Merge branch 'main' into refinedelay

.github/workflows/codespell.yml

@@ -0,0 +1,25 @@
+# Codespell configuration is within pyproject.toml
+---
+name: Codespell
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+permissions:
+  contents: read
+
+jobs:
+  codespell:
+    name: Check for spelling errors
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Annotate locations with typos
+        uses: codespell-project/codespell-problem-matcher@v1
+      - name: Codespell
+        uses: codespell-project/actions-codespell@v2

CODE_OF_CONDUCT.md

@@ -5,7 +5,7 @@ participation in our project and our community to be a harassment-free
 experience for everyone.
 
 Although no list can hope to be all-encompassing, we explicitly honor diversity in age,
-body size, disability, ethnicity, gender identity and expression, level of experience, native language, education, socio-economic status, nationality, personal appearance, race, religion,
+body size, disability, ethnicity, gender identity and expression, level of experience, native language, education, socioeconomic status, nationality, personal appearance, race, religion,
 or sexual identity and orientation.
 
 ## Our Standards

docs/legacy.rst

@@ -56,7 +56,7 @@ For compatibility with old workflows, rapidtide can be called using legacy synta
           --permutationmethod=METHOD     - Method for permuting the regressor for significance estimation.  Default
                                            is shuffle
           --skipsighistfit               - Do not fit significance histogram with a Johnson SB function
-          --windowfunc=FUNC              - Use FUNC window funcion prior to correlation.  Options are
+          --windowfunc=FUNC              - Use FUNC window function prior to correlation.  Options are
                                            hamming (default), hann, blackmanharris, and None
           --nowindow                     - Disable precorrelation windowing
           -f GAUSSSIGMA                  - Spatially filter fMRI data prior to analysis using
@@ -112,7 +112,7 @@ For compatibility with old workflows, rapidtide can be called using legacy synta
           --liang                        - Use generalized cross-correlation with Liang weighting function
                                            (Liang, et al, doi:10.1109/IMCCC.2015.283)
           --eckart                       - Use generalized cross-correlation with Eckart weighting function
-          --corrmaskthresh=PCT           - Do correlations in voxels where the mean exceeeds this
+          --corrmaskthresh=PCT           - Do correlations in voxels where the mean exceeds this
                                            percentage of the robust max (default is 1.0)
           --corrmask=MASK                - Only do correlations in voxels in MASK (if set, corrmaskthresh
                                            is ignored).
@@ -224,7 +224,7 @@ For compatibility with old workflows, rapidtide can be called using legacy synta
           --acfix                        - Perform a secondary correlation to disambiguate peak location
                                            (enables --accheck).  Experimental.
           --tmask=MASKFILE               - Only correlate during epochs specified in
-                                           MASKFILE (NB: if file has one colum, the length needs to match
+                                           MASKFILE (NB: if file has one column, the length needs to match
                                            the number of TRs used.  TRs with nonzero values will be used
                                            in analysis.  If there are 2 or more columns, each line of MASKFILE
                                            contains the time (first column) and duration (second column) of an

docs/theoryofoperation.rst

@@ -480,7 +480,7 @@ Time delay determination
 
 This is the core of the program, that actually does the delay determination.
 It's currently divided into two parts -
-calculation of a time dependant similarity function between the sLFO regressor and each voxel
+calculation of a time dependent similarity function between the sLFO regressor and each voxel
 (currently using one of three methods),
 and then a fitting step to find the peak time delay and strength of association between the two signals.
 

docs/usage_general.rst

@@ -107,7 +107,7 @@ waveform, you'd run:
       happytest_desc-slicerescardfromfmri_timeseries.json:cardiacfromfmri,cardiacfromfmri_dlfiltered \
       --format separate
 
-There are some companion programs - ``showxy`` works on 2D (x, y) data; ``showhist`` is specificaly for viewing
+There are some companion programs - ``showxy`` works on 2D (x, y) data; ``showhist`` is specifically for viewing
 histograms (``_hist`` files) generated by several programs in the package, ``spectrogram`` generates and displays spectrograms of
 time series data.  Each of these is separately documented below.
 

docs/usage_rapidtide.rst

@@ -505,7 +505,7 @@ For this type of analysis, a good place to start is the following:
 
 The first option (``--numnull 0``), shuts off the calculation of the null correlation distribution.  This is used to
 determine the significance threshold, but the method currently implemented in rapidtide is a bit simplistic - it
-assumes that all the time points in the data are exchangable.  This is certainly true for resting state data (see
+assumes that all the time points in the data are exchangeable.  This is certainly true for resting state data (see
 above), but it is very much NOT true for block paradigm gas challenges.  To properly analyze those, I need to
 consider what time points are 'equivalent', and up to now, I don't, so setting the number of iterations in the
 Monte Carlo analysis to zero omits this step.
@@ -580,7 +580,7 @@ and more importantly, we have access to some much higher quality NIRS data, this
 The majority of the work was already done, I just needed to account for a few qualities that make NIRS data different from fMRI data:
 
 * NIRS data is not generally stored in NIFTI files.  While there is one now (SNIRF), at the time I started doing this, there was no standard NIRS file format.  In the absence of one, you could do worse than a multicolumn text file, with one column per data channel.  That's what I did here - if the file has a '.txt' extension rather than '.nii.', '.nii.gz', or no extension, it will assume all I/O should be done on multicolumn text files.  However, I'm a firm believer in SNIRF, and will add support for it one of these days.
-* NIRS data is often zero mean.  This turned out to mess with a lot of my assumptions about which voxels have significant data, and mask construction.  This has led to some new options for specifying mask threshholds and data averaging.
+* NIRS data is often zero mean.  This turned out to mess with a lot of my assumptions about which voxels have significant data, and mask construction.  This has led to some new options for specifying mask thresholds and data averaging.
 * NIRS data is in some sense "calibrated" as relative micromolar changes in oxy-, deoxy-, and total hemoglobin concentration, so mean and/or variance normalizing the timecourses may not be right thing to do.  I've added in some new options to mess with normalizations.
 
 

docs/usage_tidepool.rst

@@ -3,7 +3,7 @@ tidepool
 
 Description:
 ^^^^^^^^^^^^
-	Tidepool is a handy tool for displaying all of the various maps generated by rapidtide in one place, overlayed on an anatomic image.  This makes it easier to see how all the maps are related to one another.  To use it, launch tidepool from the command line, navigate to a rapidtide output directory, and then select a lag time (maxcorr) map.  tidpool will figure out the root name and pull in all of the other associated maps, timecourses, and info files.  The displays are live, and linked together, so you can explore multiple parameters efficiently.  Works in native or standard space.
+	Tidepool is a handy tool for displaying all of the various maps generated by rapidtide in one place, overlaid on an anatomic image.  This makes it easier to see how all the maps are related to one another.  To use it, launch tidepool from the command line, navigate to a rapidtide output directory, and then select a lag time (maxcorr) map.  tidpool will figure out the root name and pull in all of the other associated maps, timecourses, and info files.  The displays are live, and linked together, so you can explore multiple parameters efficiently.  Works in native or standard space.
 
 .. image:: images/tidepool_overview.jpg
    :align: center

pyproject.toml

@@ -136,3 +136,10 @@ versionfile_source = 'rapidtide/_version.py'
 versionfile_build = 'rapidtide/_version.py'
 tag_prefix = 'v'
 parentdir_prefix = 'rapidtide-'
+
+[tool.codespell]
+# Ref: https://github.com/codespell-project/codespell#using-a-config-file
+skip = '.git*,versioneer.py,*.css,exportlist.txt,data,*.bib'
+check-hidden = true
+ignore-regex = '\bsubjeT\b'
+ignore-words-list = 'thex,normall'

rapidtide/OrthoImageItem.py

@@ -148,7 +148,7 @@ def __init__(
         self.offsetz = self.imgsize * (0.5 - self.zfov / (2.0 * self.maxfov))
 
         if self.verbose > 1:
-            print("OrthoImageItem intialization:")
+            print("OrthoImageItem initialization:")
             print("    Dimensions:", self.xdim, self.ydim, self.zdim)
             print("    Voxel sizes:", self.xsize, self.ysize, self.zsize)
             print("    FOVs:", self.xfov, self.yfov, self.zfov)

rapidtide/correlate.py

@@ -489,7 +489,7 @@ def cross_mutual_info(
     -------
     if returnaxis is True:
         thexmi_x : 1D array
-            the set of offsets at which cross mutual information is calcuated
+            the set of offsets at which cross mutual information is calculated
         thexmi_y : 1D array
             the set of cross mutual information values
         len(thexmi_x): int

rapidtide/experimental/harmonogram

@@ -239,7 +239,7 @@ for o, a in opts:
         print('turning off legend label')
     elif o == '--debug':
         debug = True
-        print('turning debuggin on')
+        print('turning debugging on')
     elif o == '--nowindow':
         useHamming = False
         print('turning window off')

rapidtide/filter.py

@@ -1389,7 +1389,7 @@ 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
     -------
@@ -1451,7 +1451,7 @@ def csdfilter(
         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
     -------

rapidtide/happy_supportfuncs.py

@@ -438,7 +438,7 @@ def findbadpts(
         thresh = numsigma * sigma
         thebadpts = np.where(absdev >= thresh, 1.0, 0.0)
         print(
-            "Bad point threshhold set to",
+            "Bad point threshold set to",
             "{:.3f}".format(thresh),
             "using the",
             thetype,

rapidtide/helper_classes.py

@@ -1340,7 +1340,7 @@ def track(self, x, fs):
             print(self.times.shape, self.freqs.shape, thespectrogram.shape)
             print(self.times)
 
-        # intitialize the peak fitter
+        # initialize the peak fitter
         thefitter = SimilarityFunctionFitter(
             corrtimeaxis=self.freqs,
             lagmin=self.lowerlim,

rapidtide/refineregressor.py

@@ -627,6 +627,7 @@ def dorefine(
 
     if cleanrefined:
         thefit, R2 = tide_fit.mlregress(averagediscard, averagedata)
+
         fitcoff = rt_floatset(thefit[0, 1])
         datatoremove = rt_floatset(fitcoff * averagediscard)
         outputdata -= datatoremove

rapidtide/scripts/fingerprint

@@ -103,8 +103,8 @@ def _get_parser():
             "Atlas.  Options are: "
             "ASPECTS - ASPECTS territory atlas; "
             "ATT - Arterial transit time flow territories; "
-            "JHU1 - Johns Hopkins level 1 probabalistic arterial flow territories, without ventricles (default); "
-            "JHU2 - Johns Hopkins level 2 probabalistic arterial flow territories, without ventricles."
+            "JHU1 - Johns Hopkins level 1 probabilistic arterial flow territories, without ventricles (default); "
+            "JHU2 - Johns Hopkins level 2 probabilistic arterial flow territories, without ventricles."
         ),
         choices=["ASPECTS", "ATT", "JHU1", "JHU2"],
         default="JHU1",

rapidtide/scripts/happywarp

@@ -122,7 +122,7 @@ def _get_parser():
 
     parser.add_argument(
         "--debug",
-        help="ouput additional debugging information",
+        help="output additional debugging information",
         action="store_true",
     )
 

rapidtide/stats.py

@@ -1036,7 +1036,7 @@ def makemask(image, threshpct=25.0, verbose=False, nozero=False, noneg=False):
         print(
             f"fracval: {pctthresh:.2f}",
             f"threshpct: {threshpct:.2f}",
-            f"mask threshhold: {threshval:.2f}",
+            f"mask threshold: {threshval:.2f}",
         )
     themask = np.where(image > threshval, np.int16(1), np.int16(0))
     return themask

rapidtide/workflows/atlastool.py

@@ -75,7 +75,7 @@ def _get_parser():
         action="store",
         type=lambda x: pf.is_float(parser, x),
         metavar="FILE",
-        help=("Threshhold for autogenerated mask (default is 0.25)."),
+        help=("Threshold for autogenerated mask (default is 0.25)."),
         default=0.25,
     )
     parser.add_argument(

rapidtide/workflows/endtidalproc.py

@@ -90,7 +90,7 @@ def _get_parser():
         dest="thresh",
         metavar="PCT",
         type=float,
-        help="Amount of fall (or rise) needed, in percent, to recognize a peak (or trough).",
+        help="Amount of fall (or rise) needed, in percent, to recognize a peak (or through).",
         default=1.0,
     )
     parser.add_argument(
@@ -138,7 +138,7 @@ def endtidalproc():
     args.thestarttime = xvec[thestartpoint]
     args.theendtime = xvec[theendpoint]
 
-    # set parameters - maxtime is the longest to look ahead for a peak (or trough) in seconds
+    # set parameters - maxtime is the longest to look ahead for a peak (or through) in seconds
     # lookahead should be '(samples / period) / f' where '4 >= f >= 1.25' might be a good value
     maxtime = 1.0
     f = 2.0

rapidtide/workflows/happy.py

@@ -331,7 +331,7 @@ def happy_main(argparsingfunc):
     if args.fliparteries:
         # add another pass to refine the waveform after getting the new appflips
         numpasses += 1
-        print("Adding a pass to regenerate cardiac waveform using bettter appflips")
+        print("Adding a pass to regenerate cardiac waveform using better appflips")
 
     # output mask size
     print(f"estmask has {len(np.where(estmask_byslice[:, :] > 0)[0])} voxels above threshold.")
@@ -1073,7 +1073,7 @@ def happy_main(argparsingfunc):
                 infodict["respsamplerate"] = returnedinputfreq
                 infodict["numresppts_fullres"] = fullrespts
 
-        # account for slice time offests
+        # account for slice time offsets
         offsets_byslice = np.zeros((xsize * ysize, numslices), dtype=np.float64)
         for i in range(numslices):
             offsets_byslice[:, i] = slicetimes[i]
@@ -1543,12 +1543,12 @@ def happy_main(argparsingfunc):
                 debug=args.debug,
             )
 
-        # find vessel threshholds
+        # find vessel thresholds
         tide_util.logmem("before making vessel masks")
         hardvesselthresh = tide_stats.getfracvals(np.max(histinput, axis=1), [0.98])[0] / 2.0
         softvesselthresh = args.softvesselfrac * hardvesselthresh
         print(
-            "hard, soft vessel threshholds set to",
+            "hard, soft vessel thresholds set to",
             "{:.3f}".format(hardvesselthresh),
             "{:.3f}".format(softvesselthresh),
         )

rapidtide/workflows/happy_parser.py

@@ -677,7 +677,7 @@ def process_args(inputargs=None):
             args = _get_parser().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.")
             raise
 
     # save the raw and formatted command lines

rapidtide/workflows/pixelcomp.py

@@ -57,7 +57,7 @@ def _get_parser():
     parser.add_argument(
         "--scatter",
         action="store_true",
-        help=("Do a scatter plot intstead of a contour plot."),
+        help=("Do a scatter plot instead of a contour plot."),
         default=False,
     )
     parser.add_argument(

rapidtide/workflows/rapidtide.py

@@ -1769,7 +1769,7 @@ def rapidtide_main(argparsingfunc):
     )
     LGR.verbose(f"edgebufferfrac set to {optiondict['edgebufferfrac']}")
 
-    # intitialize the correlation fitter
+    # initialize the correlation fitter
     thefitter = tide_classes.SimilarityFunctionFitter(
         lagmod=optiondict["lagmod"],
         lthreshval=optiondict["lthreshval"],
@@ -2227,7 +2227,7 @@ def rapidtide_main(argparsingfunc):
                 if optiondict["ampthreshfromsig"]:
                     if pcts is not None:
                         LGR.info(
-                            f"setting ampthresh to the p < {1.0 - thepercentiles[0]:.3f} threshhold"
+                            f"setting ampthresh to the p < {1.0 - thepercentiles[0]:.3f} threshold"
                         )
                         optiondict["ampthresh"] = pcts[0]
                         tide_stats.printthresholds(

rapidtide/workflows/rapidtide_parser.py

@@ -1080,7 +1080,7 @@ def _get_parser():
         metavar="THRESH",
         type=float,
         help=(
-            "Threshhold value (fraction of maximum) in a histogram "
+            "Threshold value (fraction of maximum) in a histogram "
             f"to be considered the start of a peak.  Default is {DEFAULT_PICKLEFT_THRESH}."
         ),
         default=DEFAULT_PICKLEFT_THRESH,
@@ -1160,7 +1160,7 @@ def _get_parser():
         type=int,
         metavar="MAXPASSES",
         help=(
-            "Terminate refinement after MAXPASSES passes, whether or not convergence has occured. "
+            "Terminate refinement after MAXPASSES passes, whether or not convergence has occurred. "
             f"Default is {DEFAULT_MAXPASSES}."
         ),
         default=DEFAULT_MAXPASSES,
@@ -1656,7 +1656,7 @@ def _get_parser():
         "--focaldebug",
         dest="focaldebug",
         action="store_true",
-        help=("Enable targetted additional debugging output (used during development)."),
+        help=("Enable targeted additional debugging output (used during development)."),
         default=False,
     )
     debugging.add_argument(
@@ -1798,7 +1798,7 @@ def process_args(inputargs=None):
     # what fraction of the correlation window to avoid on either end when
     # fitting
     args["edgebufferfrac"] = 0.0
-    # only do fits in voxels that exceed threshhold
+    # only do fits in voxels that exceed threshold
     args["enforcethresh"] = True
     # if set to the location of the first autocorrelation sidelobe,
     # this will fold back sidelobes

rapidtide/workflows/showarbcorr.py

@@ -377,7 +377,7 @@ def showarbcorr(args):
 
     thepxcorr = pearsonr(filtereddata1, filtereddata2)
 
-    # intitialize the correlation fitter
+    # initialize the correlation fitter
     thexsimfuncfitter = tide_classes.SimilarityFunctionFitter(
         corrtimeaxis=xcorr_x,
         lagmin=args.lagmin,

rapidtide/workflows/showxcorrx.py

@@ -558,7 +558,7 @@ def showxcorrx(args):
         )
 
     if args.similaritymetric == "mutualinfo":
-        # intitialize the similarity function fitter
+        # initialize the similarity function fitter
         themifitter = tide_classes.SimilarityFunctionFitter(
             corrtimeaxis=MI_x_trim,
             lagmin=args.lagmin,
@@ -573,7 +573,7 @@ def showxcorrx(args):
         )
         maxdelaymi = MI_x_trim[np.argmax(theMI_trim)]
     else:
-        # intitialize the correlation fitter
+        # initialize the correlation fitter
         thexsimfuncfitter = tide_classes.SimilarityFunctionFitter(
             corrtimeaxis=xcorr_x,
             lagmin=args.lagmin,

setup.py

@@ -49,7 +49,6 @@
     "rapidtide/multiproc",
     "rapidtide/patchmatch",
     "rapidtide/peakeval",
-    "rapidtide/refine",
     "rapidtide/refinedelay",
     "rapidtide/refineregressor",
     "rapidtide/resample",