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fslfeatsetup-0.4.8
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مشاهده بیشترتوضیحات
a small set of tools for create FSL FEAT .fsf configuration files
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fslfeatsetup-0.4.8 |
| نام | fslfeatsetup |
| نسخه کتابخانه | 0.4.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Chris Langfield |
| ایمیل نویسنده | cal2254@cumc.columbia.edu |
| آدرس صفحه اصلی | https://github.com/chris-langfield/pyfsf |
| آدرس اینترنتی | https://pypi.org/project/fslfeatsetup/ |
| مجوز | Attribution-NonCommercial-ShareAlike |
 
# fslfeatsetup
Python functions to create an FSL FEAT configuration file (.fsf)
Can be run from python command line/REPL or incorporated into a script
**Alpha version available on PyPI**
```pip install fslfeatsetup```
Feel free to contribute
[DOCUMENTATION](build/docs/content/api-documentation.md) generated by [pydoc-markdown](https://github.com/NiklasRosenstein/pydoc-markdown)
[On Python Package Index](https://pypi.org/project/fslfeatsetup/)
## Overview
The .fsf file is represented by the class `FeatSettings`, which is constructed with the analysis level and analysis type options.
Each panel of the FEAT GUI is represented by a separate class, taking the initial `FeatSettings` object as its argument. Each of these objects has a `Configure()` function taking keyword arguments specifying the options available in that panel of the GUI. These are the `MiscOptions`, `DataOptions`, `PreStatsOptions`, `RegOptions`, `StatsOptions`, and `PostStatsOptions` classes.
For example the checkboxes and inputs on the Misc Options GUI panel correspond to the key-word arguments in the `MiscOptions.Configure()` function.
`MiscOptions.Configure(brainThreshold=10, noiseLevel=0.66, temporalSmoothness=0.05, zThreshold=5.3, cleanupFirstLevel=False, overwriteOriginalPostStats = False, estimateNoiseFromData=False)`
(*Some of these options pertain only to higher-level analyses*)
Dropdown lists are represented by enum-like classes:
`PreStatsOptions.Configure(st = FeatSliceTiming.REGULAR_UP)`
`PreStatsOptions.Configure(st = FeatSliceTiming.TIMING_FILE, sliceTimingFile = "path/to/file")`
## First-level analysis example
```python
from fslfeatsetup.FSF import *
from fslfeatsetup.EVs import *
from fslfeatsetup.FSFLabels import *
SubjectFMRI = [ ... ]
SubjectStructurals = [ ... ]
for i in range(len(SubjectFMRI)):
# initialize the FeatSettings object
FSF = FeatSettings(FeatLevel.FIRST_LEVEL, FeatAnalysis.FULL_ANALYSIS)
# Configure the Data options
Data = DataOptions(FSF)
# The only required inputs are the output FEAT directory, and the list of
# FMRI files (or lower-level feats, see Higher Level Analysis example
Data.Configure("path/to/output/subject_i",[SubjectFMRI[i]])
# Configure the Miscellaneous options
Misc = MiscOptions(FSF)
# There are NO required inputs. Using the defaults specified in my FSL installation.
# If fslfeatsetup needs an option that is not in the defaults, it will let you know
Misc.Configure()
# Configure Registration options
Reg = RegOptions(FSF)
# I can specify a standard to use, or I can go with the default 2mm MNI152, as I am here
Reg.ConfigureStandardSpace()
# The only required argument
Reg.ConfigureMainStructural([SubjectStructurals[i]])
# If I don't want to use expanded functional data, I simply don't configure it
# Reg.ConfigureExpandedFunctional([ this would be a list of your expanded functional images ])
# Configure Pre-Stats options
PreStats = PreStatsOptions(FSF)
# The library has built-in enum-like structures that hardcode the FEAT options
PreStats.Configure(sliceTiming=FeatSliceTiming.TIMING_FILE,
sliceTimingFile="path/to/slice/timing/file",
bet=True)
# Configure Stats options
Stats = StatsOptions(FSF)
# using all defaults, so I don't need to specify keyword arguments
Stats.Configure()
# Add EVs from custom 3 column text formats.
# Note that ONLY the 3-column text file format is currently supported
# specify the parameters of the convolution function, or use defaults
Stats.AddFirstLevelEV("myEV1","path/to/my/EV1",Gamma(phase=0, stdev=3, lag=6))
Stats.AddFirstLevelEV("myEV2","path/to/my/EV2",Gamma())
Stats.AddFirstLevelEV("myEV3","path/to/my/EV3",Gamma())
# orthogonalize
# The argument is a pythonic matrix (list of lists)
# the size of this matrix will be one larger than the number of EVs
Stats.OrthogonalizeEVs([ [ 0 for x in range(4)] for y in range(4)])
# Configure Post-Stats options
PostStats = PostStatsOptions(FSF)
# using all defaults except for min and max Z-threshold for rendering
PostStats.Configure(zmin = 2, zmax= 8)
# write to .fsf file
FSF.write("path/to/subject_i/fsf")
```
## Higher-level analysis steps
todo
## Changelog
| PyPI version | Description |
| ------ | ------ |
| 0.3.4 | fixes related to the Issues page. No methods or classes are different |
| 0.2.8 | Generated an .fsf accepted by FEAT. There are a bunch of hard-coded defaults that still need to be fixed |
| 0.2.5 | Fixed gammadelay being blank and FSLDIR not found |
| 0.2.3 | Patch: quotations for custom EV files |
| 0.2.1 | Patched a silly bug |
| 0.2.0 | first "complete" version, with post-stats options and auto generated comments. still very much a work in progress |
| 0.1.2 | first stable version - package still a work in progress |
نیازمندی
| مقدار | نام |
|---|---|
| - | fslpy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl fslfeatsetup-0.4.8:
pip install fslfeatsetup-0.4.8.whl
نصب پکیج tar.gz fslfeatsetup-0.4.8:
pip install fslfeatsetup-0.4.8.tar.gz