Fixed errors found by codespell

Author: bbfrederick

CHANGELOG.md

@@ -76,10 +76,10 @@ History of changes
 * Added reset component keystroke to PICAchooser and melodicomp.
 
 ## Version 1.2.2 (4/5/20)
-* More fiddling to get deplyment working again.
+* More fiddling to get deployment working again.
 
 ## Version 1.2.1 (4/5/20)
-* Fiddled with .gitignore to try to get deplyment working again.
+* Fiddled with .gitignore to try to get deployment working again.
 * Added a help line to the bottom of grader GUI window.
 
 ## Version 1.2.0 (4/5/20)

INSTALL.rst

@@ -11,7 +11,8 @@ PICAchooser requires some external libraries to be installed first:
 -  scipy
 -  pandas
 -  nibabel
--  pyqt5
+-  pyqt6
+-  pyqt6-sip
 -  pyqtgraph
 -  pillow
 
@@ -37,7 +38,7 @@ 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 PICAchooser installed by default. You can get it here:
+dependencies for PICAchooser 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

README.rst

@@ -4,7 +4,7 @@ PICAchooser (the package)
 .. figure:: https://raw.githubusercontent.com/bbfrederick/picachooser/master/images/picalogo.png
    :alt: Logo
 
-A set of simple gui tools for scanning through MELODIC probabalistic ICA
+A set of simple gui tools for scanning through MELODIC probabilistic ICA
 runs and quickly making decisions about which components to retain, and
 what relates to what. These tools each only do one thing, but they do them
 quickly and easily using only keyboard input. Current programs are
@@ -18,7 +18,7 @@ What's in here?
 PICAchooser
 -----------
 Lets you step through the components in an ICA analysis (from many sources), and select which components you
-want to retain.  In addition to showing the spatial ICs, it also displays the componnent timecourses,
+want to retain.  In addition to showing the spatial ICs, it also displays the component timecourses,
 motion traces, and the correlation between them, to help with your decision making.
 
 Once you launch, you do everything with keyboard commands, and it's been optimized to go as fast as possible, so

picachooser/LightboxItem.py

@@ -492,7 +492,7 @@ def __init__(
         self.setorient(self.orientation)
 
         if self.verbose:
-            print("LightboxItem intialization:")
+            print("LightboxItem initialization:")
             print("    Dimensions:", self.xdim, self.ydim, self.zdim)
             print("    Voxel sizes:", self.xsize, self.ysize)
             print("    FOVs:", self.xfov, self.yfov)

picachooser/scripts/PICAchooser.py

@@ -835,7 +835,7 @@ def main():
         metavar="MIXFILE",
         type=lambda x: pica_util.is_valid_file(parser, x),
         help=(
-            "The timecourses of the independant components (usually found in MELODICDIR/melodic_mix),"
+            "The timecourses of the independent components (usually found in MELODICDIR/melodic_mix),"
         ),
         default=None,
     )

picachooser/scripts/grader.py

@@ -400,7 +400,7 @@ def main():
     try:
         args = parser.parse_args()
     except SystemExit:
-        print("Use --help option for detailed informtion on options.")
+        print("Use --help option for detailed information on options.")
         raise
 
     runmode = "aroma"

picachooser/stats.py

@@ -517,7 +517,7 @@ def getfracvals(datamat, thefracs, numbins=200, displayplots=False, nozero=False
         maskmat = datamat[np.where(datamat != 0.0)]
         if len(maskmat) == 0:
             for thisfrac in thefracs:
-                thevals.appen(0.0)
+                thevals.append(0.0)
             return thevals
     else:
         maskmat = datamat
@@ -613,7 +613,7 @@ def makemask(image, threshpct=25.0, verbose=False, nozero=False):
         print(
             " threshpct:",
             threshpct,
-            " mask threshhold:",
+            " mask threshold:",
             threshval,
         )
     themask = np.where(image > threshval, np.int16(1), np.int16(0))