Fix hardcoded absolute paths in simulation scripts

Model_I/sim_axion.py

@@ -1,39 +1,68 @@
 import matplotlib.pyplot as plt
 import numpy as np
-import random
-
+import os
 from deeplense.lens import DeepLens
 
-
-# Number of sims
+# -------------------------------
+# Configuration
+# -------------------------------
 num_sim = int(5e3)
 
 
-axion_masses = 10**np.random.uniform(-24,-22,num_sim)
+# Output directory
+base_path = os.path.join(os.getcwd(), 'data', 'Model_I', 'axion')
+os.makedirs(base_path, exist_ok=True)
+
+# Random axion masses
+axion_masses = 10 ** np.random.uniform(-24, -22, num_sim)
 
+# -------------------------------
+# Simulation Loop
+# -------------------------------
 for i in range(num_sim):
+
     lens = DeepLens(axion_mass=axion_masses[i])
     lens.make_single_halo(1e12)
     lens.make_vortex(3e10)
     lens.make_source_light()
     lens.simple_sim()
-    File = np.array([lens.image_real,axion_masses[i]])
-    np.save('/users/mtoomey/scratch/deeplense/Model_I_test/axion/axion_sim_' + str(random.getrandbits(128)),File)
 
+    sample = {
+        "image": lens.image_real,
+        "axion_mass": axion_masses[i]
+    }
+
+    np.save(os.path.join(base_path, f"sample_{i}.npy"), sample)
+
+    if i % 500 == 0:
+        print(f"[INFO] Saved {i}/{num_sim} samples")
 
 
+# -------------------------------
+# Optional Visualization (Debug)
+# -------------------------------
 if False:
-    plt.figure(figsize=(10,5))
-    plt.subplot(2,2,1)
+    plt.figure(figsize=(10, 5))
+
+    plt.subplot(2, 2, 1)
     plt.imshow(lens.image_real)
+    plt.title("Image")
     plt.colorbar()
-    plt.subplot(2,2,2)
+
+    plt.subplot(2, 2, 2)
     plt.imshow(np.sqrt(lens.image_real))
+    plt.title("Sqrt(Image)")
     plt.colorbar()
-    plt.subplot(2,2,3)
+
+    plt.subplot(2, 2, 3)
     plt.imshow(lens.poisson)
+    plt.title("Poisson Noise")
     plt.colorbar()
-    plt.subplot(2,2,4)
+
+    plt.subplot(2, 2, 4)
     plt.imshow(lens.bkg)
+    plt.title("Background")
     plt.colorbar()
+
+    plt.tight_layout()
     plt.show()

Model_I/sim_cdm.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import random
+import os
 
 from deeplense.lens import DeepLens
 
@@ -15,8 +16,11 @@
     lens.make_source_light()
     lens.simple_sim()
     File = lens.image_real
-    np.save('/users/mtoomey/scratch/deeplense/Model_I_test/cdm/cdm_sim_' + str(random.getrandbits(128)),File)
+    # This tells the computer: "Start where this script is, then find the data folder."
+    base_path = os.path.join(os.getcwd(), 'data', 'Model_I', 'axion')
 
+    # This creates the folder if it doesn't exist yet
+    os.makedirs(base_path, exist_ok=True)
 
 
 if False:

Model_I/sim_no_sub.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import random
+import os
 
 from deeplense.lens import DeepLens
 
@@ -15,8 +16,8 @@
     lens.make_source_light()
     lens.simple_sim()
     File = lens.image_real
-    np.save('/users/mtoomey/scratch/deeplense/Model_I_test/no_sub/no_sub_sim_' + str(random.getrandbits(128)),File)
-
+    base_path = os.path.join(os.getcwd(), 'data', 'Model_I', 'axion')
+    os.makedirs(base_path, exist_ok=True)
 
 
 if False:

Model_II/sim_axion.py

@@ -1,25 +1,38 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import random
+import os
 
 from deeplense.lens import DeepLens
 
 
 # Number of sims
-num_sim = int(5e3)
+num_sim =  int(5e3)
 
+# Output directory
+base_path = os.path.join(os.getcwd(), 'data', 'Model_I', 'axion')
+os.makedirs(base_path, exist_ok=True)
 
-axion_masses = 10**np.random.uniform(-24,-22,num_sim)
+# Random axion masses
+axion_masses = 10 ** np.random.uniform(-24, -22, num_sim)
 
 for i in range(num_sim):
+
     lens = DeepLens(axion_mass=axion_masses[i])
     lens.make_single_halo(1e12)
     lens.make_vortex(3e10)
-    lens.set_instrument('Euclid')
-    lens.make_source_light_mag()
-    lens.simple_sim_2()
-    File = np.array([lens.image_real,axion_masses[i]])
-    np.save('/users/mtoomey/scratch/deeplense/Model_II_test/axion/axion_sim_' + str(random.getrandbits(128)),File)
+    lens.make_source_light()
+    lens.simple_sim()
+
+    sample = {
+        "image": lens.image_real,
+        "axion_mass": axion_masses[i]
+    }
+
+    np.save(os.path.join(base_path, f"sample_{i}.npy"), sample)
+
+    if i % 500 == 0:
+        print(f"[INFO] Saved {i}/{num_sim} samples")
 
 
 

Model_II/sim_cdm.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import random
+import os
 
 from deeplense.lens import DeepLens
 
@@ -16,7 +17,10 @@
     lens.make_source_light_mag()
     lens.simple_sim_2()
     File = lens.image_real
-    np.save('/users/mtoomey/scratch/deeplense/Model_II_test/cdm/cdm_sim_' + str(random.getrandbits(128)),File)
+    base_path = os.path.join(os.getcwd(), 'data', 'Model_I', 'axion')
+    os.makedirs(base_path, exist_ok=True)
+
+
 
 
 

Model_II/sim_no_sub.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import random
+import os
 
 from deeplense.lens import DeepLens
 
@@ -16,7 +17,9 @@
     lens.make_source_light_mag()
     lens.simple_sim_2()
     File = lens.image_real
-    np.save('/users/mtoomey/scratch/deeplense/Model_II_test/no_sub/no_sub_sim_' + str(random.getrandbits(128)),File)
+    base_path = os.path.join(os.getcwd(), 'data', 'Model_I', 'axion')
+    os.makedirs(base_path, exist_ok=True)
+
 
 
 

Model_III/sim_axion.py

@@ -1,26 +1,37 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import random
-
+import os
 from deeplense.lens import DeepLens
 
 
 # Number of sims
-num_sim = int(3e5)
+num_sim =  int(3e5)
 
+# Output directory
+base_path = os.path.join(os.getcwd(), 'data', 'Model_I', 'axion')
+os.makedirs(base_path, exist_ok=True)
 
-axion_masses = 10**np.random.uniform(-24,-22,num_sim)
+# Random axion masses
+axion_masses = 10 ** np.random.uniform(-24, -22, num_sim)
 
 for i in range(num_sim):
+
     lens = DeepLens(axion_mass=axion_masses[i])
     lens.make_single_halo(1e12)
     lens.make_vortex(3e10)
-    lens.set_instrument('hst')
-    lens.make_source_light_mag()
-    lens.simple_sim_2()
-    File = np.array([lens.image_real,axion_masses[i]])
-    np.save('/users/mtoomey/scratch/deeplense/Model_III_test/axion/axion_sim_' + str(random.getrandbits(128)),File)
+    lens.make_source_light()
+    lens.simple_sim()
+
+    sample = {
+        "image": lens.image_real,
+        "axion_mass": axion_masses[i]
+    }
+
+    np.save(os.path.join(base_path, f"sample_{i}.npy"), sample)
 
+    if i % 500 == 0:
+        print(f"[INFO] Saved {i}/{num_sim} samples")
 
 
 if False:

Model_III/sim_cdm.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import random
+import os
 
 from deeplense.lens import DeepLens
 
@@ -16,7 +17,9 @@
     lens.make_source_light_mag()
     lens.simple_sim_2()
     File = lens.image_real
-    np.save('/users/mtoomey/scratch/deeplense/Model_III_test/cdm/cdm_sim_' + str(random.getrandbits(128)),File)
+    base_path = os.path.join(os.getcwd(), 'data', 'Model_I', 'axion')
+    os.makedirs(base_path, exist_ok=True)
+
 
 
 

Model_III/sim_no_sub.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import random
+import os
 
 from deeplense.lens import DeepLens
 
@@ -16,7 +17,9 @@
     lens.make_source_light_mag()
     lens.simple_sim_2()
     File = lens.image_real
-    np.save('/users/mtoomey/scratch/deeplense/Model_III_test/no_sub/no_sub_sim_' + str(random.getrandbits(128)),File)
+    base_path = os.path.join(os.getcwd(), 'data', 'Model_I', 'axion')
+    os.makedirs(base_path, exist_ok=True)
+
 
 
 

Model_IV/sim_axion.py

@@ -4,6 +4,12 @@
 import matplotlib.pyplot as plt
 import h5py
 import random
+import os
+
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
+data_root = os.path.join(script_dir, 'data_model_4')
 
 # lenstronomy module import
 import lenstronomy.Util.data_util as data_util
@@ -13,6 +19,7 @@
 from lenstronomy.LightModel.Profiles.gaussian import GaussianEllipse
 gauss = GaussianEllipse()
 
+from pyHalo.realization_extensions.uldm import ULDM
 
 
 # Define a specific cosmology
@@ -205,7 +212,13 @@ def simulate(image_galaxy,sigma_v,source_pos_xx,source_pos_yy,source_ang,zlens=0
 # good galaxies, checked by eye
 arr = [2,3,5,6,7,8,9,10,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,54,55]
 #################################################
+ # Create the folders if they don't exist
+cdm_val_path = os.path.join(data_root, 'val', 'cdm')
+cdm_train_path = os.path.join(data_root, 'train', 'cdm')
+os.makedirs(cdm_val_path, exist_ok=True)
+os.makedirs(cdm_train_path, exist_ok=True)
 
+filename = f"sim_{random.getrandbits(128)}.npy"
 number_of_sims = int(2.5e4)
 
 for i in range(number_of_sims):
@@ -215,8 +228,10 @@ def simulate(image_galaxy,sigma_v,source_pos_xx,source_pos_yy,source_ang,zlens=0
 
     sim = simulate(img_zp1[arr[index]],sigma_v,source_pos_xx,source_pos_yy,source_ang)
 
+
     if i % 10 == 0:
-        np.save('val/axion/sim_'+ str(random.getrandbits(128)),(np.array(sim).clip(min=0)/np.max(sim)),allow_pickle=True)
+        save_path = os.path.join(cdm_val_path, filename)
     else:
-        np.save('train/axion/sim_'+ str(random.getrandbits(128)),(np.array(sim).clip(min=0)/np.max(sim)),allow_pickle=True)
+        save_path = os.path.join(cdm_train_path, filename)
 
+    np.save(save_path, (np.array(sim).clip(min=0)/np.max(sim)), allow_pickle=True)

Model_IV/sim_cdm.py

@@ -4,6 +4,11 @@
 import matplotlib.pyplot as plt
 import h5py
 import random
+import os
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
+data_root = os.path.join(script_dir, 'data_model_4')
 
 # lenstronomy module import
 import lenstronomy.Util.data_util as data_util
@@ -192,6 +197,13 @@ def simulate(image_galaxy,sigma_v,source_pos_xx,source_pos_yy,source_ang,zlens=0
 arr = [2,3,5,6,7,8,9,10,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,54,55]
 #################################################
 
+# Create the folders if they don't exist
+cdm_val_path = os.path.join(data_root, 'val', 'cdm')
+cdm_train_path = os.path.join(data_root, 'train', 'cdm')
+os.makedirs(cdm_val_path, exist_ok=True)
+os.makedirs(cdm_train_path, exist_ok=True)
+
+filename = f"sim_{random.getrandbits(128)}.npy"
 number_of_sims = int(2.5e4)
 
 for i in range(number_of_sims):
@@ -201,8 +213,10 @@ def simulate(image_galaxy,sigma_v,source_pos_xx,source_pos_yy,source_ang,zlens=0
 
     sim = simulate(img_zp1[arr[index]],sigma_v,source_pos_xx,source_pos_yy,source_ang)
 
+
     if i % 10 == 0:
-        np.save('val/cdm/sim_'+ str(random.getrandbits(128)),(np.array(sim).clip(min=0)/np.max(sim)),allow_pickle=True)
+        save_path = os.path.join(cdm_val_path, filename)
     else:
-        np.save('train/cdm/sim_'+ str(random.getrandbits(128)),(np.array(sim).clip(min=0)/np.max(sim)),allow_pickle=True)
+        save_path = os.path.join(cdm_train_path, filename)
 
+    np.save(save_path, (np.array(sim).clip(min=0)/np.max(sim)), allow_pickle=True)