diff --git a/mypy.ini b/mypy.ini
index 38010937..f7bc0335 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -1,4 +1,11 @@
 [mypy]
+plugins = pydantic.mypy
 
 ignore_missing_imports = True
 python_version = 3.8
+
+[pydantic-mypy]
+init_typed = true
+
+init_forbid_extra = True
+warn_untyped_fields = True
diff --git a/src/ramanchada2/io/experimental/read_txt.py b/src/ramanchada2/io/experimental/read_txt.py
index bb2e6740..7a85a970 100644
--- a/src/ramanchada2/io/experimental/read_txt.py
+++ b/src/ramanchada2/io/experimental/read_txt.py
@@ -21,9 +21,9 @@ def read_txt(data_in: TextIO) -> Tuple[NDArray, NDArray, Dict]:
     lines = data_in.readlines()
     if lines[0].startswith('File Version;BW'):
         data, meta = bw_format(lines)
-        positions = data['RamanShift']
-        intensities = data['DarkSubtracted#1']
+        positions = data['RamanShift'].to_numpy()
+        intensities = data['DarkSubtracted#1'].to_numpy()
     else:  # assume two column spectrum
         positions, intensities = two_column_spe(lines)
         meta = dict()
-    return positions.to_numpy(), intensities.to_numpy(), meta
+    return positions, intensities, meta
diff --git a/src/ramanchada2/spectrum/peaks/find_peaks.py b/src/ramanchada2/spectrum/peaks/find_peaks.py
index 1b9e8ac8..5b66c7aa 100644
--- a/src/ramanchada2/spectrum/peaks/find_peaks.py
+++ b/src/ramanchada2/spectrum/peaks/find_peaks.py
@@ -2,7 +2,7 @@
 
 from typing import Union, Tuple, List
 from scipy import signal
-from pydantic import validate_arguments, Field
+from pydantic import validate_arguments, PositiveFloat
 
 from ..spectrum import Spectrum
 from ramanchada2.misc.spectrum_deco import (add_spectrum_method,
@@ -44,7 +44,7 @@ def find_peak_groups(
         prominence: float = 1e-2,
         wlen=None,
         width: Union[int, Tuple[int, int]] = 1,
-        n_sigma_group: float = Field(5, ge=0)
+        n_sigma_group: PositiveFloat = 5.
         ) -> List[PeakCandidatesListModel]:
     res = signal.find_peaks(spe.y, prominence=prominence, width=width, wlen=wlen)
     return PeakCandidatesListModel.from_find_peaks(res).group_neighbours(n_sigma=n_sigma_group)