From e193897ca29aca21790b316f92f95985767ec297 Mon Sep 17 00:00:00 2001
From: Andrecho <37243793+andrewendlinger@users.noreply.github.com>
Date: Fri, 20 Feb 2026 19:39:10 +0000
Subject: [PATCH 1/2] fix: handle NaN expressions in PriorKnowledge to prevent
 TypeError

---
 pyAMARES/kernel/PriorKnowledge.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/pyAMARES/kernel/PriorKnowledge.py b/pyAMARES/kernel/PriorKnowledge.py
index dc4a4ba..1a0e99a 100644
--- a/pyAMARES/kernel/PriorKnowledge.py
+++ b/pyAMARES/kernel/PriorKnowledge.py
@@ -369,6 +369,11 @@ def generateparameter(
             lval = df_lb2[peak].iloc[i]
             uval = df_ub2[peak].iloc[i]
             expr = df_expr[peak].iloc[i]
+
+            # --- FIX: Ensure expr is None if it evaluates to NaN ---
+            if pd.isna(expr):
+                expr = None
+
             # Handle NaN values for bounds
             if np.isnan(lval):
                 lval = -np.inf

From 816ae0cc245bb987d83b7e429c98cffc2e7d23ae Mon Sep 17 00:00:00 2001
From: Andrecho <37243793+andrewendlinger@users.noreply.github.com>
Date: Fri, 20 Feb 2026 19:52:44 +0000
Subject: [PATCH 2/2] fix: prevent pandas LossySetitemError during unit
 conversion

Removes float32 downcasting in `safe_convert_to_numeric` and
ensures numeric columns are cast to float64 in `unitconverter`. This
resolves a `LossySetitemError` in pandas 2.2+ caused by attempting to
insert float64 constants (e.g., `np.pi`) into float32 columns.
---
 pyAMARES/kernel/PriorKnowledge.py | 35 ++++++++++++++++++++++++-------
 1 file changed, 27 insertions(+), 8 deletions(-)

diff --git a/pyAMARES/kernel/PriorKnowledge.py b/pyAMARES/kernel/PriorKnowledge.py
index 1a0e99a..a182aae 100644
--- a/pyAMARES/kernel/PriorKnowledge.py
+++ b/pyAMARES/kernel/PriorKnowledge.py
@@ -15,9 +15,14 @@
 
 def safe_convert_to_numeric(x):
     try:
-        return pd.to_numeric(
-            x, downcast="float", errors="raise"
-        )  # Use float as default output type
+        res = pd.to_numeric(x, errors="raise")
+        # Ensure float64 to prevent LossySetitemError in pandas 2.2+
+        if isinstance(res, pd.Series):
+            if np.issubdtype(res.dtype, np.number):
+                return res.astype(float)
+        elif isinstance(res, (int, float, np.number)) and not isinstance(res, bool):
+            return float(res)
+        return res
     except (ValueError, TypeError):
         return x
 
@@ -181,16 +186,30 @@ def unitconverter(df_ini, MHz=120.0):
         pandas.DataFrame: A DataFrame with converted unit values in specified rows.
     """
     df = deepcopy(df_ini)
+
+    # Pre-cast numeric columns to float64 to prevent pandas 2.2+ LossySetitemError
+    for col in df.columns:
+        if np.issubdtype(df[col].dtype, np.number):
+            df[col] = df[col].astype(float)
+
     if "chemicalshift" in df.index:
-        df.loc["chemicalshift", df.notna().loc["chemicalshift"]] *= MHz
+        mask = df.notna().loc["chemicalshift"]
+        if mask.any():
+            df.loc["chemicalshift", mask] = df.loc["chemicalshift", mask].astype(
+                float
+            ) * float(MHz)
 
     if "linewidth" in df.index:
-        df.loc["linewidth", df.notna().loc["linewidth"]] *= np.pi
+        mask = df.notna().loc["linewidth"]
+        if mask.any():
+            df.loc["linewidth", mask] = df.loc["linewidth", mask].astype(float) * float(
+                np.pi
+            )
 
     if "phase" in df.index:
-        df.loc["phase", df.notna().loc["phase"]] = np.deg2rad(
-            df.loc["phase"][df.notna().loc["phase"]].astype(float)
-        )
+        mask = df.notna().loc["phase"]
+        if mask.any():
+            df.loc["phase", mask] = np.deg2rad(df.loc["phase"][mask].astype(float))
 
     return df