Clear redundant modality configuration files

examples/article_figures/figS_NPC_quantification.py

@@ -17,7 +17,7 @@
 probe_template = "GFP_w_nanobody"
 probe_target_type = "Sequence"
 probe_target_value = "ELAVGSL" # Sequence in the C-terminal of Nup96
-modalities = ["STED_Thev2016", ]
+modalities = ["STED", ]
 
 # fetch experimental image from URL 
 STED_example = "https://ftp.ebi.ac.uk/biostudies/fire/S-BIAD/S-BIAD0-99/S-BIAD8/Files/Library/Gallery%20Fig%201/STED/GFP_Gallery-STED_181026_1.tif"
@@ -36,7 +36,6 @@
     probe_target_type=probe_target_type,
     probe_target_value=probe_target_value,
     multimodal=modalities,
-    STED_Thev2016={"exp_time": 0.0002},
     clear_experiment=True,
     run_simulation=False,
     random_seed=random_seed
@@ -66,7 +65,19 @@
     min_distance=min_distance)
 
 # Run simulation
-experiment.set_modality_acq(modality_name="STED_Thev2016", exp_time=0.0002)
+## Modality parameter of pixelsize_nm is set to match Thevathasan et al. 2019.
+## All other parameters are estimations to emulate Thevathasan et al. 2019 STED modality
+experiment.update_modality(
+    modality_name="STED",
+    lateral_resolution_nm=15,
+    axial_resolution_nm=20,
+    psf_voxel_nm=5,
+    depth_of_field_nm=100,
+    pixelsize_nm=15)
+experiment.set_modality_acq(
+    modality_name="STED",
+    exp_time=0.0002)
+experiment.imager.modalities["STED"]["detector"]["noise_model"]["binomial"]["p"] = 0.7
 images, noiselsess = experiment.run_simulation()
 
 # Analyse images: fit circles
@@ -79,13 +90,13 @@
 maxRadius_round = np.ceil(MAX_radius_px).astype('int64')
 minDist = minRadius_round
 
-if images["STED_Thev2016"]["ch0"][0].min() < 0:
-    images["STED_Thev2016"]["ch0"][0] += -images["STED_Thev2016"]["ch0"][0].min()
+if images["STED"]["ch0"][0].min() < 0:
+    images["STED"]["ch0"][0] += -images["STED"]["ch0"][0].min()
 
 #### Simulated Data
 HCparams = dict(dp=1, minDist=maxRadius_round, 
         param1=10, param2=7, minRadius=minRadius_round, maxRadius=maxRadius_round)
-circles_sim, img_blurred_sim, c_params_sim = metrics.get_circles(images["STED_Thev2016"]["ch0"][0].astype(np.uint8), **HCparams)
+circles_sim, img_blurred_sim, c_params_sim = metrics.get_circles(images["STED"]["ch0"][0].astype(np.uint8), **HCparams)
 radii_simulated= []
 for (x, y, r) in circles_sim[0]:
     radii_simulated.append((r*pixelsize))
@@ -103,7 +114,7 @@
 fig, axs = plt.subplots(1, 3)
 axs[0].imshow(experimental_img_processed, cmap="grey")
 axs[0].set_title("Modified from Thevathasan et al. 2019. Nature Methods.")
-axs[1].imshow(images["STED_Thev2016"]["ch0"][0], cmap="grey")
+axs[1].imshow(images["STED"]["ch0"][0], cmap="grey")
 axs[1].set_title("VLab4Mic simulated image")
 
 df = pd.DataFrame({
@@ -115,7 +126,7 @@
 
 length_nm = 1000
 nm = 1e-09
-pixelsize = (experiment.imaging_modalities["STED_Thev2016"]["detector"]["scale"] / nm) * experiment.imaging_modalities["STED_Thev2016"]["detector"]["pixelsize"]
+pixelsize = (experiment.imaging_modalities["STED"]["detector"]["scale"] / nm) * experiment.imaging_modalities["STED"]["detector"]["pixelsize"]
 pixelsize = np.ceil(pixelsize)
 length_px = length_nm / pixelsize
 hight_px = length_px / 10

examples/real_vs_simulation.py

@@ -7,13 +7,13 @@
 import matplotlib.pyplot as plt
 from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
 import matplotlib.font_manager as fm
-
+random_seed = 24
 # parameters for simulation
 structure = "7R5K"
 probe_template = "GFP_w_nanobody"
 probe_target_type = "Sequence"
 probe_target_value = "ELAVGSL" # Sequence in the C-terminal of Nup96
-modalities = ["STED_Thev2016", ]
+modalities = ["STED", ]
 
 # fetch experimental image from URL 
 STED_example = "https://ftp.ebi.ac.uk/biostudies/fire/S-BIAD/S-BIAD0-99/S-BIAD8/Files/Library/Gallery%20Fig%201/STED/GFP_Gallery-STED_181026_1.tif"
@@ -44,9 +44,10 @@
     probe_target_type=probe_target_type,
     probe_target_value=probe_target_value,
     multimodal=modalities,
-    STED_Thev2016={"exp_time": 0.0004},
+    STED={"exp_time": 0.0004},
     clear_experiment=True,
     run_simulation=False,
+    random_seed=random_seed
 )
 
 # Use experimental image for positioning
@@ -60,6 +61,19 @@
     min_distance=min_distance)
 
 # Run simulation
+## Modality parameter of pixelsize_nm is set to match Thevathasan et al. 2019.
+## All other parameters are estimations to emulate Thevathasan et al. 2019 STED modality
+experiment.update_modality(
+    modality_name="STED",
+    lateral_resolution_nm=15,
+    axial_resolution_nm=20,
+    psf_voxel_nm=5,
+    depth_of_field_nm=100,
+    pixelsize_nm=15)
+experiment.set_modality_acq(
+    modality_name="STED",
+    exp_time=0.0004)
+experiment.imager.modalities["STED"]["detector"]["noise_model"]["binomial"]["p"] = 0.7
 images, noiselsess = experiment.run_simulation()
 
 # Plot experimental vs simulated image
@@ -70,7 +84,7 @@
 fig, axs = plt.subplots(1, 2)
 axs[0].imshow(experimental_img_processed, cmap="grey")
 axs[0].set_title("Modified from Thevathasan et al. 2019. Nature Methods.")
-axs[1].imshow(images["STED_Thev2016"]["ch0"][0], cmap="grey")
+axs[1].imshow(images["STED"]["ch0"][0], cmap="grey")
 axs[1].set_title("VLab4Mic simulated image")
 
 fontprops = fm.FontProperties(size=20)