Fixed spelling errors

Author: bbfrederick

CHANGELOG.md

@@ -39,7 +39,7 @@
 * (capfromtcs) Revert change to max_iter when specifying initialcenters.
 
 ## Version 1.3.3 (1/10/23)
-* (capfromtcs) Changed intialization of kmeans from initialcenters, save more intermediate results.
+* (capfromtcs) Changed initialization of kmeans from initialcenters, save more intermediate results.
 * (utils.py) Factored out some transition array calculations.
 
 ## Version 1.3.2 (11/15/22)
@@ -58,7 +58,7 @@
 * (capfromtcs, clustercomp) Used newer, non-deprecated method to access nifti files with nibabel.
 * (package) Added docker and singularity test scripts.
 * (package) Reformatted several files with black.
-* (package) Made many (unsuccesful) attempts to get the documentation to build.
+* (package) Made many (unsuccessful) attempts to get the documentation to build.
 
 ## Version 1.2.2.5 (8/30/22)
 * (package) Bump to trigger github deployment.

INSTALL.rst

@@ -36,7 +36,7 @@ The other simple way to get this all done is to use Anaconda python
 from Continuum Analytics. It’s a free, curated scientific Python
 distribution that is easy to maintain and takes a lot of headaches out
 of maintaining a distribution. It also already comes with almost all of the
-dependancies for capcalc installed by default. You can get it here:
+dependencies for capcalc installed by default. You can get it here:
 https://www.continuum.io. You should download the most recent Python 3 version.
 
 After installing Anaconda python, install the remaining dependency

capcalc/filter.py

@@ -1191,7 +1191,7 @@ def harmonicnotchfilter(timecourse, Fs, Ffundamental, notchpct=1.0, debug=False)
     notchpct: float, optional
         Width of the notch relative to the filter frequency in percent.  Default is 1.0.
     debug: bool, optional
-        Set to True for additiona information on function internals.  Default is False.
+        Set to True for additional information on function internals.  Default is False.
 
     Returns
     -------
@@ -1251,7 +1251,7 @@ def csdfilter(obsdata, commondata, padlen=20, cyclic=False, debug=False):
         If True, pad by wrapping the data in a cyclic manner rather than reflecting at the ends
 
     debug: bool, optional
-        Set to True for additiona information on function internals.  Default is False.
+        Set to True for additional information on function internals.  Default is False.
 
     Returns
     -------

capcalc/parser_funcs.py

@@ -768,7 +768,7 @@ def setargs(thegetparserfunc, inputargs=None):
             args = thegetparserfunc().parse_args()
             argstowrite = sys.argv
         except SystemExit:
-            print("Use --help option for detailed informtion on options.")
+            print("Use --help option for detailed information on options.")
             # thegetparserfunc().print_help()
             raise
     else:
@@ -779,7 +779,7 @@ def setargs(thegetparserfunc, inputargs=None):
             args = thegetparserfunc().parse_args(inputargs)
             argstowrite = inputargs
         except SystemExit:
-            print("Use --help option for detailed informtion on options.")
+            print("Use --help option for detailed information on options.")
             # thegetparserfunc().print_help()
             raise
 

capcalc/scripts/clusternifti.py

@@ -264,8 +264,8 @@ def _get_parser():
         action="store",
         type=str,
         choices=["euclidean"],
-        help=("Select affinity type.  Default is euclidian. "),
-        default="euclidian",
+        help=("Select affinity type.  Default is euclidean. "),
+        default="euclidean",
     )
     parser.add_argument(
         "--n_clusters",
@@ -289,7 +289,7 @@ def _get_parser():
         "--display",
         dest="display",
         action="store_true",
-        help=("Display intitial tSNE map."),
+        help=("Display initial tSNE map."),
         default=False,
     )
     parser.add_argument(

capcalc/stats.py

@@ -788,7 +788,7 @@ def makemask(image, threshpct=25.0, verbose=False, nozero=False, noneg=False):
             pctthresh,
             " threshpct:",
             threshpct,
-            " mask threshhold:",
+            " mask threshold:",
             threshval,
         )
     themask = np.where(image > threshval, np.int16(1), np.int16(0))

docs/usage.rst

@@ -113,7 +113,7 @@ Usage:
 				  [--pca] [--ica] [-p NUMCOMPONENTS]
 
 		required arguments:
-		    -i, --infile=TIMECOURSEFILE  - text file mulitple timeseries
+		    -i, --infile=TIMECOURSEFILE  - text file multiple timeseries
 		    -o, --outfile=OUTNAME        - the root name of the output files
 
 		    --samplefreq=FREQ            - sample frequency of all timecourses is FREQ 
@@ -158,7 +158,7 @@ Usage:
 		    --pca                        - perform PCA dimensionality reduction prior to analysis
 		    --ica                        - perform ICA dimensionality reduction prior to analysis
 		    -p NUMCOMPONENTS             - set the number of p/ica components to NUMCOMPONENTS (default is 8).  Set to -1 to estimate
-		    --noscale                    - do not apply standard scaler befor cluster fitting
+		    --noscale                    - do not apply standard scaler before cluster fitting
 
 		  Other:
 		    --GBR                        - apply gradient boosting regressor testing on clusters