clean up

.gitignore

@@ -5,3 +5,6 @@
 ~$*
 *~
 *.bak
+
+# user defined things
+figs/*

data/.gitignore

@@ -0,0 +1,2 @@
+*.npy
+*.prd

python/.gitignore

@@ -0,0 +1,2 @@
+*.png
+*.npy

python/demo_read_reconstruct.py

@@ -5,7 +5,7 @@
 from array_api_compat import to_device
 import matplotlib.pyplot as plt
 from io_yardl import read_yardl
-
+from pathlib import Path
 
 dev = "cpu"
 
@@ -20,13 +20,13 @@
 # ----------------------------------------------------------------------------
 # ----------------------------------------------------------------------------
 
-event_det_id_1, event_det_id_2, scanner_lut = read_yardl("write_test.prd")
+event_det_id_1, event_det_id_2, scanner_lut = read_yardl("../data/write_test.prd")
 
 xstart = scanner_lut[event_det_id_1, :]
 xend = scanner_lut[event_det_id_2, :]
 
 # HACK: load the sensitivity image
-sens_img = np.load("sensitivity_image.npy")
+sens_img = np.load("../data/sensitivity_image.npy")
 
 # ----------------------------------------------------------------------------
 # ----------------------------------------------------------------------------
@@ -63,13 +63,16 @@
 # ----------------------------------------------------------------------------
 # ----------------------------------------------------------------------------
 
+fig_dir = Path("../figs")
+fig_dir.mkdir(exist_ok=True)
+
 vmax = 0.055
 fig, ax = plt.subplots(1, recon.shape[2], figsize=(recon.shape[2] * 2, 2))
 for i in range(recon.shape[2]):
     ax[i].imshow(
         np.asarray(to_device(recon[:, :, i], "cpu")), vmin=0, vmax=vmax, cmap="Greys"
     )
-    ax[i].set_title(f"ground truth sl {i+1}", fontsize="small")
+    ax[i].set_title(f"LM recon sl {i+1}", fontsize="small")
 
 fig.tight_layout()
-fig.savefig("lm_reconstruction.png")
+fig.savefig(fig_dir / "lm_reconstruction.png")

python/demo_write.py

@@ -6,6 +6,7 @@
 import matplotlib.pyplot as plt
 from array_api_compat import to_device
 from io_yardl import write_yardl
+from pathlib import Path
 
 # device variable (cpu or cuda) that determines whether calculations
 # are performed on the cpu or cuda gpu
@@ -122,22 +123,27 @@
 scanner_lut = lor_descriptor.scanner.all_lor_endpoints
 
 # write the data to PETSIRD
-write_yardl(event_det_id_1, event_det_id_2, scanner_lut, output_file="write_test.prd")
+write_yardl(
+    event_det_id_1, event_det_id_2, scanner_lut, output_file="../data/write_test.prd"
+)
 
 # HACK: write the sensitivity image to file
-np.save("sensitivity_image.npy", sens_img)
+np.save("../data/sensitivity_image.npy", sens_img)
 
 # -----------------------------------------------------------------------------
 # -----------------------------------------------------------------------------
 # -----------------------------------------------------------------------------
 
+fig_dir = Path("../figs")
+fig_dir.mkdir(exist_ok=True)
+
 vmax = 1.2 * img.max()
-fig2, ax2 = plt.subplots(1, img.shape[2], figsize=(img.shape[2] * 2, 2))
+fig, ax = plt.subplots(1, img.shape[2], figsize=(img.shape[2] * 2, 2))
 for i in range(img.shape[2]):
-    ax2[i].imshow(
+    ax[i].imshow(
         np.asarray(to_device(img[:, :, i], "cpu")), vmin=0, vmax=vmax, cmap="Greys"
     )
-    ax2[i].set_title(f"ground truth sl {i+1}", fontsize="small")
+    ax[i].set_title(f"ground truth sl {i+1}", fontsize="small")
 
-fig2.tight_layout()
-fig2.savefig("simulated_phantom.png")
+fig.tight_layout()
+fig.savefig(fig_dir / "simulated_phantom.png")