From b9f35d34b690d13ba242bb7de11115e1f410c269 Mon Sep 17 00:00:00 2001
From: Dimitri Papadopoulos Orfanos
 <3234522+DimitriPapadopoulos@users.noreply.github.com>
Date: Thu, 25 Jan 2024 05:07:17 +0100
Subject: [PATCH] Fix more typos found by codespell (#209)

---
 nmrglue/analysis/tests/test_analysis_integration.py |  6 +++---
 nmrglue/process/proc_base.py                        | 12 ++++++------
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/nmrglue/analysis/tests/test_analysis_integration.py b/nmrglue/analysis/tests/test_analysis_integration.py
index 81e1301f..4660e1ab 100644
--- a/nmrglue/analysis/tests/test_analysis_integration.py
+++ b/nmrglue/analysis/tests/test_analysis_integration.py
@@ -58,7 +58,7 @@ def test_1d_integrate():
     assert_array_almost_equal(values_2,  [0.5 * 3, 3.])
 
 
-def test_1d_integrate_withnoise():
+def test_1d_integrate_with_noise():
     """ Test integration of synthetic 1D data with two peaks and noise"""
 
     # seed random number
@@ -88,11 +88,11 @@ def test_1d_integrate_withnoise():
     assert abs(integrate(data, uc, limits) - 1) <= max_error
 
     # Test renormalization of norms
-    resutls = integrate(data, uc, ((4, 6), (7, 9)), noise_limits=(1, 2),
+    results = integrate(data, uc, ((4, 6), (7, 9)), noise_limits=(1, 2),
                         norm_to_range=1)
 
     # Test renormalization of values.
-    assert abs(resutls[0, 0] - 0.5) <= max_error
+    assert abs(results[0, 0] - 0.5) <= max_error
 
 
 def test_1d_ndintegrate():
diff --git a/nmrglue/process/proc_base.py b/nmrglue/process/proc_base.py
index 43b79e64..d2bcfd6b 100644
--- a/nmrglue/process/proc_base.py
+++ b/nmrglue/process/proc_base.py
@@ -2667,18 +2667,18 @@ def expand_nus(data, shape, nuslist, aqorder=None, quadrature_order=None, allow_
     aqorder : list | None
         the order in which indirect dimensions are acquired.
         defaults to [1, 0] for 3D and [2, 1, 0] for 4D datasets. 
-        All other possibilites compatible with the dimension are 
+        All other possibilities compatible with the dimension are 
         allowed, for eg, [0, 1] for 3D, and [0, 1, 2], [0, 2, 1], 
         [2, 0, 1], [1, 0, 2], [1, 2, 0] for 4D data.  
     quadrature_order : list | None
-        ordering of quadrature points. by defualt, this uses
+        ordering of quadrature points. by default, this uses
         the order from itertools.product, which seems to be 
         fine most of the common acquistions. For example, for a 2D dataset,
         this will be [(0,), (1,)], for 3D, this will be [(0, 0), (0, 1), (1, 0), (1, 1)]
         and for 4D [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1),
         (1, 1, 0), (1, 1, 1)], where 0=real and 1=imag point
     allow_high_dims : bool
-        flag that allows datasets higher than 4D to be genereted.
+        flag that allows datasets higher than 4D to be generated.
         By default, this is not allowed to prevent generation of large datasets
         by mistake
 
@@ -2693,11 +2693,11 @@ def expand_nus(data, shape, nuslist, aqorder=None, quadrature_order=None, allow_
     ------
     ValueError
         if shape of the final data is not compatible
-        with the expeted shape from nuslist
+        with the expected shape from nuslist
     ValueError
         if dataset dimension is less than 2
     ValueError
-        if dataset dimension is greated than 4 and
+        if dataset dimension is greater than 4 and
         the flag to allow this is False
 
     """
@@ -2709,7 +2709,7 @@ def expand_nus(data, shape, nuslist, aqorder=None, quadrature_order=None, allow_
         raise ValueError(f"expected {ndim}D data but got {ndim}D nuslist")
 
     if ndim < 2:
-        raise ValueError(f"Needs to be be atleast a 2D dataset (not {ndim}D)")
+        raise ValueError(f"Needs to be be at least a 2D dataset (not {ndim}D)")
 
     # protection against unintended generation of large datasets
     if (ndim > 4) and (allow_high_dims == False):