Merge pull request #24 from OSIPI/analysis/workflow

Analysis/workflow

.github/workflows/analysis.yml

@@ -0,0 +1,63 @@
+name: Algorithm Analysis
+
+on:
+  push:
+    branches:
+      - 'main'
+      - 'analysis/**'
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    continue-on-error: false
+    strategy:
+      fail-fast: false
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+      - name: Set up R
+        uses: r-lib/actions/setup-r@v2
+        with: 
+          use-public-rspm: true
+      - name: Install R dependencies
+        uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          packages: |
+            any::plyr
+            any::dplyr
+            any::tidyverse
+            any::data.table
+            any::ggplot2
+      - name: Generate fitting data
+        run: |
+          pip install pytest
+          python -m pytest -m slow --saveFileName test_output.csv --SNR 10 30 50 100 200 --fitCount 300 --saveDurationFileName test_duration.csv
+      - name: Generate figures
+        run: Rscript --vanilla tests/IVIMmodels/unit_tests/analyze.r test_output.csv test_duration.csv
+      - name: Upload raw data
+        uses: actions/upload-artifact@v3
+        with:
+            name: Raw data
+            path: |
+              test_output.csv
+              test_duration.csv
+      - name: Upload figures
+        uses: actions/upload-artifact@v3
+        with:
+            name: Fit figures
+            path: |
+              D.pdf
+              f.pdf
+              Dp.pdf
+              D_limited.pdf
+              f_limited.pdf
+              Dp_limited.pdf
+              durations.pdf

README.md

@@ -30,4 +30,4 @@ The **test** folder contains the test files corresponding to the contributed cod
 The **utils** folder contains various helpful tools.
 
 ## View Testing Reports
-*to be added*
+[![Unit tests](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/unit_test.yml/badge.svg?branch=main)](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/unit_test.yml)

conftest.py

@@ -19,6 +19,12 @@ def pytest_addoption(parser):
         type=bool,
         help="Use Rician noise, non-rician is gaussian",
     )
+    parser.addoption(
+        "--usePrior",
+        default=False,
+        type=bool,
+        help="Use a prior where accepted",
+    )
     parser.addoption(
         "--algorithmFile",
         default="tests/IVIMmodels/unit_tests/algorithms.json",
@@ -110,6 +116,10 @@ def fit_count(request):
 def rician_noise(request):
     return request.config.getoption("--ricianNoise")
 
+@pytest.fixture(scope="session")
+def use_prior(request):
+    return request.config.getoption("--usePrior")
+
 
 def pytest_generate_tests(metafunc):
     if "SNR" in metafunc.fixturenames:

tests/IVIMmodels/unit_tests/analyze.r

@@ -25,7 +25,7 @@ plot_ivim <- function(data, fileExtension) {
     f_plot <- ggplot(data, aes(x=Algorithm)) + geom_boxplot(aes(y=f_fitted)) + geom_boxplot(color="red", aes(y=f)) + facet_grid(SNR ~ Region) + scale_x_discrete(guide = guide_axis(angle = 90)) + ylim(0, 1) + ggtitle("Perfusion fraction grid") + ylab("Perfusion fraction")
     print(f_plot)
     ggsave(paste("f", fileExtension, sep=""), plot=f_plot, width = 50, height = 50, units = "cm")
-    D_plot <- ggplot(data, aes(x=Algorithm)) + geom_boxplot(aes(y=D_fitted)) + geom_boxplot(color="red", aes(y=D)) + facet_grid(SNR ~ Region) + scale_x_discrete(guide = guide_axis(angle = 90)) + ggtitle("Diffusion grid") + ylab("Diffusion")
+    D_plot <- ggplot(data, aes(x=Algorithm)) + geom_boxplot(aes(y=D_fitted)) + geom_boxplot(color="red", aes(y=D)) + facet_grid(SNR ~ Region) + scale_x_discrete(guide = guide_axis(angle = 90)) + ylim(0, 0.005) + ggtitle("Diffusion grid") + ylab("Diffusion")
     print(D_plot)
     ggsave(paste("D", fileExtension, sep=""), plot=D_plot, width = 50, height = 50, units = "cm")
     Dp_plot <- ggplot(data, aes(x=Algorithm)) + geom_boxplot(aes(y=Dp_fitted)) + geom_boxplot(color="red", aes(y=Dp)) + facet_grid(SNR ~ Region) + scale_x_discrete(guide = guide_axis(angle = 90)) + ylim(0, 0.25) + ggtitle("Perfusion grid") + ylab("Perfusion")
@@ -43,8 +43,8 @@ plot_ivim(data_restricted, "_limited.pdf")
 data_duration <- read.csv(duration_name)
 data_duration <- data_duration %>% mutate_if(is.character, as.factor)
 data_duration$ms <- data_duration$Duration..us./data_duration$Count/1000
-ggplot(data_duration, aes(x=Algorithm, y=ms)) + geom_boxplot() + scale_x_discrete(guide = guide_axis(angle = 90)) + ggtitle("Fit Duration") + ylab("Time (ms)")
-ggsave("durations.pdf", width = 20, height = 20, units = "cm")
+duration_plot <- ggplot(data_duration, aes(x=Algorithm, y=ms)) + geom_boxplot() + scale_x_discrete(guide = guide_axis(angle = 90)) + ggtitle("Fit Duration") + ylab("Time (ms)")
+ggsave("durations.pdf", plot=duration_plot, width = 20, height = 20, units = "cm")
 
 
 if (runPrediction) {

tests/IVIMmodels/unit_tests/test_ivim_synthetic.py

@@ -14,7 +14,7 @@
 #run using pytest <path_to_this_file> --saveFileName test_output.txt --SNR 50 100 200
 #e.g. pytest -m slow tests/IVIMmodels/unit_tests/test_ivim_synthetic.py  --saveFileName test_output.csv --SNR 10 50 100 200 --fitCount 20
 @pytest.mark.slow
-def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician_noise, save_file, save_duration_file):
+def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician_noise, save_file, save_duration_file, use_prior):
     # assert save_file == "test"
     random.seed(42)
     S0 = 1
@@ -24,8 +24,8 @@ def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician
     f = data["f"]
     Dp = data["Dp"]
     fit = OsipiBase(algorithm=ivim_algorithm)
-    # here try a prior, but it's not seeing it, why?
-    if hasattr(fit, "accepts_priors") and fit.accepts_priors:
+    # here is a prior
+    if use_prior and hasattr(fit, "accepts_priors") and fit.accepts_priors:
         prior = [np.random.normal(D, D/3, 10), np.random.normal(f, f/3, 10), np.random.normal(Dp, Dp/3, 10), np.random.normal(1, 1/3, 10)]
         # prior = [np.repeat(D, 10)+np.random.normal(0,D/3,np.shape(np.repeat(D, 10))), np.repeat(f, 10)+np.random.normal(0,f/3,np.shape(np.repeat(D, 10))), np.repeat(Dp, 10)+np.random.normal(0,Dp/3,np.shape(np.repeat(D, 10))),np.repeat(np.ones_like(Dp), 10)+np.random.normal(0,1/3,np.shape(np.repeat(D, 10)))]  # D, f, D*
         fit.initialize(prior_in=prior)