pip install lsfmpy

lsfmpy_create_autocomplete --output-file autocomplete_lsfmpy.sh
source autocomplete_lsfmpy.sh

git clone -b tutorial https://github.com/Neuroinflab/lsfm_image_server.git
cd lsfm_image_server
export TUTORIAL=${PWD}

bash -xe step_00_download_data.sh

mkdir ${TUTORIAL}/example_data/autofluorescence
mv auto_channel_part_*.zip ${TUTORIAL}/example_data/autofluorescence
mkdir ${TUTORIAL}/example_data/cfos
mv cfos_channel_part_*.zip ${TUTORIAL}/example_data/cfos
cd ${TUTORIAL}/example_data/autofluroescence
unzip *. zip
cd ${TUTORIAL}/example_data/cfos
unzip *. zip

# rm -f ${TUTORIAL}/example_data/autofluorescence/auto_channel_part_*.zip 
# rm -f ${TUTORIAL}/example_data/cfos/cfos_channel_part_*.zip

lsfmpy_tutorial/
└── defaults.sh		# set up constants and logging
└── case_id 		   # unique specimen ID
└── step_00_download_data.sh 
└── step_01_set_up_images.sh 
└── step_02_preprocess.sh
└── step_03_signal_and_structural_coregistration.sh
└── step_04_set_up_template_coregistration.sh
└── step_05_core_registration.sh
└── step_06_write_to_hdf.sh
└── step_07_examples.sh
└── functions.sh 	# convenience functions for setting registration fidelity
├── scripts       	# python scripts (should work with python 2.7 and 3.8)
│   └── header_converter.py
│   └── clahe.py
│   └── rescale_displacement_field.py
│   └── tqdm.py
├── example_data/
│   └── autofluorescence/
│   │   └── Z000000.tif
│   │   └── ...
│   └── cfos/
│   │   └── Z000000.tif
│   │   └── ...
└── 00_reference/
│   └── template.nii.gz 
│   └── segmentation.nii.gz 
│   └── template_25um_both_hemispheres.nii.gz 
│   └── segmentation_25um_both_hemispheres.nii.gz 
│   └── labels.txt # labels for segmentation
│   └── template_mask.nii.gz  	# template mask clipped to match the extent of the target brain image
└── 01_input_data/
    └── 00_signal/
    │   └── 001_25_mm_mask.nii.gz # mask outlining the brain matter in signal channel
    └── 01_structural/
        └── 001_25_mm_mask.nii.gz # mask outlining the brain matter in autofluorescence channel
	

cd ${TUTORIAL}
dump_metadata \
	--input-file ./example_data/autofluorescence/Z000000.tif \
	--output-file ./autofluo.json

cd ${TUTORIAL}
dump_metadata \
	--input-file ./example_data/cfos/Z000000.tif \
	--output-file ./cfos.json

lsfmpy \
    # use the write command
    write \
        # path to the target hdf file,
	  # which will be created if not found
        --hdf-path ./000001.h5 \
	  # path to image metadata
        --metadata-path ./autofluo.json \
	  #  file name pattern 
        --file-name-format Z%06d.tif \
	  # channel name for this modality
        --channel-name autofluo \
	  # path to first file in the sequence
        --image-path ./example_data/autofluorescence/Z000000.tif \
   	  # file name for BigDataViewer-compatible xml
        --bdv-xml 000001.xml

lsfmpy write \
        --hdf-path ./000001.h5 \
        --metadata-path ./cfos.json \
        --file-name-format Z%06d.tif \
        --channel-name cfos \
        --image-path ./example_data/cfos/Z000000.tif \
        --bdv-xml 000001.xml

bash step_01_set_up_images.sh 

bash step_02_preprocess.sh

bash step_03_signal_and_structural_coregistration.sh

source functions.sh

# load setting for high quality mapping:
lsfm_set_mapping_high_quality
bash step_04_set_up_template_coregistration.sh
# before proceeding with registration, the script will check if following executables are available:
# mkdir rm cp tar WarpImageMultiTransform c3d antsRegistration
# this will also execute step_05_core_registration.sh

lsfmpy_tutorial/
└── example_data/	
└── 00_reference/	# preprocessed allen ccfv3	
└── 01_input_data/	# images exported for registration
└── workdir_fixed_{md5}_moving_{md5}/ 
└── 02_preprocessed_input_images/ # preprocessed images
└── 03_coregistration/ 		# signal channel mapped to structural channel
└── 04_transforms/		# affine and deformable transformations
└── 05_results/			# both channels mapped to template, 
				# template and segmentation mapped to the physical space of 
				# signal and structural image

bash step_06_write_to_hdf.sh

lsfmpy \
    # you can iteratively add multiple transformations
    # both affine and deformable, indicating the order
    # in which they should be executed 
    add-transform \
	    # name of the transformation as it was written to the hdf file
          --name inverse_warp \
          # indicate the position of the transformation in the chain:
          --ordn 0 \
          # indicate type of transformation (deformable)
    --transform-type df \


    # add another transformation
    - add-transform \
    --name structural_to_template \
    # this is second transformation in chain
    --ordn 1 \
    # this is an affine transformation
    --transform-type affine \
    # and it should be inverted
    --invert True \
	


    # now we can export the data
    - export \
	    # from the autofluorescence channel
    --channel-name autofluo \
    # at 10 µm isotropic resolution
    --output-resolution 0.01,0.01,0.01 \
    # indicate output file
    --output-path ./000001_auto_in_template_10um.nii.gz \
    # indicate source hdf
    --hdf-path ./000001.h5 \
    # indicate anatomical orientation of the image
    # (if you are not sure, set the orientation to RAS
          # and check results in ITKSnap)
    --input-orientation RPI

lsfmpy \
    # specify the mapping from signal channel to the template
    --add-transform \
        --transform-type affine \
        --name signal_to_structural \
        --ordn 0 \
        --invert True \
    –-add-transform \
        --name inverse_warp \
        --ordn 1 \
        --transform-type df \
    –-add-transform \
        --name structural_to_template \
        --ordn 2 \
        --transform-type affine \
        --invert True \
    # export Anterodorsal nucleus from the signal channel at 2 µm voxel size
    -export \
        --channel-name cfos \
        --output-resolution 0.002,0.002,0.002 \
        --output-path ./000001_cfos_in_template_AD_2um.nii.gz \
        --hdf-path ./000001.h5 \
        --input-orientation RPI \
        --segmentation-name cfos_segmentation \
        --region_id 64