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-rw-r--r--numpy/core/src/common/npy_cpu_features.h4
-rw-r--r--numpy/core/tests/test_array_coercion.py2
-rw-r--r--numpy/linalg/lapack_lite/f2c_c_lapack.c8
-rw-r--r--numpy/linalg/lapack_lite/f2c_d_lapack.c10
-rw-r--r--numpy/linalg/lapack_lite/f2c_s_lapack.c10
-rw-r--r--numpy/linalg/lapack_lite/f2c_z_lapack.c8
-rw-r--r--numpy/ma/extras.py2
-rw-r--r--numpy/random/_generator.pyx2
-rw-r--r--numpy/random/mtrand.pyx4
9 files changed, 25 insertions, 25 deletions
diff --git a/numpy/core/src/common/npy_cpu_features.h b/numpy/core/src/common/npy_cpu_features.h
index 3d5f2e75c..96c543e70 100644
--- a/numpy/core/src/common/npy_cpu_features.h
+++ b/numpy/core/src/common/npy_cpu_features.h
@@ -137,7 +137,7 @@ npy_cpu_features_dict(void);
* that supported by the compiler and platform according to the specified
* values to command argument '--cpu-baseline'.
*
- * This function is mainly used to implement umath's attrbute '__cpu_baseline__',
+ * This function is mainly used to implement umath's attribute '__cpu_baseline__',
* and the items are sorted from the lowest to highest interest.
*
* For example, according to the default build configuration and by assuming the compiler
@@ -159,7 +159,7 @@ npy_cpu_baseline_list(void);
* that supported by the compiler and platform according to the specified
* values to command argument '--cpu-dispatch'.
*
- * This function is mainly used to implement umath's attrbute '__cpu_dispatch__',
+ * This function is mainly used to implement umath's attribute '__cpu_dispatch__',
* and the items are sorted from the lowest to highest interest.
*
* For example, according to the default build configuration and by assuming the compiler
diff --git a/numpy/core/tests/test_array_coercion.py b/numpy/core/tests/test_array_coercion.py
index d349f9d02..e858cd8b6 100644
--- a/numpy/core/tests/test_array_coercion.py
+++ b/numpy/core/tests/test_array_coercion.py
@@ -135,7 +135,7 @@ def scalar_instances(times=True, extended_precision=True, user_dtype=True):
def is_parametric_dtype(dtype):
- """Returns True if the the dtype is a parametric legacy dtype (itemsize
+ """Returns True if the dtype is a parametric legacy dtype (itemsize
is 0, or a datetime without units)
"""
if dtype.itemsize == 0:
diff --git a/numpy/linalg/lapack_lite/f2c_c_lapack.c b/numpy/linalg/lapack_lite/f2c_c_lapack.c
index c36c0e368..a7d1f836b 100644
--- a/numpy/linalg/lapack_lite/f2c_c_lapack.c
+++ b/numpy/linalg/lapack_lite/f2c_c_lapack.c
@@ -14509,7 +14509,7 @@ L60:
}
/*
- ==== Use up to NS of the the smallest magnatiude
+ ==== Use up to NS of the smallest magnatiude
. shifts. If there aren't NS shifts available,
. then use them all, possibly dropping one to
. make the number of shifts even. ====
@@ -16624,7 +16624,7 @@ L60:
}
/*
- ==== Use up to NS of the the smallest magnatiude
+ ==== Use up to NS of the smallest magnatiude
. shifts. If there aren't NS shifts available,
. then use them all, possibly dropping one to
. make the number of shifts even. ====
@@ -17341,7 +17341,7 @@ L80:
/*
==== Accumulate U. (If necessary, update Z later
- . with with an efficient matrix-matrix
+ . with an efficient matrix-matrix
. multiply.) ====
*/
@@ -25242,7 +25242,7 @@ L160:
===============
The algorithm used in this program is basically backward (forward)
- substitution, with scaling to make the the code robust against
+ substitution, with scaling to make the code robust against
possible overflow.
Each eigenvector is normalized so that the element of largest
diff --git a/numpy/linalg/lapack_lite/f2c_d_lapack.c b/numpy/linalg/lapack_lite/f2c_d_lapack.c
index 233db74b9..10e22ff1b 100644
--- a/numpy/linalg/lapack_lite/f2c_d_lapack.c
+++ b/numpy/linalg/lapack_lite/f2c_d_lapack.c
@@ -16423,7 +16423,7 @@ L90:
N1 (input) INTEGER
N2 (input) INTEGER
- These arguements contain the respective lengths of the two
+ These arguments contain the respective lengths of the two
sorted lists to be merged.
A (input) DOUBLE PRECISION array, dimension (N1+N2)
@@ -18032,7 +18032,7 @@ L60:
}
/*
- ==== Use up to NS of the the smallest magnatiude
+ ==== Use up to NS of the smallest magnatiude
. shifts. If there aren't NS shifts available,
. then use them all, possibly dropping one to
. make the number of shifts even. ====
@@ -20271,7 +20271,7 @@ L60:
}
/*
- ==== Use up to NS of the the smallest magnatiude
+ ==== Use up to NS of the smallest magnatiude
. shifts. If there aren't NS shifts available,
. then use them all, possibly dropping one to
. make the number of shifts even. ====
@@ -20870,7 +20870,7 @@ L90:
/*
==== Accumulate U. (If necessary, update Z later
- . with with an efficient matrix-matrix
+ . with an efficient matrix-matrix
. multiply.) ====
*/
@@ -40074,7 +40074,7 @@ L180:
===============
The algorithm used in this program is basically backward (forward)
- substitution, with scaling to make the the code robust against
+ substitution, with scaling to make the code robust against
possible overflow.
Each eigenvector is normalized so that the element of largest
diff --git a/numpy/linalg/lapack_lite/f2c_s_lapack.c b/numpy/linalg/lapack_lite/f2c_s_lapack.c
index 2a32315c7..26e7c18ac 100644
--- a/numpy/linalg/lapack_lite/f2c_s_lapack.c
+++ b/numpy/linalg/lapack_lite/f2c_s_lapack.c
@@ -16365,7 +16365,7 @@ L90:
N1 (input) INTEGER
N2 (input) INTEGER
- These arguements contain the respective lengths of the two
+ These arguments contain the respective lengths of the two
sorted lists to be merged.
A (input) REAL array, dimension (N1+N2)
@@ -17968,7 +17968,7 @@ L60:
}
/*
- ==== Use up to NS of the the smallest magnatiude
+ ==== Use up to NS of the smallest magnatiude
. shifts. If there aren't NS shifts available,
. then use them all, possibly dropping one to
. make the number of shifts even. ====
@@ -20194,7 +20194,7 @@ L60:
}
/*
- ==== Use up to NS of the the smallest magnatiude
+ ==== Use up to NS of the smallest magnatiude
. shifts. If there aren't NS shifts available,
. then use them all, possibly dropping one to
. make the number of shifts even. ====
@@ -20794,7 +20794,7 @@ L90:
/*
==== Accumulate U. (If necessary, update Z later
- . with with an efficient matrix-matrix
+ . with an efficient matrix-matrix
. multiply.) ====
*/
@@ -39901,7 +39901,7 @@ L180:
===============
The algorithm used in this program is basically backward (forward)
- substitution, with scaling to make the the code robust against
+ substitution, with scaling to make the code robust against
possible overflow.
Each eigenvector is normalized so that the element of largest
diff --git a/numpy/linalg/lapack_lite/f2c_z_lapack.c b/numpy/linalg/lapack_lite/f2c_z_lapack.c
index 8234eca41..64e41d082 100644
--- a/numpy/linalg/lapack_lite/f2c_z_lapack.c
+++ b/numpy/linalg/lapack_lite/f2c_z_lapack.c
@@ -14582,7 +14582,7 @@ L60:
}
/*
- ==== Use up to NS of the the smallest magnatiude
+ ==== Use up to NS of the smallest magnatiude
. shifts. If there aren't NS shifts available,
. then use them all, possibly dropping one to
. make the number of shifts even. ====
@@ -16718,7 +16718,7 @@ L60:
}
/*
- ==== Use up to NS of the the smallest magnatiude
+ ==== Use up to NS of the smallest magnatiude
. shifts. If there aren't NS shifts available,
. then use them all, possibly dropping one to
. make the number of shifts even. ====
@@ -17439,7 +17439,7 @@ L80:
/*
==== Accumulate U. (If necessary, update Z later
- . with with an efficient matrix-matrix
+ . with an efficient matrix-matrix
. multiply.) ====
*/
@@ -25358,7 +25358,7 @@ L160:
===============
The algorithm used in this program is basically backward (forward)
- substitution, with scaling to make the the code robust against
+ substitution, with scaling to make the code robust against
possible overflow.
Each eigenvector is normalized so that the element of largest
diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py
index 641f4746f..d90831b9b 100644
--- a/numpy/ma/extras.py
+++ b/numpy/ma/extras.py
@@ -1844,7 +1844,7 @@ def notmasked_contiguous(a, axis=None):
a = asarray(a)
nd = a.ndim
if nd > 2:
- raise NotImplementedError("Currently limited to atmost 2D array.")
+ raise NotImplementedError("Currently limited to at most 2D array.")
if axis is None or nd == 1:
return flatnotmasked_contiguous(a)
#
diff --git a/numpy/random/_generator.pyx b/numpy/random/_generator.pyx
index 5218c6d0e..85dfef034 100644
--- a/numpy/random/_generator.pyx
+++ b/numpy/random/_generator.pyx
@@ -2990,7 +2990,7 @@ cdef class Generator:
probability of success, :math:`N+n` is the number of trials, and
:math:`\\Gamma` is the gamma function. When :math:`n` is an integer,
:math:`\\frac{\\Gamma(N+n)}{N!\\Gamma(n)} = \\binom{N+n-1}{N}`, which is
- the more common form of this term in the the pmf. The negative
+ the more common form of this term in the pmf. The negative
binomial distribution gives the probability of N failures given n
successes, with a success on the last trial.
diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx
index fcc1f27d2..408d5a332 100644
--- a/numpy/random/mtrand.pyx
+++ b/numpy/random/mtrand.pyx
@@ -139,7 +139,7 @@ cdef class RandomState:
'RandomState' methods using the same parameters will always produce the
same results up to roundoff error except when the values were incorrect.
`RandomState` is effectively frozen and will only receive updates that
- are required by changes in the the internals of Numpy. More substantial
+ are required by changes in the internals of Numpy. More substantial
changes, including algorithmic improvements, are reserved for
`Generator`.
@@ -3478,7 +3478,7 @@ cdef class RandomState:
probability of success, :math:`N+n` is the number of trials, and
:math:`\\Gamma` is the gamma function. When :math:`n` is an integer,
:math:`\\frac{\\Gamma(N+n)}{N!\\Gamma(n)} = \\binom{N+n-1}{N}`, which is
- the more common form of this term in the the pmf. The negative
+ the more common form of this term in the pmf. The negative
binomial distribution gives the probability of N failures given n
successes, with a success on the last trial.
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