Fixed installation issues of #3

README.md

@@ -1,4 +1,4 @@
-# **NiChart<sup>Workflows</sup>** 
+# **NiChart<sup>Workflows</sup>**
 
 A toolkit that allows users to apply **NiChart** processing methods and pre-trained models on their data.  **NiChart<sup>Workflows</sup>** includes tools for [data harmonization](https://neuroimagingchart.com/components/#Harmonization) and [machine learning](https://neuroimagingchart.com/components/#Machine%20Learning), using pre-trained models and reference distributions provided by [**NiChart<sup>Engine</sup>**](https://github.com/gurayerus/NiChart_Engine). **NiChart<sup>Workflow</sup>** utilizes [Snakemake](https://snakemake.github.io) workflows to define multi-step image processing and data analytics pipelines.
 
@@ -13,7 +13,7 @@ Install dependencies for the **NiChart<sup>Workflows</sup>** in a conda environm
 ```bash
 conda install -n base -c conda-forge mamba # If needed
 mamba init # If first time installing, then potentially source ~/.bashrc
-mamba create -c conda-forge -c bioconda -n NiChart_Workflows python=3.8 
+mamba create -c conda-forge -c bioconda -n NiChart_Workflows python=3.12
 mamba activate NiChart_Workflows
 pip install NiChart-Viewer-Demo spare-scores NiChartHarmonize
 mamba install -c conda-forge -c bioconda snakemake
@@ -58,7 +58,7 @@ If the workflow works successfully, workflow steps will be run on the data. Resu
 
 > "../../output/vTest1",
 
-and the viewer will be launched with the combined result file as input. 
+and the viewer will be launched with the combined result file as input.
 
 ## Contributing:
 
@@ -76,4 +76,3 @@ This project is licensed under the [License Name] license. Please refer to the L
 ## Contact:
 
 For any inquiries, please contact [email protected]. (Last Updated: 5/24/2024)
-

input/vTest2/Study1/Images/Images

@@ -0,0 +1 @@
+/Users/spirosmag/Documents/NiChart_Workflows/input/vTest2/Study1/Images

utils/dlmuse/util_dlmuse_apply.sh

@@ -1,10 +1,23 @@
 #! /bin/bash -x
 
+# download NiChart DLMUSE package
+echo "Downloading NiChart DLMUSE..."
+if [ -d "/NiChart_DLMUSE" ]
+then
+    echo "Clone already exists!"
+else
+    git clone https://github.com/CBICA/NiChart_DLMUSE.git
+fi
+cd NiChart_DLMUSE
+pip install .
+cd ..
+echo "NiChart DLMUSE downloaded!"
 echo "About to run: $0 $@"
 
+
 ## Read input
 in_csv=$(realpath $1)
-out_csv=$(realpath $2)
+out_csv=$2
 
 ## Prep init data
 in_dir=$(dirname ${in_csv})
@@ -14,10 +27,10 @@ tmp_dir="${out_dir}/tmprun_dlmuse"
 mkdir -pv "${tmp_dir}/nnUNet_preprocessed"
 mkdir -pv "${tmp_dir}/nnUNet_raw_database"
 mkdir -pv "${tmp_dir}/nnUNet_out"
-ln -s `realpath ../../../../NiChart/NiChart_Models/nnUNet_model` ${tmp_dir}/nnUNet_model
+ln -s `realpath ./nnUNet_model` "${tmp_dir}/nnUNet_model"
 ln -s `realpath ${in_dir}/Images` "${tmp_dir}/nnUNet_raw_database/nnUNet_raw_data"
-droi=`realpath ../../../../NiChart/NiChart_DLMUSE/shared/dicts/MUSE_mapping_derived_rois.csv`
-roi=`realpath ../../../../NiChart/NiChart_DLMUSE/shared/dicts/MUSE_mapping_consecutive_indices.csv`
+droi=`realpath NiChart_DLMUSE/shared/dicts/MUSE_mapping_derived_rois.csv`
+roi=`realpath NiChart_DLMUSE/shared/dicts/MUSE_mapping_consecutive_indices.csv`
 
 ## Apply dlmuse test
 cmd="NiChart_DLMUSE --indir ${tmp_dir}/nnUNet_raw_database/nnUNet_raw_data --outdir ${tmp_dir}/nnUNet_out --pipelinetype structural --derived_ROI_mappings_file $droi --MUSE_ROI_mappings_file $roi --nnUNet_raw_data_base ${tmp_dir}/nnUNet_raw_database --nnUNet_preprocessed ${tmp_dir}/nnUNet_preprocessed --model_folder ${tmp_dir}/nnUNet_model --all_in_gpu True --mode fastest --disable_tta"

utils/spare/util_spare_test.sh

@@ -1,5 +1,20 @@
 #! /bin/bash
 
+# download spare package
+echo "Downloading spare score package..."
+if [ -d "/spare_score" ]
+then
+    echo "spare score clone already exists!"
+else
+    git clone https://github.com/CBICA/spare_score.git
+fi
+python setup.py bdist_wheel
+cd dist
+WHEEL_FILE=$(ls spare_scores*)
+pip install "$WHEEL_FILE"
+cd ..
+echo "spare score is downloaded!"
+
 ## Read input
 in_csv=$1
 in_mdl=$2

workflows/w_sMRI/Snakefile

@@ -9,10 +9,10 @@ import os
 #report: "report/workflow.rst"
 
 ###################################
-## Default config file 
+## Default config file
 ## Runs the workflow with the test dataset
 ## Edit this for running it on other datasets
-in_config = "../../config/vTest1/config.yaml"
+in_config = "../../config/vTest2/config.yaml"
 #in_config = "../../config/vTest2/config.yaml"
 configfile: f"{in_config}"
 ###################################
@@ -192,7 +192,7 @@ rule calc_derived_ROIs:
     '''
     Calculate derived ROIs from harmonized data
     - If the input data already includes derived ROIs, they will not be updated
-    '''        
+    '''
     input:
         in_csv=f"../../{dir_output}/{{study}}/out_combat/{{study}}_{{dtype}}_COMBAT_single.csv",
         dict=f"../../{derived_rois}"
@@ -293,7 +293,7 @@ rule launch_viewer:
         touch(f"../../{dir_output}/flag_VIEWED.csv"),
     shell:
         "NiChart_Viewer --data_file {input}"
-        
+
 ## Delete flag for the viewer, so that it will launch the viewer next time
 if os.path.exists(f"../../{dir_output}/flag_VIEWED.csv"):
     os.remove(f"../../{dir_output}/flag_VIEWED.csv")