Merge pull request #18 from NYU-DiffusionMRI/rpg_multithread

Rpg multithread

.github/workflows/build_wheels.yml

@@ -94,28 +94,42 @@ jobs:
       - name: Build x86_64 wheel
         env:
           ARCHFLAGS: "-arch x86_64"
+          CFLAGS: "-arch x86_64"
+          LDFLAGS: "-arch x86_64"
           MACOSX_DEPLOYMENT_TARGET: "11.0"
         run: |
-          mkdir -p $GITHUB_WORKSPACE/dist
+          mkdir -p $GITHUB_WORKSPACE/dist/x86_64
           python setup.py bdist_wheel
+          mv dist/*.whl $GITHUB_WORKSPACE/dist/x86_64/
       - name: Repair x86_64 wheel
         run: |
           pip install delocate
-          delocate-wheel dist/*-macosx_11_0_x86_64.whl
+          delocate-wheel $GITHUB_WORKSPACE/dist/x86_64/*.whl
       - name: Build arm64 wheel
         env:
           ARCHFLAGS: "-arch arm64"
+          CFLAGS: "-arch arm64"
+          LDFLAGS: "-arch arm64"
           MACOSX_DEPLOYMENT_TARGET: "11.0"
         run: |
+          mkdir -p $GITHUB_WORKSPACE/dist/arm64
           python setup.py bdist_wheel
+          mv dist/*.whl $GITHUB_WORKSPACE/dist/arm64/
       - name: Repair arm64 wheel
         run: |
-          delocate-wheel dist/*-macosx_11_0_arm64.whl
+          delocate-wheel dist/*.whl
       - name: Verify repaired wheels
         run: |
-          ls -al $GITHUB_WORKSPACE/dist/
-      - name: Upload to PyPI
+          ls -al $GITHUB_WORKSPACE/dist/x86_64/
+          ls -al $GITHUB_WORKSPACE/dist/arm64/
+      - name: Upload x86_64 wheel to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages_dir: $GITHUB_WORKSPACE/dist/x86_64
+          password: ${{ secrets.PYPI_API_TOKEN }}
+
+      - name: Upload arm64 wheel to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          packages_dir: dist
+          packages_dir: $GITHUB_WORKSPACE/dist/arm64
           password: ${{ secrets.PYPI_API_TOKEN }}

.vscode/launch.json

@@ -1,16 +1,26 @@
 {
-    // Use IntelliSense to learn about possible attributes.
-    // Hover to view descriptions of existing attributes.
-    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "Python Debugger: Current File with Arguments",
+            "name": "Attach to Python Script",
             "type": "debugpy",
+            "request": "attach",
+            "connect": {
+                "host": "localhost",
+                "port": 5678
+            },
+            "pathMappings": [
+                {
+                    "localRoot": "/Users/benaron/Documents/DESIGNER-v2/lib",  // Update with your Python script directory
+                    "remoteRoot": "/path_to_your_python_script_on_remote_if_any"
+                }
+            ]
+        },
+        {
+            "name": "Debug Shell Script",
+            "type": "bashdb",
             "request": "launch",
-            "program": "${file}",
-            "console": "integratedTerminal",
-            "args": "${command:pickArgs}"
+            "program": "/Users/benaron/Documents/scripts/test_d2.sh",
         }
     ]
 }

designer2/tmi.py

@@ -582,16 +582,15 @@ def execute(): #pylint: disable=unused-variable
 
         logger.info("Initializing SMI fitting...")
         echo_times = dwi_metadata['echo_time_per_volume']
-        if np.max(echo_times) > 1.0:
-            logger.info("Echo times in ms, converting to seconds.")
-            echo_times /= 1000
+        if (np.min(echo_times) < 1.0) and (np.min(echo_times) > 0):
+            logger.info("Echo times in s, converting to ms.")
+            echo_times *= 1000
 
         if multi_te_beta:
-            smi = SMI(bval=bval_orig, bvec=bvec_orig, rotinv_lmax=lmax)
-            smi.set_compartments(compartments)
+            smi = SMI(bval=bval_orig, bvec=bvec_orig, rotinv_lmax=lmax,
+                      compartments=compartments, echo_time=echo_times,
+                      beta=dwi_metadata['bshape_per_volume'])
 
-            smi.set_echotime(echo_times)
-            smi.set_bshape(dwi_metadata['bshape_per_volume'])
             logger.info("SMI model initialized for multi-TE/beta data.")
 
             params_smi = smi.fit(dwi_orig, mask=mask, sigma=sigma)
@@ -600,10 +599,10 @@ def execute(): #pylint: disable=unused-variable
             save_params(params_smi, nii, model='smi', outdir=outdir)
             logger.info("SMI parameters saved for multi-TE/beta data.", extra={"outdir": outdir})
         else:
-            smi = SMI(bval=bval, bvec=bvec, rotinv_lmax=lmax)
-            smi.set_compartments(compartments)
-            smi.set_echotime(echo_times)
-            smi.set_bshape(dwi_metadata['bshape_per_volume'])
+            smi = SMI(bval=bval, bvec=bvec, rotinv_lmax=lmax,
+                      compartments=compartments, echo_time=echo_times,
+                      beta=dwi_metadata['bshape_per_volume'])
+
             logger.info("SMI model initialized for single-TE/beta data.")
 
             params_smi = smi.fit(dwi, mask=mask, sigma=sigma)

lib/smi.py

@@ -41,14 +41,18 @@ def __init__(self, bval, bvec, beta=None, echo_time=None, merge_distance=None, c
         self.rotinv_lmax = rotinv_lmax
         self.n_training = int(n_training)
 
+        # set up required inputs
         self.set_compartments(compartments)
+        self.set_bvals_bvecs(bval, bvec)
+        self.set_bshape(beta)
+        self.set_echotime(echo_time)
 
         if not l_max_training:
             self.l_max_training = 6
         else:
             self.l_max_training = l_max_training
 
-        if training_bounds is not None and training_prior is not None:
+        if (training_bounds is not None) and (training_prior is not None):
             self.prior = training_prior
             if not self.fit_T2:
                 self.prior = np.hstack(
@@ -69,17 +73,15 @@ def __init__(self, bval, bvec, beta=None, echo_time=None, merge_distance=None, c
         elif noise_bias == 'rician':
             self.flag_rician_bias = True
 
+        if self.fit_T2:
+            self.n_training = int(2e5)
+
         if seed is not None:
             self.seed = seed
             np.random.seed(self.seed)
         else:
             self.seed = None
 
-        # set up required inputs
-        self.set_bvals_bvecs(bval, bvec)
-        self.set_bshape(beta)
-        self.set_echotime(echo_time)
-
     def set_mask(self, mask):
         """
         Function to add a brain mask to the class if the mask was input
@@ -290,9 +292,9 @@ def get_uniformly_distributed_SM_prior(self):
         for i in range(len(dims) - 1):
             dim = dims[i]
             dim_next = dims[i + 1]
-            px = self.l2norm(plm[dim:(dim+dim_next+1), :])
+            px = self.l2norm(plm[dim+1:(dim+dim_next+1), :])
             prior = np.vstack((prior, px))
-
+        
         return prior.T
 
     def vectorize(self, image, mask):