usage: __main__.py [-h] [--out_colorbar OUT_COLORBAR] [--show_colorbar]
[--horizontal_cbar]
(--use_dps DPS_KEY | --use_dpp DPP_KEY | --load_dps DPS_FILE | --load_dpp DPP_FILE | --from_anatomy FILE | --along_profile | --local_orientation | --local_angle)
[--ambiant_occlusion [AMBIANT_OCCLUSION]]
[--colormap COLORMAP] [--clip_outliers]
[--min_range MIN_RANGE] [--max_range MAX_RANGE]
[--min_cmap MIN_CMAP] [--max_cmap MAX_CMAP] [--log]
[--LUT FILE] [--reference REFERENCE]
[-v [{DEBUG,INFO,WARNING}]] [-f]
in_tractogram out_tractogram
The script uses scalars from an anatomy, data_per_point or data_per_streamline
(e.g. commit_weights) to visualize them on the streamlines.
Saves the RGB values in the data_per_point 'color' with 3 values per point:
(color_x, color_y, color_z).
If called with .tck, the output will always be .trk, because data_per_point has
no equivalent in tck file.
If used with a visualization software like MI-Brain
(https://github.com/imeka/mi-brain), the 'color' dps is applied by default at
loading time.
COLORING METHOD
This script maps the raw values from these sources to RGB using a colormap.
--use_dpp: The data from each point is converted to a color.
--use_dps: The same color is applied to all points of the streamline.
--from_anatomy: The voxel's color is used for the points of the streamlines
crossing it. See also scil_tractogram_project_map_to_streamlines.py. You
can have more options to project maps to dpp, and then use --use_dpp here.
--along_profile: The data used here is each point's position in the
streamline. To have nice results, you should first uniformize head/tail.
See scil_tractogram_uniformize_endpoints.py.
--local_angle.
COLORING OPTIONS
A minimum and a maximum range can be provided to clip values. If the range of
values is too large for intuitive visualization, a log transform can be
applied.
If the data provided from --use_dps, --use_dpp and --from_anatomy are integer
labels, they can be mapped using a LookUp Table (--LUT).
The file provided as a LUT should be either .txt or .npy and if the size is
N=20, then the data provided should be between 1-20.
A custom colormap can be provided using --colormap. It should be a string
containing a colormap name OR multiple Matplotlib named colors separated by -.
The colormap used for mapping values to colors can be saved to a png/jpg image
using the --out_colorbar option.
See also: scil_tractogram_assign_uniform_color.py, for simplified options.
Formerly: scil_assign_custom_color_to_tractogram.py
positional arguments:
in_tractogram Input tractogram (.trk or .tck).
out_tractogram Output tractogram (.trk or .tck).
options:
-h, --help show this help message and exit
--reference REFERENCE
Reference anatomy for tck/vtk/fib/dpy file
support (.nii or .nii.gz).
-v [{DEBUG,INFO,WARNING}]
Produces verbose output depending on the provided level.
Default level is warning, default when using -v is info.
-f Force overwriting of the output files.
Colorbar options:
--out_colorbar OUT_COLORBAR
Optional output colorbar (.png, .jpg or any format
supported by matplotlib).
--show_colorbar Will show the colorbar. Must be used with --out_colorbar
to be effective.
--horizontal_cbar Draw horizontal colorbar (vertical by default).
Coloring method:
--use_dps DPS_KEY Use the data_per_streamline (scalar) for coloring.
--use_dpp DPP_KEY Use the data_per_point (scalar) for coloring.
--load_dps DPS_FILE Load data per streamline (scalar) for coloring
--load_dpp DPP_FILE Load data per point (scalar) for coloring
--from_anatomy FILE Use the voxel data for coloring,
linear scaling from minmax.
--along_profile Color streamlines according to each point positionalong its length.
--local_orientation Color streamlines according to the angle between each segment (in degree).
Angles at first and last points are set to 0.
--local_angle Color streamlines according to the angle between each segment (in degree).
Angles at first and last points are set to 0.
Coloring options:
--ambiant_occlusion [AMBIANT_OCCLUSION]
Impact factor of the ambiant occlusion approximation. [None]
--colormap COLORMAP Select the colormap for colored trk (dps/dpp) [jet].
Use two Matplotlib named color separeted by a - to create your own colormap.
--clip_outliers If set, we will clip the outliers (first and last 5% quantile). Strongly suggested if your data comes from COMMIT!
--min_range MIN_RANGE
Set the minimum value when using dps/dpp/anatomy.
--max_range MAX_RANGE
Set the maximum value when using dps/dpp/anatomy.
--min_cmap MIN_CMAP Set the minimum value of the colormap.
--max_cmap MAX_CMAP Set the maximum value of the colormap.
--log Apply a base 10 logarithm for colored trk (dps/dpp).
--LUT FILE If the dps/dpp or anatomy contain integer labels, the value will be substituted.
If the LUT has 20 elements, integers from 1-20 in the data will be
replaced by the value in the file (.npy or .txt)
Scilpy version: 2.0.2