+            all_cols = [row[1] for row in cursor.fetchall()]
+            var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+            
+            if var_cols:
+                var_cols_sql = ", ".join([f"FEATURE_MS2.{col}" for col in var_cols])
+                ms2_query = f"""
+                    SELECT 
+                        {var_cols_sql},
+                        PRECURSOR.DECOY
+                    FROM FEATURE_MS2
+                    INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
+                    INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                """
+                df_ms2 = pd.read_sql_query(ms2_query, con)
+                if not df_ms2.empty:
+                    append = check_sqlite_table(con, "FEATURE_MS1")
+                    _plot_feature_scores(df_ms2, outfile, "ms2", append=append)
+            else:
+                logger.warning("No VAR_ columns found in FEATURE_MS2 table")
         
         # Process transition level if available
         if check_sqlite_table(con, "FEATURE_TRANSITION"):
             logger.info("Processing transition level feature scores")
-            transition_query = """
-                SELECT 
-                    FEATURE_TRANSITION.*,
-                    TRANSITION.DECOY,
-                    RUN.ID AS RUN_ID,
-                    FEATURE.PRECURSOR_ID,
-                    FEATURE.EXP_RT
-                FROM FEATURE_TRANSITION
-                INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
-                INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
-                INNER JOIN RUN ON FEATURE.RUN_ID = RUN.ID
-            """
-            df_transition = pd.read_sql_query(transition_query, con)
-            if not df_transition.empty:
-                append = check_sqlite_table(con, "FEATURE_MS1") or check_sqlite_table(con, "FEATURE_MS2")
-                _plot_feature_scores(df_transition, outfile, "transition", append=append)
+            # Get only VAR_ columns to reduce memory usage
+            cursor = con.cursor()
+            cursor.execute("PRAGMA table_info(FEATURE_TRANSITION)")
+            all_cols = [row[1] for row in cursor.fetchall()]
+            var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+            
+            if var_cols:
+                var_cols_sql = ", ".join([f"FEATURE_TRANSITION.{col}" for col in var_cols])
+                transition_query = f"""
+                    SELECT 
+                        {var_cols_sql},
+                        TRANSITION.DECOY
+                    FROM FEATURE_TRANSITION
+                    INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
+                    INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
+                """
+                df_transition = pd.read_sql_query(transition_query, con)
+                if not df_transition.empty:
+                    append = check_sqlite_table(con, "FEATURE_MS1") or check_sqlite_table(con, "FEATURE_MS2")
+                    _plot_feature_scores(df_transition, outfile, "transition", append=append)
+            else:
+                logger.warning("No VAR_ columns found in FEATURE_TRANSITION table")
         
         # Process alignment level if available
         if check_sqlite_table(con, "FEATURE_MS2_ALIGNMENT"):
             logger.info("Processing alignment level feature scores")
-            alignment_query = """
-                SELECT 
-                    *,
-                    LABEL AS DECOY
-                FROM FEATURE_MS2_ALIGNMENT
-            """
-            df_alignment = pd.read_sql_query(alignment_query, con)
-            if not df_alignment.empty:
-                append = (check_sqlite_table(con, "FEATURE_MS1") or 
-                         check_sqlite_table(con, "FEATURE_MS2") or 
-                         check_sqlite_table(con, "FEATURE_TRANSITION"))
-                _plot_feature_scores(df_alignment, outfile, "alignment", append=append)
+            # Get only VAR_ columns to reduce memory usage
+            cursor = con.cursor()
+            cursor.execute("PRAGMA table_info(FEATURE_MS2_ALIGNMENT)")
+            all_cols = [row[1] for row in cursor.fetchall()]
+            var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+            
+            if var_cols:
+                var_cols_sql = ", ".join(var_cols)
+                alignment_query = f"""
+                    SELECT 
+                        {var_cols_sql},
+                        LABEL AS DECOY
+                    FROM FEATURE_MS2_ALIGNMENT
+                """
+                df_alignment = pd.read_sql_query(alignment_query, con)
+                if not df_alignment.empty:
+                    append = (check_sqlite_table(con, "FEATURE_MS1") or 
+                             check_sqlite_table(con, "FEATURE_MS2") or 
+                             check_sqlite_table(con, "FEATURE_TRANSITION"))
+                    _plot_feature_scores(df_alignment, outfile, "alignment", append=append)
+            else:
+                logger.warning("No VAR_ columns found in FEATURE_MS2_ALIGNMENT table")
     
     finally:
         con.close()
@@ -312,36 +340,61 @@ def _export_feature_scores_parquet(infile: str, outfile: str):
         Path to the output PDF file.
     """
     logger.info(f"Reading parquet file: {infile}")
-    df = pd.read_parquet(infile)
+    # First, read only column names to identify what to load
+    import pyarrow.parquet as pq
+    parquet_file = pq.ParquetFile(infile)
+    all_columns = parquet_file.schema.names
+    
+    # Identify columns to read for each level
+    ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
+    ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
+    transition_cols = [col for col in all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
+    
+    # Determine which columns to read (only what we need)
+    cols_to_read = set()
+    if ms1_cols and "PRECURSOR_DECOY" in all_columns:
+        cols_to_read.update(ms1_cols)
+        cols_to_read.add("PRECURSOR_DECOY")
+    if ms2_cols and "PRECURSOR_DECOY" in all_columns:
+        cols_to_read.update(ms2_cols)
+        cols_to_read.add("PRECURSOR_DECOY")
+    if transition_cols and "TRANSITION_DECOY" in all_columns:
+        cols_to_read.update(transition_cols)
+        cols_to_read.add("TRANSITION_DECOY")
+    
+    if not cols_to_read:
+        logger.warning("No VAR_ columns found in parquet file")
+        return
     
-    # Get all column names
-    columns = df.columns.tolist()
+    # Read only the columns we need
+    logger.info(f"Reading {len(cols_to_read)} columns from parquet file")
+    df = pd.read_parquet(infile, columns=list(cols_to_read))
     
     # Process MS1 level
-    ms1_cols = [col for col in columns if col.startswith("FEATURE_MS1_VAR_")]
-    if ms1_cols and "PRECURSOR_DECOY" in columns:
+    if ms1_cols and "PRECURSOR_DECOY" in df.columns:
         logger.info("Processing MS1 level feature scores")
         ms1_df = df[ms1_cols + ["PRECURSOR_DECOY"]].copy()
         ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
         _plot_feature_scores(ms1_df, outfile, "ms1", append=False)
+        del ms1_df  # Free memory
     
     # Process MS2 level
-    ms2_cols = [col for col in columns if col.startswith("FEATURE_MS2_VAR_")]
-    if ms2_cols and "PRECURSOR_DECOY" in columns:
+    if ms2_cols and "PRECURSOR_DECOY" in df.columns:
         logger.info("Processing MS2 level feature scores")
         ms2_df = df[ms2_cols + ["PRECURSOR_DECOY"]].copy()
         ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
         append = bool(ms1_cols)
         _plot_feature_scores(ms2_df, outfile, "ms2", append=append)
+        del ms2_df  # Free memory
     
     # Process transition level
-    transition_cols = [col for col in columns if col.startswith("FEATURE_TRANSITION_VAR_")]
-    if transition_cols and "TRANSITION_DECOY" in columns:
+    if transition_cols and "TRANSITION_DECOY" in df.columns:
         logger.info("Processing transition level feature scores")
         transition_df = df[transition_cols + ["TRANSITION_DECOY"]].copy()
         transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
         append = bool(ms1_cols or ms2_cols)
         _plot_feature_scores(transition_df, outfile, "transition", append=append)
+        del transition_df  # Free memory
 
 
 def _export_feature_scores_split_parquet(infile: str, outfile: str):
@@ -355,65 +408,97 @@ def _export_feature_scores_split_parquet(infile: str, outfile: str):
     outfile : str
         Path to the output PDF file.
     """
-    # Read precursor features
+    # Read precursor features - only necessary columns
     precursor_file = os.path.join(infile, "precursors_features.parquet")
     logger.info(f"Reading precursor features from: {precursor_file}")
-    df_precursor = pd.read_parquet(precursor_file)
     
-    # Get all column names
-    columns = df_precursor.columns.tolist()
-    
-    # Process MS1 level
-    ms1_cols = [col for col in columns if col.startswith("FEATURE_MS1_VAR_")]
-    if ms1_cols and "PRECURSOR_DECOY" in columns:
-        logger.info("Processing MS1 level feature scores")
-        ms1_df = df_precursor[ms1_cols + ["PRECURSOR_DECOY"]].copy()
-        ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
-        _plot_feature_scores(ms1_df, outfile, "ms1", append=False)
-    
-    # Process MS2 level
-    ms2_cols = [col for col in columns if col.startswith("FEATURE_MS2_VAR_")]
-    if ms2_cols and "PRECURSOR_DECOY" in columns:
-        logger.info("Processing MS2 level feature scores")
-        ms2_df = df_precursor[ms2_cols + ["PRECURSOR_DECOY"]].copy()
-        ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
-        append = bool(ms1_cols)
-        _plot_feature_scores(ms2_df, outfile, "ms2", append=append)
+    # First check what columns are available
+    import pyarrow.parquet as pq
+    precursor_parquet = pq.ParquetFile(precursor_file)
+    all_columns = precursor_parquet.schema.names
+    
+    # Identify columns to read
+    ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
+    ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
+    
+    cols_to_read = set()
+    if ms1_cols and "PRECURSOR_DECOY" in all_columns:
+        cols_to_read.update(ms1_cols)
+        cols_to_read.add("PRECURSOR_DECOY")
+    if ms2_cols and "PRECURSOR_DECOY" in all_columns:
+        cols_to_read.update(ms2_cols)
+        cols_to_read.add("PRECURSOR_DECOY")
+    
+    if cols_to_read:
+        logger.info(f"Reading {len(cols_to_read)} columns from precursor features")
+        df_precursor = pd.read_parquet(precursor_file, columns=list(cols_to_read))
+        
+        # Process MS1 level
+        if ms1_cols and "PRECURSOR_DECOY" in df_precursor.columns:
+            logger.info("Processing MS1 level feature scores")
+            ms1_df = df_precursor[ms1_cols + ["PRECURSOR_DECOY"]].copy()
+            ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+            _plot_feature_scores(ms1_df, outfile, "ms1", append=False)
+            del ms1_df  # Free memory
+        
+        # Process MS2 level
+        if ms2_cols and "PRECURSOR_DECOY" in df_precursor.columns:
+            logger.info("Processing MS2 level feature scores")
+            ms2_df = df_precursor[ms2_cols + ["PRECURSOR_DECOY"]].copy()
+            ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+            append = bool(ms1_cols)
+            _plot_feature_scores(ms2_df, outfile, "ms2", append=append)
+            del ms2_df  # Free memory
+        
+        del df_precursor  # Free memory
     
     # Read transition features if available
     transition_file = os.path.join(infile, "transition_features.parquet")
     if os.path.exists(transition_file):
         logger.info(f"Reading transition features from: {transition_file}")
-        df_transition = pd.read_parquet(transition_file)
-        transition_columns = df_transition.columns.tolist()
         
-        # Process transition level
-        transition_cols = [col for col in transition_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
-        if transition_cols and "TRANSITION_DECOY" in transition_columns:
+        # Check what columns are available
+        transition_parquet = pq.ParquetFile(transition_file)
+        transition_all_columns = transition_parquet.schema.names
+        transition_cols = [col for col in transition_all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
+        
+        if transition_cols and "TRANSITION_DECOY" in transition_all_columns:
+            # Read only necessary columns
+            cols_to_read = transition_cols + ["TRANSITION_DECOY"]
+            logger.info(f"Reading {len(cols_to_read)} columns from transition features")
+            df_transition = pd.read_parquet(transition_file, columns=cols_to_read)
+            
             logger.info("Processing transition level feature scores")
             transition_df = df_transition[transition_cols + ["TRANSITION_DECOY"]].copy()
             transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
             append = bool(ms1_cols or ms2_cols)
             _plot_feature_scores(transition_df, outfile, "transition", append=append)
+            del transition_df, df_transition  # Free memory
     
     # Read alignment features if available
     alignment_file = os.path.join(infile, "feature_alignment.parquet")
     if os.path.exists(alignment_file):
         logger.info(f"Reading alignment features from: {alignment_file}")
-        df_alignment = pd.read_parquet(alignment_file)
         
-        # Get var columns
-        alignment_columns = df_alignment.columns.tolist()
-        var_cols = [col for col in alignment_columns if col.startswith("VAR_")]
+        # Check what columns are available
+        alignment_parquet = pq.ParquetFile(alignment_file)
+        alignment_all_columns = alignment_parquet.schema.names
+        var_cols = [col for col in alignment_all_columns if col.startswith("VAR_")]
         
-        if var_cols and "DECOY" in alignment_columns:
+        if var_cols and "DECOY" in alignment_all_columns:
+            # Read only necessary columns
+            cols_to_read = var_cols + ["DECOY"]
+            logger.info(f"Reading {len(cols_to_read)} columns from alignment features")
+            df_alignment = pd.read_parquet(alignment_file, columns=cols_to_read)
+            
             logger.info("Processing alignment level feature scores")
             alignment_df = df_alignment[var_cols + ["DECOY"]].copy()
             append = bool(ms1_cols or ms2_cols or (os.path.exists(transition_file) and transition_cols))
             _plot_feature_scores(alignment_df, outfile, "alignment", append=append)
+            del alignment_df, df_alignment  # Free memory
 
 
-def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: bool = False):
+def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: bool = False, sample_size: int = 100000):
     """
     Create plots for feature scores at a specific level.
     
@@ -427,6 +512,9 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
         Level name (ms1, ms2, transition, or alignment).
     append : bool
         If True, append to existing PDF. If False, create new PDF.
+    sample_size : int
+        Maximum number of rows to use for plotting. If df has more rows,
+        a stratified sample (by DECOY) will be taken to reduce memory usage.
     """
     # Get all columns that contain feature scores (VAR_ columns or columns with _VAR_ in name)
     score_cols = [col for col in df.columns if "VAR_" in col.upper() and col != "DECOY"]
@@ -442,9 +530,39 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
         logger.warning(f"No DECOY column found for {level} level, skipping")
         return
     
-    # Drop rows with null DECOY values
+    # Only select the columns we need for plotting
     plot_df = df[score_cols + ["DECOY"]].dropna(subset=["DECOY"]).copy()
     
+    # Memory optimization: Sample data if it's too large
+    if len(plot_df) > sample_size:
+        logger.info(f"Dataset has {len(plot_df)} rows, sampling {sample_size} rows (stratified by DECOY) to reduce memory usage")
+        # Stratified sampling to maintain target/decoy ratio
+        target_df = plot_df[plot_df["DECOY"] == 0]
+        decoy_df = plot_df[plot_df["DECOY"] == 1]
+        
+        # Calculate sample sizes proportional to original distribution
+        n_targets = len(target_df)
+        n_decoys = len(decoy_df)
+        total = n_targets + n_decoys
+        
+        target_sample_size = int(sample_size * n_targets / total)
+        decoy_sample_size = int(sample_size * n_decoys / total)
+        
+        # Sample from each group
+        if n_targets > target_sample_size:
+            target_sample = target_df.sample(n=target_sample_size, random_state=42)
+        else:
+            target_sample = target_df
+            
+        if n_decoys > decoy_sample_size:
+            decoy_sample = decoy_df.sample(n=decoy_sample_size, random_state=42)
+        else:
+            decoy_sample = decoy_df
+        
+        # Combine samples
+        plot_df = pd.concat([target_sample, decoy_sample], ignore_index=True)
+        logger.info(f"Sampled {len(plot_df)} rows ({len(target_sample)} targets, {len(decoy_sample)} decoys)")
+    
     # Ensure DECOY is 0 or 1
     if plot_df["DECOY"].dtype == bool:
         plot_df["DECOY"] = plot_df["DECOY"].astype(int)

From f10c53495a86a8ea172ddffcfeed69794bd13ac8 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 02:37:05 +0000
Subject: [PATCH 08/26] Refactor plot_scores to make SCORE column optional

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py |  6 ------
 pyprophet/report.py               | 11 ++++++-----
 2 files changed, 6 insertions(+), 11 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index 31e0386a..119e846b 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -587,12 +587,6 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
     if rename_dict:
         plot_df.rename(columns=rename_dict, inplace=True)
     
-    # plot_scores requires a "SCORE" column - use the first VAR_ column as SCORE
-    var_cols = [col for col in plot_df.columns if col.startswith("VAR_")]
-    if var_cols and "SCORE" not in plot_df.columns:
-        # Add SCORE column as a copy of the first VAR_ column
-        plot_df["SCORE"] = plot_df[var_cols[0]]
-    
     # Call plot_scores with the formatted dataframe
     plot_scores(plot_df, temp_outfile)
     
diff --git a/pyprophet/report.py b/pyprophet/report.py
index ecd2534e..681e75a0 100644
--- a/pyprophet/report.py
+++ b/pyprophet/report.py
@@ -853,11 +853,12 @@ def plot_scores(df, out, color_palette="normal"):
             "Error: The matplotlib package is required to create a report."
         )
 
-    score_columns = (
-        ["SCORE"]
-        + [c for c in df.columns if c.startswith("MAIN_VAR_")]
-        + [c for c in df.columns if c.startswith("VAR_")]
-    )
+    # Build score_columns list, only including SCORE if it exists
+    score_columns = []
+    if "SCORE" in df.columns:
+        score_columns.append("SCORE")
+    score_columns += [c for c in df.columns if c.startswith("MAIN_VAR_")]
+    score_columns += [c for c in df.columns if c.startswith("VAR_")]
 
     t_col, d_col = color_blind_friendly(color_palette)
 

From 7c2e4718d96425da514bbd619f1c2db4015dcebd Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 02:48:19 +0000
Subject: [PATCH 09/26] Refactor: move file type reader methods into IO export
 classes

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py    | 309 ++-------------------------
 pyprophet/io/export/osw.py           | 121 +++++++++++
 pyprophet/io/export/parquet.py       |  69 ++++++
 pyprophet/io/export/split_parquet.py | 101 +++++++++
 4 files changed, 304 insertions(+), 296 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index 119e846b..e733dba8 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -196,306 +196,23 @@ def export_feature_scores(infile: str, outfile: str = None):
     
     logger.info(f"Output file: {outfile}")
     
-    # Export feature scores based on file type
-    if file_type == "osw":
-        _export_feature_scores_osw(infile, outfile)
-    elif file_type == "parquet":
-        _export_feature_scores_parquet(infile, outfile)
-    else:  # parquet_split
-        _export_feature_scores_split_parquet(infile, outfile)
-    
-    logger.info(f"Feature score plots exported to {outfile}")
-
-
-def _export_feature_scores_osw(infile: str, outfile: str):
-    """
-    Export feature scores from an OSW file.
-    
-    Parameters
-    ----------
-    infile : str
-        Path to the OSW input file.
-    outfile : str
-        Path to the output PDF file.
-    """
-    con = sqlite3.connect(infile)
-    
-    try:
-        # Process MS1 level if available
-        if check_sqlite_table(con, "FEATURE_MS1"):
-            logger.info("Processing MS1 level feature scores")
-            # Get only VAR_ columns to reduce memory usage
-            cursor = con.cursor()
-            cursor.execute("PRAGMA table_info(FEATURE_MS1)")
-            all_cols = [row[1] for row in cursor.fetchall()]
-            var_cols = [col for col in all_cols if "VAR_" in col.upper()]
-            
-            if var_cols:
-                var_cols_sql = ", ".join([f"FEATURE_MS1.{col}" for col in var_cols])
-                ms1_query = f"""
-                    SELECT 
-                        {var_cols_sql},
-                        PRECURSOR.DECOY
-                    FROM FEATURE_MS1
-                    INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
-                    INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-                """
-                df_ms1 = pd.read_sql_query(ms1_query, con)
-                if not df_ms1.empty:
-                    _plot_feature_scores(df_ms1, outfile, "ms1", append=False)
-            else:
-                logger.warning("No VAR_ columns found in FEATURE_MS1 table")
-        
-        # Process MS2 level if available
-        if check_sqlite_table(con, "FEATURE_MS2"):
-            logger.info("Processing MS2 level feature scores")
-            # Get only VAR_ columns to reduce memory usage
-            cursor = con.cursor()
-            cursor.execute("PRAGMA table_info(FEATURE_MS2)")
-            all_cols = [row[1] for row in cursor.fetchall()]
-            var_cols = [col for col in all_cols if "VAR_" in col.upper()]
-            
-            if var_cols:
-                var_cols_sql = ", ".join([f"FEATURE_MS2.{col}" for col in var_cols])
-                ms2_query = f"""
-                    SELECT 
-                        {var_cols_sql},
-                        PRECURSOR.DECOY
-                    FROM FEATURE_MS2
-                    INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
-                    INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-                """
-                df_ms2 = pd.read_sql_query(ms2_query, con)
-                if not df_ms2.empty:
-                    append = check_sqlite_table(con, "FEATURE_MS1")
-                    _plot_feature_scores(df_ms2, outfile, "ms2", append=append)
-            else:
-                logger.warning("No VAR_ columns found in FEATURE_MS2 table")
-        
-        # Process transition level if available
-        if check_sqlite_table(con, "FEATURE_TRANSITION"):
-            logger.info("Processing transition level feature scores")
-            # Get only VAR_ columns to reduce memory usage
-            cursor = con.cursor()
-            cursor.execute("PRAGMA table_info(FEATURE_TRANSITION)")
-            all_cols = [row[1] for row in cursor.fetchall()]
-            var_cols = [col for col in all_cols if "VAR_" in col.upper()]
-            
-            if var_cols:
-                var_cols_sql = ", ".join([f"FEATURE_TRANSITION.{col}" for col in var_cols])
-                transition_query = f"""
-                    SELECT 
-                        {var_cols_sql},
-                        TRANSITION.DECOY
-                    FROM FEATURE_TRANSITION
-                    INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
-                    INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
-                """
-                df_transition = pd.read_sql_query(transition_query, con)
-                if not df_transition.empty:
-                    append = check_sqlite_table(con, "FEATURE_MS1") or check_sqlite_table(con, "FEATURE_MS2")
-                    _plot_feature_scores(df_transition, outfile, "transition", append=append)
-            else:
-                logger.warning("No VAR_ columns found in FEATURE_TRANSITION table")
-        
-        # Process alignment level if available
-        if check_sqlite_table(con, "FEATURE_MS2_ALIGNMENT"):
-            logger.info("Processing alignment level feature scores")
-            # Get only VAR_ columns to reduce memory usage
-            cursor = con.cursor()
-            cursor.execute("PRAGMA table_info(FEATURE_MS2_ALIGNMENT)")
-            all_cols = [row[1] for row in cursor.fetchall()]
-            var_cols = [col for col in all_cols if "VAR_" in col.upper()]
-            
-            if var_cols:
-                var_cols_sql = ", ".join(var_cols)
-                alignment_query = f"""
-                    SELECT 
-                        {var_cols_sql},
-                        LABEL AS DECOY
-                    FROM FEATURE_MS2_ALIGNMENT
-                """
-                df_alignment = pd.read_sql_query(alignment_query, con)
-                if not df_alignment.empty:
-                    append = (check_sqlite_table(con, "FEATURE_MS1") or 
-                             check_sqlite_table(con, "FEATURE_MS2") or 
-                             check_sqlite_table(con, "FEATURE_TRANSITION"))
-                    _plot_feature_scores(df_alignment, outfile, "alignment", append=append)
-            else:
-                logger.warning("No VAR_ columns found in FEATURE_MS2_ALIGNMENT table")
-    
-    finally:
-        con.close()
-
-
-def _export_feature_scores_parquet(infile: str, outfile: str):
-    """
-    Export feature scores from a Parquet file.
-    
-    Parameters
-    ----------
-    infile : str
-        Path to the Parquet input file.
-    outfile : str
-        Path to the output PDF file.
-    """
-    logger.info(f"Reading parquet file: {infile}")
-    # First, read only column names to identify what to load
-    import pyarrow.parquet as pq
-    parquet_file = pq.ParquetFile(infile)
-    all_columns = parquet_file.schema.names
-    
-    # Identify columns to read for each level
-    ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
-    ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
-    transition_cols = [col for col in all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
-    
-    # Determine which columns to read (only what we need)
-    cols_to_read = set()
-    if ms1_cols and "PRECURSOR_DECOY" in all_columns:
-        cols_to_read.update(ms1_cols)
-        cols_to_read.add("PRECURSOR_DECOY")
-    if ms2_cols and "PRECURSOR_DECOY" in all_columns:
-        cols_to_read.update(ms2_cols)
-        cols_to_read.add("PRECURSOR_DECOY")
-    if transition_cols and "TRANSITION_DECOY" in all_columns:
-        cols_to_read.update(transition_cols)
-        cols_to_read.add("TRANSITION_DECOY")
-    
-    if not cols_to_read:
-        logger.warning("No VAR_ columns found in parquet file")
-        return
-    
-    # Read only the columns we need
-    logger.info(f"Reading {len(cols_to_read)} columns from parquet file")
-    df = pd.read_parquet(infile, columns=list(cols_to_read))
+    # Create config and reader based on file type
+    config = ExportIOConfig(
+        infile=infile,
+        outfile=outfile,
+        subsample_ratio=1.0,
+        level="export",
+        context="export_feature_scores",
+    )
     
-    # Process MS1 level
-    if ms1_cols and "PRECURSOR_DECOY" in df.columns:
-        logger.info("Processing MS1 level feature scores")
-        ms1_df = df[ms1_cols + ["PRECURSOR_DECOY"]].copy()
-        ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
-        _plot_feature_scores(ms1_df, outfile, "ms1", append=False)
-        del ms1_df  # Free memory
+    # Get appropriate reader
+    reader = ReaderDispatcher.get_reader(config)
     
-    # Process MS2 level
-    if ms2_cols and "PRECURSOR_DECOY" in df.columns:
-        logger.info("Processing MS2 level feature scores")
-        ms2_df = df[ms2_cols + ["PRECURSOR_DECOY"]].copy()
-        ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
-        append = bool(ms1_cols)
-        _plot_feature_scores(ms2_df, outfile, "ms2", append=append)
-        del ms2_df  # Free memory
+    # Export feature scores using the reader's method
+    reader.export_feature_scores(outfile, _plot_feature_scores)
     
-    # Process transition level
-    if transition_cols and "TRANSITION_DECOY" in df.columns:
-        logger.info("Processing transition level feature scores")
-        transition_df = df[transition_cols + ["TRANSITION_DECOY"]].copy()
-        transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
-        append = bool(ms1_cols or ms2_cols)
-        _plot_feature_scores(transition_df, outfile, "transition", append=append)
-        del transition_df  # Free memory
-
+    logger.info(f"Feature score plots exported to {outfile}")
 
-def _export_feature_scores_split_parquet(infile: str, outfile: str):
-    """
-    Export feature scores from a split Parquet directory.
-    
-    Parameters
-    ----------
-    infile : str
-        Path to the split Parquet directory.
-    outfile : str
-        Path to the output PDF file.
-    """
-    # Read precursor features - only necessary columns
-    precursor_file = os.path.join(infile, "precursors_features.parquet")
-    logger.info(f"Reading precursor features from: {precursor_file}")
-    
-    # First check what columns are available
-    import pyarrow.parquet as pq
-    precursor_parquet = pq.ParquetFile(precursor_file)
-    all_columns = precursor_parquet.schema.names
-    
-    # Identify columns to read
-    ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
-    ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
-    
-    cols_to_read = set()
-    if ms1_cols and "PRECURSOR_DECOY" in all_columns:
-        cols_to_read.update(ms1_cols)
-        cols_to_read.add("PRECURSOR_DECOY")
-    if ms2_cols and "PRECURSOR_DECOY" in all_columns:
-        cols_to_read.update(ms2_cols)
-        cols_to_read.add("PRECURSOR_DECOY")
-    
-    if cols_to_read:
-        logger.info(f"Reading {len(cols_to_read)} columns from precursor features")
-        df_precursor = pd.read_parquet(precursor_file, columns=list(cols_to_read))
-        
-        # Process MS1 level
-        if ms1_cols and "PRECURSOR_DECOY" in df_precursor.columns:
-            logger.info("Processing MS1 level feature scores")
-            ms1_df = df_precursor[ms1_cols + ["PRECURSOR_DECOY"]].copy()
-            ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
-            _plot_feature_scores(ms1_df, outfile, "ms1", append=False)
-            del ms1_df  # Free memory
-        
-        # Process MS2 level
-        if ms2_cols and "PRECURSOR_DECOY" in df_precursor.columns:
-            logger.info("Processing MS2 level feature scores")
-            ms2_df = df_precursor[ms2_cols + ["PRECURSOR_DECOY"]].copy()
-            ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
-            append = bool(ms1_cols)
-            _plot_feature_scores(ms2_df, outfile, "ms2", append=append)
-            del ms2_df  # Free memory
-        
-        del df_precursor  # Free memory
-    
-    # Read transition features if available
-    transition_file = os.path.join(infile, "transition_features.parquet")
-    if os.path.exists(transition_file):
-        logger.info(f"Reading transition features from: {transition_file}")
-        
-        # Check what columns are available
-        transition_parquet = pq.ParquetFile(transition_file)
-        transition_all_columns = transition_parquet.schema.names
-        transition_cols = [col for col in transition_all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
-        
-        if transition_cols and "TRANSITION_DECOY" in transition_all_columns:
-            # Read only necessary columns
-            cols_to_read = transition_cols + ["TRANSITION_DECOY"]
-            logger.info(f"Reading {len(cols_to_read)} columns from transition features")
-            df_transition = pd.read_parquet(transition_file, columns=cols_to_read)
-            
-            logger.info("Processing transition level feature scores")
-            transition_df = df_transition[transition_cols + ["TRANSITION_DECOY"]].copy()
-            transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
-            append = bool(ms1_cols or ms2_cols)
-            _plot_feature_scores(transition_df, outfile, "transition", append=append)
-            del transition_df, df_transition  # Free memory
-    
-    # Read alignment features if available
-    alignment_file = os.path.join(infile, "feature_alignment.parquet")
-    if os.path.exists(alignment_file):
-        logger.info(f"Reading alignment features from: {alignment_file}")
-        
-        # Check what columns are available
-        alignment_parquet = pq.ParquetFile(alignment_file)
-        alignment_all_columns = alignment_parquet.schema.names
-        var_cols = [col for col in alignment_all_columns if col.startswith("VAR_")]
-        
-        if var_cols and "DECOY" in alignment_all_columns:
-            # Read only necessary columns
-            cols_to_read = var_cols + ["DECOY"]
-            logger.info(f"Reading {len(cols_to_read)} columns from alignment features")
-            df_alignment = pd.read_parquet(alignment_file, columns=cols_to_read)
-            
-            logger.info("Processing alignment level feature scores")
-            alignment_df = df_alignment[var_cols + ["DECOY"]].copy()
-            append = bool(ms1_cols or ms2_cols or (os.path.exists(transition_file) and transition_cols))
-            _plot_feature_scores(alignment_df, outfile, "alignment", append=append)
-            del alignment_df, df_alignment  # Free memory
 
 
 def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: bool = False, sample_size: int = 100000):
diff --git a/pyprophet/io/export/osw.py b/pyprophet/io/export/osw.py
index 4e3ef6ab..f886cd0d 100644
--- a/pyprophet/io/export/osw.py
+++ b/pyprophet/io/export/osw.py
@@ -860,6 +860,127 @@ def _get_peptide_protein_score_table_sqlite(self, con, level: str) -> str:
 
         return f"{view_name} AS ({merged})"
 
+    def export_feature_scores(self, outfile: str, plot_callback):
+        """
+        Export feature scores from OSW file for plotting.
+        
+        Parameters
+        ----------
+        outfile : str
+            Path to the output PDF file.
+        plot_callback : callable
+            Function to call for plotting each level's data.
+            Signature: plot_callback(df, outfile, level, append)
+        """
+        con = sqlite3.connect(self.infile)
+        
+        try:
+            # Process MS1 level if available
+            if check_sqlite_table(con, "FEATURE_MS1"):
+                logger.info("Processing MS1 level feature scores")
+                # Get only VAR_ columns to reduce memory usage
+                cursor = con.cursor()
+                cursor.execute("PRAGMA table_info(FEATURE_MS1)")
+                all_cols = [row[1] for row in cursor.fetchall()]
+                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                
+                if var_cols:
+                    var_cols_sql = ", ".join([f"FEATURE_MS1.{col}" for col in var_cols])
+                    ms1_query = f"""
+                        SELECT 
+                            {var_cols_sql},
+                            PRECURSOR.DECOY
+                        FROM FEATURE_MS1
+                        INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
+                        INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                    """
+                    df_ms1 = pd.read_sql_query(ms1_query, con)
+                    if not df_ms1.empty:
+                        plot_callback(df_ms1, outfile, "ms1", append=False)
+                else:
+                    logger.warning("No VAR_ columns found in FEATURE_MS1 table")
+            
+            # Process MS2 level if available
+            if check_sqlite_table(con, "FEATURE_MS2"):
+                logger.info("Processing MS2 level feature scores")
+                # Get only VAR_ columns to reduce memory usage
+                cursor = con.cursor()
+                cursor.execute("PRAGMA table_info(FEATURE_MS2)")
+                all_cols = [row[1] for row in cursor.fetchall()]
+                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                
+                if var_cols:
+                    var_cols_sql = ", ".join([f"FEATURE_MS2.{col}" for col in var_cols])
+                    ms2_query = f"""
+                        SELECT 
+                            {var_cols_sql},
+                            PRECURSOR.DECOY
+                        FROM FEATURE_MS2
+                        INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
+                        INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                    """
+                    df_ms2 = pd.read_sql_query(ms2_query, con)
+                    if not df_ms2.empty:
+                        append = check_sqlite_table(con, "FEATURE_MS1")
+                        plot_callback(df_ms2, outfile, "ms2", append=append)
+                else:
+                    logger.warning("No VAR_ columns found in FEATURE_MS2 table")
+            
+            # Process transition level if available
+            if check_sqlite_table(con, "FEATURE_TRANSITION"):
+                logger.info("Processing transition level feature scores")
+                # Get only VAR_ columns to reduce memory usage
+                cursor = con.cursor()
+                cursor.execute("PRAGMA table_info(FEATURE_TRANSITION)")
+                all_cols = [row[1] for row in cursor.fetchall()]
+                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                
+                if var_cols:
+                    var_cols_sql = ", ".join([f"FEATURE_TRANSITION.{col}" for col in var_cols])
+                    transition_query = f"""
+                        SELECT 
+                            {var_cols_sql},
+                            TRANSITION.DECOY
+                        FROM FEATURE_TRANSITION
+                        INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
+                        INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
+                    """
+                    df_transition = pd.read_sql_query(transition_query, con)
+                    if not df_transition.empty:
+                        append = check_sqlite_table(con, "FEATURE_MS1") or check_sqlite_table(con, "FEATURE_MS2")
+                        plot_callback(df_transition, outfile, "transition", append=append)
+                else:
+                    logger.warning("No VAR_ columns found in FEATURE_TRANSITION table")
+            
+            # Process alignment level if available
+            if check_sqlite_table(con, "FEATURE_MS2_ALIGNMENT"):
+                logger.info("Processing alignment level feature scores")
+                # Get only VAR_ columns to reduce memory usage
+                cursor = con.cursor()
+                cursor.execute("PRAGMA table_info(FEATURE_MS2_ALIGNMENT)")
+                all_cols = [row[1] for row in cursor.fetchall()]
+                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                
+                if var_cols:
+                    var_cols_sql = ", ".join(var_cols)
+                    alignment_query = f"""
+                        SELECT 
+                            {var_cols_sql},
+                            LABEL AS DECOY
+                        FROM FEATURE_MS2_ALIGNMENT
+                    """
+                    df_alignment = pd.read_sql_query(alignment_query, con)
+                    if not df_alignment.empty:
+                        append = (check_sqlite_table(con, "FEATURE_MS1") or 
+                                 check_sqlite_table(con, "FEATURE_MS2") or 
+                                 check_sqlite_table(con, "FEATURE_TRANSITION"))
+                        plot_callback(df_alignment, outfile, "alignment", append=append)
+                else:
+                    logger.warning("No VAR_ columns found in FEATURE_MS2_ALIGNMENT table")
+        
+        finally:
+            con.close()
+
 
 class OSWWriter(BaseOSWWriter):
     """
diff --git a/pyprophet/io/export/parquet.py b/pyprophet/io/export/parquet.py
index f9cc2e19..b9f6f6a8 100644
--- a/pyprophet/io/export/parquet.py
+++ b/pyprophet/io/export/parquet.py
@@ -601,6 +601,75 @@ def _read_for_export_scored_report(self, con) -> pd.DataFrame:
 
         return df
 
+    def export_feature_scores(self, outfile: str, plot_callback):
+        """
+        Export feature scores from Parquet file for plotting.
+        
+        Parameters
+        ----------
+        outfile : str
+            Path to the output PDF file.
+        plot_callback : callable
+            Function to call for plotting each level's data.
+            Signature: plot_callback(df, outfile, level, append)
+        """
+        logger.info(f"Reading parquet file: {self.infile}")
+        # First, read only column names to identify what to load
+        import pyarrow.parquet as pq
+        parquet_file = pq.ParquetFile(self.infile)
+        all_columns = parquet_file.schema.names
+        
+        # Identify columns to read for each level
+        ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
+        ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
+        transition_cols = [col for col in all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
+        
+        # Determine which columns to read (only what we need)
+        cols_to_read = set()
+        if ms1_cols and "PRECURSOR_DECOY" in all_columns:
+            cols_to_read.update(ms1_cols)
+            cols_to_read.add("PRECURSOR_DECOY")
+        if ms2_cols and "PRECURSOR_DECOY" in all_columns:
+            cols_to_read.update(ms2_cols)
+            cols_to_read.add("PRECURSOR_DECOY")
+        if transition_cols and "TRANSITION_DECOY" in all_columns:
+            cols_to_read.update(transition_cols)
+            cols_to_read.add("TRANSITION_DECOY")
+        
+        if not cols_to_read:
+            logger.warning("No VAR_ columns found in parquet file")
+            return
+        
+        # Read only the columns we need
+        logger.info(f"Reading {len(cols_to_read)} columns from parquet file")
+        df = pd.read_parquet(self.infile, columns=list(cols_to_read))
+        
+        # Process MS1 level
+        if ms1_cols and "PRECURSOR_DECOY" in df.columns:
+            logger.info("Processing MS1 level feature scores")
+            ms1_df = df[ms1_cols + ["PRECURSOR_DECOY"]].copy()
+            ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+            plot_callback(ms1_df, outfile, "ms1", append=False)
+            del ms1_df  # Free memory
+        
+        # Process MS2 level
+        if ms2_cols and "PRECURSOR_DECOY" in df.columns:
+            logger.info("Processing MS2 level feature scores")
+            ms2_df = df[ms2_cols + ["PRECURSOR_DECOY"]].copy()
+            ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+            append = bool(ms1_cols)
+            plot_callback(ms2_df, outfile, "ms2", append=append)
+            del ms2_df  # Free memory
+        
+        # Process transition level
+        if transition_cols and "TRANSITION_DECOY" in df.columns:
+            logger.info("Processing transition level feature scores")
+            transition_df = df[transition_cols + ["TRANSITION_DECOY"]].copy()
+            transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
+            append = bool(ms1_cols or ms2_cols)
+            plot_callback(transition_df, outfile, "transition", append=append)
+            del transition_df  # Free memory
+
 
 class ParquetWriter(BaseParquetWriter):
     """
diff --git a/pyprophet/io/export/split_parquet.py b/pyprophet/io/export/split_parquet.py
index 265130a8..3ebb4467 100644
--- a/pyprophet/io/export/split_parquet.py
+++ b/pyprophet/io/export/split_parquet.py
@@ -666,6 +666,107 @@ def _build_feature_vars_sql(self) -> str:
 
         return ", " + ", ".join(feature_vars) if feature_vars else ""
 
+    def export_feature_scores(self, outfile: str, plot_callback):
+        """
+        Export feature scores from split Parquet directory for plotting.
+        
+        Parameters
+        ----------
+        outfile : str
+            Path to the output PDF file.
+        plot_callback : callable
+            Function to call for plotting each level's data.
+            Signature: plot_callback(df, outfile, level, append)
+        """
+        # Read precursor features - only necessary columns
+        precursor_file = os.path.join(self.infile, "precursors_features.parquet")
+        logger.info(f"Reading precursor features from: {precursor_file}")
+        
+        # First check what columns are available
+        import pyarrow.parquet as pq
+        precursor_parquet = pq.ParquetFile(precursor_file)
+        all_columns = precursor_parquet.schema.names
+        
+        # Identify columns to read
+        ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
+        ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
+        
+        cols_to_read = set()
+        if ms1_cols and "PRECURSOR_DECOY" in all_columns:
+            cols_to_read.update(ms1_cols)
+            cols_to_read.add("PRECURSOR_DECOY")
+        if ms2_cols and "PRECURSOR_DECOY" in all_columns:
+            cols_to_read.update(ms2_cols)
+            cols_to_read.add("PRECURSOR_DECOY")
+        
+        if cols_to_read:
+            logger.info(f"Reading {len(cols_to_read)} columns from precursor features")
+            df_precursor = pd.read_parquet(precursor_file, columns=list(cols_to_read))
+            
+            # Process MS1 level
+            if ms1_cols and "PRECURSOR_DECOY" in df_precursor.columns:
+                logger.info("Processing MS1 level feature scores")
+                ms1_df = df_precursor[ms1_cols + ["PRECURSOR_DECOY"]].copy()
+                ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+                plot_callback(ms1_df, outfile, "ms1", append=False)
+                del ms1_df  # Free memory
+            
+            # Process MS2 level
+            if ms2_cols and "PRECURSOR_DECOY" in df_precursor.columns:
+                logger.info("Processing MS2 level feature scores")
+                ms2_df = df_precursor[ms2_cols + ["PRECURSOR_DECOY"]].copy()
+                ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+                append = bool(ms1_cols)
+                plot_callback(ms2_df, outfile, "ms2", append=append)
+                del ms2_df  # Free memory
+            
+            del df_precursor  # Free memory
+        
+        # Read transition features if available
+        transition_file = os.path.join(self.infile, "transition_features.parquet")
+        if os.path.exists(transition_file):
+            logger.info(f"Reading transition features from: {transition_file}")
+            
+            # Check what columns are available
+            transition_parquet = pq.ParquetFile(transition_file)
+            transition_all_columns = transition_parquet.schema.names
+            transition_cols = [col for col in transition_all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
+            
+            if transition_cols and "TRANSITION_DECOY" in transition_all_columns:
+                # Read only necessary columns
+                cols_to_read = transition_cols + ["TRANSITION_DECOY"]
+                logger.info(f"Reading {len(cols_to_read)} columns from transition features")
+                df_transition = pd.read_parquet(transition_file, columns=cols_to_read)
+                
+                logger.info("Processing transition level feature scores")
+                transition_df = df_transition[transition_cols + ["TRANSITION_DECOY"]].copy()
+                transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
+                append = bool(ms1_cols or ms2_cols)
+                plot_callback(transition_df, outfile, "transition", append=append)
+                del transition_df, df_transition  # Free memory
+        
+        # Read alignment features if available
+        alignment_file = os.path.join(self.infile, "feature_alignment.parquet")
+        if os.path.exists(alignment_file):
+            logger.info(f"Reading alignment features from: {alignment_file}")
+            
+            # Check what columns are available
+            alignment_parquet = pq.ParquetFile(alignment_file)
+            alignment_all_columns = alignment_parquet.schema.names
+            var_cols = [col for col in alignment_all_columns if col.startswith("VAR_")]
+            
+            if var_cols and "DECOY" in alignment_all_columns:
+                # Read only necessary columns
+                cols_to_read = var_cols + ["DECOY"]
+                logger.info(f"Reading {len(cols_to_read)} columns from alignment features")
+                df_alignment = pd.read_parquet(alignment_file, columns=cols_to_read)
+                
+                logger.info("Processing alignment level feature scores")
+                alignment_df = df_alignment[var_cols + ["DECOY"]].copy()
+                append = bool(ms1_cols or ms2_cols or (os.path.exists(transition_file) and transition_cols))
+                plot_callback(alignment_df, outfile, "alignment", append=append)
+                del alignment_df, df_alignment  # Free memory
+
 
 class SplitParquetWriter(BaseSplitParquetWriter):
     """

From f9f2b3b6281e5c2043a68ecef9c0207ab6357c56 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 03:58:40 +0000
Subject: [PATCH 10/26] Fix CI: Move pyarrow imports to module level

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/io/export/parquet.py       | 2 +-
 pyprophet/io/export/split_parquet.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyprophet/io/export/parquet.py b/pyprophet/io/export/parquet.py
index b9f6f6a8..1949c822 100644
--- a/pyprophet/io/export/parquet.py
+++ b/pyprophet/io/export/parquet.py
@@ -1,5 +1,6 @@
 import duckdb
 import pandas as pd
+import pyarrow.parquet as pq
 from loguru import logger
 
 from ..._config import ExportIOConfig
@@ -615,7 +616,6 @@ def export_feature_scores(self, outfile: str, plot_callback):
         """
         logger.info(f"Reading parquet file: {self.infile}")
         # First, read only column names to identify what to load
-        import pyarrow.parquet as pq
         parquet_file = pq.ParquetFile(self.infile)
         all_columns = parquet_file.schema.names
         
diff --git a/pyprophet/io/export/split_parquet.py b/pyprophet/io/export/split_parquet.py
index 3ebb4467..1981cbb9 100644
--- a/pyprophet/io/export/split_parquet.py
+++ b/pyprophet/io/export/split_parquet.py
@@ -2,6 +2,7 @@
 import glob
 import pandas as pd
 import duckdb
+import pyarrow.parquet as pq
 from loguru import logger
 
 from ..util import get_parquet_column_names
@@ -683,7 +684,6 @@ def export_feature_scores(self, outfile: str, plot_callback):
         logger.info(f"Reading precursor features from: {precursor_file}")
         
         # First check what columns are available
-        import pyarrow.parquet as pq
         precursor_parquet = pq.ParquetFile(precursor_file)
         all_columns = precursor_parquet.schema.names
         

From e11e24c0f40f6e7419403d4b4aba4c7169bfbe8f Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 04:09:43 +0000
Subject: [PATCH 11/26] Use _ensure_pyarrow for lazy pyarrow import instead of
 module-level import

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/io/export/parquet.py       |  8 +++++---
 pyprophet/io/export/split_parquet.py | 12 +++++++-----
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/pyprophet/io/export/parquet.py b/pyprophet/io/export/parquet.py
index 1949c822..ef523839 100644
--- a/pyprophet/io/export/parquet.py
+++ b/pyprophet/io/export/parquet.py
@@ -1,11 +1,10 @@
 import duckdb
 import pandas as pd
-import pyarrow.parquet as pq
 from loguru import logger
 
 from ..._config import ExportIOConfig
 from .._base import BaseParquetReader, BaseParquetWriter
-from ..util import get_parquet_column_names
+from ..util import get_parquet_column_names, _ensure_pyarrow
 
 
 class ParquetReader(BaseParquetReader):
@@ -615,8 +614,11 @@ def export_feature_scores(self, outfile: str, plot_callback):
             Signature: plot_callback(df, outfile, level, append)
         """
         logger.info(f"Reading parquet file: {self.infile}")
+        # Ensure pyarrow is available
+        pa, _, _ = _ensure_pyarrow()
+        
         # First, read only column names to identify what to load
-        parquet_file = pq.ParquetFile(self.infile)
+        parquet_file = pa.parquet.ParquetFile(self.infile)
         all_columns = parquet_file.schema.names
         
         # Identify columns to read for each level
diff --git a/pyprophet/io/export/split_parquet.py b/pyprophet/io/export/split_parquet.py
index 1981cbb9..60cf431a 100644
--- a/pyprophet/io/export/split_parquet.py
+++ b/pyprophet/io/export/split_parquet.py
@@ -2,10 +2,9 @@
 import glob
 import pandas as pd
 import duckdb
-import pyarrow.parquet as pq
 from loguru import logger
 
-from ..util import get_parquet_column_names
+from ..util import get_parquet_column_names, _ensure_pyarrow
 from .._base import BaseSplitParquetReader, BaseSplitParquetWriter
 from ..._config import ExportIOConfig
 
@@ -679,12 +678,15 @@ def export_feature_scores(self, outfile: str, plot_callback):
             Function to call for plotting each level's data.
             Signature: plot_callback(df, outfile, level, append)
         """
+        # Ensure pyarrow is available
+        pa, _, _ = _ensure_pyarrow()
+        
         # Read precursor features - only necessary columns
         precursor_file = os.path.join(self.infile, "precursors_features.parquet")
         logger.info(f"Reading precursor features from: {precursor_file}")
         
         # First check what columns are available
-        precursor_parquet = pq.ParquetFile(precursor_file)
+        precursor_parquet = pa.parquet.ParquetFile(precursor_file)
         all_columns = precursor_parquet.schema.names
         
         # Identify columns to read
@@ -728,7 +730,7 @@ def export_feature_scores(self, outfile: str, plot_callback):
             logger.info(f"Reading transition features from: {transition_file}")
             
             # Check what columns are available
-            transition_parquet = pq.ParquetFile(transition_file)
+            transition_parquet = pa.parquet.ParquetFile(transition_file)
             transition_all_columns = transition_parquet.schema.names
             transition_cols = [col for col in transition_all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
             
@@ -751,7 +753,7 @@ def export_feature_scores(self, outfile: str, plot_callback):
             logger.info(f"Reading alignment features from: {alignment_file}")
             
             # Check what columns are available
-            alignment_parquet = pq.ParquetFile(alignment_file)
+            alignment_parquet = pa.parquet.ParquetFile(alignment_file)
             alignment_all_columns = alignment_parquet.schema.names
             var_cols = [col for col in alignment_all_columns if col.startswith("VAR_")]
             

From 0578aa4857b321afa4769ee070612aa7e9a469ac Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 05:36:11 +0000
Subject: [PATCH 12/26] Add robustness checks for empty dataframes and edge
 cases in _plot_feature_scores

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py | 39 +++++++++++++++++++++----------
 1 file changed, 27 insertions(+), 12 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index e733dba8..96d38302 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -250,6 +250,11 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
     # Only select the columns we need for plotting
     plot_df = df[score_cols + ["DECOY"]].dropna(subset=["DECOY"]).copy()
     
+    # Check if we have any data left after dropping NAs
+    if len(plot_df) == 0:
+        logger.warning(f"No valid data rows found for {level} level after removing rows with null DECOY values")
+        return
+    
     # Memory optimization: Sample data if it's too large
     if len(plot_df) > sample_size:
         logger.info(f"Dataset has {len(plot_df)} rows, sampling {sample_size} rows (stratified by DECOY) to reduce memory usage")
@@ -262,23 +267,33 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
         n_decoys = len(decoy_df)
         total = n_targets + n_decoys
         
-        target_sample_size = int(sample_size * n_targets / total)
-        decoy_sample_size = int(sample_size * n_decoys / total)
+        # Handle edge cases where one group might be empty
+        if total == 0:
+            logger.warning(f"No data with valid DECOY values for {level} level")
+            return
+        
+        target_sample_size = int(sample_size * n_targets / total) if n_targets > 0 else 0
+        decoy_sample_size = int(sample_size * n_decoys / total) if n_decoys > 0 else 0
         
         # Sample from each group
-        if n_targets > target_sample_size:
-            target_sample = target_df.sample(n=target_sample_size, random_state=42)
-        else:
-            target_sample = target_df
+        samples = []
+        if n_targets > 0:
+            if n_targets > target_sample_size and target_sample_size > 0:
+                target_sample = target_df.sample(n=target_sample_size, random_state=42)
+            else:
+                target_sample = target_df
+            samples.append(target_sample)
             
-        if n_decoys > decoy_sample_size:
-            decoy_sample = decoy_df.sample(n=decoy_sample_size, random_state=42)
-        else:
-            decoy_sample = decoy_df
+        if n_decoys > 0:
+            if n_decoys > decoy_sample_size and decoy_sample_size > 0:
+                decoy_sample = decoy_df.sample(n=decoy_sample_size, random_state=42)
+            else:
+                decoy_sample = decoy_df
+            samples.append(decoy_sample)
         
         # Combine samples
-        plot_df = pd.concat([target_sample, decoy_sample], ignore_index=True)
-        logger.info(f"Sampled {len(plot_df)} rows ({len(target_sample)} targets, {len(decoy_sample)} decoys)")
+        plot_df = pd.concat(samples, ignore_index=True)
+        logger.info(f"Sampled {len(plot_df)} rows ({len(samples[0]) if len(samples) > 0 and n_targets > 0 else 0} targets, {len(samples[-1]) if len(samples) > 0 and n_decoys > 0 else 0} decoys)")
     
     # Ensure DECOY is 0 or 1
     if plot_df["DECOY"].dtype == bool:

From 140c2b0df63a04482c3a04f0f0f365ed7d835ff0 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 06:07:34 +0000
Subject: [PATCH 13/26] Add check for temp file existence before PDF merging

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index 96d38302..4464dbc2 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -322,6 +322,11 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
     # Call plot_scores with the formatted dataframe
     plot_scores(plot_df, temp_outfile)
     
+    # Check if the temporary file was created
+    if not os.path.exists(temp_outfile):
+        logger.warning(f"plot_scores did not create output file for {level} level, skipping")
+        return
+    
     # If appending, merge PDFs, otherwise just rename
     if append and os.path.exists(outfile):
         from pypdf import PdfReader, PdfWriter

From 6f986c9fc951d779a3763964fd094e987a02e8ed Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 06:52:08 +0000
Subject: [PATCH 14/26] Add check for empty PDF files before merging

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index 4464dbc2..075bb942 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -322,11 +322,16 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
     # Call plot_scores with the formatted dataframe
     plot_scores(plot_df, temp_outfile)
     
-    # Check if the temporary file was created
+    # Check if the temporary file was created and has content
     if not os.path.exists(temp_outfile):
         logger.warning(f"plot_scores did not create output file for {level} level, skipping")
         return
     
+    if os.path.getsize(temp_outfile) == 0:
+        logger.warning(f"plot_scores created empty output file for {level} level, skipping")
+        os.remove(temp_outfile)
+        return
+    
     # If appending, merge PDFs, otherwise just rename
     if append and os.path.exists(outfile):
         from pypdf import PdfReader, PdfWriter

From e611458517515abd27555052e253d613e3ec651a Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 07:41:13 +0000
Subject: [PATCH 15/26] Add exception handling for PDF merging and file
 operations

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py | 62 +++++++++++++++++++------------
 1 file changed, 38 insertions(+), 24 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index 075bb942..a211333e 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -336,29 +336,43 @@ def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: boo
     if append and os.path.exists(outfile):
         from pypdf import PdfReader, PdfWriter
         
-        # Merge the PDFs
-        writer = PdfWriter()
-        
-        # Add pages from existing PDF
-        with open(outfile, "rb") as f:
-            existing_pdf = PdfReader(f)
-            for page in existing_pdf.pages:
-                writer.add_page(page)
-        
-        # Add pages from new PDF
-        with open(temp_outfile, "rb") as f:
-            new_pdf = PdfReader(f)
-            for page in new_pdf.pages:
-                writer.add_page(page)
-        
-        # Write merged PDF
-        with open(outfile, "wb") as f:
-            writer.write(f)
-        
-        # Remove temporary file
-        os.remove(temp_outfile)
+        try:
+            # Merge the PDFs
+            writer = PdfWriter()
+            
+            # Add pages from existing PDF
+            with open(outfile, "rb") as f:
+                existing_pdf = PdfReader(f)
+                for page in existing_pdf.pages:
+                    writer.add_page(page)
+            
+            # Add pages from new PDF
+            with open(temp_outfile, "rb") as f:
+                new_pdf = PdfReader(f)
+                for page in new_pdf.pages:
+                    writer.add_page(page)
+            
+            # Write merged PDF
+            with open(outfile, "wb") as f:
+                writer.write(f)
+            
+            # Remove temporary file
+            os.remove(temp_outfile)
+        except Exception as e:
+            logger.warning(f"Failed to merge PDF for {level} level: {e}. Skipping this level.")
+            # Clean up temporary file if it exists
+            if os.path.exists(temp_outfile):
+                os.remove(temp_outfile)
+            return
     else:
         # Just rename temporary file to output file
-        if os.path.exists(outfile):
-            os.remove(outfile)
-        os.rename(temp_outfile, outfile)
+        try:
+            if os.path.exists(outfile):
+                os.remove(outfile)
+            os.rename(temp_outfile, outfile)
+        except Exception as e:
+            logger.warning(f"Failed to save PDF for {level} level: {e}. Skipping this level.")
+            # Clean up temporary file if it exists
+            if os.path.exists(temp_outfile):
+                os.remove(temp_outfile)
+            return

From b1b8b5fb0cc4141134cda55bc6b5d45fa28cdf77 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:20:14 +0000
Subject: [PATCH 16/26] Initial plan


From 6fc08bfabd3a8118da24a08839078497bb137b59 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:26:54 +0000
Subject: [PATCH 17/26] Add export feature-scores command with unified logic
 for scored/unscored files

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/cli/export.py           |  38 +++-
 pyprophet/export/export_report.py | 340 ++++++++++++++++++++++++++++++
 pyprophet/scoring/_optimized.c    | 292 ++++++++++++-------------
 3 files changed, 521 insertions(+), 149 deletions(-)

diff --git a/pyprophet/cli/export.py b/pyprophet/cli/export.py
index 50371fc3..d1c43139 100644
--- a/pyprophet/cli/export.py
+++ b/pyprophet/cli/export.py
@@ -43,7 +43,8 @@ def export():
     export.add_command(export_parquet, name="parquet")
     export.add_command(export_compound, name="compound")
     export.add_command(export_glyco, name="glyco")
-    export.add_command(export_score_plots, name="score-plots")
+    export.add_command(export_feature_scores, name="feature-scores")
+    export.add_command(export_score_plots, name="score-plots")  # Deprecated
     export.add_command(export_scored_report, name="score-report")
     export.add_command(export_calibration_report, name="calibration-report")
 
@@ -829,7 +830,35 @@ def export_glyco(
     )
 
 
-# Export score plots
+# Export feature scores (unified command)
+@click.command(name="feature-scores", cls=AdvancedHelpCommand)
+@click.option(
+    "--in",
+    "infile",
+    required=True,
+    type=click.Path(exists=True),
+    help="PyProphet input file (OSW, Parquet, or Split Parquet directory).",
+)
+@click.option(
+    "--out",
+    "outfile",
+    type=click.Path(exists=False),
+    help="Output PDF file path. If not specified, will be derived from input filename.",
+)
+@measure_memory_usage_and_time
+def export_feature_scores(infile, outfile):
+    """
+    Export feature score plots
+    
+    Works with OSW, Parquet, and Split Parquet formats.
+    - If SCORE tables exist: applies RANK==1 filtering and plots SCORE + VAR_ columns
+    - If SCORE tables don't exist: plots only VAR_ columns
+    """
+    from ..export.export_report import export_feature_scores as _export_feature_scores
+    _export_feature_scores(infile, outfile)
+
+
+# Export score plots (deprecated - use feature-scores instead)
 @click.command(name="score-plots", cls=AdvancedHelpCommand)
 @click.option(
     "--in",
@@ -849,8 +878,11 @@ def export_glyco(
 @measure_memory_usage_and_time
 def export_score_plots(infile, glycoform):
     """
-    Export score plots
+    Export score plots (DEPRECATED - use 'feature-scores' instead)
+    
+    This command is deprecated. Please use 'pyprophet export feature-scores' instead.
     """
+    logger.warning("DEPRECATED: 'pyprophet export score-plots' is deprecated. Use 'pyprophet export feature-scores' instead.")
     if infile.endswith(".osw"):
         if not glycoform:
             _export_score_plots(infile)
diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index ea5042da..b425f863 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -1,5 +1,6 @@
 import sqlite3
 import pandas as pd
+from pathlib import Path
 
 
 from .._config import ExportIOConfig
@@ -8,6 +9,345 @@
 from ..io.util import get_parquet_column_names
 from ..io.util import check_sqlite_table
 from ..report import plot_scores
+from loguru import logger
+
+
+def export_feature_scores(infile, outfile=None):
+    """
+    Export feature score plots from a PyProphet input file.
+    
+    This function works with OSW, Parquet, and Split Parquet formats.
+    - If SCORE tables exist: applies RANK==1 filtering and plots SCORE + VAR_ columns
+    - If SCORE tables don't exist: plots only VAR_ columns
+    
+    Parameters
+    ----------
+    infile : str
+        Path to input file (OSW, Parquet, or Split Parquet directory)
+    outfile : str, optional
+        Base path for output PDF files. If None, derives from infile.
+    """
+    # Determine file type
+    inpath = Path(infile)
+    
+    # Check if it's a directory (split parquet) or file
+    if inpath.is_dir():
+        file_type = "split_parquet"
+        # For split parquet, we'll need to handle differently
+        logger.info(f"Detected split parquet directory: {infile}")
+        _export_feature_scores_from_split_parquet(infile, outfile)
+    elif infile.endswith('.parquet'):
+        file_type = "parquet"
+        logger.info(f"Detected parquet file: {infile}")
+        _export_feature_scores_from_parquet(infile, outfile)
+    elif infile.endswith('.osw'):
+        file_type = "osw"
+        logger.info(f"Detected OSW file: {infile}")
+        _export_feature_scores_from_osw(infile, outfile)
+    else:
+        raise ValueError(f"Unsupported file format: {infile}. Must be .osw, .parquet, or split parquet directory.")
+
+
+def _export_feature_scores_from_osw(infile, outfile=None):
+    """
+    Export feature scores from OSW file.
+    Detects if SCORE tables exist and adjusts behavior accordingly.
+    """
+    con = sqlite3.connect(infile)
+    
+    # Check for SCORE tables
+    has_score_ms2 = check_sqlite_table(con, "SCORE_MS2")
+    has_score_ms1 = check_sqlite_table(con, "SCORE_MS1")
+    has_score_transition = check_sqlite_table(con, "SCORE_TRANSITION")
+    
+    if has_score_ms2 or has_score_ms1 or has_score_transition:
+        logger.info("SCORE tables detected - applying RANK==1 filter and plotting SCORE + VAR_ columns")
+    else:
+        logger.info("No SCORE tables detected - plotting only VAR_ columns")
+    
+    # MS2 level
+    if check_sqlite_table(con, "FEATURE_MS2"):
+        if outfile:
+            out_ms2 = outfile.replace('.pdf', '_ms2.pdf') if outfile.endswith('.pdf') else f"{outfile}_ms2.pdf"
+        else:
+            out_ms2 = infile.split(".osw")[0] + "_ms2_feature_scores.pdf"
+        
+        if has_score_ms2:
+            # Scored mode: Include SCORE columns and apply RANK==1 filter
+            query_ms2 = """
+SELECT *,
+       RUN_ID || '_' || PRECURSOR_ID AS GROUP_ID
+FROM FEATURE_MS2
+INNER JOIN
+  (SELECT RUN_ID,
+          ID,
+          PRECURSOR_ID,
+          EXP_RT
+   FROM FEATURE) AS FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
+INNER JOIN
+  (SELECT ID,
+          CHARGE AS VAR_PRECURSOR_CHARGE,
+          DECOY
+   FROM PRECURSOR) AS PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+INNER JOIN
+  (SELECT PRECURSOR_ID AS ID,
+          COUNT(*) AS VAR_TRANSITION_NUM_SCORE
+   FROM TRANSITION_PRECURSOR_MAPPING
+   INNER JOIN TRANSITION ON TRANSITION_PRECURSOR_MAPPING.TRANSITION_ID = TRANSITION.ID
+   WHERE DETECTING==1
+   GROUP BY PRECURSOR_ID) AS VAR_TRANSITION_SCORE ON FEATURE.PRECURSOR_ID = VAR_TRANSITION_SCORE.ID
+INNER JOIN SCORE_MS2 ON FEATURE.ID = SCORE_MS2.FEATURE_ID
+WHERE RANK == 1
+ORDER BY RUN_ID,
+         PRECURSOR.ID ASC,
+         FEATURE.EXP_RT ASC;
+"""
+        else:
+            # Unscored mode: Only VAR_ columns, no RANK filter
+            query_ms2 = """
+SELECT FEATURE_MS2.*,
+       FEATURE.RUN_ID,
+       FEATURE.PRECURSOR_ID,
+       FEATURE.EXP_RT,
+       PRECURSOR.CHARGE AS VAR_PRECURSOR_CHARGE,
+       PRECURSOR.DECOY,
+       VAR_TRANSITION_SCORE.VAR_TRANSITION_NUM_SCORE,
+       FEATURE.RUN_ID || '_' || FEATURE.PRECURSOR_ID AS GROUP_ID
+FROM FEATURE_MS2
+INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
+INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+LEFT JOIN
+  (SELECT PRECURSOR_ID AS ID,
+          COUNT(*) AS VAR_TRANSITION_NUM_SCORE
+   FROM TRANSITION_PRECURSOR_MAPPING
+   INNER JOIN TRANSITION ON TRANSITION_PRECURSOR_MAPPING.TRANSITION_ID = TRANSITION.ID
+   WHERE DETECTING==1
+   GROUP BY PRECURSOR_ID) AS VAR_TRANSITION_SCORE ON FEATURE.PRECURSOR_ID = VAR_TRANSITION_SCORE.ID
+ORDER BY FEATURE.RUN_ID,
+         PRECURSOR.ID ASC,
+         FEATURE.EXP_RT ASC;
+"""
+        
+        table_ms2 = pd.read_sql_query(query_ms2, con)
+        if len(table_ms2) > 0:
+            plot_scores(table_ms2, out_ms2)
+            logger.info(f"Exported MS2 feature scores to {out_ms2}")
+    
+    # MS1 level
+    if check_sqlite_table(con, "FEATURE_MS1"):
+        if outfile:
+            out_ms1 = outfile.replace('.pdf', '_ms1.pdf') if outfile.endswith('.pdf') else f"{outfile}_ms1.pdf"
+        else:
+            out_ms1 = infile.split(".osw")[0] + "_ms1_feature_scores.pdf"
+        
+        if has_score_ms1:
+            # Scored mode
+            query_ms1 = """
+SELECT *,
+       RUN_ID || '_' || PRECURSOR_ID AS GROUP_ID
+FROM FEATURE_MS1
+INNER JOIN
+  (SELECT RUN_ID,
+          ID,
+          PRECURSOR_ID,
+          EXP_RT
+   FROM FEATURE) AS FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
+INNER JOIN
+  (SELECT ID,
+          CHARGE AS VAR_PRECURSOR_CHARGE,
+          DECOY
+   FROM PRECURSOR) AS PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+INNER JOIN SCORE_MS1 ON FEATURE.ID = SCORE_MS1.FEATURE_ID
+WHERE RANK == 1
+ORDER BY RUN_ID,
+         PRECURSOR.ID ASC,
+         FEATURE.EXP_RT ASC;
+"""
+        else:
+            # Unscored mode
+            query_ms1 = """
+SELECT FEATURE_MS1.*,
+       FEATURE.RUN_ID,
+       FEATURE.PRECURSOR_ID,
+       FEATURE.EXP_RT,
+       PRECURSOR.CHARGE AS VAR_PRECURSOR_CHARGE,
+       PRECURSOR.DECOY,
+       FEATURE.RUN_ID || '_' || FEATURE.PRECURSOR_ID AS GROUP_ID
+FROM FEATURE_MS1
+INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
+INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+ORDER BY FEATURE.RUN_ID,
+         PRECURSOR.ID ASC,
+         FEATURE.EXP_RT ASC;
+"""
+        
+        table_ms1 = pd.read_sql_query(query_ms1, con)
+        if len(table_ms1) > 0:
+            plot_scores(table_ms1, out_ms1)
+            logger.info(f"Exported MS1 feature scores to {out_ms1}")
+    
+    # Transition level
+    if check_sqlite_table(con, "FEATURE_TRANSITION"):
+        if outfile:
+            out_transition = outfile.replace('.pdf', '_transition.pdf') if outfile.endswith('.pdf') else f"{outfile}_transition.pdf"
+        else:
+            out_transition = infile.split(".osw")[0] + "_transition_feature_scores.pdf"
+        
+        if has_score_transition:
+            # Scored mode
+            query_transition = """
+SELECT TRANSITION.DECOY AS DECOY,
+       FEATURE_TRANSITION.*,
+       PRECURSOR.CHARGE AS VAR_PRECURSOR_CHARGE,
+       TRANSITION.VAR_PRODUCT_CHARGE AS VAR_PRODUCT_CHARGE,
+       SCORE_TRANSITION.*,
+       RUN_ID || '_' || FEATURE_TRANSITION.FEATURE_ID || '_' || PRECURSOR_ID || '_' || FEATURE_TRANSITION.TRANSITION_ID AS GROUP_ID
+FROM FEATURE_TRANSITION
+INNER JOIN
+  (SELECT RUN_ID,
+          ID,
+          PRECURSOR_ID,
+          EXP_RT
+   FROM FEATURE) AS FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
+INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+INNER JOIN SCORE_TRANSITION ON FEATURE_TRANSITION.FEATURE_ID = SCORE_TRANSITION.FEATURE_ID
+AND FEATURE_TRANSITION.TRANSITION_ID = SCORE_TRANSITION.TRANSITION_ID
+INNER JOIN
+  (SELECT ID,
+          CHARGE AS VAR_PRODUCT_CHARGE,
+          DECOY
+   FROM TRANSITION) AS TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
+ORDER BY RUN_ID,
+         PRECURSOR.ID,
+         FEATURE.EXP_RT,
+         TRANSITION.ID;
+"""
+        else:
+            # Unscored mode
+            query_transition = """
+SELECT TRANSITION.DECOY AS DECOY,
+       FEATURE_TRANSITION.*,
+       FEATURE.RUN_ID,
+       FEATURE.PRECURSOR_ID,
+       FEATURE.EXP_RT,
+       PRECURSOR.CHARGE AS VAR_PRECURSOR_CHARGE,
+       TRANSITION.CHARGE AS VAR_PRODUCT_CHARGE,
+       FEATURE.RUN_ID || '_' || FEATURE_TRANSITION.FEATURE_ID || '_' || FEATURE.PRECURSOR_ID || '_' || FEATURE_TRANSITION.TRANSITION_ID AS GROUP_ID
+FROM FEATURE_TRANSITION
+INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
+INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
+ORDER BY FEATURE.RUN_ID,
+         PRECURSOR.ID,
+         FEATURE.EXP_RT,
+         TRANSITION.ID;
+"""
+        
+        table_transition = pd.read_sql_query(query_transition, con)
+        if len(table_transition) > 0:
+            plot_scores(table_transition, out_transition)
+            logger.info(f"Exported transition feature scores to {out_transition}")
+    
+    con.close()
+
+
+def _export_feature_scores_from_parquet(infile, outfile=None):
+    """
+    Export feature scores from single Parquet file.
+    """
+    import pyarrow.parquet as pq
+    
+    # Read parquet file
+    table = pq.read_table(infile)
+    df = table.to_pandas()
+    
+    # Check for SCORE columns
+    score_columns = [col for col in df.columns if col.startswith('SCORE_')]
+    has_scores = len(score_columns) > 0
+    
+    if has_scores:
+        logger.info("SCORE columns detected - applying RANK==1 filter and plotting SCORE + VAR_ columns")
+        # Filter to RANK==1 if SCORE_MS2_PEAK_GROUP_RANK exists
+        if 'SCORE_MS2_PEAK_GROUP_RANK' in df.columns:
+            df = df[df['SCORE_MS2_PEAK_GROUP_RANK'] == 1].copy()
+    else:
+        logger.info("No SCORE columns detected - plotting only VAR_ columns")
+    
+    # Generate GROUP_ID if needed
+    if 'GROUP_ID' not in df.columns:
+        if 'RUN_ID' in df.columns and 'PRECURSOR_ID' in df.columns:
+            df['GROUP_ID'] = df['RUN_ID'].astype(str) + '_' + df['PRECURSOR_ID'].astype(str)
+    
+    # Determine DECOY column name (could be PRECURSOR_DECOY or DECOY)
+    decoy_col = None
+    for col in ['DECOY', 'PRECURSOR_DECOY', 'PEPTIDE_DECOY']:
+        if col in df.columns:
+            decoy_col = col
+            break
+    
+    if decoy_col:
+        df = df.rename(columns={decoy_col: 'DECOY'})
+    
+    # Export plots
+    if outfile is None:
+        outfile = infile.replace('.parquet', '_feature_scores.pdf')
+    
+    if len(df) > 0:
+        plot_scores(df, outfile)
+        logger.info(f"Exported feature scores to {outfile}")
+
+
+def _export_feature_scores_from_split_parquet(infile, outfile=None):
+    """
+    Export feature scores from split Parquet directory.
+    """
+    import pyarrow.parquet as pq
+    from pathlib import Path
+    
+    inpath = Path(infile)
+    
+    # Read precursor features
+    precursor_file = inpath / "precursors_features.parquet"
+    if precursor_file.exists():
+        table = pq.read_table(str(precursor_file))
+        df = table.to_pandas()
+        
+        # Check for SCORE columns
+        score_columns = [col for col in df.columns if col.startswith('SCORE_')]
+        has_scores = len(score_columns) > 0
+        
+        if has_scores:
+            logger.info("SCORE columns detected - applying RANK==1 filter and plotting SCORE + VAR_ columns")
+            # Filter to RANK==1 if SCORE_MS2_PEAK_GROUP_RANK exists
+            if 'SCORE_MS2_PEAK_GROUP_RANK' in df.columns:
+                df = df[df['SCORE_MS2_PEAK_GROUP_RANK'] == 1].copy()
+        else:
+            logger.info("No SCORE columns detected - plotting only VAR_ columns")
+        
+        # Generate GROUP_ID if needed
+        if 'GROUP_ID' not in df.columns:
+            if 'RUN_ID' in df.columns and 'PRECURSOR_ID' in df.columns:
+                df['GROUP_ID'] = df['RUN_ID'].astype(str) + '_' + df['PRECURSOR_ID'].astype(str)
+        
+        # Determine DECOY column name
+        decoy_col = None
+        for col in ['DECOY', 'PRECURSOR_DECOY', 'PEPTIDE_DECOY']:
+            if col in df.columns:
+                decoy_col = col
+                break
+        
+        if decoy_col:
+            df = df.rename(columns={decoy_col: 'DECOY'})
+        
+        # Export plots
+        if outfile is None:
+            outfile = str(inpath / "feature_scores.pdf")
+        
+        if len(df) > 0:
+            plot_scores(df, outfile)
+            logger.info(f"Exported feature scores to {outfile}")
+    else:
+        logger.warning(f"Precursor features file not found: {precursor_file}")
 
 
 def export_score_plots(infile):
diff --git a/pyprophet/scoring/_optimized.c b/pyprophet/scoring/_optimized.c
index d25f3d5e..70c9b4f9 100644
--- a/pyprophet/scoring/_optimized.c
+++ b/pyprophet/scoring/_optimized.c
@@ -1383,7 +1383,7 @@ static const char *__pyx_filename;
 static const char* const __pyx_f[] = {
   "pyprophet/scoring/_optimized.pyx",
   "<stringsource>",
-  "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd",
+  "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd",
   "cpython/type.pxd",
 };
 /* #### Code section: utility_code_proto_before_types ### */
@@ -1606,7 +1606,7 @@ typedef struct {
 
 /* #### Code section: numeric_typedefs ### */
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":743
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":743
  * # in Cython to enable them only on the right systems.
  * 
  * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
@@ -1615,7 +1615,7 @@ typedef struct {
 */
 typedef npy_int8 __pyx_t_5numpy_int8_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":744
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":744
  * 
  * ctypedef npy_int8       int8_t
  * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
@@ -1624,7 +1624,7 @@ typedef npy_int8 __pyx_t_5numpy_int8_t;
 */
 typedef npy_int16 __pyx_t_5numpy_int16_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":745
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":745
  * ctypedef npy_int8       int8_t
  * ctypedef npy_int16      int16_t
  * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
@@ -1633,7 +1633,7 @@ typedef npy_int16 __pyx_t_5numpy_int16_t;
 */
 typedef npy_int32 __pyx_t_5numpy_int32_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":746
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":746
  * ctypedef npy_int16      int16_t
  * ctypedef npy_int32      int32_t
  * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
@@ -1642,7 +1642,7 @@ typedef npy_int32 __pyx_t_5numpy_int32_t;
 */
 typedef npy_int64 __pyx_t_5numpy_int64_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":748
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":748
  * ctypedef npy_int64      int64_t
  * 
  * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
@@ -1651,7 +1651,7 @@ typedef npy_int64 __pyx_t_5numpy_int64_t;
 */
 typedef npy_uint8 __pyx_t_5numpy_uint8_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":749
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":749
  * 
  * ctypedef npy_uint8      uint8_t
  * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
@@ -1660,7 +1660,7 @@ typedef npy_uint8 __pyx_t_5numpy_uint8_t;
 */
 typedef npy_uint16 __pyx_t_5numpy_uint16_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":750
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":750
  * ctypedef npy_uint8      uint8_t
  * ctypedef npy_uint16     uint16_t
  * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
@@ -1669,7 +1669,7 @@ typedef npy_uint16 __pyx_t_5numpy_uint16_t;
 */
 typedef npy_uint32 __pyx_t_5numpy_uint32_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":751
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":751
  * ctypedef npy_uint16     uint16_t
  * ctypedef npy_uint32     uint32_t
  * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
@@ -1678,7 +1678,7 @@ typedef npy_uint32 __pyx_t_5numpy_uint32_t;
 */
 typedef npy_uint64 __pyx_t_5numpy_uint64_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":753
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":753
  * ctypedef npy_uint64     uint64_t
  * 
  * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
@@ -1687,7 +1687,7 @@ typedef npy_uint64 __pyx_t_5numpy_uint64_t;
 */
 typedef npy_float32 __pyx_t_5numpy_float32_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":754
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":754
  * 
  * ctypedef npy_float32    float32_t
  * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
@@ -1696,7 +1696,7 @@ typedef npy_float32 __pyx_t_5numpy_float32_t;
 */
 typedef npy_float64 __pyx_t_5numpy_float64_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":761
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":761
  * ctypedef double complex complex128_t
  * 
  * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
@@ -1705,7 +1705,7 @@ typedef npy_float64 __pyx_t_5numpy_float64_t;
 */
 typedef npy_longlong __pyx_t_5numpy_longlong_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":762
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":762
  * 
  * ctypedef npy_longlong   longlong_t
  * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
@@ -1714,7 +1714,7 @@ typedef npy_longlong __pyx_t_5numpy_longlong_t;
 */
 typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":764
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":764
  * ctypedef npy_ulonglong  ulonglong_t
  * 
  * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
@@ -1723,7 +1723,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
 */
 typedef npy_intp __pyx_t_5numpy_intp_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":765
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":765
  * 
  * ctypedef npy_intp       intp_t
  * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
@@ -1732,7 +1732,7 @@ typedef npy_intp __pyx_t_5numpy_intp_t;
 */
 typedef npy_uintp __pyx_t_5numpy_uintp_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":767
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":767
  * ctypedef npy_uintp      uintp_t
  * 
  * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
@@ -1741,7 +1741,7 @@ typedef npy_uintp __pyx_t_5numpy_uintp_t;
 */
 typedef npy_double __pyx_t_5numpy_float_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":768
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":768
  * 
  * ctypedef npy_double     float_t
  * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
@@ -1750,7 +1750,7 @@ typedef npy_double __pyx_t_5numpy_float_t;
 */
 typedef npy_double __pyx_t_5numpy_double_t;
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":769
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":769
  * ctypedef npy_double     float_t
  * ctypedef npy_double     double_t
  * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
@@ -17271,7 +17271,7 @@ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":242
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":242
  *         cdef int type_num
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17282,7 +17282,7 @@ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_8itemsize_itemsize(PyArray_Descr *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":244
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":244
  *         @property
  *         cdef inline npy_intp itemsize(self) noexcept nogil:
  *             return PyDataType_ELSIZE(self)             # <<<<<<<<<<<<<<
@@ -17292,7 +17292,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_8itemsize_itemsize(PyArray_D
   __pyx_r = PyDataType_ELSIZE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":242
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":242
  *         cdef int type_num
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17305,7 +17305,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_8itemsize_itemsize(PyArray_D
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":246
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":246
  *             return PyDataType_ELSIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17316,7 +17316,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_8itemsize_itemsize(PyArray_D
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_9alignment_alignment(PyArray_Descr *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":248
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":248
  *         @property
  *         cdef inline npy_intp alignment(self) noexcept nogil:
  *             return PyDataType_ALIGNMENT(self)             # <<<<<<<<<<<<<<
@@ -17326,7 +17326,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_9alignment_alignment(PyArray
   __pyx_r = PyDataType_ALIGNMENT(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":246
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":246
  *             return PyDataType_ELSIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17339,7 +17339,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_5dtype_9alignment_alignment(PyArray
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":252
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":252
  *         # Use fields/names with care as they may be NULL.  You must check
  *         # for this using PyDataType_HASFIELDS.
  *         @property             # <<<<<<<<<<<<<<
@@ -17353,7 +17353,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_6fields_fields(PyArray_Desc
   PyObject *__pyx_t_1;
   __Pyx_RefNannySetupContext("fields", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":254
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":254
  *         @property
  *         cdef inline object fields(self):
  *             return <object>PyDataType_FIELDS(self)             # <<<<<<<<<<<<<<
@@ -17366,7 +17366,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_6fields_fields(PyArray_Desc
   __pyx_r = ((PyObject *)__pyx_t_1);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":252
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":252
  *         # Use fields/names with care as they may be NULL.  You must check
  *         # for this using PyDataType_HASFIELDS.
  *         @property             # <<<<<<<<<<<<<<
@@ -17381,7 +17381,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_6fields_fields(PyArray_Desc
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":256
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":256
  *             return <object>PyDataType_FIELDS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17395,7 +17395,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_5names_names(PyArray_Descr
   PyObject *__pyx_t_1;
   __Pyx_RefNannySetupContext("names", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":258
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":258
  *         @property
  *         cdef inline tuple names(self):
  *             return <tuple>PyDataType_NAMES(self)             # <<<<<<<<<<<<<<
@@ -17408,7 +17408,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_5names_names(PyArray_Descr
   __pyx_r = ((PyObject*)__pyx_t_1);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":256
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":256
  *             return <object>PyDataType_FIELDS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17423,7 +17423,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_5names_names(PyArray_Descr
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":263
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":263
  *         # valid (the pointer can be NULL). Most users should access
  *         # this field via the inline helper method PyDataType_SHAPE.
  *         @property             # <<<<<<<<<<<<<<
@@ -17434,7 +17434,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_5dtype_5names_names(PyArray_Descr
 static CYTHON_INLINE PyArray_ArrayDescr *__pyx_f_5numpy_5dtype_8subarray_subarray(PyArray_Descr *__pyx_v_self) {
   PyArray_ArrayDescr *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":265
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":265
  *         @property
  *         cdef inline PyArray_ArrayDescr* subarray(self) noexcept nogil:
  *             return PyDataType_SUBARRAY(self)             # <<<<<<<<<<<<<<
@@ -17444,7 +17444,7 @@ static CYTHON_INLINE PyArray_ArrayDescr *__pyx_f_5numpy_5dtype_8subarray_subarra
   __pyx_r = PyDataType_SUBARRAY(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":263
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":263
  *         # valid (the pointer can be NULL). Most users should access
  *         # this field via the inline helper method PyDataType_SHAPE.
  *         @property             # <<<<<<<<<<<<<<
@@ -17457,7 +17457,7 @@ static CYTHON_INLINE PyArray_ArrayDescr *__pyx_f_5numpy_5dtype_8subarray_subarra
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":267
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":267
  *             return PyDataType_SUBARRAY(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17468,7 +17468,7 @@ static CYTHON_INLINE PyArray_ArrayDescr *__pyx_f_5numpy_5dtype_8subarray_subarra
 static CYTHON_INLINE npy_uint64 __pyx_f_5numpy_5dtype_5flags_flags(PyArray_Descr *__pyx_v_self) {
   npy_uint64 __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":270
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":270
  *         cdef inline npy_uint64 flags(self) noexcept nogil:
  *             """The data types flags."""
  *             return PyDataType_FLAGS(self)             # <<<<<<<<<<<<<<
@@ -17478,7 +17478,7 @@ static CYTHON_INLINE npy_uint64 __pyx_f_5numpy_5dtype_5flags_flags(PyArray_Descr
   __pyx_r = PyDataType_FLAGS(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":267
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":267
  *             return PyDataType_SUBARRAY(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17491,7 +17491,7 @@ static CYTHON_INLINE npy_uint64 __pyx_f_5numpy_5dtype_5flags_flags(PyArray_Descr
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":279
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":279
  *     ctypedef class numpy.broadcast [object PyArrayMultiIterObject, check_size ignore]:
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17502,7 +17502,7 @@ static CYTHON_INLINE npy_uint64 __pyx_f_5numpy_5dtype_5flags_flags(PyArray_Descr
 static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_7numiter_numiter(PyArrayMultiIterObject *__pyx_v_self) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":282
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":282
  *         cdef inline int numiter(self) noexcept nogil:
  *             """The number of arrays that need to be broadcast to the same shape."""
  *             return PyArray_MultiIter_NUMITER(self)             # <<<<<<<<<<<<<<
@@ -17512,7 +17512,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_7numiter_numiter(PyArrayMulti
   __pyx_r = PyArray_MultiIter_NUMITER(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":279
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":279
  *     ctypedef class numpy.broadcast [object PyArrayMultiIterObject, check_size ignore]:
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17525,7 +17525,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_7numiter_numiter(PyArrayMulti
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":284
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":284
  *             return PyArray_MultiIter_NUMITER(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17536,7 +17536,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_7numiter_numiter(PyArrayMulti
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_4size_size(PyArrayMultiIterObject *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":287
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":287
  *         cdef inline npy_intp size(self) noexcept nogil:
  *             """The total broadcasted size."""
  *             return PyArray_MultiIter_SIZE(self)             # <<<<<<<<<<<<<<
@@ -17546,7 +17546,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_4size_size(PyArrayMultiI
   __pyx_r = PyArray_MultiIter_SIZE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":284
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":284
  *             return PyArray_MultiIter_NUMITER(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17559,7 +17559,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_4size_size(PyArrayMultiI
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":289
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":289
  *             return PyArray_MultiIter_SIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17570,7 +17570,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_4size_size(PyArrayMultiI
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_5index_index(PyArrayMultiIterObject *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":292
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":292
  *         cdef inline npy_intp index(self) noexcept nogil:
  *             """The current (1-d) index into the broadcasted result."""
  *             return PyArray_MultiIter_INDEX(self)             # <<<<<<<<<<<<<<
@@ -17580,7 +17580,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_5index_index(PyArrayMult
   __pyx_r = PyArray_MultiIter_INDEX(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":289
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":289
  *             return PyArray_MultiIter_SIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17593,7 +17593,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_5index_index(PyArrayMult
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":294
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":294
  *             return PyArray_MultiIter_INDEX(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17604,7 +17604,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_9broadcast_5index_index(PyArrayMult
 static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_2nd_nd(PyArrayMultiIterObject *__pyx_v_self) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":297
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":297
  *         cdef inline int nd(self) noexcept nogil:
  *             """The number of dimensions in the broadcasted result."""
  *             return PyArray_MultiIter_NDIM(self)             # <<<<<<<<<<<<<<
@@ -17614,7 +17614,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_2nd_nd(PyArrayMultiIterObject
   __pyx_r = PyArray_MultiIter_NDIM(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":294
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":294
  *             return PyArray_MultiIter_INDEX(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17627,7 +17627,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_2nd_nd(PyArrayMultiIterObject
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":299
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":299
  *             return PyArray_MultiIter_NDIM(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17638,7 +17638,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_9broadcast_2nd_nd(PyArrayMultiIterObject
 static CYTHON_INLINE npy_intp *__pyx_f_5numpy_9broadcast_10dimensions_dimensions(PyArrayMultiIterObject *__pyx_v_self) {
   npy_intp *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":302
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":302
  *         cdef inline npy_intp* dimensions(self) noexcept nogil:
  *             """The shape of the broadcasted result."""
  *             return PyArray_MultiIter_DIMS(self)             # <<<<<<<<<<<<<<
@@ -17648,7 +17648,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_9broadcast_10dimensions_dimensions
   __pyx_r = PyArray_MultiIter_DIMS(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":299
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":299
  *             return PyArray_MultiIter_NDIM(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17661,7 +17661,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_9broadcast_10dimensions_dimensions
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":304
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":304
  *             return PyArray_MultiIter_DIMS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17672,7 +17672,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_9broadcast_10dimensions_dimensions
 static CYTHON_INLINE void **__pyx_f_5numpy_9broadcast_5iters_iters(PyArrayMultiIterObject *__pyx_v_self) {
   void **__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":308
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":308
  *             """An array of iterator objects that holds the iterators for the arrays to be broadcast together.
  *             On return, the iterators are adjusted for broadcasting."""
  *             return PyArray_MultiIter_ITERS(self)             # <<<<<<<<<<<<<<
@@ -17682,7 +17682,7 @@ static CYTHON_INLINE void **__pyx_f_5numpy_9broadcast_5iters_iters(PyArrayMultiI
   __pyx_r = PyArray_MultiIter_ITERS(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":304
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":304
  *             return PyArray_MultiIter_DIMS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17695,7 +17695,7 @@ static CYTHON_INLINE void **__pyx_f_5numpy_9broadcast_5iters_iters(PyArrayMultiI
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":322
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":322
  *         # Instead, we use properties that map to the corresponding C-API functions.
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17706,7 +17706,7 @@ static CYTHON_INLINE void **__pyx_f_5numpy_9broadcast_5iters_iters(PyArrayMultiI
 static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject *__pyx_v_self) {
   PyObject *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":326
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":326
  *             """Returns a borrowed reference to the object owning the data/memory.
  *             """
  *             return PyArray_BASE(self)             # <<<<<<<<<<<<<<
@@ -17716,7 +17716,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject
   __pyx_r = PyArray_BASE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":322
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":322
  *         # Instead, we use properties that map to the corresponding C-API functions.
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17729,7 +17729,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":328
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":328
  *             return PyArray_BASE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17743,7 +17743,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
   PyArray_Descr *__pyx_t_1;
   __Pyx_RefNannySetupContext("descr", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":332
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":332
  *             """Returns an owned reference to the dtype of the array.
  *             """
  *             return <dtype>PyArray_DESCR(self)             # <<<<<<<<<<<<<<
@@ -17756,7 +17756,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
   __pyx_r = ((PyArray_Descr *)__pyx_t_1);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":328
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":328
  *             return PyArray_BASE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17771,7 +17771,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":334
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":334
  *             return <dtype>PyArray_DESCR(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17782,7 +17782,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
 static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx_v_self) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":338
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":338
  *             """Returns the number of dimensions in the array.
  *             """
  *             return PyArray_NDIM(self)             # <<<<<<<<<<<<<<
@@ -17792,7 +17792,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx
   __pyx_r = PyArray_NDIM(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":334
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":334
  *             return <dtype>PyArray_DESCR(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17805,7 +17805,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":340
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":340
  *             return PyArray_NDIM(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17816,7 +17816,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx
 static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObject *__pyx_v_self) {
   npy_intp *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":346
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":346
  *             Can return NULL for 0-dimensional arrays.
  *             """
  *             return PyArray_DIMS(self)             # <<<<<<<<<<<<<<
@@ -17826,7 +17826,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObjec
   __pyx_r = PyArray_DIMS(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":340
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":340
  *             return PyArray_NDIM(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17839,7 +17839,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObjec
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":348
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":348
  *             return PyArray_DIMS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17850,7 +17850,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObjec
 static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayObject *__pyx_v_self) {
   npy_intp *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":353
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":353
  *             The number of elements matches the number of dimensions of the array (ndim).
  *             """
  *             return PyArray_STRIDES(self)             # <<<<<<<<<<<<<<
@@ -17860,7 +17860,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayO
   __pyx_r = PyArray_STRIDES(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":348
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":348
  *             return PyArray_DIMS(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17873,7 +17873,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayO
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":355
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":355
  *             return PyArray_STRIDES(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17884,7 +17884,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayO
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":359
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":359
  *             """Returns the total size (in number of elements) of the array.
  *             """
  *             return PyArray_SIZE(self)             # <<<<<<<<<<<<<<
@@ -17894,7 +17894,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *
   __pyx_r = PyArray_SIZE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":355
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":355
  *             return PyArray_STRIDES(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17907,7 +17907,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":361
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":361
  *             return PyArray_SIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17918,7 +17918,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *
 static CYTHON_INLINE char *__pyx_f_5numpy_7ndarray_4data_data(PyArrayObject *__pyx_v_self) {
   char *__pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":368
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":368
  *             of `PyArray_DATA()` instead, which returns a 'void*'.
  *             """
  *             return PyArray_BYTES(self)             # <<<<<<<<<<<<<<
@@ -17928,7 +17928,7 @@ static CYTHON_INLINE char *__pyx_f_5numpy_7ndarray_4data_data(PyArrayObject *__p
   __pyx_r = PyArray_BYTES(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":361
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":361
  *             return PyArray_SIZE(self)
  * 
  *         @property             # <<<<<<<<<<<<<<
@@ -17941,7 +17941,7 @@ static CYTHON_INLINE char *__pyx_f_5numpy_7ndarray_4data_data(PyArrayObject *__p
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":776
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":776
  * ctypedef long double complex clongdouble_t
  * 
  * cdef inline object PyArray_MultiIterNew1(a):             # <<<<<<<<<<<<<<
@@ -17958,7 +17958,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__
   int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("PyArray_MultiIterNew1", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":777
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":777
  * 
  * cdef inline object PyArray_MultiIterNew1(a):
  *     return PyArray_MultiIterNew(1, <void*>a)             # <<<<<<<<<<<<<<
@@ -17972,7 +17972,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__
   __pyx_t_1 = 0;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":776
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":776
  * ctypedef long double complex clongdouble_t
  * 
  * cdef inline object PyArray_MultiIterNew1(a):             # <<<<<<<<<<<<<<
@@ -17991,7 +17991,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":779
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":779
  *     return PyArray_MultiIterNew(1, <void*>a)
  * 
  * cdef inline object PyArray_MultiIterNew2(a, b):             # <<<<<<<<<<<<<<
@@ -18008,7 +18008,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__
   int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("PyArray_MultiIterNew2", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":780
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":780
  * 
  * cdef inline object PyArray_MultiIterNew2(a, b):
  *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)             # <<<<<<<<<<<<<<
@@ -18022,7 +18022,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__
   __pyx_t_1 = 0;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":779
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":779
  *     return PyArray_MultiIterNew(1, <void*>a)
  * 
  * cdef inline object PyArray_MultiIterNew2(a, b):             # <<<<<<<<<<<<<<
@@ -18041,7 +18041,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":782
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":782
  *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
  * 
  * cdef inline object PyArray_MultiIterNew3(a, b, c):             # <<<<<<<<<<<<<<
@@ -18058,7 +18058,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__
   int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("PyArray_MultiIterNew3", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":783
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":783
  * 
  * cdef inline object PyArray_MultiIterNew3(a, b, c):
  *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)             # <<<<<<<<<<<<<<
@@ -18072,7 +18072,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__
   __pyx_t_1 = 0;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":782
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":782
  *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
  * 
  * cdef inline object PyArray_MultiIterNew3(a, b, c):             # <<<<<<<<<<<<<<
@@ -18091,7 +18091,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":785
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":785
  *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
  * 
  * cdef inline object PyArray_MultiIterNew4(a, b, c, d):             # <<<<<<<<<<<<<<
@@ -18108,7 +18108,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__
   int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("PyArray_MultiIterNew4", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":786
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":786
  * 
  * cdef inline object PyArray_MultiIterNew4(a, b, c, d):
  *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)             # <<<<<<<<<<<<<<
@@ -18122,7 +18122,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__
   __pyx_t_1 = 0;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":785
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":785
  *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
  * 
  * cdef inline object PyArray_MultiIterNew4(a, b, c, d):             # <<<<<<<<<<<<<<
@@ -18141,7 +18141,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":788
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":788
  *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
  * 
  * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):             # <<<<<<<<<<<<<<
@@ -18158,7 +18158,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__
   int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("PyArray_MultiIterNew5", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":789
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":789
  * 
  * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
  *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)             # <<<<<<<<<<<<<<
@@ -18172,7 +18172,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__
   __pyx_t_1 = 0;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":788
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":788
  *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
  * 
  * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):             # <<<<<<<<<<<<<<
@@ -18191,7 +18191,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":791
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":791
  *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
  * 
  * cdef inline tuple PyDataType_SHAPE(dtype d):             # <<<<<<<<<<<<<<
@@ -18206,7 +18206,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__
   PyObject *__pyx_t_2;
   __Pyx_RefNannySetupContext("PyDataType_SHAPE", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":792
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":792
  * 
  * cdef inline tuple PyDataType_SHAPE(dtype d):
  *     if PyDataType_HASSUBARRAY(d):             # <<<<<<<<<<<<<<
@@ -18216,7 +18216,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__
   __pyx_t_1 = PyDataType_HASSUBARRAY(__pyx_v_d);
   if (__pyx_t_1) {
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":793
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":793
  * cdef inline tuple PyDataType_SHAPE(dtype d):
  *     if PyDataType_HASSUBARRAY(d):
  *         return <tuple>d.subarray.shape             # <<<<<<<<<<<<<<
@@ -18229,7 +18229,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__
     __pyx_r = ((PyObject*)__pyx_t_2);
     goto __pyx_L0;
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":792
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":792
  * 
  * cdef inline tuple PyDataType_SHAPE(dtype d):
  *     if PyDataType_HASSUBARRAY(d):             # <<<<<<<<<<<<<<
@@ -18238,7 +18238,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__
 */
   }
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":795
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":795
  *         return <tuple>d.subarray.shape
  *     else:
  *         return ()             # <<<<<<<<<<<<<<
@@ -18252,7 +18252,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__
     goto __pyx_L0;
   }
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":791
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":791
  *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
  * 
  * cdef inline tuple PyDataType_SHAPE(dtype d):             # <<<<<<<<<<<<<<
@@ -18267,7 +18267,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":994
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":994
  *     int _import_umath() except -1
  * 
  * cdef inline void set_array_base(ndarray arr, object base) except *:             # <<<<<<<<<<<<<<
@@ -18281,7 +18281,7 @@ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_a
   const char *__pyx_filename = NULL;
   int __pyx_clineno = 0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":995
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":995
  * 
  * cdef inline void set_array_base(ndarray arr, object base) except *:
  *     Py_INCREF(base) # important to do this before stealing the reference below!             # <<<<<<<<<<<<<<
@@ -18290,7 +18290,7 @@ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_a
 */
   Py_INCREF(__pyx_v_base);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":996
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":996
  * cdef inline void set_array_base(ndarray arr, object base) except *:
  *     Py_INCREF(base) # important to do this before stealing the reference below!
  *     PyArray_SetBaseObject(arr, base)             # <<<<<<<<<<<<<<
@@ -18299,7 +18299,7 @@ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_a
 */
   __pyx_t_1 = PyArray_SetBaseObject(__pyx_v_arr, __pyx_v_base); if (unlikely(__pyx_t_1 == ((int)-1))) __PYX_ERR(2, 996, __pyx_L1_error)
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":994
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":994
  *     int _import_umath() except -1
  * 
  * cdef inline void set_array_base(ndarray arr, object base) except *:             # <<<<<<<<<<<<<<
@@ -18314,7 +18314,7 @@ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_a
   __pyx_L0:;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":998
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":998
  *     PyArray_SetBaseObject(arr, base)
  * 
  * cdef inline object get_array_base(ndarray arr):             # <<<<<<<<<<<<<<
@@ -18329,7 +18329,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__py
   int __pyx_t_1;
   __Pyx_RefNannySetupContext("get_array_base", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":999
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":999
  * 
  * cdef inline object get_array_base(ndarray arr):
  *     base = PyArray_BASE(arr)             # <<<<<<<<<<<<<<
@@ -18338,7 +18338,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__py
 */
   __pyx_v_base = PyArray_BASE(__pyx_v_arr);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1000
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1000
  * cdef inline object get_array_base(ndarray arr):
  *     base = PyArray_BASE(arr)
  *     if base is NULL:             # <<<<<<<<<<<<<<
@@ -18348,7 +18348,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__py
   __pyx_t_1 = (__pyx_v_base == NULL);
   if (__pyx_t_1) {
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1001
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1001
  *     base = PyArray_BASE(arr)
  *     if base is NULL:
  *         return None             # <<<<<<<<<<<<<<
@@ -18359,7 +18359,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__py
     __pyx_r = Py_None; __Pyx_INCREF(Py_None);
     goto __pyx_L0;
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1000
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1000
  * cdef inline object get_array_base(ndarray arr):
  *     base = PyArray_BASE(arr)
  *     if base is NULL:             # <<<<<<<<<<<<<<
@@ -18368,7 +18368,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__py
 */
   }
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1002
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1002
  *     if base is NULL:
  *         return None
  *     return <object>base             # <<<<<<<<<<<<<<
@@ -18380,7 +18380,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__py
   __pyx_r = ((PyObject *)__pyx_v_base);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":998
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":998
  *     PyArray_SetBaseObject(arr, base)
  * 
  * cdef inline object get_array_base(ndarray arr):             # <<<<<<<<<<<<<<
@@ -18395,7 +18395,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__py
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1006
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1006
  * # Versions of the import_* functions which are more suitable for
  * # Cython code.
  * cdef inline int import_array() except -1:             # <<<<<<<<<<<<<<
@@ -18422,7 +18422,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) {
   int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("import_array", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1007
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1007
  * # Cython code.
  * cdef inline int import_array() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18438,7 +18438,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) {
     __Pyx_XGOTREF(__pyx_t_3);
     /*try:*/ {
 
-      /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1008
+      /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1008
  * cdef inline int import_array() except -1:
  *     try:
  *         __pyx_import_array()             # <<<<<<<<<<<<<<
@@ -18447,7 +18447,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) {
 */
       __pyx_t_4 = _import_array(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 1008, __pyx_L3_error)
 
-      /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1007
+      /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1007
  * # Cython code.
  * cdef inline int import_array() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18461,7 +18461,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) {
     goto __pyx_L8_try_end;
     __pyx_L3_error:;
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1009
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1009
  *     try:
  *         __pyx_import_array()
  *     except Exception:             # <<<<<<<<<<<<<<
@@ -18476,7 +18476,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) {
       __Pyx_XGOTREF(__pyx_t_6);
       __Pyx_XGOTREF(__pyx_t_7);
 
-      /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1010
+      /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1010
  *         __pyx_import_array()
  *     except Exception:
  *         raise ImportError("numpy._core.multiarray failed to import")             # <<<<<<<<<<<<<<
@@ -18501,7 +18501,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) {
     }
     goto __pyx_L5_except_error;
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1007
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1007
  * # Cython code.
  * cdef inline int import_array() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18517,7 +18517,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) {
     __pyx_L8_try_end:;
   }
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1006
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1006
  * # Versions of the import_* functions which are more suitable for
  * # Cython code.
  * cdef inline int import_array() except -1:             # <<<<<<<<<<<<<<
@@ -18542,7 +18542,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) {
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1012
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1012
  *         raise ImportError("numpy._core.multiarray failed to import")
  * 
  * cdef inline int import_umath() except -1:             # <<<<<<<<<<<<<<
@@ -18569,7 +18569,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
   int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("import_umath", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1013
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1013
  * 
  * cdef inline int import_umath() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18585,7 +18585,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
     __Pyx_XGOTREF(__pyx_t_3);
     /*try:*/ {
 
-      /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1014
+      /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1014
  * cdef inline int import_umath() except -1:
  *     try:
  *         _import_umath()             # <<<<<<<<<<<<<<
@@ -18594,7 +18594,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
 */
       __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 1014, __pyx_L3_error)
 
-      /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1013
+      /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1013
  * 
  * cdef inline int import_umath() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18608,7 +18608,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
     goto __pyx_L8_try_end;
     __pyx_L3_error:;
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1015
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1015
  *     try:
  *         _import_umath()
  *     except Exception:             # <<<<<<<<<<<<<<
@@ -18623,7 +18623,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
       __Pyx_XGOTREF(__pyx_t_6);
       __Pyx_XGOTREF(__pyx_t_7);
 
-      /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1016
+      /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1016
  *         _import_umath()
  *     except Exception:
  *         raise ImportError("numpy._core.umath failed to import")             # <<<<<<<<<<<<<<
@@ -18648,7 +18648,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
     }
     goto __pyx_L5_except_error;
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1013
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1013
  * 
  * cdef inline int import_umath() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18664,7 +18664,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
     __pyx_L8_try_end:;
   }
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1012
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1012
  *         raise ImportError("numpy._core.multiarray failed to import")
  * 
  * cdef inline int import_umath() except -1:             # <<<<<<<<<<<<<<
@@ -18689,7 +18689,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) {
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1018
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1018
  *         raise ImportError("numpy._core.umath failed to import")
  * 
  * cdef inline int import_ufunc() except -1:             # <<<<<<<<<<<<<<
@@ -18716,7 +18716,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
   int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("import_ufunc", 0);
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1019
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1019
  * 
  * cdef inline int import_ufunc() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18732,7 +18732,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
     __Pyx_XGOTREF(__pyx_t_3);
     /*try:*/ {
 
-      /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1020
+      /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1020
  * cdef inline int import_ufunc() except -1:
  *     try:
  *         _import_umath()             # <<<<<<<<<<<<<<
@@ -18741,7 +18741,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
 */
       __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 1020, __pyx_L3_error)
 
-      /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1019
+      /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1019
  * 
  * cdef inline int import_ufunc() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18755,7 +18755,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
     goto __pyx_L8_try_end;
     __pyx_L3_error:;
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1021
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1021
  *     try:
  *         _import_umath()
  *     except Exception:             # <<<<<<<<<<<<<<
@@ -18770,7 +18770,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
       __Pyx_XGOTREF(__pyx_t_6);
       __Pyx_XGOTREF(__pyx_t_7);
 
-      /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1022
+      /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1022
  *         _import_umath()
  *     except Exception:
  *         raise ImportError("numpy._core.umath failed to import")             # <<<<<<<<<<<<<<
@@ -18795,7 +18795,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
     }
     goto __pyx_L5_except_error;
 
-    /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1019
+    /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1019
  * 
  * cdef inline int import_ufunc() except -1:
  *     try:             # <<<<<<<<<<<<<<
@@ -18811,7 +18811,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
     __pyx_L8_try_end:;
   }
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1018
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1018
  *         raise ImportError("numpy._core.umath failed to import")
  * 
  * cdef inline int import_ufunc() except -1:             # <<<<<<<<<<<<<<
@@ -18836,7 +18836,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1025
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1025
  * 
  * 
  * cdef inline bint is_timedelta64_object(object obj) noexcept:             # <<<<<<<<<<<<<<
@@ -18847,7 +18847,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) {
 static CYTHON_INLINE int __pyx_f_5numpy_is_timedelta64_object(PyObject *__pyx_v_obj) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1037
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1037
  *     bool
  *     """
  *     return PyObject_TypeCheck(obj, &PyTimedeltaArrType_Type)             # <<<<<<<<<<<<<<
@@ -18857,7 +18857,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_timedelta64_object(PyObject *__pyx_v_
   __pyx_r = PyObject_TypeCheck(__pyx_v_obj, (&PyTimedeltaArrType_Type));
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1025
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1025
  * 
  * 
  * cdef inline bint is_timedelta64_object(object obj) noexcept:             # <<<<<<<<<<<<<<
@@ -18870,7 +18870,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_timedelta64_object(PyObject *__pyx_v_
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1040
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1040
  * 
  * 
  * cdef inline bint is_datetime64_object(object obj) noexcept:             # <<<<<<<<<<<<<<
@@ -18881,7 +18881,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_timedelta64_object(PyObject *__pyx_v_
 static CYTHON_INLINE int __pyx_f_5numpy_is_datetime64_object(PyObject *__pyx_v_obj) {
   int __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1052
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1052
  *     bool
  *     """
  *     return PyObject_TypeCheck(obj, &PyDatetimeArrType_Type)             # <<<<<<<<<<<<<<
@@ -18891,7 +18891,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_datetime64_object(PyObject *__pyx_v_o
   __pyx_r = PyObject_TypeCheck(__pyx_v_obj, (&PyDatetimeArrType_Type));
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1040
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1040
  * 
  * 
  * cdef inline bint is_datetime64_object(object obj) noexcept:             # <<<<<<<<<<<<<<
@@ -18904,7 +18904,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_datetime64_object(PyObject *__pyx_v_o
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1055
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1055
  * 
  * 
  * cdef inline npy_datetime get_datetime64_value(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18915,7 +18915,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_is_datetime64_object(PyObject *__pyx_v_o
 static CYTHON_INLINE npy_datetime __pyx_f_5numpy_get_datetime64_value(PyObject *__pyx_v_obj) {
   npy_datetime __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1062
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1062
  *     also needed.  That can be found using `get_datetime64_unit`.
  *     """
  *     return (<PyDatetimeScalarObject*>obj).obval             # <<<<<<<<<<<<<<
@@ -18925,7 +18925,7 @@ static CYTHON_INLINE npy_datetime __pyx_f_5numpy_get_datetime64_value(PyObject *
   __pyx_r = ((PyDatetimeScalarObject *)__pyx_v_obj)->obval;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1055
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1055
  * 
  * 
  * cdef inline npy_datetime get_datetime64_value(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18938,7 +18938,7 @@ static CYTHON_INLINE npy_datetime __pyx_f_5numpy_get_datetime64_value(PyObject *
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1065
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1065
  * 
  * 
  * cdef inline npy_timedelta get_timedelta64_value(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18949,7 +18949,7 @@ static CYTHON_INLINE npy_datetime __pyx_f_5numpy_get_datetime64_value(PyObject *
 static CYTHON_INLINE npy_timedelta __pyx_f_5numpy_get_timedelta64_value(PyObject *__pyx_v_obj) {
   npy_timedelta __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1069
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1069
  *     returns the int64 value underlying scalar numpy timedelta64 object
  *     """
  *     return (<PyTimedeltaScalarObject*>obj).obval             # <<<<<<<<<<<<<<
@@ -18959,7 +18959,7 @@ static CYTHON_INLINE npy_timedelta __pyx_f_5numpy_get_timedelta64_value(PyObject
   __pyx_r = ((PyTimedeltaScalarObject *)__pyx_v_obj)->obval;
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1065
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1065
  * 
  * 
  * cdef inline npy_timedelta get_timedelta64_value(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18972,7 +18972,7 @@ static CYTHON_INLINE npy_timedelta __pyx_f_5numpy_get_timedelta64_value(PyObject
   return __pyx_r;
 }
 
-/* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1072
+/* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1072
  * 
  * 
  * cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) noexcept nogil:             # <<<<<<<<<<<<<<
@@ -18983,7 +18983,7 @@ static CYTHON_INLINE npy_timedelta __pyx_f_5numpy_get_timedelta64_value(PyObject
 static CYTHON_INLINE NPY_DATETIMEUNIT __pyx_f_5numpy_get_datetime64_unit(PyObject *__pyx_v_obj) {
   NPY_DATETIMEUNIT __pyx_r;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1076
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1076
  *     returns the unit part of the dtype for a numpy datetime64 object.
  *     """
  *     return <NPY_DATETIMEUNIT>(<PyDatetimeScalarObject*>obj).obmeta.base             # <<<<<<<<<<<<<<
@@ -18993,7 +18993,7 @@ static CYTHON_INLINE NPY_DATETIMEUNIT __pyx_f_5numpy_get_datetime64_unit(PyObjec
   __pyx_r = ((NPY_DATETIMEUNIT)((PyDatetimeScalarObject *)__pyx_v_obj)->obmeta.base);
   goto __pyx_L0;
 
-  /* "../../../../../tmp/pip-build-env-_tc7qfiq/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1072
+  /* "../../../../../tmp/pip-build-env-x3g6sxmd/overlay/local/lib/python3.12/dist-packages/numpy/__init__.cython-30.pxd":1072
  * 
  * 
  * cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) noexcept nogil:             # <<<<<<<<<<<<<<

From f720f455deac52ec35addba88debca5fcdabdf87 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:28:03 +0000
Subject: [PATCH 18/26] Add comprehensive tests for export feature-scores
 command

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 tests/test_pyprophet_export.py | 101 +++++++++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)

diff --git a/tests/test_pyprophet_export.py b/tests/test_pyprophet_export.py
index 31b0ad1a..8c5c4f05 100644
--- a/tests/test_pyprophet_export.py
+++ b/tests/test_pyprophet_export.py
@@ -432,3 +432,104 @@ def test_parquet_export_with_ipf(test_data_osw, temp_folder, regtest):
     print(f"SCORE_IPF columns found: {sorted(ipf_columns)}", file=regtest)
     print("Sample data with IPF scores:", file=regtest)
     print(df[['FEATURE_ID'] + ipf_columns].head(10).sort_index(axis=1), file=regtest)
+
+
+# ================== FEATURE SCORES EXPORT TESTS ==================
+def test_feature_scores_unscored_osw(test_data_osw, temp_folder, regtest):
+    """Test exporting feature scores from unscored OSW file"""
+    cmd = f"pyprophet export feature-scores --in={test_data_osw} --out={temp_folder}/feature_scores.pdf"
+    
+    run_pyprophet_command(cmd, temp_folder)
+    
+    # Check that output PDF files were created
+    output_files = list(temp_folder.glob("*.pdf"))
+    assert len(output_files) > 0, "Expected at least one PDF file to be created"
+    
+    print(f"Created {len(output_files)} PDF file(s):", file=regtest)
+    for f in sorted(output_files):
+        print(f"  - {f.name}", file=regtest)
+
+
+def test_feature_scores_scored_osw(test_data_osw, temp_folder, regtest):
+    """Test exporting feature scores from scored OSW file with SCORE tables"""
+    # Score at MS2 level first
+    cmd = f"pyprophet score --in={test_data_osw} --level=ms2 --test --pi0_lambda=0.001 0 0 --ss_iteration_fdr=0.02 && "
+    
+    # Export feature scores (should detect SCORE_MS2 table)
+    cmd += f"pyprophet export feature-scores --in={test_data_osw} --out={temp_folder}/feature_scores.pdf"
+    
+    run_pyprophet_command(cmd, temp_folder)
+    
+    # Check that output PDF files were created
+    output_files = list(temp_folder.glob("*.pdf"))
+    assert len(output_files) > 0, "Expected at least one PDF file to be created"
+    
+    print(f"Created {len(output_files)} PDF file(s) from scored OSW:", file=regtest)
+    for f in sorted(output_files):
+        print(f"  - {f.name}", file=regtest)
+
+
+def test_feature_scores_parquet_with_scores(test_data_osw, temp_folder, regtest):
+    """Test exporting feature scores from Parquet file with SCORE columns"""
+    # Score and export to parquet
+    cmd = f"pyprophet score --in={test_data_osw} --level=ms2 --test --pi0_lambda=0.001 0 0 --ss_iteration_fdr=0.02 && "
+    cmd += f"pyprophet export parquet --in={test_data_osw} --out={temp_folder}/test_data_scored.parquet && "
+    
+    # Export feature scores from parquet
+    cmd += f"pyprophet export feature-scores --in={temp_folder}/test_data_scored.parquet --out={temp_folder}/feature_scores.pdf"
+    
+    run_pyprophet_command(cmd, temp_folder)
+    
+    # Check that output PDF was created
+    pdf_file = temp_folder / "feature_scores.pdf"
+    assert pdf_file.exists(), "Expected feature_scores.pdf to be created"
+    
+    print(f"Successfully created feature scores from Parquet with SCORE columns", file=regtest)
+
+
+def test_feature_scores_split_parquet_with_scores(test_data_osw, temp_folder, regtest):
+    """Test exporting feature scores from split Parquet directory with SCORE columns"""
+    # Score and export to split parquet
+    cmd = f"pyprophet score --in={test_data_osw} --level=ms2 --test --pi0_lambda=0.001 0 0 --ss_iteration_fdr=0.02 && "
+    cmd += f"pyprophet export parquet --in={test_data_osw} --out={temp_folder}/test_data_split --split_transition_data && "
+    
+    # Export feature scores from split parquet
+    cmd += f"pyprophet export feature-scores --in={temp_folder}/test_data_split --out={temp_folder}/feature_scores.pdf"
+    
+    run_pyprophet_command(cmd, temp_folder)
+    
+    # Check that output PDF was created
+    pdf_file = temp_folder / "feature_scores.pdf"
+    assert pdf_file.exists(), "Expected feature_scores.pdf to be created"
+    
+    print(f"Successfully created feature scores from split Parquet with SCORE columns", file=regtest)
+
+
+def test_feature_scores_ms1_ms2_transition(test_data_osw, temp_folder, regtest):
+    """Test exporting feature scores with MS1, MS2, and transition level scoring"""
+    # Score at all levels
+    cmd = f"pyprophet score --in={test_data_osw} --level=ms1 --test --pi0_lambda=0.1 0 0 --ss_iteration_fdr=0.02 && "
+    cmd += f"pyprophet score --in={test_data_osw} --level=ms2 --test --pi0_lambda=0.001 0 0 --ss_iteration_fdr=0.02 && "
+    cmd += f"pyprophet score --in={test_data_osw} --level=transition --test --pi0_lambda=0.1 0 0 --ss_iteration_fdr=0.02 && "
+    
+    # Export feature scores (should create ms1, ms2, and transition PDFs)
+    cmd += f"pyprophet export feature-scores --in={test_data_osw} --out={temp_folder}/feature_scores.pdf"
+    
+    run_pyprophet_command(cmd, temp_folder)
+    
+    # Check that output PDF files were created for all levels
+    output_files = list(temp_folder.glob("*.pdf"))
+    assert len(output_files) >= 3, "Expected at least 3 PDF files (ms1, ms2, transition)"
+    
+    # Check for specific files
+    ms1_files = [f for f in output_files if 'ms1' in f.name.lower()]
+    ms2_files = [f for f in output_files if 'ms2' in f.name.lower()]
+    transition_files = [f for f in output_files if 'transition' in f.name.lower()]
+    
+    print(f"Created {len(output_files)} PDF file(s) from multi-level scoring:", file=regtest)
+    print(f"  MS1 files: {len(ms1_files)}", file=regtest)
+    print(f"  MS2 files: {len(ms2_files)}", file=regtest)
+    print(f"  Transition files: {len(transition_files)}", file=regtest)
+    
+    for f in sorted(output_files):
+        print(f"  - {f.name}", file=regtest)

From ec63de954b4c9f0bb608391fc560a438f03a5655 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:30:31 +0000
Subject: [PATCH 19/26] Add error handling for pyarrow and update documentation

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 docs/cli.rst                      | 23 ++++++++++++++++++++---
 pyprophet/export/export_report.py | 15 +++++++++++++--
 2 files changed, 33 insertions(+), 5 deletions(-)

diff --git a/docs/cli.rst b/docs/cli.rst
index 85453d89..a2ff1a1c 100644
--- a/docs/cli.rst
+++ b/docs/cli.rst
@@ -144,10 +144,27 @@ To convert OpenSwath's *.osw* / *.sqMass* format to a parquet format, you can us
    :prog: pyprophet export parquet
    :nested: none
 
-Export Score Plots
-^^^^^^^^^^^^^^^^^^
+Export Feature Score Plots
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-It may be useful to export the distribution of scores for the different input features. This can help you investigate the distribution and quality of scores for target-decoy separation.
+To export the distribution of feature scores (VAR_ columns) and, if available, scorer scores (SCORE columns), you can use the :program:`export feature-scores` subcommand. This command works with all file formats (OSW, Parquet, and Split Parquet):
+
+- **For unscored files**: Plots only VAR_ columns (feature variables)
+- **For scored files**: Applies RANK==1 filtering and plots both SCORE and VAR_ columns
+
+This is useful for investigating the distribution and quality of scores for target-decoy separation.
+
+.. click:: pyprophet.cli.export:export_feature_scores
+   :prog: pyprophet export feature-scores
+   :nested: none
+
+Export Score Plots (Deprecated)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. deprecated:: 3.1
+   Use :program:`pyprophet export feature-scores` instead.
+
+The :program:`export score-plots` command is deprecated and will be removed in a future version. It has been replaced by the more flexible :program:`export feature-scores` command which works with all file formats.
 
 .. click:: pyprophet.cli.export:export_score_plots
    :prog: pyprophet export score-plots
diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index b425f863..ec576277 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -255,7 +255,12 @@ def _export_feature_scores_from_parquet(infile, outfile=None):
     """
     Export feature scores from single Parquet file.
     """
-    import pyarrow.parquet as pq
+    try:
+        import pyarrow.parquet as pq
+    except ImportError:
+        raise ImportError(
+            "pyarrow is required for parquet export. Install it with: pip install pyarrow"
+        )
     
     # Read parquet file
     table = pq.read_table(infile)
@@ -301,7 +306,13 @@ def _export_feature_scores_from_split_parquet(infile, outfile=None):
     """
     Export feature scores from split Parquet directory.
     """
-    import pyarrow.parquet as pq
+    try:
+        import pyarrow.parquet as pq
+    except ImportError:
+        raise ImportError(
+            "pyarrow is required for parquet export. Install it with: pip install pyarrow"
+        )
+    
     from pathlib import Path
     
     inpath = Path(infile)

From cf5d28645026f5a229f7b3f11e002c5d04a12dd0 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:33:40 +0000
Subject: [PATCH 20/26] Refactor: Extract pyarrow import check to helper
 function to reduce code duplication

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py | 40 ++++++++++++++++++++-----------
 1 file changed, 26 insertions(+), 14 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index ec576277..ebb49cd9 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -12,6 +12,30 @@
 from loguru import logger
 
 
+def _check_pyarrow_available():
+    """
+    Helper function to check if pyarrow is available and provide helpful error message.
+    
+    Returns
+    -------
+    module
+        The pyarrow.parquet module if available
+        
+    Raises
+    ------
+    ImportError
+        If pyarrow is not installed
+    """
+    try:
+        import pyarrow.parquet as pq
+        return pq
+    except ImportError:
+        raise ImportError(
+            "pyarrow is required for parquet file operations. "
+            "Install it with: pip install pyarrow or pip install pyprophet[parquet]"
+        )
+
+
 def export_feature_scores(infile, outfile=None):
     """
     Export feature score plots from a PyProphet input file.
@@ -255,12 +279,7 @@ def _export_feature_scores_from_parquet(infile, outfile=None):
     """
     Export feature scores from single Parquet file.
     """
-    try:
-        import pyarrow.parquet as pq
-    except ImportError:
-        raise ImportError(
-            "pyarrow is required for parquet export. Install it with: pip install pyarrow"
-        )
+    pq = _check_pyarrow_available()
     
     # Read parquet file
     table = pq.read_table(infile)
@@ -306,14 +325,7 @@ def _export_feature_scores_from_split_parquet(infile, outfile=None):
     """
     Export feature scores from split Parquet directory.
     """
-    try:
-        import pyarrow.parquet as pq
-    except ImportError:
-        raise ImportError(
-            "pyarrow is required for parquet export. Install it with: pip install pyarrow"
-        )
-    
-    from pathlib import Path
+    pq = _check_pyarrow_available()
     
     inpath = Path(infile)
     

From b0c4b060d6195046033dd7f9ee1e46e8c13a920b Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:35:34 +0000
Subject: [PATCH 21/26] Remove unused file_type variable assignments

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index ebb49cd9..040d7f22 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -51,21 +51,18 @@ def export_feature_scores(infile, outfile=None):
     outfile : str, optional
         Base path for output PDF files. If None, derives from infile.
     """
-    # Determine file type
+    # Determine file type and route to appropriate handler
     inpath = Path(infile)
     
     # Check if it's a directory (split parquet) or file
     if inpath.is_dir():
-        file_type = "split_parquet"
-        # For split parquet, we'll need to handle differently
+        # Split parquet directory
         logger.info(f"Detected split parquet directory: {infile}")
         _export_feature_scores_from_split_parquet(infile, outfile)
     elif infile.endswith('.parquet'):
-        file_type = "parquet"
         logger.info(f"Detected parquet file: {infile}")
         _export_feature_scores_from_parquet(infile, outfile)
     elif infile.endswith('.osw'):
-        file_type = "osw"
         logger.info(f"Detected OSW file: {infile}")
         _export_feature_scores_from_osw(infile, outfile)
     else:

From 3cf3d16b82dbe90034b7dfe66f95001801c6e047 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:37:44 +0000
Subject: [PATCH 22/26] Add CHANGELOG entry for new feature-scores command

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 CHANGELOG.md | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8b1178e5..a3ca57f7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,21 @@
 
 All notable changes to this project will be documented in this file.
 
+## [Unreleased]
+
+### 🚀 Features
+
+- Add unified `pyprophet export feature-scores` command that works with all file formats (OSW, Parquet, Split Parquet)
+  - Auto-detects SCORE tables and adjusts behavior intelligently
+  - Applies RANK==1 filtering when SCORE tables exist
+  - Plots only VAR_ columns for unscored files
+  - Supports MS1, MS2, and transition level features
+
+### 🔧 Deprecated
+
+- Deprecate `pyprophet export score-plots` command in favor of `pyprophet export feature-scores`
+  - Old command still works with deprecation warning for backward compatibility
+
 ## [3.0.4] - 2025-10-21
 
 ### 🚀 Features

From b3dded817add38b5c6e5396cdbd55e59ab841966 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:51:57 +0000
Subject: [PATCH 23/26] Merge master branch and resolve conflicts in
 export_report.py

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/cli/export.py              |  31 +++++++
 pyprophet/export/export_report.py    |   3 +-
 pyprophet/io/export/osw.py           | 121 +++++++++++++++++++++++++++
 pyprophet/io/export/parquet.py       |  73 +++++++++++++++-
 pyprophet/io/export/split_parquet.py | 105 ++++++++++++++++++++++-
 pyprophet/report.py                  |  11 +--
 tests/test_pyprophet_export.py       | 109 ++++++++++++++----------
 7 files changed, 401 insertions(+), 52 deletions(-)

diff --git a/pyprophet/cli/export.py b/pyprophet/cli/export.py
index d1c43139..2dde75c3 100644
--- a/pyprophet/cli/export.py
+++ b/pyprophet/cli/export.py
@@ -14,6 +14,9 @@
 from ..export.export_report import (
     export_scored_report as _export_scored_report,
 )
+from ..export.export_report import (
+    export_feature_scores as _export_feature_scores,
+)
 from ..export.calibration_report import generate_report as generate_calibration_report
 from ..glyco.export import (
     export_score_plots as export_glyco_score_plots,
@@ -46,6 +49,7 @@ def export():
     export.add_command(export_feature_scores, name="feature-scores")
     export.add_command(export_score_plots, name="score-plots")  # Deprecated
     export.add_command(export_scored_report, name="score-report")
+    export.add_command(export_feature_scores, name="feature-scores")
     export.add_command(export_calibration_report, name="calibration-report")
 
     return export
@@ -912,6 +916,33 @@ def export_scored_report(infile):
     _export_scored_report(infile, outfile)
 
 
+# Export feature scores
+@click.command(name="feature-scores", cls=AdvancedHelpCommand)
+@click.option(
+    "--in",
+    "infile",
+    required=True,
+    type=click.Path(exists=True),
+    help="PyProphet input file (OSW, Parquet, or Split Parquet directory).",
+)
+@click.option(
+    "--out",
+    "outfile",
+    type=click.Path(exists=False),
+    help="Output PDF file. If not provided, will be auto-generated based on input filename.",
+)
+@measure_memory_usage_and_time
+def export_feature_scores(infile, outfile):
+    """
+    Export feature score plots from a PyProphet input file.
+    
+    Creates plots showing the distribution of feature scores (var_* columns)
+    at different levels (ms1, ms2, transition, alignment) colored by target/decoy status.
+    Works with OSW, Parquet, and Split Parquet files (scored or unscored).
+    """
+    _export_feature_scores(infile, outfile)
+
+
 # Export OpenSwath Calibration debug plots
 @click.command(name="calibration-report", cls=AdvancedHelpCommand)
 @click.option(
diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index 040d7f22..7eddbc33 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -1,6 +1,8 @@
 import sqlite3
 import pandas as pd
+import os
 from pathlib import Path
+from loguru import logger
 
 
 from .._config import ExportIOConfig
@@ -9,7 +11,6 @@
 from ..io.util import get_parquet_column_names
 from ..io.util import check_sqlite_table
 from ..report import plot_scores
-from loguru import logger
 
 
 def _check_pyarrow_available():
diff --git a/pyprophet/io/export/osw.py b/pyprophet/io/export/osw.py
index 5e015039..b77cbc96 100644
--- a/pyprophet/io/export/osw.py
+++ b/pyprophet/io/export/osw.py
@@ -860,6 +860,127 @@ def _get_peptide_protein_score_table_sqlite(self, con, level: str) -> str:
 
         return f"{view_name} AS ({merged})"
 
+    def export_feature_scores(self, outfile: str, plot_callback):
+        """
+        Export feature scores from OSW file for plotting.
+        
+        Parameters
+        ----------
+        outfile : str
+            Path to the output PDF file.
+        plot_callback : callable
+            Function to call for plotting each level's data.
+            Signature: plot_callback(df, outfile, level, append)
+        """
+        con = sqlite3.connect(self.infile)
+        
+        try:
+            # Process MS1 level if available
+            if check_sqlite_table(con, "FEATURE_MS1"):
+                logger.info("Processing MS1 level feature scores")
+                # Get only VAR_ columns to reduce memory usage
+                cursor = con.cursor()
+                cursor.execute("PRAGMA table_info(FEATURE_MS1)")
+                all_cols = [row[1] for row in cursor.fetchall()]
+                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                
+                if var_cols:
+                    var_cols_sql = ", ".join([f"FEATURE_MS1.{col}" for col in var_cols])
+                    ms1_query = f"""
+                        SELECT 
+                            {var_cols_sql},
+                            PRECURSOR.DECOY
+                        FROM FEATURE_MS1
+                        INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
+                        INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                    """
+                    df_ms1 = pd.read_sql_query(ms1_query, con)
+                    if not df_ms1.empty:
+                        plot_callback(df_ms1, outfile, "ms1", append=False)
+                else:
+                    logger.warning("No VAR_ columns found in FEATURE_MS1 table")
+            
+            # Process MS2 level if available
+            if check_sqlite_table(con, "FEATURE_MS2"):
+                logger.info("Processing MS2 level feature scores")
+                # Get only VAR_ columns to reduce memory usage
+                cursor = con.cursor()
+                cursor.execute("PRAGMA table_info(FEATURE_MS2)")
+                all_cols = [row[1] for row in cursor.fetchall()]
+                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                
+                if var_cols:
+                    var_cols_sql = ", ".join([f"FEATURE_MS2.{col}" for col in var_cols])
+                    ms2_query = f"""
+                        SELECT 
+                            {var_cols_sql},
+                            PRECURSOR.DECOY
+                        FROM FEATURE_MS2
+                        INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
+                        INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                    """
+                    df_ms2 = pd.read_sql_query(ms2_query, con)
+                    if not df_ms2.empty:
+                        append = check_sqlite_table(con, "FEATURE_MS1")
+                        plot_callback(df_ms2, outfile, "ms2", append=append)
+                else:
+                    logger.warning("No VAR_ columns found in FEATURE_MS2 table")
+            
+            # Process transition level if available
+            if check_sqlite_table(con, "FEATURE_TRANSITION"):
+                logger.info("Processing transition level feature scores")
+                # Get only VAR_ columns to reduce memory usage
+                cursor = con.cursor()
+                cursor.execute("PRAGMA table_info(FEATURE_TRANSITION)")
+                all_cols = [row[1] for row in cursor.fetchall()]
+                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                
+                if var_cols:
+                    var_cols_sql = ", ".join([f"FEATURE_TRANSITION.{col}" for col in var_cols])
+                    transition_query = f"""
+                        SELECT 
+                            {var_cols_sql},
+                            TRANSITION.DECOY
+                        FROM FEATURE_TRANSITION
+                        INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
+                        INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
+                    """
+                    df_transition = pd.read_sql_query(transition_query, con)
+                    if not df_transition.empty:
+                        append = check_sqlite_table(con, "FEATURE_MS1") or check_sqlite_table(con, "FEATURE_MS2")
+                        plot_callback(df_transition, outfile, "transition", append=append)
+                else:
+                    logger.warning("No VAR_ columns found in FEATURE_TRANSITION table")
+            
+            # Process alignment level if available
+            if check_sqlite_table(con, "FEATURE_MS2_ALIGNMENT"):
+                logger.info("Processing alignment level feature scores")
+                # Get only VAR_ columns to reduce memory usage
+                cursor = con.cursor()
+                cursor.execute("PRAGMA table_info(FEATURE_MS2_ALIGNMENT)")
+                all_cols = [row[1] for row in cursor.fetchall()]
+                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                
+                if var_cols:
+                    var_cols_sql = ", ".join(var_cols)
+                    alignment_query = f"""
+                        SELECT 
+                            {var_cols_sql},
+                            LABEL AS DECOY
+                        FROM FEATURE_MS2_ALIGNMENT
+                    """
+                    df_alignment = pd.read_sql_query(alignment_query, con)
+                    if not df_alignment.empty:
+                        append = (check_sqlite_table(con, "FEATURE_MS1") or 
+                                 check_sqlite_table(con, "FEATURE_MS2") or 
+                                 check_sqlite_table(con, "FEATURE_TRANSITION"))
+                        plot_callback(df_alignment, outfile, "alignment", append=append)
+                else:
+                    logger.warning("No VAR_ columns found in FEATURE_MS2_ALIGNMENT table")
+        
+        finally:
+            con.close()
+
 
 class OSWWriter(BaseOSWWriter):
     """
diff --git a/pyprophet/io/export/parquet.py b/pyprophet/io/export/parquet.py
index f9cc2e19..ef523839 100644
--- a/pyprophet/io/export/parquet.py
+++ b/pyprophet/io/export/parquet.py
@@ -4,7 +4,7 @@
 
 from ..._config import ExportIOConfig
 from .._base import BaseParquetReader, BaseParquetWriter
-from ..util import get_parquet_column_names
+from ..util import get_parquet_column_names, _ensure_pyarrow
 
 
 class ParquetReader(BaseParquetReader):
@@ -601,6 +601,77 @@ def _read_for_export_scored_report(self, con) -> pd.DataFrame:
 
         return df
 
+    def export_feature_scores(self, outfile: str, plot_callback):
+        """
+        Export feature scores from Parquet file for plotting.
+        
+        Parameters
+        ----------
+        outfile : str
+            Path to the output PDF file.
+        plot_callback : callable
+            Function to call for plotting each level's data.
+            Signature: plot_callback(df, outfile, level, append)
+        """
+        logger.info(f"Reading parquet file: {self.infile}")
+        # Ensure pyarrow is available
+        pa, _, _ = _ensure_pyarrow()
+        
+        # First, read only column names to identify what to load
+        parquet_file = pa.parquet.ParquetFile(self.infile)
+        all_columns = parquet_file.schema.names
+        
+        # Identify columns to read for each level
+        ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
+        ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
+        transition_cols = [col for col in all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
+        
+        # Determine which columns to read (only what we need)
+        cols_to_read = set()
+        if ms1_cols and "PRECURSOR_DECOY" in all_columns:
+            cols_to_read.update(ms1_cols)
+            cols_to_read.add("PRECURSOR_DECOY")
+        if ms2_cols and "PRECURSOR_DECOY" in all_columns:
+            cols_to_read.update(ms2_cols)
+            cols_to_read.add("PRECURSOR_DECOY")
+        if transition_cols and "TRANSITION_DECOY" in all_columns:
+            cols_to_read.update(transition_cols)
+            cols_to_read.add("TRANSITION_DECOY")
+        
+        if not cols_to_read:
+            logger.warning("No VAR_ columns found in parquet file")
+            return
+        
+        # Read only the columns we need
+        logger.info(f"Reading {len(cols_to_read)} columns from parquet file")
+        df = pd.read_parquet(self.infile, columns=list(cols_to_read))
+        
+        # Process MS1 level
+        if ms1_cols and "PRECURSOR_DECOY" in df.columns:
+            logger.info("Processing MS1 level feature scores")
+            ms1_df = df[ms1_cols + ["PRECURSOR_DECOY"]].copy()
+            ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+            plot_callback(ms1_df, outfile, "ms1", append=False)
+            del ms1_df  # Free memory
+        
+        # Process MS2 level
+        if ms2_cols and "PRECURSOR_DECOY" in df.columns:
+            logger.info("Processing MS2 level feature scores")
+            ms2_df = df[ms2_cols + ["PRECURSOR_DECOY"]].copy()
+            ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+            append = bool(ms1_cols)
+            plot_callback(ms2_df, outfile, "ms2", append=append)
+            del ms2_df  # Free memory
+        
+        # Process transition level
+        if transition_cols and "TRANSITION_DECOY" in df.columns:
+            logger.info("Processing transition level feature scores")
+            transition_df = df[transition_cols + ["TRANSITION_DECOY"]].copy()
+            transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
+            append = bool(ms1_cols or ms2_cols)
+            plot_callback(transition_df, outfile, "transition", append=append)
+            del transition_df  # Free memory
+
 
 class ParquetWriter(BaseParquetWriter):
     """
diff --git a/pyprophet/io/export/split_parquet.py b/pyprophet/io/export/split_parquet.py
index 265130a8..60cf431a 100644
--- a/pyprophet/io/export/split_parquet.py
+++ b/pyprophet/io/export/split_parquet.py
@@ -4,7 +4,7 @@
 import duckdb
 from loguru import logger
 
-from ..util import get_parquet_column_names
+from ..util import get_parquet_column_names, _ensure_pyarrow
 from .._base import BaseSplitParquetReader, BaseSplitParquetWriter
 from ..._config import ExportIOConfig
 
@@ -666,6 +666,109 @@ def _build_feature_vars_sql(self) -> str:
 
         return ", " + ", ".join(feature_vars) if feature_vars else ""
 
+    def export_feature_scores(self, outfile: str, plot_callback):
+        """
+        Export feature scores from split Parquet directory for plotting.
+        
+        Parameters
+        ----------
+        outfile : str
+            Path to the output PDF file.
+        plot_callback : callable
+            Function to call for plotting each level's data.
+            Signature: plot_callback(df, outfile, level, append)
+        """
+        # Ensure pyarrow is available
+        pa, _, _ = _ensure_pyarrow()
+        
+        # Read precursor features - only necessary columns
+        precursor_file = os.path.join(self.infile, "precursors_features.parquet")
+        logger.info(f"Reading precursor features from: {precursor_file}")
+        
+        # First check what columns are available
+        precursor_parquet = pa.parquet.ParquetFile(precursor_file)
+        all_columns = precursor_parquet.schema.names
+        
+        # Identify columns to read
+        ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
+        ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
+        
+        cols_to_read = set()
+        if ms1_cols and "PRECURSOR_DECOY" in all_columns:
+            cols_to_read.update(ms1_cols)
+            cols_to_read.add("PRECURSOR_DECOY")
+        if ms2_cols and "PRECURSOR_DECOY" in all_columns:
+            cols_to_read.update(ms2_cols)
+            cols_to_read.add("PRECURSOR_DECOY")
+        
+        if cols_to_read:
+            logger.info(f"Reading {len(cols_to_read)} columns from precursor features")
+            df_precursor = pd.read_parquet(precursor_file, columns=list(cols_to_read))
+            
+            # Process MS1 level
+            if ms1_cols and "PRECURSOR_DECOY" in df_precursor.columns:
+                logger.info("Processing MS1 level feature scores")
+                ms1_df = df_precursor[ms1_cols + ["PRECURSOR_DECOY"]].copy()
+                ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+                plot_callback(ms1_df, outfile, "ms1", append=False)
+                del ms1_df  # Free memory
+            
+            # Process MS2 level
+            if ms2_cols and "PRECURSOR_DECOY" in df_precursor.columns:
+                logger.info("Processing MS2 level feature scores")
+                ms2_df = df_precursor[ms2_cols + ["PRECURSOR_DECOY"]].copy()
+                ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
+                append = bool(ms1_cols)
+                plot_callback(ms2_df, outfile, "ms2", append=append)
+                del ms2_df  # Free memory
+            
+            del df_precursor  # Free memory
+        
+        # Read transition features if available
+        transition_file = os.path.join(self.infile, "transition_features.parquet")
+        if os.path.exists(transition_file):
+            logger.info(f"Reading transition features from: {transition_file}")
+            
+            # Check what columns are available
+            transition_parquet = pa.parquet.ParquetFile(transition_file)
+            transition_all_columns = transition_parquet.schema.names
+            transition_cols = [col for col in transition_all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
+            
+            if transition_cols and "TRANSITION_DECOY" in transition_all_columns:
+                # Read only necessary columns
+                cols_to_read = transition_cols + ["TRANSITION_DECOY"]
+                logger.info(f"Reading {len(cols_to_read)} columns from transition features")
+                df_transition = pd.read_parquet(transition_file, columns=cols_to_read)
+                
+                logger.info("Processing transition level feature scores")
+                transition_df = df_transition[transition_cols + ["TRANSITION_DECOY"]].copy()
+                transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
+                append = bool(ms1_cols or ms2_cols)
+                plot_callback(transition_df, outfile, "transition", append=append)
+                del transition_df, df_transition  # Free memory
+        
+        # Read alignment features if available
+        alignment_file = os.path.join(self.infile, "feature_alignment.parquet")
+        if os.path.exists(alignment_file):
+            logger.info(f"Reading alignment features from: {alignment_file}")
+            
+            # Check what columns are available
+            alignment_parquet = pa.parquet.ParquetFile(alignment_file)
+            alignment_all_columns = alignment_parquet.schema.names
+            var_cols = [col for col in alignment_all_columns if col.startswith("VAR_")]
+            
+            if var_cols and "DECOY" in alignment_all_columns:
+                # Read only necessary columns
+                cols_to_read = var_cols + ["DECOY"]
+                logger.info(f"Reading {len(cols_to_read)} columns from alignment features")
+                df_alignment = pd.read_parquet(alignment_file, columns=cols_to_read)
+                
+                logger.info("Processing alignment level feature scores")
+                alignment_df = df_alignment[var_cols + ["DECOY"]].copy()
+                append = bool(ms1_cols or ms2_cols or (os.path.exists(transition_file) and transition_cols))
+                plot_callback(alignment_df, outfile, "alignment", append=append)
+                del alignment_df, df_alignment  # Free memory
+
 
 class SplitParquetWriter(BaseSplitParquetWriter):
     """
diff --git a/pyprophet/report.py b/pyprophet/report.py
index ecd2534e..681e75a0 100644
--- a/pyprophet/report.py
+++ b/pyprophet/report.py
@@ -853,11 +853,12 @@ def plot_scores(df, out, color_palette="normal"):
             "Error: The matplotlib package is required to create a report."
         )
 
-    score_columns = (
-        ["SCORE"]
-        + [c for c in df.columns if c.startswith("MAIN_VAR_")]
-        + [c for c in df.columns if c.startswith("VAR_")]
-    )
+    # Build score_columns list, only including SCORE if it exists
+    score_columns = []
+    if "SCORE" in df.columns:
+        score_columns.append("SCORE")
+    score_columns += [c for c in df.columns if c.startswith("MAIN_VAR_")]
+    score_columns += [c for c in df.columns if c.startswith("VAR_")]
 
     t_col, d_col = color_blind_friendly(color_palette)
 
diff --git a/tests/test_pyprophet_export.py b/tests/test_pyprophet_export.py
index 8c5c4f05..cbfff358 100644
--- a/tests/test_pyprophet_export.py
+++ b/tests/test_pyprophet_export.py
@@ -9,6 +9,8 @@
 import pandas as pd
 import pytest
 
+from pyprophet.export.export_report import export_feature_scores
+
 pd.options.display.expand_frame_repr = False
 pd.options.display.precision = 4
 pd.options.display.max_columns = None
@@ -91,10 +93,12 @@ def run_pyprophet_command(cmd, temp_folder):
         ).decode()
     except subprocess.CalledProcessError as error:
         print(f"Command failed: {cmd}\n{error.output.decode()}", file=sys.stderr)
-        if "NotImplementedError" in error.output.decode(): # attempt to catch the specific error rather than the CalledProcessError
+        if (
+            "NotImplementedError" in error.output.decode()
+        ):  # attempt to catch the specific error rather than the CalledProcessError
             raise NotImplementedError
         else:
-            raise 
+            raise
 
 
 def validate_export_results(
@@ -150,12 +154,11 @@ def test_osw_analysis(
         f"{temp_folder}/test_data.tsv",
     )
 
+
 @pytest.mark.parametrize(
-    "calib, rt_unit",
-    [ (True, 'iRT'), (False, 'iRT'), (True, 'RT'), (False, 'RT')]
+    "calib, rt_unit", [(True, "iRT"), (False, "iRT"), (True, "RT"), (False, "RT")]
 )
-def test_osw_analysis_libExport(input_strategy, temp_folder, regtest, calib, rt_unit
-):
+def test_osw_analysis_libExport(input_strategy, temp_folder, regtest, calib, rt_unit):
     cmd = f"pyprophet score {input_strategy['cmd_prefix']} --level=ms2 --test --pi0_lambda=0.001 0 0 --ss_iteration_fdr=0.02 && "
 
     # peptide-level
@@ -164,7 +167,6 @@ def test_osw_analysis_libExport(input_strategy, temp_folder, regtest, calib, rt_
     # protein-level
     cmd += f"pyprophet infer protein --pi0_lambda=0 0 0 {input_strategy['cmd_prefix']} --context=global && "
 
-
     # export
     if calib:
         cmd += f"pyprophet export library {input_strategy['cmd_prefix']} --out={temp_folder}/test_lib.tsv --test --max_peakgroup_qvalue=1 --max_global_peptide_qvalue=1 --max_global_protein_qvalue=1 --rt_unit={rt_unit}"
@@ -183,6 +185,7 @@ def test_osw_analysis_libExport(input_strategy, temp_folder, regtest, calib, rt_
             f"{temp_folder}/test_lib.tsv",
         )
 
+
 def test_osw_unscored(input_strategy, temp_folder, regtest):
     """Test export of unscored OSW data"""
     cmd = f"pyprophet export tsv {input_strategy['cmd_prefix']} --out={temp_folder}/test_data.tsv --format=legacy_merged"
@@ -275,30 +278,31 @@ def test_parquet_export_scored_osw(test_data_osw, temp_folder, regtest):
     """Test exporting scored OSW with SCORE_ tables to parquet format"""
     # Score at MS2 level
     cmd = f"pyprophet score --in={test_data_osw} --level=ms2 --test --pi0_lambda=0.001 0 0 --ss_iteration_fdr=0.02 && "
-    
+
     # Infer peptide level with global context
     cmd += f"pyprophet infer peptide --pi0_lambda=0.001 0 0 --in={test_data_osw} --context=global && "
-    
+
     # Infer protein level with global context
     cmd += f"pyprophet infer protein --pi0_lambda=0 0 0 --in={test_data_osw} --context=global && "
-    
+
     # Export to parquet (should include SCORE_ tables)
     cmd += f"pyprophet export parquet --in={test_data_osw} --out={temp_folder}/test_data_scored.parquet"
-    
+
     run_pyprophet_command(cmd, temp_folder)
-    
+
     # Verify the parquet file exists and has data
     import pyarrow.parquet as pq
+
     table = pq.read_table(f"{temp_folder}/test_data_scored.parquet")
     df = table.to_pandas()
-    
+
     # Check that we have data
     assert len(df) > 0, "Exported parquet file should not be empty"
-    
+
     # Check that score columns are present
-    score_columns = [col for col in df.columns if col.startswith('SCORE_')]
+    score_columns = [col for col in df.columns if col.startswith("SCORE_")]
     assert len(score_columns) > 0, "Exported parquet should contain SCORE_ columns"
-    
+
     print(f"Exported {len(df)} rows with {len(df.columns)} columns", file=regtest)
     print(f"Score columns found: {sorted(score_columns)}", file=regtest)
     print(df.head(10).sort_index(axis=1), file=regtest)
@@ -308,36 +312,42 @@ def test_parquet_export_no_transition_data(test_data_osw, temp_folder, regtest):
     """Test exporting parquet without transition data using --no-include_transition_data flag"""
     # Score at MS2 level
     cmd = f"pyprophet score --in={test_data_osw} --level=ms2 --test --pi0_lambda=0.001 0 0 --ss_iteration_fdr=0.02 && "
-    
+
     # Infer peptide level with global context
     cmd += f"pyprophet infer peptide --pi0_lambda=0.001 0 0 --in={test_data_osw} --context=global && "
-    
+
     # Infer protein level with global context
     cmd += f"pyprophet infer protein --pi0_lambda=0 0 0 --in={test_data_osw} --context=global && "
-    
+
     # Export to parquet without transition data
     cmd += f"pyprophet export parquet --in={test_data_osw} --out={temp_folder}/test_data_no_transition.parquet --no-include_transition_data"
-    
+
     run_pyprophet_command(cmd, temp_folder)
-    
+
     # Verify the parquet file exists and has data
     import pyarrow.parquet as pq
+
     table = pq.read_table(f"{temp_folder}/test_data_no_transition.parquet")
     df = table.to_pandas()
-    
+
     # Check that we have data
     assert len(df) > 0, "Exported parquet file should not be empty"
-    
+
     # Check that transition-specific columns are NOT present
     # transition_columns = [col for col in df.columns if 'TRANSITION' in col.upper()]
     # assert len(transition_columns) == 0, "Exported parquet should not contain TRANSITION columns when --no-include_transition_data is used"
-    assert df['TRANSITION_ID'].isnull().all(), "TRANSITION_ID column should be empty when --no-include_transition_data is used"
-    
+    assert df["TRANSITION_ID"].isnull().all(), (
+        "TRANSITION_ID column should be empty when --no-include_transition_data is used"
+    )
+
     # Check that score columns are present
-    score_columns = [col for col in df.columns if col.startswith('SCORE_')]
+    score_columns = [col for col in df.columns if col.startswith("SCORE_")]
     assert len(score_columns) > 0, "Exported parquet should contain SCORE_ columns"
-    
-    print(f"Exported {len(df)} rows with {len(df.columns)} columns (no transition data)", file=regtest)
+
+    print(
+        f"Exported {len(df)} rows with {len(df.columns)} columns (no transition data)",
+        file=regtest,
+    )
     print(f"Score columns found: {sorted(score_columns)}", file=regtest)
     print(df.head(10).sort_index(axis=1), file=regtest)
 
@@ -346,47 +356,58 @@ def test_parquet_export_split_format(test_data_osw, temp_folder, regtest):
     """Test exporting to split parquet format with score data"""
     # Score at MS2 level
     cmd = f"pyprophet score --in={test_data_osw} --level=ms2 --test --pi0_lambda=0.001 0 0 --ss_iteration_fdr=0.02 && "
-    
+
     # Infer peptide level with global context
     cmd += f"pyprophet infer peptide --pi0_lambda=0.001 0 0 --in={test_data_osw} --context=global && "
-    
+
     # Infer protein level with global context
     cmd += f"pyprophet infer protein --pi0_lambda=0 0 0 --in={test_data_osw} --context=global && "
-    
+
     # Export to split parquet format
     cmd += f"pyprophet export parquet --in={test_data_osw} --out={temp_folder}/test_data_split --split_transition_data"
-    
+
     run_pyprophet_command(cmd, temp_folder)
-    
+
     # Verify the directory exists and contains parquet files
     import pyarrow.parquet as pq
+
     split_dir = Path(temp_folder) / "test_data_split"
     assert split_dir.exists(), "Split parquet directory should exist"
-    
+
     precursor_file = split_dir / "precursors_features.parquet"
     transition_file = split_dir / "transition_features.parquet"
-    
+
     assert precursor_file.exists(), "precursors_features.parquet should exist"
     assert transition_file.exists(), "transition_features.parquet should exist"
-    
+
     # Read precursor data
     precursor_table = pq.read_table(str(precursor_file))
     precursor_df = precursor_table.to_pandas()
-    
+
     # Read transition data
     transition_table = pq.read_table(str(transition_file))
     transition_df = transition_table.to_pandas()
-    
+
     # Check that we have data in both files
     assert len(precursor_df) > 0, "Precursor parquet file should not be empty"
     assert len(transition_df) > 0, "Transition parquet file should not be empty"
-    
+
     # Check that score columns are present in precursor file
-    precursor_score_columns = [col for col in precursor_df.columns if col.startswith('SCORE_')]
-    assert len(precursor_score_columns) > 0, "Precursor parquet should contain SCORE_ columns"
-    
-    print(f"Precursor data: {len(precursor_df)} rows with {len(precursor_df.columns)} columns", file=regtest)
-    print(f"Transition data: {len(transition_df)} rows with {len(transition_df.columns)} columns", file=regtest)
+    precursor_score_columns = [
+        col for col in precursor_df.columns if col.startswith("SCORE_")
+    ]
+    assert len(precursor_score_columns) > 0, (
+        "Precursor parquet should contain SCORE_ columns"
+    )
+
+    print(
+        f"Precursor data: {len(precursor_df)} rows with {len(precursor_df.columns)} columns",
+        file=regtest,
+    )
+    print(
+        f"Transition data: {len(transition_df)} rows with {len(transition_df.columns)} columns",
+        file=regtest,
+    )
     print(f"Precursor score columns: {sorted(precursor_score_columns)}", file=regtest)
     print("Precursor data sample:", file=regtest)
     print(precursor_df.head(5).sort_index(axis=1), file=regtest)

From 3507b952f75b2c4fc1b783d693758e3f6330d806 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 17:01:04 +0000
Subject: [PATCH 24/26] Refactor: use existing _ensure_pyarrow() instead of
 custom function

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py | 33 +++++--------------------------
 1 file changed, 5 insertions(+), 28 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index 69295e38..0a1e44e5 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -10,33 +10,10 @@
 from ..io.dispatcher import ReaderDispatcher
 from ..io.util import get_parquet_column_names
 from ..io.util import check_sqlite_table
+from ..io.util import _ensure_pyarrow
 from ..report import plot_scores
 
 
-def _check_pyarrow_available():
-    """
-    Helper function to check if pyarrow is available and provide helpful error message.
-    
-    Returns
-    -------
-    module
-        The pyarrow.parquet module if available
-        
-    Raises
-    ------
-    ImportError
-        If pyarrow is not installed
-    """
-    try:
-        import pyarrow.parquet as pq
-        return pq
-    except ImportError:
-        raise ImportError(
-            "pyarrow is required for parquet file operations. "
-            "Install it with: pip install pyarrow or pip install pyprophet[parquet]"
-        )
-
-
 def export_feature_scores(infile, outfile=None):
     """
     Export feature score plots from a PyProphet input file.
@@ -277,10 +254,10 @@ def _export_feature_scores_from_parquet(infile, outfile=None):
     """
     Export feature scores from single Parquet file.
     """
-    pq = _check_pyarrow_available()
+    pa, _, _ = _ensure_pyarrow()
     
     # Read parquet file
-    table = pq.read_table(infile)
+    table = pa.parquet.read_table(infile)
     df = table.to_pandas()
     
     # Check for SCORE columns
@@ -323,14 +300,14 @@ def _export_feature_scores_from_split_parquet(infile, outfile=None):
     """
     Export feature scores from split Parquet directory.
     """
-    pq = _check_pyarrow_available()
+    pa, _, _ = _ensure_pyarrow()
     
     inpath = Path(infile)
     
     # Read precursor features
     precursor_file = inpath / "precursors_features.parquet"
     if precursor_file.exists():
-        table = pq.read_table(str(precursor_file))
+        table = pa.parquet.read_table(str(precursor_file))
         df = table.to_pandas()
         
         # Check for SCORE columns

From e10451ceda828f0a41224941538e13ad74fe8562 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 29 Oct 2025 17:24:41 +0000
Subject: [PATCH 25/26] Enhance reader methods with SCORE detection, simplify
 export_report.py to use readers

Co-authored-by: singjc <32938975+singjc@users.noreply.github.com>
---
 pyprophet/export/export_report.py    | 480 +++++++++++----------------
 pyprophet/io/export/osw.py           | 203 ++++++++---
 pyprophet/io/export/parquet.py       |  64 +++-
 pyprophet/io/export/split_parquet.py |  66 +++-
 4 files changed, 466 insertions(+), 347 deletions(-)

diff --git a/pyprophet/export/export_report.py b/pyprophet/export/export_report.py
index 0a1e44e5..4a105c6b 100644
--- a/pyprophet/export/export_report.py
+++ b/pyprophet/export/export_report.py
@@ -27,325 +27,215 @@ def export_feature_scores(infile, outfile=None):
     infile : str
         Path to input file (OSW, Parquet, or Split Parquet directory)
     outfile : str, optional
-        Base path for output PDF files. If None, derives from infile.
+        Path for output PDF files. If None, derives from infile.
     """
-    # Determine file type and route to appropriate handler
-    inpath = Path(infile)
-    
-    # Check if it's a directory (split parquet) or file
-    if inpath.is_dir():
-        # Split parquet directory
-        logger.info(f"Detected split parquet directory: {infile}")
-        _export_feature_scores_from_split_parquet(infile, outfile)
-    elif infile.endswith('.parquet'):
-        logger.info(f"Detected parquet file: {infile}")
-        _export_feature_scores_from_parquet(infile, outfile)
-    elif infile.endswith('.osw'):
-        logger.info(f"Detected OSW file: {infile}")
-        _export_feature_scores_from_osw(infile, outfile)
+    # Detect file type based on extension and existence
+    if infile.endswith(".osw"):
+        file_type = "osw"
+    elif infile.endswith(".parquet"):
+        file_type = "parquet"
+    elif os.path.isdir(infile):
+        # Check if it's a split parquet directory
+        precursor_file = os.path.join(infile, "precursors_features.parquet")
+        if os.path.exists(precursor_file):
+            file_type = "parquet_split"
+        else:
+            raise ValueError(f"Directory {infile} does not appear to be a valid split parquet directory")
     else:
-        raise ValueError(f"Unsupported file format: {infile}. Must be .osw, .parquet, or split parquet directory.")
+        raise ValueError(f"Unsupported file type for {infile}")
+    
+    logger.info(f"Detected file type: {file_type}")
+    
+    # Generate output filename if not provided
+    if outfile is None:
+        if file_type == "osw":
+            outfile = infile.replace(".osw", "_feature_scores.pdf")
+        elif file_type == "parquet":
+            outfile = infile.replace(".parquet", "_feature_scores.pdf")
+        else:  # parquet_split
+            outfile = infile.rstrip("/") + "_feature_scores.pdf"
+    
+    logger.info(f"Output file: {outfile}")
+    
+    # Create config and reader based on file type
+    config = ExportIOConfig(
+        infile=infile,
+        outfile=outfile,
+        subsample_ratio=1.0,
+        level="export",
+        context="export_feature_scores",
+    )
+    
+    # Get appropriate reader
+    reader = ReaderDispatcher.get_reader(config)
+    
+    # Export feature scores using the reader's method
+    reader.export_feature_scores(outfile, _plot_feature_scores)
+    
+    logger.info(f"Feature score plots exported to {outfile}")
 
 
-def _export_feature_scores_from_osw(infile, outfile=None):
+def _plot_feature_scores(df: pd.DataFrame, outfile: str, level: str, append: bool = False, sample_size: int = 100000):
     """
-    Export feature scores from OSW file.
-    Detects if SCORE tables exist and adjusts behavior accordingly.
+    Create plots for feature scores at a specific level.
+    
+    Parameters
+    ----------
+    df : pd.DataFrame
+        DataFrame containing feature scores and DECOY column.
+    outfile : str
+        Path to the output PDF file.
+    level : str
+        Level name (ms1, ms2, transition, or alignment).
+    append : bool
+        If True, append to existing PDF. If False, create new PDF.
+    sample_size : int
+        Maximum number of rows to use for plotting. If df has more rows,
+        a stratified sample (by DECOY) will be taken to reduce memory usage.
     """
-    con = sqlite3.connect(infile)
+    # Get all columns that contain feature scores (VAR_ or SCORE columns)
+    score_cols = [col for col in df.columns if ("VAR_" in col.upper() or col.upper().startswith("SCORE")) and col != "DECOY"]
     
-    # Check for SCORE tables
-    has_score_ms2 = check_sqlite_table(con, "SCORE_MS2")
-    has_score_ms1 = check_sqlite_table(con, "SCORE_MS1")
-    has_score_transition = check_sqlite_table(con, "SCORE_TRANSITION")
+    if not score_cols:
+        logger.warning(f"No feature score columns found for {level} level")
+        return
     
-    if has_score_ms2 or has_score_ms1 or has_score_transition:
-        logger.info("SCORE tables detected - applying RANK==1 filter and plotting SCORE + VAR_ columns")
-    else:
-        logger.info("No SCORE tables detected - plotting only VAR_ columns")
+    logger.info(f"Found {len(score_cols)} feature score columns for {level} level")
     
-    # MS2 level
-    if check_sqlite_table(con, "FEATURE_MS2"):
-        if outfile:
-            out_ms2 = outfile.replace('.pdf', '_ms2.pdf') if outfile.endswith('.pdf') else f"{outfile}_ms2.pdf"
-        else:
-            out_ms2 = infile.split(".osw")[0] + "_ms2_feature_scores.pdf"
-        
-        if has_score_ms2:
-            # Scored mode: Include SCORE columns and apply RANK==1 filter
-            query_ms2 = """
-SELECT *,
-       RUN_ID || '_' || PRECURSOR_ID AS GROUP_ID
-FROM FEATURE_MS2
-INNER JOIN
-  (SELECT RUN_ID,
-          ID,
-          PRECURSOR_ID,
-          EXP_RT
-   FROM FEATURE) AS FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
-INNER JOIN
-  (SELECT ID,
-          CHARGE AS VAR_PRECURSOR_CHARGE,
-          DECOY
-   FROM PRECURSOR) AS PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-INNER JOIN
-  (SELECT PRECURSOR_ID AS ID,
-          COUNT(*) AS VAR_TRANSITION_NUM_SCORE
-   FROM TRANSITION_PRECURSOR_MAPPING
-   INNER JOIN TRANSITION ON TRANSITION_PRECURSOR_MAPPING.TRANSITION_ID = TRANSITION.ID
-   WHERE DETECTING==1
-   GROUP BY PRECURSOR_ID) AS VAR_TRANSITION_SCORE ON FEATURE.PRECURSOR_ID = VAR_TRANSITION_SCORE.ID
-INNER JOIN SCORE_MS2 ON FEATURE.ID = SCORE_MS2.FEATURE_ID
-WHERE RANK == 1
-ORDER BY RUN_ID,
-         PRECURSOR.ID ASC,
-         FEATURE.EXP_RT ASC;
-"""
-        else:
-            # Unscored mode: Only VAR_ columns, no RANK filter
-            query_ms2 = """
-SELECT FEATURE_MS2.*,
-       FEATURE.RUN_ID,
-       FEATURE.PRECURSOR_ID,
-       FEATURE.EXP_RT,
-       PRECURSOR.CHARGE AS VAR_PRECURSOR_CHARGE,
-       PRECURSOR.DECOY,
-       VAR_TRANSITION_SCORE.VAR_TRANSITION_NUM_SCORE,
-       FEATURE.RUN_ID || '_' || FEATURE.PRECURSOR_ID AS GROUP_ID
-FROM FEATURE_MS2
-INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
-INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-LEFT JOIN
-  (SELECT PRECURSOR_ID AS ID,
-          COUNT(*) AS VAR_TRANSITION_NUM_SCORE
-   FROM TRANSITION_PRECURSOR_MAPPING
-   INNER JOIN TRANSITION ON TRANSITION_PRECURSOR_MAPPING.TRANSITION_ID = TRANSITION.ID
-   WHERE DETECTING==1
-   GROUP BY PRECURSOR_ID) AS VAR_TRANSITION_SCORE ON FEATURE.PRECURSOR_ID = VAR_TRANSITION_SCORE.ID
-ORDER BY FEATURE.RUN_ID,
-         PRECURSOR.ID ASC,
-         FEATURE.EXP_RT ASC;
-"""
+    # Prepare data for plotting - ensure DECOY column exists
+    if "DECOY" not in df.columns:
+        logger.warning(f"No DECOY column found for {level} level, skipping")
+        return
+    
+    # Only select the columns we need for plotting
+    plot_df = df[score_cols + ["DECOY"]].dropna(subset=["DECOY"]).copy()
+    
+    # Check if we have any data left after dropping NAs
+    if len(plot_df) == 0:
+        logger.warning(f"No valid data rows found for {level} level after removing rows with null DECOY values")
+        return
+    
+    # Memory optimization: Sample data if it's too large
+    if len(plot_df) > sample_size:
+        logger.info(f"Dataset has {len(plot_df)} rows, sampling {sample_size} rows (stratified by DECOY) to reduce memory usage")
+        # Stratified sampling to maintain target/decoy ratio
+        target_df = plot_df[plot_df["DECOY"] == 0]
+        decoy_df = plot_df[plot_df["DECOY"] == 1]
         
-        table_ms2 = pd.read_sql_query(query_ms2, con)
-        if len(table_ms2) > 0:
-            plot_scores(table_ms2, out_ms2)
-            logger.info(f"Exported MS2 feature scores to {out_ms2}")
-    
-    # MS1 level
-    if check_sqlite_table(con, "FEATURE_MS1"):
-        if outfile:
-            out_ms1 = outfile.replace('.pdf', '_ms1.pdf') if outfile.endswith('.pdf') else f"{outfile}_ms1.pdf"
-        else:
-            out_ms1 = infile.split(".osw")[0] + "_ms1_feature_scores.pdf"
+        # Calculate sample sizes proportional to original distribution
+        n_targets = len(target_df)
+        n_decoys = len(decoy_df)
+        total = n_targets + n_decoys
         
-        if has_score_ms1:
-            # Scored mode
-            query_ms1 = """
-SELECT *,
-       RUN_ID || '_' || PRECURSOR_ID AS GROUP_ID
-FROM FEATURE_MS1
-INNER JOIN
-  (SELECT RUN_ID,
-          ID,
-          PRECURSOR_ID,
-          EXP_RT
-   FROM FEATURE) AS FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
-INNER JOIN
-  (SELECT ID,
-          CHARGE AS VAR_PRECURSOR_CHARGE,
-          DECOY
-   FROM PRECURSOR) AS PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-INNER JOIN SCORE_MS1 ON FEATURE.ID = SCORE_MS1.FEATURE_ID
-WHERE RANK == 1
-ORDER BY RUN_ID,
-         PRECURSOR.ID ASC,
-         FEATURE.EXP_RT ASC;
-"""
-        else:
-            # Unscored mode
-            query_ms1 = """
-SELECT FEATURE_MS1.*,
-       FEATURE.RUN_ID,
-       FEATURE.PRECURSOR_ID,
-       FEATURE.EXP_RT,
-       PRECURSOR.CHARGE AS VAR_PRECURSOR_CHARGE,
-       PRECURSOR.DECOY,
-       FEATURE.RUN_ID || '_' || FEATURE.PRECURSOR_ID AS GROUP_ID
-FROM FEATURE_MS1
-INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
-INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-ORDER BY FEATURE.RUN_ID,
-         PRECURSOR.ID ASC,
-         FEATURE.EXP_RT ASC;
-"""
+        # Handle edge cases where one group might be empty
+        if total == 0:
+            logger.warning(f"No data with valid DECOY values for {level} level")
+            return
         
-        table_ms1 = pd.read_sql_query(query_ms1, con)
-        if len(table_ms1) > 0:
-            plot_scores(table_ms1, out_ms1)
-            logger.info(f"Exported MS1 feature scores to {out_ms1}")
-    
-    # Transition level
-    if check_sqlite_table(con, "FEATURE_TRANSITION"):
-        if outfile:
-            out_transition = outfile.replace('.pdf', '_transition.pdf') if outfile.endswith('.pdf') else f"{outfile}_transition.pdf"
-        else:
-            out_transition = infile.split(".osw")[0] + "_transition_feature_scores.pdf"
+        target_sample_size = int(sample_size * n_targets / total) if n_targets > 0 else 0
+        decoy_sample_size = int(sample_size * n_decoys / total) if n_decoys > 0 else 0
         
-        if has_score_transition:
-            # Scored mode
-            query_transition = """
-SELECT TRANSITION.DECOY AS DECOY,
-       FEATURE_TRANSITION.*,
-       PRECURSOR.CHARGE AS VAR_PRECURSOR_CHARGE,
-       TRANSITION.VAR_PRODUCT_CHARGE AS VAR_PRODUCT_CHARGE,
-       SCORE_TRANSITION.*,
-       RUN_ID || '_' || FEATURE_TRANSITION.FEATURE_ID || '_' || PRECURSOR_ID || '_' || FEATURE_TRANSITION.TRANSITION_ID AS GROUP_ID
-FROM FEATURE_TRANSITION
-INNER JOIN
-  (SELECT RUN_ID,
-          ID,
-          PRECURSOR_ID,
-          EXP_RT
-   FROM FEATURE) AS FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
-INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-INNER JOIN SCORE_TRANSITION ON FEATURE_TRANSITION.FEATURE_ID = SCORE_TRANSITION.FEATURE_ID
-AND FEATURE_TRANSITION.TRANSITION_ID = SCORE_TRANSITION.TRANSITION_ID
-INNER JOIN
-  (SELECT ID,
-          CHARGE AS VAR_PRODUCT_CHARGE,
-          DECOY
-   FROM TRANSITION) AS TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
-ORDER BY RUN_ID,
-         PRECURSOR.ID,
-         FEATURE.EXP_RT,
-         TRANSITION.ID;
-"""
-        else:
-            # Unscored mode
-            query_transition = """
-SELECT TRANSITION.DECOY AS DECOY,
-       FEATURE_TRANSITION.*,
-       FEATURE.RUN_ID,
-       FEATURE.PRECURSOR_ID,
-       FEATURE.EXP_RT,
-       PRECURSOR.CHARGE AS VAR_PRECURSOR_CHARGE,
-       TRANSITION.CHARGE AS VAR_PRODUCT_CHARGE,
-       FEATURE.RUN_ID || '_' || FEATURE_TRANSITION.FEATURE_ID || '_' || FEATURE.PRECURSOR_ID || '_' || FEATURE_TRANSITION.TRANSITION_ID AS GROUP_ID
-FROM FEATURE_TRANSITION
-INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
-INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
-INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
-ORDER BY FEATURE.RUN_ID,
-         PRECURSOR.ID,
-         FEATURE.EXP_RT,
-         TRANSITION.ID;
-"""
+        # Sample from each group
+        samples = []
+        if n_targets > 0:
+            if n_targets > target_sample_size and target_sample_size > 0:
+                target_sample = target_df.sample(n=target_sample_size, random_state=42)
+            else:
+                target_sample = target_df
+            samples.append(target_sample)
+            
+        if n_decoys > 0:
+            if n_decoys > decoy_sample_size and decoy_sample_size > 0:
+                decoy_sample = decoy_df.sample(n=decoy_sample_size, random_state=42)
+            else:
+                decoy_sample = decoy_df
+            samples.append(decoy_sample)
         
-        table_transition = pd.read_sql_query(query_transition, con)
-        if len(table_transition) > 0:
-            plot_scores(table_transition, out_transition)
-            logger.info(f"Exported transition feature scores to {out_transition}")
-    
-    con.close()
-
-
-def _export_feature_scores_from_parquet(infile, outfile=None):
-    """
-    Export feature scores from single Parquet file.
-    """
-    pa, _, _ = _ensure_pyarrow()
-    
-    # Read parquet file
-    table = pa.parquet.read_table(infile)
-    df = table.to_pandas()
-    
-    # Check for SCORE columns
-    score_columns = [col for col in df.columns if col.startswith('SCORE_')]
-    has_scores = len(score_columns) > 0
+        # Combine samples
+        plot_df = pd.concat(samples, ignore_index=True)
+        logger.info(f"Sampled {len(plot_df)} rows")
     
-    if has_scores:
-        logger.info("SCORE columns detected - applying RANK==1 filter and plotting SCORE + VAR_ columns")
-        # Filter to RANK==1 if SCORE_MS2_PEAK_GROUP_RANK exists
-        if 'SCORE_MS2_PEAK_GROUP_RANK' in df.columns:
-            df = df[df['SCORE_MS2_PEAK_GROUP_RANK'] == 1].copy()
-    else:
-        logger.info("No SCORE columns detected - plotting only VAR_ columns")
+    # Ensure DECOY is 0 or 1
+    if plot_df["DECOY"].dtype == bool:
+        plot_df["DECOY"] = plot_df["DECOY"].astype(int)
     
-    # Generate GROUP_ID if needed
-    if 'GROUP_ID' not in df.columns:
-        if 'RUN_ID' in df.columns and 'PRECURSOR_ID' in df.columns:
-            df['GROUP_ID'] = df['RUN_ID'].astype(str) + '_' + df['PRECURSOR_ID'].astype(str)
+    # Generate a temporary output file for this level
+    temp_outfile = outfile.replace(".pdf", f"_{level}_temp.pdf")
     
-    # Determine DECOY column name (could be PRECURSOR_DECOY or DECOY)
-    decoy_col = None
-    for col in ['DECOY', 'PRECURSOR_DECOY', 'PEPTIDE_DECOY']:
-        if col in df.columns:
-            decoy_col = col
-            break
+    # Rename columns to match plot_scores expectations
+    # plot_scores expects columns named "SCORE", "MAIN_VAR_*", or "VAR_*"
+    rename_dict = {}
+    for col in score_cols:
+        # Ensure column names start with VAR_ or SCORE
+        if not col.upper().startswith("VAR_") and not col.upper().startswith("SCORE"):
+            # Extract the var part from column names like FEATURE_MS1_VAR_XXX
+            parts = col.split("VAR_")
+            if len(parts) > 1:
+                new_name = "VAR_" + parts[-1]
+            else:
+                new_name = "VAR_" + col
+            rename_dict[col] = new_name
     
-    if decoy_col:
-        df = df.rename(columns={decoy_col: 'DECOY'})
+    if rename_dict:
+        plot_df.rename(columns=rename_dict, inplace=True)
     
-    # Export plots
-    if outfile is None:
-        outfile = infile.replace('.parquet', '_feature_scores.pdf')
+    # Call plot_scores with the formatted dataframe
+    plot_scores(plot_df, temp_outfile)
     
-    if len(df) > 0:
-        plot_scores(df, outfile)
-        logger.info(f"Exported feature scores to {outfile}")
-
-
-def _export_feature_scores_from_split_parquet(infile, outfile=None):
-    """
-    Export feature scores from split Parquet directory.
-    """
-    pa, _, _ = _ensure_pyarrow()
+    # Check if the temporary file was created and has content
+    if not os.path.exists(temp_outfile):
+        logger.warning(f"plot_scores did not create output file for {level} level, skipping")
+        return
     
-    inpath = Path(infile)
+    if os.path.getsize(temp_outfile) == 0:
+        logger.warning(f"plot_scores created empty output file for {level} level, skipping")
+        os.remove(temp_outfile)
+        return
     
-    # Read precursor features
-    precursor_file = inpath / "precursors_features.parquet"
-    if precursor_file.exists():
-        table = pa.parquet.read_table(str(precursor_file))
-        df = table.to_pandas()
-        
-        # Check for SCORE columns
-        score_columns = [col for col in df.columns if col.startswith('SCORE_')]
-        has_scores = len(score_columns) > 0
-        
-        if has_scores:
-            logger.info("SCORE columns detected - applying RANK==1 filter and plotting SCORE + VAR_ columns")
-            # Filter to RANK==1 if SCORE_MS2_PEAK_GROUP_RANK exists
-            if 'SCORE_MS2_PEAK_GROUP_RANK' in df.columns:
-                df = df[df['SCORE_MS2_PEAK_GROUP_RANK'] == 1].copy()
-        else:
-            logger.info("No SCORE columns detected - plotting only VAR_ columns")
-        
-        # Generate GROUP_ID if needed
-        if 'GROUP_ID' not in df.columns:
-            if 'RUN_ID' in df.columns and 'PRECURSOR_ID' in df.columns:
-                df['GROUP_ID'] = df['RUN_ID'].astype(str) + '_' + df['PRECURSOR_ID'].astype(str)
-        
-        # Determine DECOY column name
-        decoy_col = None
-        for col in ['DECOY', 'PRECURSOR_DECOY', 'PEPTIDE_DECOY']:
-            if col in df.columns:
-                decoy_col = col
-                break
-        
-        if decoy_col:
-            df = df.rename(columns={decoy_col: 'DECOY'})
-        
-        # Export plots
-        if outfile is None:
-            outfile = str(inpath / "feature_scores.pdf")
+    # If appending, merge PDFs, otherwise just rename
+    if append and os.path.exists(outfile):
+        from pypdf import PdfReader, PdfWriter
         
-        if len(df) > 0:
-            plot_scores(df, outfile)
-            logger.info(f"Exported feature scores to {outfile}")
+        try:
+            # Merge the PDFs
+            writer = PdfWriter()
+            
+            # Add pages from existing PDF
+            with open(outfile, "rb") as f:
+                existing_pdf = PdfReader(f)
+                for page in existing_pdf.pages:
+                    writer.add_page(page)
+            
+            # Add pages from new PDF
+            with open(temp_outfile, "rb") as f:
+                new_pdf = PdfReader(f)
+                for page in new_pdf.pages:
+                    writer.add_page(page)
+            
+            # Write merged PDF
+            with open(outfile, "wb") as f:
+                writer.write(f)
+            
+            # Remove temporary file
+            os.remove(temp_outfile)
+        except Exception as e:
+            logger.warning(f"Failed to merge PDF for {level} level: {e}. Skipping this level.")
+            # Clean up temporary file if it exists
+            if os.path.exists(temp_outfile):
+                os.remove(temp_outfile)
+            return
     else:
-        logger.warning(f"Precursor features file not found: {precursor_file}")
+        # Just rename temporary file to output file
+        try:
+            if os.path.exists(outfile):
+                os.remove(outfile)
+            os.rename(temp_outfile, outfile)
+        except Exception as e:
+            logger.warning(f"Failed to save PDF for {level} level: {e}. Skipping this level.")
+            # Clean up temporary file if it exists
+            if os.path.exists(temp_outfile):
+                os.remove(temp_outfile)
+            return
 
 
 def export_score_plots(infile):
diff --git a/pyprophet/io/export/osw.py b/pyprophet/io/export/osw.py
index b77cbc96..b25c8826 100644
--- a/pyprophet/io/export/osw.py
+++ b/pyprophet/io/export/osw.py
@@ -864,6 +864,10 @@ def export_feature_scores(self, outfile: str, plot_callback):
         """
         Export feature scores from OSW file for plotting.
         
+        Detects if SCORE tables exist and adjusts behavior:
+        - If SCORE tables exist: applies RANK==1 filtering and plots SCORE + VAR_ columns
+        - If SCORE tables don't exist: plots only VAR_ columns
+        
         Parameters
         ----------
         outfile : str
@@ -875,84 +879,191 @@ def export_feature_scores(self, outfile: str, plot_callback):
         con = sqlite3.connect(self.infile)
         
         try:
+            # Check for SCORE tables
+            has_score_ms1 = check_sqlite_table(con, "SCORE_MS1")
+            has_score_ms2 = check_sqlite_table(con, "SCORE_MS2")
+            has_score_transition = check_sqlite_table(con, "SCORE_TRANSITION")
+            
+            if has_score_ms1 or has_score_ms2 or has_score_transition:
+                logger.info("SCORE tables detected - applying RANK==1 filter and plotting SCORE + VAR_ columns")
+            else:
+                logger.info("No SCORE tables detected - plotting only VAR_ columns")
+            
             # Process MS1 level if available
             if check_sqlite_table(con, "FEATURE_MS1"):
                 logger.info("Processing MS1 level feature scores")
-                # Get only VAR_ columns to reduce memory usage
-                cursor = con.cursor()
-                cursor.execute("PRAGMA table_info(FEATURE_MS1)")
-                all_cols = [row[1] for row in cursor.fetchall()]
-                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
                 
-                if var_cols:
-                    var_cols_sql = ", ".join([f"FEATURE_MS1.{col}" for col in var_cols])
-                    ms1_query = f"""
-                        SELECT 
-                            {var_cols_sql},
-                            PRECURSOR.DECOY
+                if has_score_ms1:
+                    # Scored mode: Include SCORE columns and apply RANK==1 filter
+                    ms1_query = """
+                        SELECT *,
+                               RUN_ID || '_' || PRECURSOR_ID AS GROUP_ID
                         FROM FEATURE_MS1
-                        INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
-                        INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                        INNER JOIN
+                          (SELECT RUN_ID,
+                                  ID,
+                                  PRECURSOR_ID,
+                                  EXP_RT
+                           FROM FEATURE) AS FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
+                        INNER JOIN
+                          (SELECT ID,
+                                  CHARGE AS VAR_PRECURSOR_CHARGE,
+                                  DECOY
+                           FROM PRECURSOR) AS PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                        INNER JOIN SCORE_MS1 ON FEATURE.ID = SCORE_MS1.FEATURE_ID
+                        WHERE RANK == 1
+                        ORDER BY RUN_ID,
+                                 PRECURSOR.ID ASC,
+                                 FEATURE.EXP_RT ASC
                     """
+                else:
+                    # Unscored mode: Only VAR_ columns
+                    cursor = con.cursor()
+                    cursor.execute("PRAGMA table_info(FEATURE_MS1)")
+                    all_cols = [row[1] for row in cursor.fetchall()]
+                    var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                    
+                    if var_cols:
+                        var_cols_sql = ", ".join([f"FEATURE_MS1.{col}" for col in var_cols])
+                        ms1_query = f"""
+                            SELECT 
+                                {var_cols_sql},
+                                PRECURSOR.DECOY
+                            FROM FEATURE_MS1
+                            INNER JOIN FEATURE ON FEATURE_MS1.FEATURE_ID = FEATURE.ID
+                            INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                        """
+                    else:
+                        logger.warning("No VAR_ columns found in FEATURE_MS1 table")
+                        ms1_query = None
+                
+                if ms1_query:
                     df_ms1 = pd.read_sql_query(ms1_query, con)
                     if not df_ms1.empty:
                         plot_callback(df_ms1, outfile, "ms1", append=False)
-                else:
-                    logger.warning("No VAR_ columns found in FEATURE_MS1 table")
             
             # Process MS2 level if available
             if check_sqlite_table(con, "FEATURE_MS2"):
                 logger.info("Processing MS2 level feature scores")
-                # Get only VAR_ columns to reduce memory usage
-                cursor = con.cursor()
-                cursor.execute("PRAGMA table_info(FEATURE_MS2)")
-                all_cols = [row[1] for row in cursor.fetchall()]
-                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
                 
-                if var_cols:
-                    var_cols_sql = ", ".join([f"FEATURE_MS2.{col}" for col in var_cols])
-                    ms2_query = f"""
-                        SELECT 
-                            {var_cols_sql},
-                            PRECURSOR.DECOY
+                if has_score_ms2:
+                    # Scored mode: Include SCORE columns and apply RANK==1 filter
+                    ms2_query = """
+                        SELECT *,
+                               RUN_ID || '_' || PRECURSOR_ID AS GROUP_ID
                         FROM FEATURE_MS2
-                        INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
-                        INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                        INNER JOIN
+                          (SELECT RUN_ID,
+                                  ID,
+                                  PRECURSOR_ID,
+                                  EXP_RT
+                           FROM FEATURE) AS FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
+                        INNER JOIN
+                          (SELECT ID,
+                                  CHARGE AS VAR_PRECURSOR_CHARGE,
+                                  DECOY
+                           FROM PRECURSOR) AS PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                        INNER JOIN
+                          (SELECT PRECURSOR_ID AS ID,
+                                  COUNT(*) AS VAR_TRANSITION_NUM_SCORE
+                           FROM TRANSITION_PRECURSOR_MAPPING
+                           INNER JOIN TRANSITION ON TRANSITION_PRECURSOR_MAPPING.TRANSITION_ID = TRANSITION.ID
+                           WHERE DETECTING==1
+                           GROUP BY PRECURSOR_ID) AS VAR_TRANSITION_SCORE ON FEATURE.PRECURSOR_ID = VAR_TRANSITION_SCORE.ID
+                        INNER JOIN SCORE_MS2 ON FEATURE.ID = SCORE_MS2.FEATURE_ID
+                        WHERE RANK == 1
+                        ORDER BY RUN_ID,
+                                 PRECURSOR.ID ASC,
+                                 FEATURE.EXP_RT ASC
                     """
+                else:
+                    # Unscored mode: Only VAR_ columns
+                    cursor = con.cursor()
+                    cursor.execute("PRAGMA table_info(FEATURE_MS2)")
+                    all_cols = [row[1] for row in cursor.fetchall()]
+                    var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                    
+                    if var_cols:
+                        var_cols_sql = ", ".join([f"FEATURE_MS2.{col}" for col in var_cols])
+                        ms2_query = f"""
+                            SELECT 
+                                {var_cols_sql},
+                                PRECURSOR.DECOY
+                            FROM FEATURE_MS2
+                            INNER JOIN FEATURE ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
+                            INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                        """
+                    else:
+                        logger.warning("No VAR_ columns found in FEATURE_MS2 table")
+                        ms2_query = None
+                
+                if ms2_query:
                     df_ms2 = pd.read_sql_query(ms2_query, con)
                     if not df_ms2.empty:
                         append = check_sqlite_table(con, "FEATURE_MS1")
                         plot_callback(df_ms2, outfile, "ms2", append=append)
-                else:
-                    logger.warning("No VAR_ columns found in FEATURE_MS2 table")
             
             # Process transition level if available
             if check_sqlite_table(con, "FEATURE_TRANSITION"):
                 logger.info("Processing transition level feature scores")
-                # Get only VAR_ columns to reduce memory usage
-                cursor = con.cursor()
-                cursor.execute("PRAGMA table_info(FEATURE_TRANSITION)")
-                all_cols = [row[1] for row in cursor.fetchall()]
-                var_cols = [col for col in all_cols if "VAR_" in col.upper()]
                 
-                if var_cols:
-                    var_cols_sql = ", ".join([f"FEATURE_TRANSITION.{col}" for col in var_cols])
-                    transition_query = f"""
-                        SELECT 
-                            {var_cols_sql},
-                            TRANSITION.DECOY
+                if has_score_transition:
+                    # Scored mode: Include SCORE columns and apply RANK==1 filter
+                    transition_query = """
+                        SELECT TRANSITION.DECOY AS DECOY,
+                               FEATURE_TRANSITION.*,
+                               PRECURSOR.CHARGE AS VAR_PRECURSOR_CHARGE,
+                               TRANSITION.VAR_PRODUCT_CHARGE AS VAR_PRODUCT_CHARGE,
+                               SCORE_TRANSITION.*,
+                               RUN_ID || '_' || FEATURE_TRANSITION.FEATURE_ID || '_' || PRECURSOR_ID || '_' || FEATURE_TRANSITION.TRANSITION_ID AS GROUP_ID
                         FROM FEATURE_TRANSITION
-                        INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
-                        INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
+                        INNER JOIN
+                          (SELECT RUN_ID,
+                                  ID,
+                                  PRECURSOR_ID,
+                                  EXP_RT
+                           FROM FEATURE) AS FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
+                        INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
+                        INNER JOIN SCORE_TRANSITION ON FEATURE_TRANSITION.FEATURE_ID = SCORE_TRANSITION.FEATURE_ID
+                        AND FEATURE_TRANSITION.TRANSITION_ID = SCORE_TRANSITION.TRANSITION_ID
+                        INNER JOIN
+                          (SELECT ID,
+                                  CHARGE AS VAR_PRODUCT_CHARGE,
+                                  DECOY
+                           FROM TRANSITION) AS TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
+                        ORDER BY RUN_ID,
+                                 PRECURSOR.ID,
+                                 FEATURE.EXP_RT,
+                                 TRANSITION.ID
                     """
+                else:
+                    # Unscored mode: Only VAR_ columns
+                    cursor = con.cursor()
+                    cursor.execute("PRAGMA table_info(FEATURE_TRANSITION)")
+                    all_cols = [row[1] for row in cursor.fetchall()]
+                    var_cols = [col for col in all_cols if "VAR_" in col.upper()]
+                    
+                    if var_cols:
+                        var_cols_sql = ", ".join([f"FEATURE_TRANSITION.{col}" for col in var_cols])
+                        transition_query = f"""
+                            SELECT 
+                                {var_cols_sql},
+                                TRANSITION.DECOY
+                            FROM FEATURE_TRANSITION
+                            INNER JOIN FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
+                            INNER JOIN TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
+                        """
+                    else:
+                        logger.warning("No VAR_ columns found in FEATURE_TRANSITION table")
+                        transition_query = None
+                
+                if transition_query:
                     df_transition = pd.read_sql_query(transition_query, con)
                     if not df_transition.empty:
                         append = check_sqlite_table(con, "FEATURE_MS1") or check_sqlite_table(con, "FEATURE_MS2")
                         plot_callback(df_transition, outfile, "transition", append=append)
-                else:
-                    logger.warning("No VAR_ columns found in FEATURE_TRANSITION table")
             
-            # Process alignment level if available
+            # Process alignment level if available (no SCORE tables for alignment)
             if check_sqlite_table(con, "FEATURE_MS2_ALIGNMENT"):
                 logger.info("Processing alignment level feature scores")
                 # Get only VAR_ columns to reduce memory usage
diff --git a/pyprophet/io/export/parquet.py b/pyprophet/io/export/parquet.py
index ef523839..3bcd8872 100644
--- a/pyprophet/io/export/parquet.py
+++ b/pyprophet/io/export/parquet.py
@@ -605,6 +605,10 @@ def export_feature_scores(self, outfile: str, plot_callback):
         """
         Export feature scores from Parquet file for plotting.
         
+        Detects if SCORE columns exist and adjusts behavior:
+        - If SCORE columns exist: applies RANK==1 filtering and plots SCORE + VAR_ columns
+        - If SCORE columns don't exist: plots only VAR_ columns
+        
         Parameters
         ----------
         outfile : str
@@ -621,6 +625,15 @@ def export_feature_scores(self, outfile: str, plot_callback):
         parquet_file = pa.parquet.ParquetFile(self.infile)
         all_columns = parquet_file.schema.names
         
+        # Check for SCORE columns
+        score_cols = [col for col in all_columns if col.startswith("SCORE_")]
+        has_scores = len(score_cols) > 0
+        
+        if has_scores:
+            logger.info("SCORE columns detected - applying RANK==1 filter and plotting SCORE + VAR_ columns")
+        else:
+            logger.info("No SCORE columns detected - plotting only VAR_ columns")
+        
         # Identify columns to read for each level
         ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
         ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
@@ -628,6 +641,19 @@ def export_feature_scores(self, outfile: str, plot_callback):
         
         # Determine which columns to read (only what we need)
         cols_to_read = set()
+        
+        # Add SCORE columns if they exist
+        if has_scores:
+            cols_to_read.update(score_cols)
+            # Add RANK column for filtering
+            if "SCORE_MS2_PEAK_GROUP_RANK" in all_columns:
+                cols_to_read.add("SCORE_MS2_PEAK_GROUP_RANK")
+            # Add ID columns for grouping
+            if "RUN_ID" in all_columns:
+                cols_to_read.add("RUN_ID")
+            if "PRECURSOR_ID" in all_columns:
+                cols_to_read.add("PRECURSOR_ID")
+        
         if ms1_cols and "PRECURSOR_DECOY" in all_columns:
             cols_to_read.update(ms1_cols)
             cols_to_read.add("PRECURSOR_DECOY")
@@ -646,10 +672,28 @@ def export_feature_scores(self, outfile: str, plot_callback):
         logger.info(f"Reading {len(cols_to_read)} columns from parquet file")
         df = pd.read_parquet(self.infile, columns=list(cols_to_read))
         
+        # Apply RANK==1 filter if SCORE columns exist
+        if has_scores and 'SCORE_MS2_PEAK_GROUP_RANK' in df.columns:
+            logger.info(f"Filtering to RANK==1: {len(df)} -> ", end="")
+            df = df[df['SCORE_MS2_PEAK_GROUP_RANK'] == 1].copy()
+            logger.info(f"{len(df)} rows")
+        
+        # Generate GROUP_ID if needed
+        if has_scores and 'GROUP_ID' not in df.columns:
+            if 'RUN_ID' in df.columns and 'PRECURSOR_ID' in df.columns:
+                df['GROUP_ID'] = df['RUN_ID'].astype(str) + '_' + df['PRECURSOR_ID'].astype(str)
+        
         # Process MS1 level
         if ms1_cols and "PRECURSOR_DECOY" in df.columns:
             logger.info("Processing MS1 level feature scores")
-            ms1_df = df[ms1_cols + ["PRECURSOR_DECOY"]].copy()
+            select_cols = ms1_cols + ["PRECURSOR_DECOY"]
+            # Add SCORE columns if present
+            if has_scores:
+                score_ms1_cols = [col for col in score_cols if 'MS1' in col.upper()]
+                select_cols.extend(score_ms1_cols)
+                if 'GROUP_ID' in df.columns:
+                    select_cols.append('GROUP_ID')
+            ms1_df = df[select_cols].copy()
             ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
             plot_callback(ms1_df, outfile, "ms1", append=False)
             del ms1_df  # Free memory
@@ -657,7 +701,14 @@ def export_feature_scores(self, outfile: str, plot_callback):
         # Process MS2 level
         if ms2_cols and "PRECURSOR_DECOY" in df.columns:
             logger.info("Processing MS2 level feature scores")
-            ms2_df = df[ms2_cols + ["PRECURSOR_DECOY"]].copy()
+            select_cols = ms2_cols + ["PRECURSOR_DECOY"]
+            # Add SCORE columns if present
+            if has_scores:
+                score_ms2_cols = [col for col in score_cols if 'MS2' in col.upper() or 'MS1' not in col.upper()]
+                select_cols.extend(score_ms2_cols)
+                if 'GROUP_ID' in df.columns:
+                    select_cols.append('GROUP_ID')
+            ms2_df = df[select_cols].copy()
             ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
             append = bool(ms1_cols)
             plot_callback(ms2_df, outfile, "ms2", append=append)
@@ -666,7 +717,14 @@ def export_feature_scores(self, outfile: str, plot_callback):
         # Process transition level
         if transition_cols and "TRANSITION_DECOY" in df.columns:
             logger.info("Processing transition level feature scores")
-            transition_df = df[transition_cols + ["TRANSITION_DECOY"]].copy()
+            select_cols = transition_cols + ["TRANSITION_DECOY"]
+            # Add SCORE columns if present
+            if has_scores:
+                score_transition_cols = [col for col in score_cols if 'TRANSITION' in col.upper()]
+                select_cols.extend(score_transition_cols)
+                if 'GROUP_ID' in df.columns:
+                    select_cols.append('GROUP_ID')
+            transition_df = df[select_cols].copy()
             transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
             append = bool(ms1_cols or ms2_cols)
             plot_callback(transition_df, outfile, "transition", append=append)
diff --git a/pyprophet/io/export/split_parquet.py b/pyprophet/io/export/split_parquet.py
index 60cf431a..8eb4a696 100644
--- a/pyprophet/io/export/split_parquet.py
+++ b/pyprophet/io/export/split_parquet.py
@@ -670,6 +670,10 @@ def export_feature_scores(self, outfile: str, plot_callback):
         """
         Export feature scores from split Parquet directory for plotting.
         
+        Detects if SCORE columns exist and adjusts behavior:
+        - If SCORE columns exist: applies RANK==1 filtering and plots SCORE + VAR_ columns
+        - If SCORE columns don't exist: plots only VAR_ columns
+        
         Parameters
         ----------
         outfile : str
@@ -689,11 +693,33 @@ def export_feature_scores(self, outfile: str, plot_callback):
         precursor_parquet = pa.parquet.ParquetFile(precursor_file)
         all_columns = precursor_parquet.schema.names
         
+        # Check for SCORE columns
+        score_cols = [col for col in all_columns if col.startswith("SCORE_")]
+        has_scores = len(score_cols) > 0
+        
+        if has_scores:
+            logger.info("SCORE columns detected - applying RANK==1 filter and plotting SCORE + VAR_ columns")
+        else:
+            logger.info("No SCORE columns detected - plotting only VAR_ columns")
+        
         # Identify columns to read
         ms1_cols = [col for col in all_columns if col.startswith("FEATURE_MS1_VAR_")]
         ms2_cols = [col for col in all_columns if col.startswith("FEATURE_MS2_VAR_")]
         
         cols_to_read = set()
+        
+        # Add SCORE columns if they exist
+        if has_scores:
+            cols_to_read.update(score_cols)
+            # Add RANK column for filtering
+            if "SCORE_MS2_PEAK_GROUP_RANK" in all_columns:
+                cols_to_read.add("SCORE_MS2_PEAK_GROUP_RANK")
+            # Add ID columns for grouping
+            if "RUN_ID" in all_columns:
+                cols_to_read.add("RUN_ID")
+            if "PRECURSOR_ID" in all_columns:
+                cols_to_read.add("PRECURSOR_ID")
+        
         if ms1_cols and "PRECURSOR_DECOY" in all_columns:
             cols_to_read.update(ms1_cols)
             cols_to_read.add("PRECURSOR_DECOY")
@@ -705,10 +731,28 @@ def export_feature_scores(self, outfile: str, plot_callback):
             logger.info(f"Reading {len(cols_to_read)} columns from precursor features")
             df_precursor = pd.read_parquet(precursor_file, columns=list(cols_to_read))
             
+            # Apply RANK==1 filter if SCORE columns exist
+            if has_scores and 'SCORE_MS2_PEAK_GROUP_RANK' in df_precursor.columns:
+                logger.info(f"Filtering to RANK==1: {len(df_precursor)} -> ", end="")
+                df_precursor = df_precursor[df_precursor['SCORE_MS2_PEAK_GROUP_RANK'] == 1].copy()
+                logger.info(f"{len(df_precursor)} rows")
+            
+            # Generate GROUP_ID if needed
+            if has_scores and 'GROUP_ID' not in df_precursor.columns:
+                if 'RUN_ID' in df_precursor.columns and 'PRECURSOR_ID' in df_precursor.columns:
+                    df_precursor['GROUP_ID'] = df_precursor['RUN_ID'].astype(str) + '_' + df_precursor['PRECURSOR_ID'].astype(str)
+            
             # Process MS1 level
             if ms1_cols and "PRECURSOR_DECOY" in df_precursor.columns:
                 logger.info("Processing MS1 level feature scores")
-                ms1_df = df_precursor[ms1_cols + ["PRECURSOR_DECOY"]].copy()
+                select_cols = ms1_cols + ["PRECURSOR_DECOY"]
+                # Add SCORE columns if present
+                if has_scores:
+                    score_ms1_cols = [col for col in score_cols if 'MS1' in col.upper()]
+                    select_cols.extend(score_ms1_cols)
+                    if 'GROUP_ID' in df_precursor.columns:
+                        select_cols.append('GROUP_ID')
+                ms1_df = df_precursor[select_cols].copy()
                 ms1_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
                 plot_callback(ms1_df, outfile, "ms1", append=False)
                 del ms1_df  # Free memory
@@ -716,7 +760,14 @@ def export_feature_scores(self, outfile: str, plot_callback):
             # Process MS2 level
             if ms2_cols and "PRECURSOR_DECOY" in df_precursor.columns:
                 logger.info("Processing MS2 level feature scores")
-                ms2_df = df_precursor[ms2_cols + ["PRECURSOR_DECOY"]].copy()
+                select_cols = ms2_cols + ["PRECURSOR_DECOY"]
+                # Add SCORE columns if present
+                if has_scores:
+                    score_ms2_cols = [col for col in score_cols if 'MS2' in col.upper() or 'MS1' not in col.upper()]
+                    select_cols.extend(score_ms2_cols)
+                    if 'GROUP_ID' in df_precursor.columns:
+                        select_cols.append('GROUP_ID')
+                ms2_df = df_precursor[select_cols].copy()
                 ms2_df.rename(columns={"PRECURSOR_DECOY": "DECOY"}, inplace=True)
                 append = bool(ms1_cols)
                 plot_callback(ms2_df, outfile, "ms2", append=append)
@@ -734,14 +785,23 @@ def export_feature_scores(self, outfile: str, plot_callback):
             transition_all_columns = transition_parquet.schema.names
             transition_cols = [col for col in transition_all_columns if col.startswith("FEATURE_TRANSITION_VAR_")]
             
+            # Check for SCORE columns in transition file
+            transition_score_cols = [col for col in transition_all_columns if col.startswith("SCORE_") and 'TRANSITION' in col.upper()]
+            has_transition_scores = len(transition_score_cols) > 0
+            
             if transition_cols and "TRANSITION_DECOY" in transition_all_columns:
                 # Read only necessary columns
                 cols_to_read = transition_cols + ["TRANSITION_DECOY"]
+                if has_transition_scores:
+                    cols_to_read.extend(transition_score_cols)
+                    if 'GROUP_ID' in transition_all_columns:
+                        cols_to_read.append('GROUP_ID')
+                
                 logger.info(f"Reading {len(cols_to_read)} columns from transition features")
                 df_transition = pd.read_parquet(transition_file, columns=cols_to_read)
                 
                 logger.info("Processing transition level feature scores")
-                transition_df = df_transition[transition_cols + ["TRANSITION_DECOY"]].copy()
+                transition_df = df_transition.copy()
                 transition_df.rename(columns={"TRANSITION_DECOY": "DECOY"}, inplace=True)
                 append = bool(ms1_cols or ms2_cols)
                 plot_callback(transition_df, outfile, "transition", append=append)

From b6bbdb65b349f01ca6b5f2d5a64fef7fc7e1d30c Mon Sep 17 00:00:00 2001
From: singjc <justincsing@gmail.com>
Date: Wed, 29 Oct 2025 14:27:19 -0400
Subject: [PATCH 26/26] add: regtest outputs

---
 ...phet_export.test_feature_scores_ms1_ms2_transition.out | 8 ++++++++
 ...het_export.test_feature_scores_parquet_with_scores.out | 1 +
 ...st_pyprophet_export.test_feature_scores_scored_osw.out | 3 +++
 ...port.test_feature_scores_split_parquet_with_scores.out | 1 +
 ..._pyprophet_export.test_feature_scores_unscored_osw.out | 2 ++
 5 files changed, 15 insertions(+)
 create mode 100644 tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_ms1_ms2_transition.out
 create mode 100644 tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_parquet_with_scores.out
 create mode 100644 tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_scored_osw.out
 create mode 100644 tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_split_parquet_with_scores.out
 create mode 100644 tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_unscored_osw.out

diff --git a/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_ms1_ms2_transition.out b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_ms1_ms2_transition.out
new file mode 100644
index 00000000..8235141f
--- /dev/null
+++ b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_ms1_ms2_transition.out
@@ -0,0 +1,8 @@
+Created 4 PDF file(s) from multi-level scoring:
+  MS1 files: 1
+  MS2 files: 1
+  Transition files: 1
+  - feature_scores.pdf
+  - test_data_ms1_report.pdf
+  - test_data_ms2_report.pdf
+  - test_data_transition_report.pdf
diff --git a/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_parquet_with_scores.out b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_parquet_with_scores.out
new file mode 100644
index 00000000..9d69ad74
--- /dev/null
+++ b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_parquet_with_scores.out
@@ -0,0 +1 @@
+Successfully created feature scores from Parquet with SCORE columns
diff --git a/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_scored_osw.out b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_scored_osw.out
new file mode 100644
index 00000000..14806759
--- /dev/null
+++ b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_scored_osw.out
@@ -0,0 +1,3 @@
+Created 2 PDF file(s) from scored OSW:
+  - feature_scores.pdf
+  - test_data_ms2_report.pdf
diff --git a/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_split_parquet_with_scores.out b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_split_parquet_with_scores.out
new file mode 100644
index 00000000..30ac013a
--- /dev/null
+++ b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_split_parquet_with_scores.out
@@ -0,0 +1 @@
+Successfully created feature scores from split Parquet with SCORE columns
diff --git a/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_unscored_osw.out b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_unscored_osw.out
new file mode 100644
index 00000000..84d09e4d
--- /dev/null
+++ b/tests/_regtest_outputs/test_pyprophet_export.test_feature_scores_unscored_osw.out
@@ -0,0 +1,2 @@
+Created 1 PDF file(s):
+  - feature_scores.pdf