[DATALAD RUNCMD] Do interactive fixing of some ambigous typos

=== Do not change lines below ===
{
 "chain": [],
 "cmd": "codespell -w -i 3 -C 2",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^

doxyconfig

@@ -1735,7 +1735,7 @@ UML_LOOK               = NO
 # the class node. If there are many fields or methods and many nodes the
 # graph may become too big to be useful. The UML_LIMIT_NUM_FIELDS
 # threshold limits the number of items for each type to make the size more
-# managable. Set this to 0 for no limit. Note that the threshold may be
+# manageable. Set this to 0 for no limit. Note that the threshold may be
 # exceeded by 50% before the limit is enforced.
 
 UML_LIMIT_NUM_FIELDS   = 10

scripts/affine_kspace.sh

@@ -15,7 +15,7 @@ A = (- R - | a )
 
 The transform needs to be defined following the usual convention:
 1.) Shifts are measured in units of FOV as the trajectory measures k-space coordinates in units 1/FOV
-2.) The image origin (x=0) is at grid position N//2 (integer devision for odd numbers)
+2.) The image origin (x=0) is at grid position N//2 (integer division for odd numbers)
 
 The transformed k-space is given by
 F[IR] (R^T k) = exp(i2pi ak) F[IM](k)

scripts/kspace_precond.sh

@@ -11,7 +11,7 @@ set -eu
 
 helpstr=$(cat <<- EOF
 Compute k-space preconditioner P such that ||P^2 AA^H - 1|| is minimal
-Note the square in the definition. The preconditioner can be used directly as wights in PICS.
+Note the square in the definition. The preconditioner can be used directly as weights in PICS.
 
 <ones>	contains ones with image dimensions
 

src/iter/proj.c

@@ -294,7 +294,7 @@ static void proj_sphere_del(const operator_data_t* _data)
  * @param N
  * @param dims
  * @param bflag
- * @param real if true, real and imaginary part are handeled independently (as bflag is set for dimension real/imag)
+ * @param real if true, real and imaginary part are handled independently (as bflag is set for dimension real/imag)
  */
 const struct operator_p_s* operator_project_sphere_create(long N, const long dims[N], unsigned long bflag, bool real)
 {
@@ -317,7 +317,7 @@ const struct operator_p_s* operator_project_sphere_create(long N, const long dim
  * @param N
  * @param dims
  * @param bflag
- * @param real if real, real and imaginary part are handeled independently (as bflag is set for dimension real/imag)
+ * @param real if real, real and imaginary part are handled independently (as bflag is set for dimension real/imag)
  */
 
 const struct operator_p_s* operator_project_mean_free_sphere_create(long N, const long dims[N], unsigned long bflag, bool real)

src/linops/someops.c

@@ -474,7 +474,7 @@ struct linop_s* linop_resize_center_create(int N, const long out_dims[N], const
 
 struct linop_s* linop_resize_create(int N, const long out_dims[N], const long in_dims[N])
 {
-	//FIXME: inconstent with md_resize
+	//FIXME: inconsistent with md_resize
 	return linop_resize_center_create(N, out_dims, in_dims);
 }
 

src/misc/version.c

@@ -33,7 +33,7 @@ bool version_parse(unsigned int v[5], const char* version)
 	v[3] = 0;	// patch level
 
 	// simple version string format, for when git describe fails
-	// This might happen if the .git directory exsits, but git is not installed on a system
+	// This might happen if the .git directory exists, but git is not installed on a system
 	int ret = sscanf(version, "v%u.%u.%u-dirty%n", &v[0], &v[1], &v[2], &s);
 
 	if ((3 == ret) && (len == s)) {

src/nlops/nltest.c

@@ -125,7 +125,7 @@ static float nlop_test_derivative_priv(const struct nlop_s* op, const complex fl
 
 		float scale = 1.;
 		float vall = 0.;
-		val0 = 1.; // do not devide by zero if val0 is never changed
+		val0 = 1.; // do not divide by zero if val0 is never changed
 		val = 0.;
 
 		for (int i = 0; i < 10; i++) {

src/nn/layers.c

@@ -531,7 +531,7 @@ static bool calc_pooling_working_dims(int N, long dims_working[N], const long di
  * @param o output index of network, the layer is appended
  * @param N
  * @param pool_size {px, py, pz} size of pooling
- * @param conv_pad must be PAD_VALID/PAD_SAME if image size is not a multiple of padding size, the image is shrinked/expanded to a multiple
+ * @param conv_pad must be PAD_VALID/PAD_SAME if image size is not a multiple of padding size, the image is shrunk/expanded to a multiple
  */
 const struct nlop_s* append_maxpool_layer_generic(const struct nlop_s* network, int o, int N, const long pool_size[N], enum PADDING conv_pad)
 {
@@ -568,7 +568,7 @@ const struct nlop_s* append_maxpool_layer_generic(const struct nlop_s* network,
  * @param network operator to append the layer (the operator is freed)
  * @param o output index of network, the layer is appended
  * @param pool_size {px, py, pz} size of pooling
- * @param conv_pad must be PAD_VALID/PAD_SAME if image size is not a multiple of padding size, the image is shrinked/expanded to a multiple
+ * @param conv_pad must be PAD_VALID/PAD_SAME if image size is not a multiple of padding size, the image is shrunk/expanded to a multiple
  * @param channel_first data layout is {c, x, y, z} if true, {x, y, z, c}else
  */
 const struct nlop_s* append_maxpool_layer(const struct nlop_s* network, int o, const long pool_size[3], enum PADDING conv_pad, bool channel_first)

src/nn/layers_nn.c

@@ -107,7 +107,7 @@ nn_t nn_append_transposed_convcorr_layer_generic(
  * @param oname
  * @param N
  * @param pool_size size of pooling
- * @param conv_pad must be PAD_VALID/PAD_SAME if image size is not a multiple of padding size, the image is shrinked/expanded to a multiple
+ * @param conv_pad must be PAD_VALID/PAD_SAME if image size is not a multiple of padding size, the image is shrunk/expanded to a multiple
  */
 nn_t nn_append_maxpool_layer_generic(nn_t network, int o, const char* oname, int N, const long pool_size[N], enum PADDING conv_pad)
 {
@@ -256,7 +256,7 @@ nn_t nn_append_batchnorm_layer(nn_t network, int o, const char* oname, const cha
  * @param o output index of network, the layer is appended
  * @param oname
  * @param pool_size {px, py, pz} size of pooling
- * @param conv_pad must be PAD_VALID/PAD_SAME if image size is not a multiple of padding size, the image is shrinked/expanded to a multiple
+ * @param conv_pad must be PAD_VALID/PAD_SAME if image size is not a multiple of padding size, the image is shrunk/expanded to a multiple
  * @param channel_first data layout is {c, x, y, z} if true, {x, y, z, c} else
  */
 nn_t nn_append_maxpool_layer(nn_t network, int o, const char* oname, const long pool_size[3], enum PADDING conv_pad, bool channel_first)

src/nn/losses.c

@@ -1145,7 +1145,7 @@ static void dice_del(const nlop_data_t* _data)
  * D = 1 - 2 * [sum_l w_l sum_i MIN(p_li, t_li)] / [sum_l w_l sum_i (p_li + t_li)]
  * where:	i - batch index
  *		l - label index
- *		w_l - wighting factor
+ *		w_l - weighting factor
  *		t_ij = target prediction (usually 0 or 1 and sum_j t_ij = 1)
  *		p_ij = probability predicted by the network (usually p_i(x) in [0, 1] and sum_j p_ij(x) = 1 (softmax activation))
  *
@@ -1217,7 +1217,7 @@ const struct nlop_s* nlop_dice_generic_create(int N, const long dims[N], unsigne
  * D = 1 - 2 * [sum_l w_l sum_i MIN(p_li, t_li)] / [sum_l w_l sum_i (p_li + t_li)]
  * where:	i - batch index
  *		l - label index
- *		w_l - wighting factor
+ *		w_l - weighting factor
  *		t_ij = target prediction (usually 0 or 1 and sum_j t_ij = 1)
  *		p_ij = probability predicted by the network (usually p_i(x) in [0, 1] and sum_j p_ij(x) = 1 (softmax activation))
  *

src/nn/tf_wrapper.c

@@ -646,7 +646,7 @@ static struct tf_arg process_arg(const struct tf_shared_graph_s* graph, const ch
 	enum TF_DataType type = TF_OperationOutputType(arg.out);
 
 	if (! ((TF_COMPLEX64 == type) || (TF_FLOAT == type)))
-		error("TensorFlow: Argument \"%s:%d\" has unsupported type. Only single precission (complex) floats are supported.\n");
+		error("TensorFlow: Argument \"%s:%d\" has unsupported type. Only single precision (complex) floats are supported.\n");
 
 	long tdims[arg.N ?: 1];
 

src/num/cudnn_wrapper.c

@@ -521,7 +521,7 @@ static cudnnConvolutionDescriptor_t get_conv_descriptor(struct conv_desc_s conv_
 	CUDNN_ERROR(cudnnCreateConvolutionDescriptor(&result));
 
 	#if (8 <= CUDNN_MAJOR)
-	// FIXME: Tensor Cores reduce precission, are we fine with that?
+	// FIXME: Tensor Cores reduce precision, are we fine with that?
 	//	  Deactivate it for now to have default case from cuDNN 7
 	CUDNN_ERROR(cudnnSetConvolutionMathType(result, CUDNN_FMA_MATH));
 	#endif

utests/test_mpi_flpmath.c

@@ -134,7 +134,7 @@ static bool test_mpi_z3op(z3op_t test_fun, unsigned long mpi_flags)
 	md_free(ref);
 	md_free(ret_copy);
 
-	UT_RETURN_ASSERT(err < UT_TOL * 10); //because single precission
+	UT_RETURN_ASSERT(err < UT_TOL * 10); //because single precision
 }
 
 static bool test_mpi_zfmac2_3(void)	{ return test_mpi_z3op(md_zfmac2,	MD_BIT(3))		; }