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| """ Implementation of diagnostic command line tools | ||
| Tools are: | ||
| * nipy_diagnose | ||
| * nipy_tsdiffana | ||
| This module has the logic for each command. | ||
| The command script files deal with argument parsing and any custom imports. | ||
| The implementation here accepts the ``args`` object from ``argparse`` and does | ||
| the work. | ||
| """ | ||
| from os.path import split as psplit, join as pjoin | ||
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| import numpy as np | ||
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| from nibabel import AnalyzeHeader | ||
| from nibabel.filename_parser import splitext_addext | ||
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| import nipy | ||
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| from .tsdiffplot import plot_tsdiffs | ||
| from .timediff import time_slice_diffs_image | ||
| from .screens import screen, write_screen_res | ||
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| def parse_fname_axes(img_fname, time_axis, slice_axis): | ||
| """ Load `img_fname`, check `time_axis`, `slice_axis` or use default | ||
| Parameters | ||
| ---------- | ||
| img_fname : str | ||
| filename of image on which to do diagnostics | ||
| time_axis : None or str or int, optional | ||
| Axis indexing time-points. None is default, will be replaced by a value | ||
| of 't'. If `time_axis` is an integer, gives the index of the input | ||
| (domain) axis of `img`. If `time_axis` is a str, can be an input | ||
| (domain) name, or an output (range) name, that maps to an input | ||
| (domain) name. | ||
| slice_axis : None or str or int, optional | ||
| Axis indexing MRI slices. If `slice_axis` is an integer, gives the | ||
| index of the input (domain) axis of `img`. If `slice_axis` is a str, | ||
| can be an input (domain) name, or an output (range) name, that maps to | ||
| an input (domain) name. If None (the default) then 1) try the name | ||
| 'slice' to select the axis - if this fails, and `fname` refers to an | ||
| Analyze type image (such as Nifti), then 2) default to the third image | ||
| axis, otherwise 3) raise a ValueError | ||
| Returns | ||
| ------- | ||
| img : ``Image`` instance | ||
| Image as loaded from `img_fname` | ||
| time_axis : int or str | ||
| Time axis, possibly filled with default | ||
| slice_axis : int or str | ||
| Slice axis, possibly filled with default | ||
| """ | ||
| # Check whether this is an Analyze-type image | ||
| img = nipy.load_image(img_fname) | ||
| # Check for axes | ||
| if not time_axis is None: | ||
| # Try converting to an integer in case that was what was passed | ||
| try: | ||
| time_axis = int(time_axis) | ||
| except ValueError: | ||
| # Maybe a string | ||
| pass | ||
| else: # was None | ||
| time_axis = 't' | ||
| if not slice_axis is None: | ||
| # Try converting to an integer in case that was what was passed | ||
| try: | ||
| slice_axis = int(slice_axis) | ||
| except ValueError: | ||
| # Maybe a string | ||
| pass | ||
| else: # slice axis was None - search for default | ||
| input_names = img.coordmap.function_domain.coord_names | ||
| is_analyze = ('header' in img.metadata and | ||
| isinstance(img.metadata['header'], AnalyzeHeader)) | ||
| if 'slice' in input_names: | ||
| slice_axis = 'slice' | ||
| elif is_analyze and img.ndim == 4: | ||
| slice_axis = 2 | ||
| else: | ||
| raise ValueError('No slice axis specified, not analyze type ' | ||
| 'image; refusing to guess') | ||
| return img, time_axis, slice_axis | ||
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| def tsdiffana(args): | ||
| """ Generate tsdiffana plots from command line params `args` | ||
| Parameters | ||
| ---------- | ||
| args : object | ||
| object with attributes | ||
| * filename : str - 4D image filename | ||
| * out_file : str - graphics file to write to instead of leaving | ||
| graphics on screen | ||
| * time_axis : str - name or number of time axis in `filename` | ||
| * slice_axis : str - name or number of slice axis in `filename` | ||
| * write_results : bool - if True, write images and plots to files | ||
| * out_path : None or str - path to which to write results | ||
| * out_fname_label : None or filename - suffix of output results files | ||
| Returns | ||
| ------- | ||
| axes : Matplotlib axes | ||
| Axes on which we have done the plots. | ||
| """ | ||
| if not args.out_file is None and args.write_results: | ||
| raise ValueError("Cannot have OUT_FILE and WRITE_RESULTS options " | ||
| "together") | ||
| img, time_axis, slice_axis = parse_fname_axes(args.filename, | ||
| args.time_axis, | ||
| args.slice_axis) | ||
| results = time_slice_diffs_image(img, time_axis, slice_axis) | ||
| axes = plot_tsdiffs(results) | ||
| if args.out_file is None and not args.write_results: | ||
| # interactive mode | ||
| return axes | ||
| if not args.out_file is None: | ||
| # plot only mode | ||
| axes[0].figure.savefig(args.out_file) | ||
| return axes | ||
| # plot and images mode | ||
| froot, ext, addext = splitext_addext(args.filename) | ||
| fpath, fbase = psplit(froot) | ||
| fpath = fpath if args.out_path is None else args.out_path | ||
| fbase = fbase if args.out_fname_label is None else args.out_fname_label | ||
| axes[0].figure.savefig(pjoin(fpath, 'tsdiff_' + fbase + '.png')) | ||
| # Save image volumes | ||
| for key, prefix in (('slice_diff2_max_vol', 'dv2_max_'), | ||
| ('diff2_mean_vol', 'dv2_mean_')): | ||
| fname = pjoin(fpath, prefix + fbase + ext + addext) | ||
| nipy.save_image(results[key], fname) | ||
| # Save time courses into npz | ||
| np.savez(pjoin(fpath, 'tsdiff_' + fbase + '.npz'), | ||
| volume_means=results['volume_means'], | ||
| slice_mean_diff2=results['slice_mean_diff2'], | ||
| ) | ||
| return axes | ||
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| def diagnose(args): | ||
| """ Calculate, write results from diagnostic screen | ||
| Parameters | ||
| ---------- | ||
| args : object | ||
| object with attributes: | ||
| * filename : str - 4D image filename | ||
| * time_axis : str - name or number of time axis in `filename` | ||
| * slice_axis : str - name or number of slice axis in `filename` | ||
| * out_path : None or str - path to which to write results | ||
| * out_fname_label : None or filename - suffix of output results files | ||
| * ncomponents : int - number of PCA components to write images for | ||
| Returns | ||
| ------- | ||
| res : dict | ||
| Results of running :func:`screen` on `filename` | ||
| """ | ||
| img, time_axis, slice_axis = parse_fname_axes(args.filename, | ||
| args.time_axis, | ||
| args.slice_axis) | ||
| res = screen(img, args.ncomponents, time_axis, slice_axis) | ||
| froot, ext, addext = splitext_addext(args.filename) | ||
| fpath, fbase = psplit(froot) | ||
| fpath = fpath if args.out_path is None else args.out_path | ||
| fbase = fbase if args.out_fname_label is None else args.out_fname_label | ||
| write_screen_res(res, fpath, fbase, ext + addext) | ||
| return res |
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