Merge pull request #57 from awoimbee/aw/fix-typos

fix typos

docs/diffcrash/run.md

@@ -7,7 +7,7 @@ analysis of simulation runs.
 It can be used as a set of independent executables or as a postprocessor plugin.
 [Diffcrash] itself must be licensed.
 Please therefore contact [Sidact] directly.
-This commmand line utility makes running a Diffcrash analysis much
+This command line utility makes running a Diffcrash analysis much
 easier.
 
 [diffcrash]: https://www.sidact.de/diffcrash

docs/dimred/command_line_interface.md

@@ -209,7 +209,7 @@ optional arguments:
                         Arguments for clustering algorithms. If not set,
                         clustering will be skipped.
   --outlier-args [OUTLIER_ARGS [OUTLIER_ARGS ...]]
-                        Arguments for outlier detection befor clustering.
+                        Arguments for outlier detection before clustering.
 ```
 
 Following arguments are required for the analysis:
@@ -322,8 +322,8 @@ through:
 Next, we will take a look at the `--start-stage` and `--end-stage` arguments.
 These allow to restart and end the command-line utility at certain points in the
 process.
-This is usefull if you don't want to repeat certain stages to save time, or want
-to end the process prematurly, e.g. don't want to generate the `.html` output.
+This is useful if you don't want to repeat certain stages to save time, or want
+to end the process prematurely, e.g. don't want to generate the `.html` output.
 
 To set the desired start and end stage, use the the following keywords:
 
@@ -431,7 +431,7 @@ ref_sample = h5file["subsample"][:]
 np.stack(h5file["subsampled_runs"][entry][:]
          for entry in h5file["subsampled_runs"].keys()])
 
-# create a numpy array containg the right reduced order basis for projection:
+# create a numpy array containing the right reduced order basis for projection:
 v_rob = h5file["v_rob"][:]
 
 # the subsampled runs are projected into the right reduced order basis and called betas:

docs/dyna/performance_info.md

@@ -70,4 +70,4 @@ Since different results may be dumped at different frequencies some state
 folders might contain more information than others.
 This inherently prevents efficient memory reading.
 The python version used here is slower than the original C-based version but
-one therefore gains better portability accross operating systems.
+one therefore gains better portability across operating systems.

docs/femzip/index.md

@@ -19,7 +19,7 @@ functions such as a femzip file check if desired.
 ```
 
 Here we check if we can use the extended FEMZIP-API.
-The extended FEMZIP-API allows reading selected arrays, but reqires a license
+The extended FEMZIP-API allows reading selected arrays, but requires a license
 with the feature "FEMUNZIPLIB-DYNA", which can be attained from SIDACT or
 femzip distributors.
 
@@ -37,7 +37,7 @@ True
 ```
 
 Check the file and library version.
-This is usually not neccessary.
+This is usually not necessary.
 
 ``` python
 >>> api.is_femunzip_version_ok(filepath)

lasso/diffcrash/diffcrash_run.py

@@ -1141,7 +1141,7 @@ def _create_merge_input_file(self, directory: str) -> str:
             merge_input_file.write("eigen_all        ! Name of eigen input file\n")
             merge_input_file.write(
                 "mode_            ! Name of Output file "
-                + "(string will be apended with mode information)\n"
+                + "(string will be appended with mode information)\n"
             )
             merge_input_file.write("1 1              ! Mode number to be generated\n")
             merge_input_file.write("'d+ d-'          ! Mode type to be generated\n")

lasso/dimred/dimred_run.py

@@ -657,7 +657,7 @@ def _parse_html_name(self, html_name_string: str) -> str:
         if replace_count > 0:
             msg = (
                 f"Replaced {replace_count} invalid characters for the html file name. "
-                f"The new hmtl name is: {html_name}"
+                f"The new html name is: {html_name}"
             )
             self.log(msg)
 

lasso/dimred/graph_laplacian.py

@@ -126,7 +126,7 @@ def _laplacian(lapl: csgraph, n_eigenmodes: int = 5):
     n_eigenmodes : int
         number of eigenmodes to compute
     points : np.ndarray
-        coordintes of graph nodes (only for plotting)
+        coordinates of graph nodes (only for plotting)
 
     Returns
     -------

lasso/dimred/sphere/algorithms.py

@@ -182,7 +182,7 @@ def compute_similarity(embeddings: np.ndarray) -> np.ndarray:
     return smatrix
 
 
-def create_historgram(
+def create_histogram(
     cloud: np.ndarray, sphere_axis: str = "Z", planar: bool = False
 ) -> BinnedStatistic2dResult:
     """Builds a histogram using the blocks of a sphered globe and returns a

lasso/dimred/svd/clustering_betas.py

@@ -25,7 +25,7 @@ def __apply_spectral_clustering(betas, runids, datasets, idsets, random_state=11
     idsets: list
         List where the grouped ids corresponding to the grouped Betas will be saved
     **kwargs: keyword arguments
-        Keyword arguments specific for the SpectralClustering algorythm
+        Keyword arguments specific for the SpectralClustering algorithm
 
     See Also
     --------
@@ -62,7 +62,7 @@ def __apply_k_means(betas, runids, datasets, idsets, random_state=11, **kwargs):
     idsets: list
         List where the grouped ids corresponding to the grouped Betas will be saved
     **kwargs: keyword arguments
-        Keyword arguments specific fot the KMeans algorythm
+        Keyword arguments specific for the KMeans algorithm
 
     See Also
     --------
@@ -85,7 +85,7 @@ def __apply_k_means(betas, runids, datasets, idsets, random_state=11, **kwargs):
 def __apply_dbscan(betas, runids, datasets, idsets, **kwargs):
     """
     Method to group the input Betas.
-    Defautl keyword arguments: eps=0.08
+    Default keyword arguments: eps=0.08
 
     Parameters
     ----------
@@ -98,7 +98,7 @@ def __apply_dbscan(betas, runids, datasets, idsets, **kwargs):
     idsets: list
         List where the grouped ids corresponding to the grouped Betas will be saved
     **kwags: keyword arguments
-        Keyword arguments for the DBSCAN algorythm
+        Keyword arguments for the DBSCAN algorithm
 
     See Also
     --------
@@ -158,7 +158,7 @@ def __detect_outliers_isolation_forest(
     betas, ids, beta_clusters, id_clusters, random_state=11, **kwargs
 ):
     """
-    Detects outliers based on the IsolationForest algorythm from sklearn.
+    Detects outliers based on the IsolationForest algorithm from sklearn.
     Detected outliers will be appended into the provided lists
     Default keyword parameters: random_state=12, behaviour="new", contamination=0.005
 
@@ -173,7 +173,7 @@ def __detect_outliers_isolation_forest(
     id_clusters: list
         List where each cluster of ids will be appended
     **kwargs: keyword argument
-        Keywords specific to the IsolationForest algorythm
+        Keywords specific to the IsolationForest algorithm
     Returns
     -------
     inlier_betas: np.array
@@ -198,7 +198,7 @@ def __detect_outliers_isolation_forest(
 
 def __detect_outliers_local_outlier_factor(betas, ids, beta_clusters, id_clusters, **kwargs):
     """
-    Detects outliers based on the LocalOutlierFactor algorythm from sklearn.
+    Detects outliers based on the LocalOutlierFactor algorithm from sklearn.
     Detected outliers will be appended into the provided lists
     Default keyword parameters: contamination=0.01
 
@@ -213,7 +213,7 @@ def __detect_outliers_local_outlier_factor(betas, ids, beta_clusters, id_cluster
     id_clusters: list
         List where each cluster of ids will be appended
     **kwargs: keyword argument
-        Keywords specific to the LocalOutlierFactor algorythm.
+        Keywords specific to the LocalOutlierFactor algorithm.
     Returns
     -------
     inlier_betas: np.ndarray
@@ -238,9 +238,9 @@ def __detect_outliers_local_outlier_factor(betas, ids, beta_clusters, id_cluster
 
 def __detect_outliers_one_class_svm(betas, ids, beta_clusters, id_clusters, **kwargs):
     """
-    Detects outliers based on the OneClassSVM algorythm from sklearn.
+    Detects outliers based on the OneClassSVM algorithm from sklearn.
     Detected outliers will be appended into the provided lists
-    Defautl keyword arguments: gamma=0.1, nu=0.01
+    Default keyword arguments: gamma=0.1, nu=0.01
 
     Parameters
     ----------
@@ -253,7 +253,7 @@ def __detect_outliers_one_class_svm(betas, ids, beta_clusters, id_clusters, **kw
     id_clusters: list
         List where each cluster of ids will be appended
     **kwargs: keyword argument
-        Keywords specific to the OneClassSVM algorythm.
+        Keywords specific to the OneClassSVM algorithm.
 
     Returns
     -------
@@ -280,7 +280,7 @@ def __detect_outliers_one_class_svm(betas, ids, beta_clusters, id_clusters, **kw
 
 def __experimental_outlier_detector(betas, ids, **kwargs):
     """
-    Detects outliers by applying LocalOutlierFactor algorythm from sklearn over multiple slices of betas .
+    Detects outliers by applying LocalOutlierFactor algorithm from sklearn over multiple slices of betas .
     Detected outliers will be appended into the provided lists
     Default keyword arguments:  contamination=0.01
     Parameters
@@ -290,7 +290,7 @@ def __experimental_outlier_detector(betas, ids, **kwargs):
     ids: np.ndarray
         Numpy array containing the ids of each beta
     **kwargs: keyword argument
-        Keywords specific to the LocalOutlierFactor algorythm
+        Keywords specific to the LocalOutlierFactor algorithm
     Returns
     -------
     outliers: np.array
@@ -583,9 +583,9 @@ def group_betas(
 ) -> Union[Tuple[list, list], str]:
     """
     Base function to to group betas into groups, detect outliers. Provides that all different
-    clustering and outlier detection algorythms are implemented in an easy to access environment.
+    clustering and outlier detection algorithms are implemented in an easy to access environment.
     To select different clustering and outlier detection algoyrthms, please use appropriate
-    KeywordTypes. A description of each function can be accessed with document_algorythm(keyword)
+    KeywordTypes. A description of each function can be accessed with document_algorithm(keyword)
     A list of all functions can be accessed with list_detectors_and_clusters()
 
     Parameters
@@ -595,25 +595,25 @@ def group_betas(
     betas: np.ndarray
         Numpy array containing the betas.
         Betas are expected to be of shape (samples, timestep, 3)
-        The three entries per beta can either be dimesnions (x,y,z) or any three betas/eigenvalues
+        The three entries per beta can either be dimensions (x,y,z) or any three betas/eigenvalues
     cluster: str, optional, default : "KMeans".
-        String specifying which clustering algorythm shall be applied.
+        String specifying which clustering algorithm shall be applied.
         Use ClusterTypefor easier access
     detector: str, optional, default: None.
-        String specifying which outlier detection algorythm shall be applied.
+        String specifying which outlier detection algorithm shall be applied.
         Use DetectorType for easier access
     cluster_params: dict, optional
-        Dictionary containing parameters for the clustering algorythms.
+        Dictionary containing parameters for the clustering algorithms.
         See the sklearn documentation for the function to learn more.
     detector_params: dict, optional
-        Dictionary containing parameters for the outlier detection algorythms.
+        Dictionary containing parameters for the outlier detection algorithms.
         See the sklearn documentation for the function to learn more
 
     Returns
     -------
     beta_clusters: list
         List containing Numpy Arrays of betas in one cluster.
-        If a detector was selected, or the clustering algorythm has its
+        If a detector was selected, or the clustering algorithm has its
         own outlier detection, the first entry in the list will be oultier betas
     id_clusters: list
         List containing lists of beta ids. Each id corresponds to the beta in
@@ -626,13 +626,13 @@ def group_betas(
     document_algorithm:
         Prints docstring of each function into console
     list_detectors_and_clusters:
-        Prints out all detection and clustering algorythms into console
+        Prints out all detection and clustering algorithms into console
     Sklearn Userguide chapter 2.3 Clustering:
         https://scikit-learn.org/stable/modules/clustering.html
-        Detailed overview of different clustering algorythms
+        Detailed overview of different clustering algorithms
     Sklearn Examples outlier detection:
         https://scikit-learn.org/stable/auto_examples/plot_anomaly_comparison.html
-        Example of different used outlier detection algorythms
+        Example of different used outlier detection algorithms
     """
 
     # pylint: disable = too-many-arguments, too-many-locals, too-many-branches
@@ -659,9 +659,9 @@ def group_betas(
             betas = betas[inlier_index]
             beta_index = beta_index[inlier_index]
         else:
-            empy_list = []
-            beta_clusters.append(empy_list)
-            id_clusters.append(empy_list)
+            empty_list = []
+            beta_clusters.append(empty_list)
+            id_clusters.append(empty_list)
 
         detector = None