diff --git a/.gitignore b/.gitignore
index f8a9584..58aac05 100644
--- a/.gitignore
+++ b/.gitignore
@@ -148,3 +148,4 @@ docs/make.bat
 docs/Makefile
 examples/ta_data_spec.txt
 *.txt
+examples/*.png
\ No newline at end of file
diff --git a/docs/environments.rst b/docs/environments.rst
new file mode 100644
index 0000000..1241eeb
--- /dev/null
+++ b/docs/environments.rst
@@ -0,0 +1,7 @@
+Environments
+=====================================
+
+We definitely have bugs. Please report them in `GitHub issues`_ or submit a pull request!
+
+.. _`GitHub issues`: https://github.com/castacks/tartanairpy/issues
+
diff --git a/docs/examples.rst b/docs/examples.rst
index c34d78e..a525693 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -20,11 +20,11 @@ Download via Python API
     ta.init(tartanair_data_root)
 
     # Download a trajectory.
-    ta.download(env = "DesertGasStation", 
-                difficulty = ['easy', 'hard'], 
-                modality = ['image', 'depth'],  
-                camera_name = ['lcam_front'],
-                unzip = True)
+    ta.download(env = "ArchVizTinyHouseDay", 
+                difficulty = ['easy'], # this can be 'easy', and/or 'hard'
+                modality = ['image', 'depth', 'seg', 'imu'], # available modalities are: image', 'depth', 'seg', 'imu', 'lidar', 'flow', 'pose'
+                camera_name = ['lcam_front', 'lcam_left', 'lcam_right', 'lcam_back', 'lcam_top', 'lcam_bottom'], 
+                unzip = True) # unzip files autonomously after download
 
 
 Download via a yaml config file
@@ -37,10 +37,10 @@ The config file if of the following format:
 
 .. code-block:: yaml
 
-    env: ['DesertGasStation']
+    env: ['ArchVizTinyHouseDay']
     difficulty: ['easy']
-    modality: ['image']
-    camera_name: ['lcam_front']
+    modality: ['image', 'depth']
+    camera_name: ['lcam_front', 'lcam_left', 'lcam_right', 'lcam_back', 'lcam_top', 'lcam_bottom']
     unzip: True
 
 Customization Example
@@ -54,7 +54,6 @@ TartanAir V2 allows you to synthesize your own dataset by modifying the raw data
 
     # For help with rotations.
     from scipy.spatial.transform import Rotation
-    import numpy as np
 
     # Initialize TartanAir.
     tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
@@ -77,8 +76,8 @@ TartanAir V2 allows you to synthesize your own dataset by modifying the raw data
     R_raw_new1 = Rotation.from_euler('xyz', [45, 0, 0], degrees=True).as_matrix().tolist()
 
     cam_model_1 = {'name': 'doublesphere',
-                    'raw_side': 'left',
-                    'params':
+                   'raw_side': 'left',
+                   'params':
                             {'fx': 300, 
                             'fy': 300, 
                             'cx': 500, 
@@ -88,15 +87,16 @@ TartanAir V2 allows you to synthesize your own dataset by modifying the raw data
                             'alpha': 0.6, 
                             'xi': -0.2, 
                             'fov_degree': 195},
-                    'R_raw_new': R_raw_new1}
+                   'R_raw_new': R_raw_new1}
 
     # Customize the dataset.
-    ta.customize(env = 'SupermarketExposure', 
+    ta.customize(env = 'ArchVizTinyHouseDay', 
                  difficulty = 'easy', 
                  trajectory_id = ['P000'], 
-                 modality = ['image'], 
-                 new_camera_models_params=[cam_model_0, cam_model_1], 
-                 num_workers = 2)
+                 modality = ['image', 'depth'], 
+                 new_camera_models_params=[cam_model_1, cam_model_0], 
+                 num_workers = 4,
+                 device = "cuda") # or cpu
 
 Dataloader Example
 -------------------------------------
@@ -113,33 +113,33 @@ TartanAir-V2 includes a powerful parallelized dataloader. It can be used to load
     ta.init(tartanair_data_root)
 
     # Specify the environments, difficulties, and trajectory ids to load.
-    envs = ['ArchVizTinyHouseDayExposure']
-    difficulties = ['hard']
-    trajectory_ids = [] #['P000', 'P001']
+    envs = ['ArchVizTinyHouseDay']
+    difficulties = ['easy']
+    trajectory_ids = ['P000', 'P001']
 
     # Specify the modalities to load.
-    modalities = ['image', 'depth', 'pose']
-    camnames = ['lcam_front', 'lcam_left']
+    modalities = ['image', 'pose', 'imu']
+    camnames = ['lcam_front', 'lcam_left', 'lcam_right', 'lcam_back', 'lcam_top', 'lcam_bottom']
 
     # Specify the dataloader parameters.
     new_image_shape_hw = [640, 640] # If None, no resizing is performed. If a value is passed, then the image is resized to this shape.
-    subset_framenum = 364 # This is the number of frames in a subset. Notice that this is an upper bound on the batch size. Ideally, make this number large to utilize your RAM efficiently. Information about the allocated memory will be provided in the console.
-    seq_length = {'image': 2, 'depth': 1} # This is the length of the data-sequences. For example, if the sequence length is 2, then the dataloader will load pairs of images.
+    subset_framenum = 200 # This is the number of frames in a subset. Notice that this is an upper bound on the batch size. Ideally, make this number large to utilize your RAM efficiently. Information about the allocated memory will be provided in the console.
+    seq_length = {'image': 2, 'pose': 2, 'imu': 10} # This is the length of the data-sequences. For example, if the sequence length is 2, then the dataloader will load pairs of images.
     seq_stride = 1 # This is the stride between the data-sequences. For example, if the sequence length is 2 and the stride is 1, then the dataloader will load pairs of images [0,1], [1,2], [2,3], etc. If the stride is 2, then the dataloader will load pairs of images [0,1], [2,3], [4,5], etc.
     frame_skip = 0 # This is the number of frames to skip between each frame. For example, if the frame skip is 2 and the sequence length is 3, then the dataloader will load frames [0, 3, 6], [1, 4, 7], [2, 5, 8], etc.
     batch_size = 8 # This is the number of data-sequences in a mini-batch.
     num_workers = 4 # This is the number of workers to use for loading the data.
-    shuffle = False # Whether to shuffle the data. Let's set this to False for now, so that we can see the data loading in a nice video. Yes it is nice don't argue with me please. Just look at it! So nice. :)
+    shuffle = True # Whether to shuffle the data. Let's set this to False for now, so that we can see the data loading in a nice video. Yes it is nice don't argue with me please. Just look at it! So nice. :)
 
     # Create a dataloader object.
     dataloader = ta.dataloader(env = envs, 
                 difficulty = difficulties, 
                 trajectory_id = trajectory_ids, 
                 modality = modalities, 
-                camear_name = camnames, 
+                camera_name = camnames, 
                 new_image_shape_hw = new_image_shape_hw, 
-                subset_framenum = subset_framenum, 
                 seq_length = seq_length, 
+                subset_framenum = subset_framenum, 
                 seq_stride = seq_stride, 
                 frame_skip = frame_skip, 
                 batch_size = batch_size, 
@@ -151,21 +151,37 @@ TartanAir-V2 includes a powerful parallelized dataloader. It can be used to load
     for i in range(100):    
         # Get the next batch.
         batch = dataloader.load_sample()
-        # Check if the batch is None.
-        if batch is None:
-            break
-        print("Batch number: {}".format(i), "Loaded {} samples so far.".format(i * batch_size))
+        # Visualize some images.
+        # The shape of an image batch is (B, S, H, W, C), where B is the batch size, S is the sequence length, H is the height, W is the width, and C is the number of channels.
+
+        print("Batch number: {}".format(i+1), "Loaded {} samples so far.".format((i+1) * batch_size))
 
         for b in range(batch_size):
-            # Visualize some images.
-            # The shape of an image batch is (B, S, H, W, C), where B is the batch size, S is the sequence length, H is the height, W is the width, and C is the number of channels.
-            img0 = batch['rgb_lcam_front'][b][0] 
-            img1 = batch['rgb_lcam_front'][b][1]
-
-            # Visualize the images.
-            outimg = np.concatenate((img0, img1), axis = 1)
-            cv2.imshow('outimg', outimg)
-            cv2.waitKey(1)
+
+            # Create image cross.
+            left = batch['image_lcam_left'][b][0].numpy().transpose(1,2,0)
+            front = batch['image_lcam_front'][b][0].numpy().transpose(1,2,0)
+            right = batch['image_lcam_right'][b][0].numpy().transpose(1,2,0)
+            back = batch['image_lcam_back'][b][0].numpy().transpose(1,2,0)
+            top = batch['image_lcam_top'][b][0].numpy().transpose(1,2,0)
+            bottom = batch['image_lcam_bottom'][b][0].numpy().transpose(1,2,0)
+            cross_mid = np.concatenate([left, front, right, back], axis=1)
+            cross_top = np.concatenate([np.zeros_like(top), top, np.zeros_like(top), np.zeros_like(top)], axis=1)
+            cross_bottom = np.concatenate([np.zeros_like(bottom), bottom, np.zeros_like(bottom), np.zeros_like(bottom)], axis=1)
+            cross = np.concatenate([cross_top, cross_mid, cross_bottom], axis=0)
+
+            pose = batch['pose_lcam_front'].numpy()
+            imu = batch['imu'].numpy()
+
+            # Resize.
+            cross = cv2.resize(cross, (cross.shape[1]//4, cross.shape[0]//4))
+
+            # Show the image cross.
+            cv2.imshow('cross', cross)
+            cv2.waitKey(100)
+
+        print("  Pose: ", pose[0][0])
+        print("  IMU: ", imu[0][0])
             
     dataloader.stop_cachers()
 
@@ -184,11 +200,11 @@ Create a data iterator to get samples from the TartanAir V2 dataset. The samples
     ta.init(tartanair_data_root)
 
     # Create iterator.
-    ta_iterator = ta.iterator(env = 'ConstructionSite',
-                              difficulty = 'easy',
-                              trajectory_id = 'P000',
-                              modality = 'image',
-                              camera_name = 'lcam_front')
+    ta_iterator = ta.iterator(env = ['ArchVizTinyHouseDay'], 
+                            difficulty = 'easy', 
+                            trajectory_id = [], 
+                            modality = 'image', 
+                            camera_name = ['lcam_left'])
 
     for i in range(100):
         sample = next(ta_iterator)
@@ -208,7 +224,7 @@ TartanAir also provides tools for evaluating estimated trajectories against the
     ta.init(tartanair_data_root)
 
     # Create an example trajectory. This is a noisy version of the ground truth trajectory.
-    env = 'AbandonedCableExposure'
+    env = 'ArchVizTinyHouseDay'
     difficulty = 'easy'
     trajectory_id = 'P002'
     camera_name = 'lcam_front'
@@ -216,11 +232,26 @@ TartanAir also provides tools for evaluating estimated trajectories against the
     est_traj = gt_traj + np.random.normal(0, 0.1, gt_traj.shape)  
 
     # Pass the ground truth trajectory directly to the evaluation function.
-    results = ta.evaluate_traj(est_traj, gt_traj = gt_traj, enforce_length = True, plot = True, plot_out_path = plot_out_path, do_scale = True, do_align = True)
+    results = ta.evaluate_traj(est_traj, 
+                               gt_traj = gt_traj, 
+                               enforce_length = True, 
+                               plot = True, 
+                               plot_out_path = plot_out_path, 
+                               do_scale = True, 
+                               do_align = True)
 
     # Or pass the environment, difficulty, and trajectory id to the evaluation function.
     plot_out_path = "evaluator_example_plot.png"
-    results = ta.evaluate_traj(est_traj, env, difficulty, trajectory_id, camera_name, enforce_length = True, plot = True, plot_out_path = plot_out_path, do_scale = True, do_align = True)
+    results = ta.evaluate_traj(est_traj, 
+                               env = env, 
+                               difficulty = difficulty, 
+                               trajectory_id = trajectory_id, 
+                               camera_name = camera_name, 
+                               enforce_length = True, 
+                               plot = True, 
+                               plot_out_path = plot_out_path, 
+                               do_scale = True, 
+                               do_align = True)
 
 Flow Sampling Example
 -------------------------------------
diff --git a/docs/index.rst b/docs/index.rst
index 579185e..8ffc7e6 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -18,7 +18,7 @@ Welcome to TartanAir V2!
 Let's go on an adventure to beautiful mountains, to dark caves, to stylish homes, to the Moon 🚀, and to other exciting places. And there is more! You, your models, and your robots can experience these worlds via a variety of sensors: LiDAR, IMU, optical cameras with any lense configuration you want (we provide customizable fisheye, pinhole, and equirectangular camera models), depth cameras, segmentation "cameras", and event cameras.
 
 .. image::
-   web_cover_figure.png
+   _images/tartanair_ending.gif
 
 All the environments have recorded trajectories that were designed to be challenging and realistic. Can we improve the state of the art in SLAM, navigation, and robotics? 
 
@@ -30,6 +30,8 @@ All the environments have recorded trajectories that were designed to be challen
    installation
    examples
    usage
+   modalities
+   environments
    troubleshooting
    
 License
diff --git a/docs/modalities.rst b/docs/modalities.rst
new file mode 100644
index 0000000..501a512
--- /dev/null
+++ b/docs/modalities.rst
@@ -0,0 +1,7 @@
+Modalities
+=====================================
+
+Images
+Depth
+Flow
+LiDAR
diff --git a/examples/customization_example.py b/examples/customization_example.py
index cb8d5bf..ba736e9 100644
--- a/examples/customization_example.py
+++ b/examples/customization_example.py
@@ -7,7 +7,6 @@
 
 # General imports.
 import sys
-import numpy as np
 from scipy.spatial.transform import Rotation
 
 # Local imports.
@@ -15,14 +14,14 @@
 import tartanair as ta
 
 # Create a TartanAir object.
-tartanair_data_root = '/media/yoraish/overflow/data/tartanair-v2'
+tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
 ta.init(tartanair_data_root)
 
 # Create the requested camera models and their parameters.
 R_raw_new0 = Rotation.from_euler('y', 90, degrees=True).as_matrix().tolist()
 
 cam_model_0 = {'name': 'pinhole', 
-                'raw_side': 'left', # TartanAir has two cameras, one on the left and one on the right. This parameter specifies which camera to use.
+               'raw_side': 'left', # TartanAir has two cameras, one on the left and one on the right. This parameter specifies which camera to use.
                'params': 
                         {'fx': 320, 'fy': 320, 'cx': 320, 'cy': 320, 'width': 640, 'height': 640},
                 'R_raw_new': R_raw_new0}
@@ -43,4 +42,10 @@
                         'fov_degree': 195},
                 'R_raw_new': R_raw_new1}
 
-ta.customize(env = 'ModularNeighborhoodIntExt', difficulty = 'easy', trajectory_id = ['P000'], modality = ['image'], new_camera_models_params=[cam_model_1, cam_model_0], num_workers = 2, device='cuda') # Or cpu.
+ta.customize(env = 'ArchVizTinyHouseDay', 
+             difficulty = 'easy', 
+             trajectory_id = ['P000'], 
+             modality = ['image', 'depth'], 
+             new_camera_models_params=[cam_model_1, cam_model_0], 
+             num_workers = 4,
+             device = "cuda") # or cpu
diff --git a/examples/dataloader_example.py b/examples/dataloader_example.py
index 710f714..4f37d99 100644
--- a/examples/dataloader_example.py
+++ b/examples/dataloader_example.py
@@ -15,20 +15,19 @@
 import tartanair as ta
 
 # Create a TartanAir object.
-# tartanair_data_root = './data/tartanair-v2'
-tartanair_data_root = '/media/yoraish/overflow/data/tartanair-v2'
+tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
 ta.init(tartanair_data_root)
 
 
 # Specify the environments, difficulties, and trajectory ids to load.
 envs = [
-    "ArchVizTinyHouseDayExposure",
+    "ArchVizTinyHouseDay",
 ]
-difficulties = []
-trajectory_ids = ['P000']
+difficulties = ['easy']
+trajectory_ids = ['P000','P001']
 
 # Specify the modalities to load.
-modalities = ['image', 'pose', 'imu_gyro', 'imu_time']
+modalities = ['image', 'pose', 'imu']
 camnames = ['lcam_front', 'lcam_left', 'lcam_right', 'lcam_back', 'lcam_top', 'lcam_bottom']
 
 # Specify dataloader configuration. The dataloader operates in the following way:
@@ -38,8 +37,8 @@
 
 new_image_shape_hw = [640, 640] # If None, no resizing is performed. If a value is passed, then the image is resized to this shape.
 subset_framenum = 200 # This is the number of frames in a subset. Notice that this is an upper bound on the batch size. Ideally, make this number large to utilize your RAM efficiently. Information about the allocated memory will be provided in the console.
+seq_length = {'image': 2, 'pose': 2, 'imu': 10} # This is the length of the data-sequences. For example, if the sequence length is 2, then the dataloader will load pairs of images.
 frame_skip = 0 # This is the number of frames to skip between each frame. For example, if the frame skip is 2 and the sequence length is 3, then the dataloader will load frames [0, 3, 6], [1, 4, 7], [2, 5, 8], etc.
-seq_length = {'image': 2, 'pose': 2, 'imu_gyro': 10, 'imu_time': 10} # This is the length of the data-sequences. For example, if the sequence length is 2, then the dataloader will load pairs of images.
 seq_stride = 1 # This is the stride between the data-sequences. For example, if the sequence length is 2 and the stride is 1, then the dataloader will load pairs of images [0,1], [1,2], [2,3], etc. If the stride is 2, then the dataloader will load pairs of images [0,1], [2,3], [4,5], etc.
 batch_size = 4 # This is the number of data-sequences in a mini-batch.
 num_workers = 8 # This is the number of workers to use for loading the data.
@@ -52,8 +51,8 @@
             modality = modalities, 
             camera_name = camnames, 
             new_image_shape_hw = new_image_shape_hw, 
-            subset_framenum = subset_framenum, 
             seq_length = seq_length, 
+            subset_framenum = subset_framenum, 
             seq_stride = seq_stride, 
             frame_skip = frame_skip, 
             batch_size = batch_size, 
@@ -65,32 +64,39 @@
 # Iterate over the dataloader and visualize the output.
 
 # Iterate over the batches.
-for i in range(1000):    
+for i in range(100):    
     # Get the next batch.
     batch = dataloader.load_sample()
-    # Check if the batch is None.
-    if batch is None:
-        break
     # Visualize some images.
     # The shape of an image batch is (B, S, H, W, C), where B is the batch size, S is the sequence length, H is the height, W is the width, and C is the number of channels.
 
-    # Create image cross.
-    left = batch['rgb_lcam_left'][0][0]
-    front = batch['rgb_lcam_front'][0][0]
-    right = batch['rgb_lcam_right'][0][0]
-    back = batch['rgb_lcam_back'][0][0]
-    top = batch['rgb_lcam_top'][0][0]
-    bottom = batch['rgb_lcam_bottom'][0][0]
-    cross_mid = np.concatenate([left, front, right, back], axis=1)
-    cross_top = np.concatenate([np.zeros_like(top), top, np.zeros_like(top), np.zeros_like(top)], axis=1)
-    cross_bottom = np.concatenate([np.zeros_like(bottom), bottom, np.zeros_like(bottom), np.zeros_like(bottom)], axis=1)
-    cross = np.concatenate([cross_top, cross_mid, cross_bottom], axis=0)
-
-    # Resize.
-    cross = cv2.resize(cross, (cross.shape[1]//4, cross.shape[0]//4))
-
-    # Show the image cross.
-    cv2.imshow('cross', cross)
-    cv2.waitKey(1)
+    print("Batch number: {}".format(i+1), "Loaded {} samples so far.".format((i+1) * batch_size))
+
+    for b in range(batch_size):
+
+        # Create image cross.
+        left = batch['image_lcam_left'][b][0].numpy().transpose(1,2,0)
+        front = batch['image_lcam_front'][b][0].numpy().transpose(1,2,0)
+        right = batch['image_lcam_right'][b][0].numpy().transpose(1,2,0)
+        back = batch['image_lcam_back'][b][0].numpy().transpose(1,2,0)
+        top = batch['image_lcam_top'][b][0].numpy().transpose(1,2,0)
+        bottom = batch['image_lcam_bottom'][b][0].numpy().transpose(1,2,0)
+        cross_mid = np.concatenate([left, front, right, back], axis=1)
+        cross_top = np.concatenate([np.zeros_like(top), top, np.zeros_like(top), np.zeros_like(top)], axis=1)
+        cross_bottom = np.concatenate([np.zeros_like(bottom), bottom, np.zeros_like(bottom), np.zeros_like(bottom)], axis=1)
+        cross = np.concatenate([cross_top, cross_mid, cross_bottom], axis=0)
+
+        pose = batch['pose_lcam_front'].numpy()
+        imu = batch['imu'].numpy()
+
+        # Resize.
+        cross = cv2.resize(cross, (cross.shape[1]//4, cross.shape[0]//4))
+
+        # Show the image cross.
+        cv2.imshow('cross', cross)
+        cv2.waitKey(100)
+
+    print("  Pose: ", pose[0][0])
+    print("  IMU: ", imu[0][0])
 
 dataloader.stop_cachers()
\ No newline at end of file
diff --git a/examples/dataset_example.py b/examples/dataset_example.py
index c603380..30a2083 100644
--- a/examples/dataset_example.py
+++ b/examples/dataset_example.py
@@ -13,7 +13,7 @@
 import tartanair as ta
 
 # Create a TartanAir object.
-tartanair_data_root = '/media/yoraish/overflow/data/tartanair-v2_training_data'
+tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
 ta.init(tartanair_data_root)
 
 #####################
@@ -21,7 +21,7 @@
 #####################
 
 # Set up the dataset.
-dataset = ta.create_image_dataset(env = ['ConstructionSite', 'SupermarketExposure'], difficulty = [], trajectory_id = [], modality = ['image', 'depth'], camera_name = ['lcam_front', 'lcam_back', 'lcam_right', 'lcam_left', 'lcam_top', 'lcam_bottom'], transform = None, num_workers=10)
+dataset = ta.create_image_dataset(env = ['ArchVizTinyHouseDay'], difficulty = [], trajectory_id = [], modality = ['image', 'depth'], camera_name = ['lcam_front', 'lcam_back', 'lcam_right', 'lcam_left', 'lcam_top', 'lcam_bottom'], transform = None, num_workers=10)
 
 # Print the dataset.
 print(dataset)
@@ -74,7 +74,7 @@
 ])
 
 # Set up a dataset.
-dataset = ta.create_image_dataset(env = [], difficulty = 'easy', trajectory_id = [], modality = ['image', 'depth'], camera_name = ['lcam_fish', 'lcam_front'], transform = transform)
+dataset = ta.create_image_dataset(env = ['ArchVizTinyHouseDay'], difficulty = 'easy', trajectory_id = [], modality = ['image', 'depth'], camera_name = ['lcam_fish', 'lcam_front'], transform = transform)
 
 dataloader = DataLoader(dataset, batch_size = 3, shuffle = True, num_workers = 0)
 
@@ -104,37 +104,3 @@
 
     if i_batch == 5:
         break
-
-####################
-# Ask for all the data from an environment.
-####################
-dataset = ta.create_image_dataset(env = 'ConstructionSite')
-
-dataloader = DataLoader(dataset, batch_size = 3, shuffle = True, num_workers = 0)
-
-# Show a few images.
-
-for i_batch, sample_batched in enumerate(dataloader):
-    print(i_batch, sample_batched['lcam_fish']['image_0'].size())    
-
-    # Show the batch side by side.
-    import cv2
-    import numpy as np
-    imgs1 = sample_batched['lcam_fish']['image_0'].numpy()
-    imgs2 = sample_batched['lcam_fish']['image_1'].numpy()
-
-    # Move the color channel to the end.
-    imgs1 = np.transpose(imgs1, (0, 2, 3, 1))
-    imgs2 = np.transpose(imgs2, (0, 2, 3, 1))
-
-
-    img = np.concatenate((imgs1[0], imgs1[1], imgs1[2]), axis = 1)
-    img = np.concatenate((img, np.concatenate((imgs2[0], imgs2[1], imgs2[2]), axis = 1)), axis = 0)
-    img = cv2.resize(img, (0,0), fx = 0.5, fy = 0.5)
-    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-
-    cv2.imshow('image transform', img)
-    cv2.waitKey(0)
-
-    if i_batch == 5:
-        break
\ No newline at end of file
diff --git a/examples/download_config.yaml b/examples/download_config.yaml
index 88007f5..7f16e9e 100644
--- a/examples/download_config.yaml
+++ b/examples/download_config.yaml
@@ -1,7 +1,7 @@
 # Config specifying which trajectories to download from the remote.
 
-env: ['AmericanDiner']
-difficulty: ['easy']
-modality: ['image', 'depth']
-camera_name: ['lcam_fish', 'lcam_front']
-unzip: True
+  env: ['ArchVizTinyHouseDay']
+  difficulty: ['easy']
+  modality: ['image', 'depth', 'seg', 'imu'] 
+  camera_name: ['lcam_fish', 'lcam_front', 'lcam_left', 'lcam_right', 'lcam_back', 'lcam_top', 'lcam_bottom']
+  unzip: True
diff --git a/examples/download_example.py b/examples/download_example.py
index b9f35c8..e640996 100644
--- a/examples/download_example.py
+++ b/examples/download_example.py
@@ -13,25 +13,21 @@
 import tartanair as ta
 
 # Create a TartanAir object.
-tartanair_data_root = '/media/yoraish/overflow/data/tartanair-v2'
-azure_token = "?sv=2021-10-04&st=2023-03-01T16%3A34%3A50Z&se=2023-03-30T15%3A34%3A00Z&sr=c&sp=rl&sig=LojCTa60TcA9ApMiMofliedxujeX0AOZdoC3O5u5cxg%3D"
- 
-ta.init(tartanair_data_root, azure_token)
-
-# Download a trajectory.
-# env = [
-#                 "HQWesternSaloonExposure",
-#                 "ModularNeighborhoodIntExt",
-#                 "PolarSciFiExposure",
-#                 "PrisonExposure",
-#                 "RuinsExposure",
-#                 "TerrainBlendingExposure",
-#                 "UrbanConstructionExposure",
-#                 "VictorianStreetExposure",
-#                 "WaterMillDayExposure",
-#                 "WaterMillNightExposure"
-# ]
-# ta.download(env = "OldScandinaviaExposure", difficulty = ['easy', 'hard'], trajectory_id = ["P004", "P005", "P003", "P008"],  modality = ['image', 'depth'],  camera_name = ['lcam_front', 'lcam_right', 'lcam_back', 'lcam_left', 'lcam_top', 'lcam_bottom'])
+tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
+
+ta.init(tartanair_data_root)
+
+# Download data from following environments.
+env = [ "Prison",
+        "Ruins",
+        "UrbanConstruction",
+]
+
+ta.download(env = env, 
+              difficulty = ['easy', 'hard'], 
+              modality = ['image', 'depth'],  
+              camera_name = ['lcam_front', 'lcam_right', 'lcam_back', 'lcam_left', 'lcam_top', 'lcam_bottom'], 
+              unzip = True)
 
 # Can also download via a yaml config file.
 ta.download(config = 'download_config.yaml')
diff --git a/examples/evaluator_example.py b/examples/evaluator_example.py
index fc994a7..8690151 100644
--- a/examples/evaluator_example.py
+++ b/examples/evaluator_example.py
@@ -14,13 +14,13 @@
 import tartanair as ta
 
 # Create a TartanAir object.
-tartanair_data_root = '/media/yoraish/overflow/data/tartanair-v2'
- 
+tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
+
 # Initialize the toolbox.
 ta.init(tartanair_data_root)
 
 # Create an example trajectory. This is a noisy version of the ground truth trajectory.
-env = 'AbandonedCableExposure'
+env = 'ArchVizTinyHouseDay'
 difficulty = 'easy'
 trajectory_id = 'P002'
 camera_name = 'lcam_front'
@@ -31,7 +31,16 @@
 
 # Get the evaluation results.
 plot_out_path = "evaluator_example.png"
-results = ta.evaluate_traj(est_traj, env, difficulty, trajectory_id, camera_name, enforce_length = True, plot = True, plot_out_path = plot_out_path, do_scale = True, do_align = True)
+results = ta.evaluate_traj(est_traj, 
+                           env = env, 
+                           difficulty = difficulty, 
+                           trajectory_id = trajectory_id, 
+                           camera_name = camera_name, 
+                           enforce_length = True, 
+                           plot = True, 
+                           plot_out_path = plot_out_path, 
+                           do_scale = True, 
+                           do_align = True)
 
 # Optionally pass the ground truth trajectory directly to the evaluation function.
 results = ta.evaluate_traj(est_traj, gt_traj = gt_traj, enforce_length = True, plot = True, plot_out_path = plot_out_path, do_scale = True, do_align = True)
\ No newline at end of file
diff --git a/examples/get_traj_example.py b/examples/get_traj_example.py
index 84f94e3..046edd8 100644
--- a/examples/get_traj_example.py
+++ b/examples/get_traj_example.py
@@ -14,12 +14,15 @@
 import tartanair as ta
 
 # Create a TartanAir object.
-tartanair_data_root = '/media/yoraish/overflow/data/tartanair-v2'
+tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
  
 ta.init(tartanair_data_root)
 
 # List available trajectories.
-traj = ta.get_traj_np(env = 'ConstructionSite', difficulty = 'easy', trajectory_id = "P000",  camera_name = 'lcam_front')
+traj = ta.get_traj_np(env = 'ArchVizTinyHouseDay', 
+                      difficulty = 'easy', 
+                      trajectory_id = "P000",  
+                      camera_name = 'lcam_front')
 print(traj.shape)
 np.set_printoptions(precision=3, suppress=True)
 print(traj[0:10, :])
\ No newline at end of file
diff --git a/examples/iterator_example.py b/examples/iterator_example.py
index 5208f68..3940594 100644
--- a/examples/iterator_example.py
+++ b/examples/iterator_example.py
@@ -13,12 +13,16 @@
 import tartanair as ta
 
 # Create a TartanAir object.
-tartanair_data_root = '/media/yoraish/overflow/data/tartanair-v2'
- 
+tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
+
 ta.init(tartanair_data_root)
 
 # List available trajectories.
-ta_iterator = ta.iterator(env = ['PolarSciFiExposure'], difficulty = 'easy', trajectory_id = [], modality = 'image', camera_name = ['lcam_left'])
+ta_iterator = ta.iterator(env = ['ArchVizTinyHouseDay'], 
+                          difficulty = 'easy', 
+                          trajectory_id = [], 
+                          modality = 'image', 
+                          camera_name = ['lcam_left'])
 
 for i in range(100):
     print(next(ta_iterator))
\ No newline at end of file
diff --git a/examples/list_example.py b/examples/list_example.py
index d41be16..09629b1 100644
--- a/examples/list_example.py
+++ b/examples/list_example.py
@@ -13,8 +13,8 @@
 import tartanair as ta
 
 # Create a TartanAir object.
-tartanair_data_root = '/media/yoraish/overflow/data/tartanair-v2'
- 
+tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
+
 ta.init(tartanair_data_root)
 
 # List available trajectories.
diff --git a/examples/visualization_example.py b/examples/visualization_example.py
index 5d39ab2..4e233e4 100644
--- a/examples/visualization_example.py
+++ b/examples/visualization_example.py
@@ -13,9 +13,13 @@
 import tartanair as ta
 
 # Create a TartanAir object.
-tartanair_data_root = '/media/yoraish/overflow/data/tartanair-v2_training_data'
- 
+tartanair_data_root = '/my/path/to/root/folder/for/tartanair-v2'
+
 ta.init(tartanair_data_root)
 
 # List available trajectories.
-ta.visualize('ConstructionSite', difficulty='easy', trajectory_id = 'P000', modality = ['image', 'depth', 'seg'], camera_name = ['lcam_front', 'lcam_right', 'lcam_back', 'lcam_left'])
\ No newline at end of file
+ta.visualize('ArchVizTinyHouseDay', 
+              difficulty='easy', 
+              trajectory_id = 'P000', 
+              modality = ['image', 'depth', 'seg'], 
+              camera_name = ['lcam_front', 'lcam_right', 'lcam_back', 'lcam_left'])
\ No newline at end of file
diff --git a/tartanair/customizer.py b/tartanair/customizer.py
index b10371c..591d17e 100644
--- a/tartanair/customizer.py
+++ b/tartanair/customizer.py
@@ -21,13 +21,15 @@
 
 # Local imports.
 from .tartanair_module import TartanAirModule
+from .reader import TartanAirImageReader
 
 # Image resampling.
-from .image_resampling.image_sampler import SixPlanarNumba
+from .image_resampling.image_sampler import SixPlanarNumba, SixPlanarTorch
 
 from .image_resampling.mvs_utils.camera_models import Pinhole, DoubleSphere, LinearSphere, Equirectangular #, PinholeRadTanFast, EUCM
 from .image_resampling.mvs_utils.shape_struct import ShapeStruct
-from .image_resampling.image_sampler.blend_function import BlendBy2ndOrderGradTorch
+from .image_resampling.mvs_utils.ftensor import FTensor
+from .image_resampling.image_sampler.blend_function import BlendBy2ndOrderGradTorch, BlendBy2ndOrderGradOcv
 
 class TartanAirCustomizer(TartanAirModule):
     def __init__(self, tartanair_data_root):
@@ -136,9 +138,10 @@ def customize(self, env, difficulty = [], trajectory_id = [], modality = [], new
 
 
         # Some mappings between attributes and parameters.
-        modality_to_reader = {"image": self.read_rgb, "depth": self.read_dist, "seg": self.read_seg}
+        self.reader = TartanAirImageReader()
+        modality_to_reader = {"image": self.reader.read_bgr, "depth": self.reader.read_dist, "seg": self.reader.read_seg}
         modality_to_interpolation = {"image": "linear", "seg": "nearest", "depth": "blend"}
-        modality_to_writer = {"image": self.write_as_is, "seg": self.write_as_is, "depth": self.write_float_depth}
+        modality_to_writer = {"image": self.reader.write_as_is, "seg": self.reader.write_as_is, "depth": self.reader.write_float_depth}
         
         ###############################
         # Enumerate the trajectories.
@@ -293,10 +296,14 @@ def customize(self, env, difficulty = [], trajectory_id = [], modality = [], new
     def sample_image_worker(self, argslist): 
         frame_ix, new_cam_model_object, R_raw_new, modality, new_cam_model_name, cam_name, side_to_frame_gfps, new_data_dir_path, modality_to_reader, modality_to_interpolation, modality_to_writer = argslist
 
-        sampler = SixPlanarNumba(new_cam_model_object.fov_degree, new_cam_model_object, R_raw_new)
+        # sampler = SixPlanarNumba(new_cam_model_object.fov_degree, new_cam_model_object, R_raw_new)
+        R_raw_new = FTensor(R_raw_new, f0='raw', f1='fisheye', rotation=True) # TODO: double check
+        sampler = SixPlanarTorch(new_cam_model_object, R_raw_new)
+        # sampler = SixPlanarNumba(new_cam_model_object, R_raw_new)
+
         sampler.device = self.device
         create_figures = False
-
+        
         raw_images = {}
         for side in ['front', 'back', 'left', 'right', 'top', 'bottom']:
             # Revert to below:
@@ -318,13 +325,13 @@ def sample_image_worker(self, argslist):
                     cv2.putText(img, side, (280, 320), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
                 
                 raw_images[side] = img
-
+        
         # Resample the six raw images to the new camera model.
         if modality_to_interpolation[modality] == "blend":
             blend_func = BlendBy2ndOrderGradTorch(0.01) # hard code
             new_image, new_image_valid_mask = sampler.blend_interpolation(raw_images, blend_func, invalid_pixel_value=0)  
         else:
-            new_image, new_image_valid_mask = sampler(raw_images, interpolation= modality_to_interpolation[modality])
+            new_image, new_image_valid_mask = sampler(raw_images, interpolation= modality_to_interpolation[modality], invalid_pixel_value=0)
 
         # Write the new image.
         out_fn = str(frame_ix).zfill(6) + "_{}_{}_{}.png".format(cam_name, modality, new_cam_model_name)
@@ -390,8 +397,8 @@ def check_six_images_exist(self, env = [], difficulty = [], trajectory_id = [],
                 # Iterate trajectory.
                 if not trajectory_id:
                     diff_path = os.path.join(self.data_root, env_folder, difficulty_folder)
-                    env_trajectory_id = os.listdir(diff_path)                
-                for traj_id_folder in env_trajectory_id:
+                    trajectory_id = os.listdir(diff_path)                
+                for traj_id_folder in trajectory_id:
 
                     # Iterate modality.
                     if not modality:
@@ -415,101 +422,3 @@ def check_six_images_exist(self, env = [], difficulty = [], trajectory_id = [],
                         print("Success: {env_folder}/{difficulty_folder}/{traj_id_folder} All files are available for {modality} on {raw_side} side.".format(env_folder = env_folder, difficulty_folder = difficulty_folder, traj_id_folder = traj_id_folder, modality = modality, raw_side = raw_side))
             
         return True
-
-    ###############################
-    # Data enumeration.
-    ###############################
-    def enumerate_trajs(self, data_folders = ['Data_easy','Data_hard']):
-        '''
-        Return a dict:
-            res['env0']: ['Data_easy/P000', 'Data_easy/P001', ...], 
-            res['env1']: ['Data_easy/P000', 'Data_easy/P001', ...], 
-        '''
-        env_folders = os.listdir(self.data_root)    
-        env_folders = [ee for ee in env_folders if os.path.isdir(os.path.join(self.data_root, ee))]
-        env_folders.sort()
-        trajlist = {}
-        for env_folder in env_folders:
-            env_dir = os.path.join(self.data_root, env_folder)
-            trajlist[env_folder] = []
-            for data_folder in data_folders:
-                datapath = os.path.join(env_dir, data_folder)
-                if not os.path.isdir(datapath):
-                    continue
-
-                trajfolders = os.listdir(datapath)
-                trajfolders = [ os.path.join(data_folder, tf) for tf in trajfolders if tf[0]=='P' ]
-                trajfolders.sort()
-                trajlist[env_folder].extend(trajfolders)
-        return trajlist
-
-    ###############################
-    # Data reading, writing, and processing.
-    ###############################
-    def depth_to_dist(self, depth):
-        '''
-        assume: fov = 90 on both x and y axes, and optical center is at image center.
-        '''
-        # import ipdb;ipdb.set_trace()
-        if self.depth_shape is None or \
-            depth.shape != self.depth_shape or \
-            self.conv_matrix is None: # only calculate once if the depth shape has not changed
-            hh, ww = depth.shape
-            f = ww/2
-            wIdx = np.linspace(0, ww - 1, ww, dtype=np.float32) + 0.5 - ww/2 # put the optical center at the middle of the image
-            hIdx = np.linspace(0, hh - 1, hh, dtype=np.float32) + 0.5 - hh/2 # put the optical center at the middle of the image
-            u, v = np.meshgrid(wIdx, hIdx)
-            dd = np.sqrt(u * u + v * v + f * f)/f
-            self.conv_matrix = dd
-        self.depth_shape = depth.shape
-        disp = self.conv_matrix * depth
-        return disp
-
-    def ocv_read(self, fn ):
-        image = cv2.imread(fn, cv2.IMREAD_UNCHANGED)
-        assert image is not None, \
-            f'{fn} read error. '
-        return image
-
-    def read_rgb(self, fn ):
-        return self.ocv_read(fn)
-
-    def read_dist(self, fn ): # read a depth image and convert it to distance
-        depth = self.read_dep(fn)
-        return self.depth_to_dist(depth)
-
-    def read_dep(self, fn ):
-        image = self.ocv_read(fn)
-        depth = np.squeeze( image.view('<f4'), axis=-1 )
-        return depth
-
-    def vis_dep(self, fn):
-        depth = self.read_dep(fn)
-        depthvis = np.clip(1/depth * 400, 0, 255).astype(np.uint8)
-        return cv2.applyColorMap(depthvis, cv2.COLORMAP_JET)
-
-    def read_seg(self, fn ):
-        image = self.ocv_read(fn)
-        return image.astype(np.uint8)
-
-    def ocv_write(self, fn, image ):
-        cv2.imwrite(fn, image)
-    
-    def write_as_is(self, fn, image ):
-        self.ocv_write( fn, image )
-    
-    def write_float_compressed(self, fn, image):
-        assert(image.ndim == 2), 'image.ndim = {}'.format(image.ndim)
-    
-        # Check if the input array is contiguous.
-        if ( not image.flags['C_CONTIGUOUS'] ):
-            image = np.ascontiguousarray(image)
-
-        dummy = np.expand_dims( image, 2 )
-        self.ocv_write( fn, dummy )
-        
-    def write_float_depth(self, fn, image):
-        if len(image.shape) == 2:
-            image = image[...,np.newaxis]
-        depth_rgba = image.view("<u1")
-        self.ocv_write(fn, depth_rgba)
diff --git a/tartanair/data_cacher b/tartanair/data_cacher
index 6544968..69e887d 160000
--- a/tartanair/data_cacher
+++ b/tartanair/data_cacher
@@ -1 +1 @@
-Subproject commit 65449682e3b86382eed8967f4fa584670bdf3e05
+Subproject commit 69e887d87b9f91c30e62a12b6f004bd0419bb1db
diff --git a/tartanair/dataloader.py b/tartanair/dataloader.py
index f331c4d..b538be5 100644
--- a/tartanair/dataloader.py
+++ b/tartanair/dataloader.py
@@ -12,12 +12,12 @@
 
 # General imports.
 import os
-from colorama import Fore, Back, Style
-
+from os.path import join, isfile
 
 # Local imports.
-from .tartanair_module import TartanAirModule
+from .tartanair_module import TartanAirModule, print_error, print_highlight, print_warn
 from .data_cacher.MultiDatasets import MultiDatasets
+from .data_cacher.datafile_editor import generate_datafile, enumerate_frames, breakdown_trajectories
 
 class TartanAirDataLoader(TartanAirModule):
     '''
@@ -27,60 +27,116 @@ def __init__(self, tartanair_data_root):
         # Call the parent class constructor.
         super(TartanAirDataLoader, self).__init__(tartanair_data_root)
 
-        # Remap some modality names to support naming inconsistencies.
-        self.modality_name_remaps = {
-            'rgb': 'rgb',
-            'image': 'rgb',
-            'imu': 'imu_acc',
-            'imu_acc': 'imu_acc',
-            'imu_gyro': 'imu_gyro',
-            'imu_time': 'imu_time',
-            'depth': 'depth',
-            'lidar': 'lidar',
-            'pose': 'pose',
-            'seg': 'seg',
-            'flow': 'flow',
-            'cam_time': 'cam_time',
-            'imu_pos': 'imu_pos'
-        }
-
-
         self.modality_default_seq_length = {
-            'rgb': 1,
+            'image': 1,
             'depth': 1,
             'seg': 1,
             'flow': 1,
             'pose': 1,
-            'imu_acc': 10,
-            'imu_gyro': 10,
-            'imu_time': 10,
+            'imu': 10,
             'lidar': 1,
-            'cam_time': 1,
-            'imu_pos': 10
         }
 
         self.modality_default_cacher_size = {
-            'rgb': [640, 640],
+            'image': [640, 640],
             'depth': [640, 640],
             'seg': [640, 640],
             'flow': [640, 640],
             'pose': [7],
-            'imu_acc': [3],
-            'imu_gyro': [3],
-            'imu_time': [1],
-            'imu_pos': [3],
+            'imu': [6],
             'lidar': [3],
         }
 
+    def generate_data_file(self, env, difficulty, trajectory_id, onemodfolder, breakdown = True):
+        print_highlight("Generating datafile...")
+
+        # find all the local trajectories with the given difficulty level
+        difficulty = ['Data_' + dd for dd in difficulty]
+        local_traj_dict = self.enumerate_trajs(difficulty)
+
+        # Iterate over all environments and trajectories, and fine all the trajectories the user is interested.
+        trajstrlist, framelist = [], []
+        for env_name in env:
+            print("Building data cacher for env {}...".format(env_name))
+            if not (env_name in local_traj_dict):
+                print_error("Could not find env {} in the local directory {}".format(env_name, local_traj_dict.keys))
+                return None
+
+            # find the trajectories that satisfy the user requirement
+            local_traj_env = local_traj_dict[env_name]
+            for traj_str in local_traj_env:
+                dif_str, trajid = traj_str.split('/')
+                if trajid in trajectory_id or len(trajectory_id) == 0:
+                    trajstr = join(env_name, traj_str)
+                    trajstrlist.append(trajstr)
+                    frames = enumerate_frames(join(self.tartanair_data_root, trajstr, onemodfolder))
+                    framelist.append(frames)
+
+            if len(trajstrlist) == 0:
+                print_error("Could not find any trajectory to load! ")
+                return None
+
+        # hard coded, we use the following datafile
+        datafile = 'ta_datafile.txt'
+        if isfile(datafile):
+            print_warn("Removing the existing datafile {}".format(datafile))
+            os.remove(datafile)
+
+        if breakdown: # break down the trajectory so that it is more random when the buffer size is limited
+            trajstrlist, framelist = breakdown_trajectories(trajstrlist, framelist)
+
+        generate_datafile(datafile, trajstrlist, framelist)
+        return datafile
+
+
+    def generate_config_file(self, datafile, foldernames, image_shape_hw, seq_length, 
+                                    subset_framenum, seq_stride, frame_skip, num_workers):
+        config = {}
+        config['task'] = 'tartanair'
+        config['global'] = {}
+
+        config['global']['modality'] = {}
+
+        config['global']['cacher'] = {}
+        config['global']['cacher']['load_traj'] = False
+        config['global']['cacher']['data_root_path_override'] = self.tartanair_data_root
+        config['global']['cacher']['subset_framenum'] = subset_framenum
+        config['global']['cacher']['worker_num'] = num_workers
+
+        config['global']['dataset'] = {}
+        config['global']['dataset']['frame_skip'] = frame_skip
+        config['global']['dataset']['seq_stride'] = seq_stride
+        config['global']['dataset']['frame_dir'] = True
+
+        config['global']['parameter'] = {}
+
+        # Build the data entry. It is shared for the entire dataset.
+        data_entry = {}
+        data_entry['file'] = datafile
+        data_entry['modality'] = {}
+        data_entry['cacher'] = {}
+        data_entry['dataset'] = {}
+        data_entry['parameter'] = {}
+
+        self.add_modality_entries(data_entry['modality'], foldernames, image_shape_hw, seq_length)
+
+        config['data'] = {}
+        config['data']['1'] = data_entry # the data_cacher support loading multiple datasets, we are only using one here
+
+        print(config)
+
+        return config
+
+
     def get_data_cacher(self, 
                         env, 
                         difficulty = None, 
                         trajectory_id = None, 
                         modality = None, 
                         camera_name = None, 
-                        new_image_shape_hw = [640, 640],
-                        subset_framenum = 56, # <--- Note in the docs that this is an upper bound on the batch size.
+                        new_image_shape_hw = [640, 640],  # This can also be a dictionary, mapping each modality name to a shape.
                         seq_length = 1, # This can also be a dictionary, mapping each modality name to a sequence length.
+                        subset_framenum = 56, # <--- Note in the docs that this is an upper bound on the batch size. In general, this should be as large as possible 
                         seq_stride = 1,
                         frame_skip = 0,
                         batch_size=1,
@@ -94,14 +150,14 @@ def get_data_cacher(self,
 
         # Add default values to empty inputs.
         if not difficulty:
-            difficulty = []
+            difficulty = self.difficulty_names # default to all difficulties 
         if not trajectory_id:
             trajectory_id = [] # Empty list will default to all trajs down the line.
         if not modality:
-            print("WARNING: No modality was specified. Defaulting to _some_ modalities: ('image', 'depth', 'seg')")
+            print_warn("WARNING: No modality was specified. Defaulting to _some_ modalities: ('image', 'depth', 'seg')")
             modality = ['image', 'depth', 'seg']
         if not camera_name:
-            camera_name = ['lcam_front', 'lcam_back', 'lcam_left', 'lcam_right', 'lcam_top', 'lcam_bottom', 'lcam_fish', 'lcam_equirect', 'rcam_front', 'rcam_back', 'rcam_left', 'rcam_right', 'rcam_top', 'rcam_bottom', 'rcam_fish', 'rcam_equirect']
+            camera_name = self.camera_names #['lcam_front', 'lcam_back', 'lcam_left', 'lcam_right', 'lcam_top', 'lcam_bottom', 'lcam_fish', 'lcam_equirect', 'rcam_front', 'rcam_back', 'rcam_left', 'rcam_right', 'rcam_top', 'rcam_bottom', 'rcam_fish', 'rcam_equirect']
 
         # Convert all inputs to lists.
         if type(env) is not list:
@@ -115,194 +171,104 @@ def get_data_cacher(self,
         if type(camera_name) is not list:
             camera_name = [camera_name]
 
-        # Allow to take in a dictionary for the modality sequence length.
-        # If an integer is given, then use it for all modalities.
-        if type(seq_length) is int:
-            seq_length = {mod: seq_length for mod in modality}
-        elif type(seq_length) is dict:
-            # Remap modality names.
-            seq_length = {self.modality_name_remaps[mod]: seq_length[mod] for mod in seq_length}
-            for mod in modality:
-                mod = self.modality_name_remaps[mod]
-                if mod not in seq_length:
-                    print("WARNING: No sequence length was given for modality {}. Defaulting to {}.".format(mod, self.modality_default_seq_length[mod]))
-                    seq_length[mod] = self.modality_default_seq_length[mod]
-        else:
-            raise ValueError("seq_length must be an integer or a dictionary.")
-
-        config = {}
-        config['task'] = 'tartanair'
-        config['transform_data_augment'] = True # Unused.
-        config['transform_flow_norm_factor'] = 1.0 # Unused.
-        config['transform_uncertainty'] = True # Unused.
-        config['transform_input_size'] = new_image_shape_hw # Unused.
-        config['dataset_frame_skip'] = frame_skip
-        config['dataset_seq_stride'] = seq_stride
-        config['data'] = {}
-
-        # Create a composed data specification text file for this data cacher. This is saved with a common name, ta_data_spec.txt, and is stored in the root directory of the TartanAir dataset. The contents are a composition of all the data specifications for each environment, difficulty, and trajectory.
-        data_spec_fpath = 'ta_data_spec.txt'
-        if os.path.isfile(data_spec_fpath):
-            os.remove(data_spec_fpath)
-
-        # Iterate over all environments and trajectories, and build an entry for each one.
-        for env_name in env:
-            print("Building data cacher for env {}...".format(env_name))
-            # If no difficulty was given, then use all difficulties.
-            if not difficulty:
-                available_diffs = [diff for diff in ['easy', 'hard'] if os.path.isdir(os.path.join(self.tartanair_data_root, env_name, 'Data_' + diff))]
-                print([os.path.join(self.tartanair_data_root, env_name, 'Data_' + diff) for diff in ['easy', 'hard']])
-                print(Fore.GREEN + "WARNING: No difficulty was specified for env {}. Defaulting to all available difficulties: {}".format(env_name, available_diffs),  Style.RESET_ALL)
-
-            else:
-                available_diffs = difficulty
-
-            for diff in available_diffs:
-
-                # If no trajectory id was given, then use all trajectories.
-                available_traj_ids = self.get_available_trajectory_ids(env_name, diff)
-                if not trajectory_id:
-                    print(Fore.GREEN + "WARNING: No trajectory id was specified for env {} and difficulty {}. Defaulting to all available trajectories: {}".format(env_name, diff, available_traj_ids),  Style.RESET_ALL)
-                else:
-                    for traj_id in trajectory_id:
-                        if traj_id not in available_traj_ids:
-                            print(Fore.RED + "WARNING: Trajectory id {} was specified for env {} and difficulty {}, but it is not available. It is skipped.".format(traj_id, env_name, diff),  Style.RESET_ALL)
-                    available_traj_ids = [traj_id for traj_id in available_traj_ids if traj_id in trajectory_id]
-                
-                for traj_id in available_traj_ids:
-                    # Build the data entry.         
-
-                    # Read the data specification file and concatenate it to the data spec file.
-                    with open(data_spec_fpath, 'a') as f:
-                        env_diff_traj_data_spec_fpath = os.path.join(self.tartanair_data_root, env_name, "analyze", 'data_' + env_name + '_Data_' + diff + '_' + traj_id + '.txt')
-
-                        # Read the data spec file.
-                        # with open(env_diff_traj_data_spec_fpath, 'r') as f2:
-                        # Create a new data spec file for this trajectory, where it is broken down into smaller pseudo-trajectories.
-                        # This is done to allow for the data cacher to cache the data in small trajectory portions, such that the data is shuffled also when the RAM memory allocation is small.
-                        pseudo_traj_length = max(max(seq_length.values()) * seq_stride * (frame_skip + 1) * 2, 20)
-                        pseudo_traj_spec_list = self.traj_spec_file_to_pseudo_traj_spec_list(env_diff_traj_data_spec_fpath, pseudo_traj_length)
-                        # Write all but the last line.
-                        # for line in f2.readlines():
-                        for line in pseudo_traj_spec_list:
-                            f.write(line)
-
-        # Build the data entry. It is shared for the entire dataset.
-        data_entry = {}
-        data_entry['modality'] = {}
-
-        self.add_modality_entries(data_entry, modality, camera_name, new_image_shape_hw, seq_length, subset_framenum, num_workers)
-
-        # Build the cacher entry.
-        data_entry['cacher'] = {}
-        data_entry['cacher']['data_root_key'] = 'tartanairv2' # TODO(yoraish): this is disregarded currently and overriden by the data_root_path_override.
-        data_entry['cacher']['subset_framenum'] = subset_framenum
-        data_entry['cacher']['worker_num'] = num_workers
-        # Add the tartanair data root to the config.
-        data_entry['cacher']['data_root_path_override'] = self.tartanair_data_root
-        # Build the transform entry.
-        data_entry['transform'] = {}
-        data_entry['transform']['resize_factor'] = 1.0
-        # Build the dataset entry.
-        data_entry['dataset'] =  None
-
-        # Add the data entry to the config with the key being the path to the frame-enumeration file.
-        config['data'][data_spec_fpath] = data_entry
+        # Check that the environments are valid.
+        if not self.check_env_valid(env):
+            return False
+        # Check that the modalities are valid
+        if not self.check_modality_valid(modality):
+            return False
+        # Check that the difficulty are valid
+        if not self.check_difficulty_valid(difficulty):
+            return False
+        # Check that the camera names are valid
+        if not self.check_camera_valid(camera_name):
+            return False
+
+        # figuring out the combination of modality and camera_name
+        # folderlist consists all the folders that need to be load under each trajectory
+        folderlist = self.compile_modality_and_cameraname(modality, camera_name)
+        # find one folder that's not imu, because imu is not frame-based
+        onemodfolder = None
+        for fl in folderlist:
+            if not fl.endswith('imu'):
+                onemodfolder = fl
+                break
+        if not onemodfolder:
+            print_error("No frame-based modality is available")
+
+        datafile = self.generate_data_file(env, difficulty, trajectory_id, onemodfolder)
+        if not datafile:
+            return False
+
+        # process pose modality seperately here because it is not frame-based 
+        if 'pose' in modality:
+            for camname in camera_name:
+                folderlist.append('pose_' + camname)
+
+        config = self.generate_config_file(datafile, folderlist, new_image_shape_hw, seq_length, 
+                                            subset_framenum, seq_stride, frame_skip, num_workers)
 
-        print(config)
         # Create the data loader from the config.
         trainDataloader = MultiDatasets(config, 
                         'local', 
                         batch= batch_size, 
-                        workernum= num_workers,
+                        workernum= 1,
                         shuffle= shuffle,
                         verbose= verbose)
 
         return trainDataloader
 
-    def traj_spec_file_to_pseudo_traj_spec_list(self, traj_spec_fpath, pseudo_traj_length):
-        '''
-        Read the trajectory specification file and break it down into smaller pseudo-trajectories. This is done to allow for the data cacher to cache the data in small trajectory portions, such that the data is shuffled also when the RAM memory allocation is small.
-        '''
-        pseudo_traj_spec_list = []
-        with open(traj_spec_fpath, 'r') as f:
-            lines = f.readlines()
-
-            # Get the current traj header.
-            traj_header = lines[0]
-            lines = lines[1:]
-
-            # The only thing that we'll change is the number of files, which is the last number in the string.
-            numless_header = traj_header[:traj_header.rfind(' ') + 1]
-
-            for i in range(0, len(lines)):
-                if i % pseudo_traj_length == 0:
-                    # Add a new line. If we have more than traj_len lines left, then add the full traj_len. Otherwise, add the remaining lines.
-                    if i + pseudo_traj_length < len(lines):
-                        pseudo_traj_spec_list.append(numless_header + str(pseudo_traj_length) + '\n')
-
-                    else:
-                        pseudo_traj_spec_list.append(numless_header + str(len(lines) - i) + '\n')
-                
-                # Append the line.
-                pseudo_traj_spec_list.append(lines[i])
-
-        return pseudo_traj_spec_list
-
-    def add_modality_entries(self, data_entry, modality, camera_name, new_image_shape_hw, seq_length, subset_framenum, num_workers):
+    def add_modality_entries(self, data_entry, foldernames, image_shape_hw, seq_length):
 
         '''
         Mutate the data_entry dictionary to add modality entries for all types in the combined modality and camera_name lists.
         Convert a modality list and camera name list to a list of modcamalities. Those are the modality names that remain after combining the modality and camera name lists. For example, if 'image' and 'lidar' is in modality, and 'lcam_front' is in camera_name, then 'rgb_lcam_front', 'lidar' will be in the returned list, as 'lidar' is not a modality that requires a camera name.
         '''
+        # Allow to take in a dictionary for the modality sequence length.
+        # If an integer is given, then use it for all modalities.
+        if type(seq_length) is int:
+            new_seq_length = {mod: seq_length for mod in foldernames}
+        elif type(seq_length) is dict:
+            new_seq_length = {}
+            for foldername in foldernames:
+                modname = foldername.split('_')[0]
+                if modname in seq_length:
+                    new_seq_length[foldername] = seq_length[modname]
+                else:
+                    print_warn("WARNING: No sequence length was given for modality {}. Defaulting to {}.".format(modname, self.modality_default_seq_length[modname]))
+                    new_seq_length[foldername] = self.modality_default_seq_length[modname]
+        else:
+            raise ValueError("seq_length must be an integer or a dictionary.")
 
-        # Create the list of types. Each type is a modality_camera name, if the modality requires a camera name, otherwise it is just the modality.
-        types = []
-        for mod in modality:
-            if mod in ['image', 'depth', 'seg', 'flow', 'pose']:
-                for cam in camera_name:
-                    mod = self.modality_name_remaps[mod]
-                    types.append(mod + '_' + cam)
-            else:
-                types.append(mod)
-
-        for type in types:
-            # Build the modality entry.
-            modality_entry = {}
-            mod = type.split('_')[0]
-
-            # Account for modality names that have `_` in them.
-            if mod == 'imu':
-                mod = '_'.join(type.split('_')[:2])
-
-            if mod in ['rgb', 'depth', 'seg', 'flow']:
-                modality_entry['cacher_size'] = new_image_shape_hw
-            else: # 'lidar', 'imu', 'pose', etc.
-                modality_entry['cacher_size'] = self.modality_default_cacher_size[mod]
-            
-            modality_entry['length'] = seq_length[mod]
-            modality_entry['subset_framenum'] = subset_framenum
-
-            # Add the modality entry to the data entry.
-            data_entry['modality'][type] = modality_entry
-            data_entry['modality'][type]['type'] = type    
-
-
-    def get_available_trajectory_ids(self, env_name, diff):
-            '''
-            Return a list of trajectory ids for the given environment and difficulty.
-            '''
-
-            # Get the path to the env directory.
-            env_dir_path = os.path.join(self.tartanair_data_root, env_name, "Data_" + diff)
-            # Check what's there of the form 'P0..'.
-            traj_ids = []
-            for f in os.listdir(env_dir_path):
-                if f.startswith('P'):
-                    traj_ids.append(f)
-
-            return traj_ids
-
-
-
-
+        if type(image_shape_hw) is list:
+            new_image_shape_hw = {mod: image_shape_hw for mod in foldernames}
+        elif type(image_shape_hw) is dict:
+            new_image_shape_hw = {}
+            for foldername in foldernames:
+                modname = foldername.split('_')[0]
+                if modname in image_shape_hw:
+                    new_image_shape_hw[foldername] = image_shape_hw[modname]
+                else:
+                    print_warn("WARNING: No shape was given for modality {}. Defaulting to {}.".format(modname, self.modality_default_cacher_size[modname]))
+                    new_image_shape_hw[foldername] = self.modality_default_cacher_size[modname]
+        else:
+            raise ValueError("image_shape_hw must be an integer or a dictionary.")
+
+        for folder in foldernames:
+            data_entry[folder] = {} # modality name
+            data_entry[folder][folder] = {} # the key name returned by the dataloader
+            data_entry[folder][folder]['cacher_size'] = new_image_shape_hw[folder]
+            data_entry[folder][folder]['length'] = new_seq_length[folder]
+
+if __name__=="__main__":
+    loader = TartanAirDataLoader('/data/tartanair_v2')
+    datafile = loader.generate_data_file(env=['coalmine'], difficulty=['easy', 'hard'], trajectory_id=[], onemodfolder='seg_rcam_right', breakdown = True)
+
+    loader.generate_config_file(datafile, 
+                                foldernames=['depth_lcam_back','image_lcam_back'], 
+                                image_shape_hw=[640, 640], 
+                                seq_length=1, 
+                                subset_framenum=100, 
+                                seq_stride=1, 
+                                frame_skip=0, 
+                                num_workers=4)
diff --git a/tartanair/dataset.py b/tartanair/dataset.py
index 5140796..e5d3c4e 100644
--- a/tartanair/dataset.py
+++ b/tartanair/dataset.py
@@ -276,8 +276,8 @@ def create_env_entries(self, env):
                 camera_name_to_motions = {}
                 camera_name_to_poses = {}
                 for camera_name in self.camera_names:
-                    # If the camera_name is one of fish or equirct, then use the front camera motions.
-                    if 'fish' in camera_name or 'equirct' in camera_name:
+                    # If the camera_name is one of fish or equirect, then use the front camera motions.
+                    if 'fish' in camera_name or 'equirect' in camera_name:
                         cam_side = 'lcam' if 'lcam' in camera_name else 'rcam'
                         posefile = traj_dir_gp + '/pose_{}_front.txt'.format(cam_side)
 
@@ -706,14 +706,13 @@ def create_env_entries(self, env):
                 # Get the trajectory poses. This is a map from a camera_name to a list of poses.
                 camera_name_to_motions = {}
                 for camera_name in self.camera_names:
-                    # If the camera_name is one of fish or equirct, then use the front camera motions.
-                    if 'fish' in camera_name or 'equirct' in camera_name:
+                    # If the camera_name is one of fish or equirect, then use the front camera motions.
+                    if 'fish' in camera_name or 'equirect' in camera_name:
                         cam_side = 'lcam' if 'lcam' in camera_name else 'rcam'
                         posefile = traj_dir_gp + '/pose_{}_front.txt'.format(cam_side)
 
                     else:
                         posefile = traj_dir_gp + f'/pose_{camera_name}.txt'
-                        
                     poselist = np.loadtxt(posefile).astype(np.float32)
                     
                     traj_poses   = self.pos_quats2SEs(poselist) # From xyz, xyzw format, to SE(3) format (camera in world).
diff --git a/tartanair/download_files.txt b/tartanair/download_files.txt
index f1e9cfe..a4c655d 100644
--- a/tartanair/download_files.txt
+++ b/tartanair/download_files.txt
@@ -1,7548 +1,7548 @@
-AbandonedCable/Data_easy/depth_lcam_back.zip 5.349899186 G
-AbandonedCable/Data_easy/depth_lcam_bottom.zip 3.889445769 G
-AbandonedCable/Data_easy/depth_lcam_equirect.zip 133.757170594 G
-AbandonedCable/Data_easy/depth_lcam_fish.zip 54.222114555 G
-AbandonedCable/Data_easy/depth_lcam_front.zip 5.443678541 G
-AbandonedCable/Data_easy/depth_lcam_left.zip 5.738004555 G
-AbandonedCable/Data_easy/depth_lcam_right.zip 5.780899248 G
-AbandonedCable/Data_easy/depth_lcam_top.zip 3.47153411 G
-AbandonedCable/Data_easy/depth_rcam_back.zip 5.352156732 G
-AbandonedCable/Data_easy/depth_rcam_bottom.zip 3.893435183 G
-AbandonedCable/Data_easy/depth_rcam_equirect.zip 133.775197998 G
-AbandonedCable/Data_easy/depth_rcam_fish.zip 54.225631046 G
-AbandonedCable/Data_easy/depth_rcam_front.zip 5.447437388 G
-AbandonedCable/Data_easy/depth_rcam_left.zip 5.753744421 G
-AbandonedCable/Data_easy/depth_rcam_right.zip 5.76643169 G
-AbandonedCable/Data_easy/depth_rcam_top.zip 3.474415416 G
-AbandonedCable/Data_easy/flow_lcam_front.zip 8.43456661 G
-AbandonedCable/Data_easy/image_lcam_back.zip 13.400718754 G
-AbandonedCable/Data_easy/image_lcam_bottom.zip 15.863814797 G
-AbandonedCable/Data_easy/image_lcam_equirect.zip 63.04157594 G
-AbandonedCable/Data_easy/image_lcam_fish.zip 30.282119177 G
-AbandonedCable/Data_easy/image_lcam_front.zip 15.294338254 G
-AbandonedCable/Data_easy/image_lcam_left.zip 14.731775641 G
-AbandonedCable/Data_easy/image_lcam_right.zip 14.866861303 G
-AbandonedCable/Data_easy/image_lcam_top.zip 10.703561667 G
-AbandonedCable/Data_easy/image_rcam_back.zip 13.404825098 G
-AbandonedCable/Data_easy/image_rcam_bottom.zip 15.862789265 G
-AbandonedCable/Data_easy/image_rcam_equirect.zip 63.043792962 G
-AbandonedCable/Data_easy/image_rcam_fish.zip 30.281609845 G
-AbandonedCable/Data_easy/image_rcam_front.zip 15.293633116 G
-AbandonedCable/Data_easy/image_rcam_left.zip 14.722622818 G
-AbandonedCable/Data_easy/image_rcam_right.zip 14.871776819 G
-AbandonedCable/Data_easy/image_rcam_top.zip 10.709628394 G
-AbandonedCable/Data_easy/imu.zip 0.114660499 G
-AbandonedCable/Data_easy/lidar.zip 12.978241849 G
-AbandonedCable/Data_easy/seg_lcam_back.zip 0.411081878 G
-AbandonedCable/Data_easy/seg_lcam_bottom.zip 0.291264949 G
-AbandonedCable/Data_easy/seg_lcam_equirect.zip 1.60930048 G
-AbandonedCable/Data_easy/seg_lcam_fish.zip 1.014240573 G
-AbandonedCable/Data_easy/seg_lcam_front.zip 0.447973675 G
-AbandonedCable/Data_easy/seg_lcam_left.zip 0.471466745 G
-AbandonedCable/Data_easy/seg_lcam_right.zip 0.476501314 G
-AbandonedCable/Data_easy/seg_lcam_top.zip 0.133550705 G
-AbandonedCable/Data_easy/seg_rcam_back.zip 0.410991414 G
-AbandonedCable/Data_easy/seg_rcam_bottom.zip 0.291351313 G
-AbandonedCable/Data_easy/seg_rcam_equirect.zip 1.610239443 G
-AbandonedCable/Data_easy/seg_rcam_fish.zip 1.014948247 G
-AbandonedCable/Data_easy/seg_rcam_front.zip 0.44843278 G
-AbandonedCable/Data_easy/seg_rcam_left.zip 0.474811108 G
-AbandonedCable/Data_easy/seg_rcam_right.zip 0.473966731 G
-AbandonedCable/Data_easy/seg_rcam_top.zip 0.13363924 G
-AbandonedCable/Data_hard/depth_lcam_back.zip 3.33551468 G
-AbandonedCable/Data_hard/depth_lcam_bottom.zip 2.920258665 G
-AbandonedCable/Data_hard/depth_lcam_equirect.zip 81.830338569 G
-AbandonedCable/Data_hard/depth_lcam_fish.zip 32.856410387 G
-AbandonedCable/Data_hard/depth_lcam_front.zip 3.381246145 G
-AbandonedCable/Data_hard/depth_lcam_left.zip 3.307921501 G
-AbandonedCable/Data_hard/depth_lcam_right.zip 3.270175277 G
-AbandonedCable/Data_hard/depth_lcam_top.zip 2.643571906 G
-AbandonedCable/Data_hard/depth_rcam_back.zip 3.336249163 G
-AbandonedCable/Data_hard/depth_rcam_bottom.zip 2.925979058 G
-AbandonedCable/Data_hard/depth_rcam_equirect.zip 81.836286245 G
-AbandonedCable/Data_hard/depth_rcam_fish.zip 32.859710883 G
-AbandonedCable/Data_hard/depth_rcam_front.zip 3.382135445 G
-AbandonedCable/Data_hard/depth_rcam_left.zip 3.32359625 G
-AbandonedCable/Data_hard/depth_rcam_right.zip 3.252483289 G
-AbandonedCable/Data_hard/depth_rcam_top.zip 2.641213799 G
-AbandonedCable/Data_hard/flow_lcam_front.zip 6.968138082 G
-AbandonedCable/Data_hard/image_lcam_back.zip 9.081356646 G
-AbandonedCable/Data_hard/image_lcam_bottom.zip 10.020730914 G
-AbandonedCable/Data_hard/image_lcam_equirect.zip 39.941156326 G
-AbandonedCable/Data_hard/image_lcam_fish.zip 18.637781575 G
-AbandonedCable/Data_hard/image_lcam_front.zip 9.11147242 G
-AbandonedCable/Data_hard/image_lcam_left.zip 9.186814775 G
-AbandonedCable/Data_hard/image_lcam_right.zip 9.093353315 G
-AbandonedCable/Data_hard/image_lcam_top.zip 7.366965958 G
-AbandonedCable/Data_hard/image_rcam_back.zip 9.086424781 G
-AbandonedCable/Data_hard/image_rcam_bottom.zip 10.038448156 G
-AbandonedCable/Data_hard/image_rcam_equirect.zip 39.952137155 G
-AbandonedCable/Data_hard/image_rcam_fish.zip 18.642733773 G
-AbandonedCable/Data_hard/image_rcam_front.zip 9.1140479 G
-AbandonedCable/Data_hard/image_rcam_left.zip 9.198893645 G
-AbandonedCable/Data_hard/image_rcam_right.zip 9.082239482 G
-AbandonedCable/Data_hard/image_rcam_top.zip 7.357949704 G
-AbandonedCable/Data_hard/imu.zip 0.069643575 G
-AbandonedCable/Data_hard/lidar.zip 7.66431553 G
-AbandonedCable/Data_hard/seg_lcam_back.zip 0.260234443 G
-AbandonedCable/Data_hard/seg_lcam_bottom.zip 0.223830799 G
-AbandonedCable/Data_hard/seg_lcam_equirect.zip 1.029171464 G
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-AbandonedCable/Data_hard/seg_lcam_front.zip 0.272873662 G
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+WaterMillDay/Data_hard/depth_rcam_right.zip 1.461124674 G
+WaterMillDay/Data_hard/depth_rcam_top.zip 1.343249376 G
+WaterMillDay/Data_hard/flow_lcam_front.zip 2.53832179 G
+WaterMillDay/Data_hard/image_lcam_back.zip 3.157560407 G
+WaterMillDay/Data_hard/image_lcam_bottom.zip 4.04615829 G
+WaterMillDay/Data_hard/image_lcam_equirect.zip 15.676404359 G
+WaterMillDay/Data_hard/image_lcam_fish.zip 7.335344051 G
+WaterMillDay/Data_hard/image_lcam_front.zip 3.885897926 G
+WaterMillDay/Data_hard/image_lcam_left.zip 3.584059942 G
+WaterMillDay/Data_hard/image_lcam_right.zip 3.498014991 G
+WaterMillDay/Data_hard/image_lcam_top.zip 2.617567642 G
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+WaterMillDay/Data_hard/image_rcam_bottom.zip 4.047528749 G
+WaterMillDay/Data_hard/image_rcam_equirect.zip 15.672682885 G
+WaterMillDay/Data_hard/image_rcam_fish.zip 7.333206755 G
+WaterMillDay/Data_hard/image_rcam_front.zip 3.884803728 G
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+WaterMillDay/Data_hard/image_rcam_right.zip 3.488732412 G
+WaterMillDay/Data_hard/image_rcam_top.zip 2.615937606 G
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+WaterMillNight/Data_easy/depth_lcam_bottom.zip 2.274849782 G
+WaterMillNight/Data_easy/depth_lcam_equirect.zip 53.153201821 G
+WaterMillNight/Data_easy/depth_lcam_fish.zip 21.724829086 G
+WaterMillNight/Data_easy/depth_lcam_front.zip 3.093245003 G
+WaterMillNight/Data_easy/depth_lcam_left.zip 3.228030786 G
+WaterMillNight/Data_easy/depth_lcam_right.zip 3.239008043 G
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+WaterMillNight/Data_easy/depth_rcam_bottom.zip 2.272364137 G
+WaterMillNight/Data_easy/depth_rcam_equirect.zip 53.155331395 G
+WaterMillNight/Data_easy/depth_rcam_fish.zip 21.725544152 G
+WaterMillNight/Data_easy/depth_rcam_front.zip 3.092595296 G
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+WaterMillNight/Data_easy/image_lcam_bottom.zip 2.238165687 G
+WaterMillNight/Data_easy/image_lcam_equirect.zip 18.454158871 G
+WaterMillNight/Data_easy/image_lcam_fish.zip 8.542108599 G
+WaterMillNight/Data_easy/image_lcam_front.zip 3.129509756 G
+WaterMillNight/Data_easy/image_lcam_left.zip 3.392114853 G
+WaterMillNight/Data_easy/image_lcam_right.zip 3.098023622 G
+WaterMillNight/Data_easy/image_lcam_top.zip 3.597861957 G
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+WaterMillNight/Data_easy/image_rcam_bottom.zip 2.231083394 G
+WaterMillNight/Data_easy/image_rcam_equirect.zip 18.439923552 G
+WaterMillNight/Data_easy/image_rcam_fish.zip 8.536897915 G
+WaterMillNight/Data_easy/image_rcam_front.zip 3.12613476 G
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+WaterMillNight/Data_easy/image_rcam_right.zip 3.09169523 G
+WaterMillNight/Data_easy/image_rcam_top.zip 3.59834029 G
+WaterMillNight/Data_easy/imu.zip 0.045353695 G
+WaterMillNight/Data_easy/lidar.zip 4.629825621 G
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+WaterMillNight/Data_easy/seg_lcam_bottom.zip 0.165949354 G
+WaterMillNight/Data_easy/seg_lcam_equirect.zip 0.999484698 G
+WaterMillNight/Data_easy/seg_lcam_fish.zip 0.570545727 G
+WaterMillNight/Data_easy/seg_lcam_front.zip 0.264839039 G
+WaterMillNight/Data_easy/seg_lcam_left.zip 0.292777017 G
+WaterMillNight/Data_easy/seg_lcam_right.zip 0.27768646 G
+WaterMillNight/Data_easy/seg_lcam_top.zip 0.133410043 G
+WaterMillNight/Data_easy/seg_rcam_back.zip 0.264317751 G
+WaterMillNight/Data_easy/seg_rcam_bottom.zip 0.165118197 G
+WaterMillNight/Data_easy/seg_rcam_equirect.zip 0.998369409 G
+WaterMillNight/Data_easy/seg_rcam_fish.zip 0.569952595 G
+WaterMillNight/Data_easy/seg_rcam_front.zip 0.264482029 G
+WaterMillNight/Data_easy/seg_rcam_left.zip 0.293959381 G
+WaterMillNight/Data_easy/seg_rcam_right.zip 0.27601999 G
+WaterMillNight/Data_easy/seg_rcam_top.zip 0.133935154 G
+WaterMillNight/Data_hard/depth_lcam_back.zip 1.868013327 G
+WaterMillNight/Data_hard/depth_lcam_bottom.zip 1.517695808 G
+WaterMillNight/Data_hard/depth_lcam_equirect.zip 31.412572839 G
+WaterMillNight/Data_hard/depth_lcam_fish.zip 12.891474691 G
+WaterMillNight/Data_hard/depth_lcam_front.zip 1.868774551 G
+WaterMillNight/Data_hard/depth_lcam_left.zip 1.790266005 G
+WaterMillNight/Data_hard/depth_lcam_right.zip 1.797877421 G
+WaterMillNight/Data_hard/depth_lcam_top.zip 1.577243376 G
+WaterMillNight/Data_hard/depth_rcam_back.zip 1.868596111 G
+WaterMillNight/Data_hard/depth_rcam_bottom.zip 1.518860468 G
+WaterMillNight/Data_hard/depth_rcam_equirect.zip 31.414446774 G
+WaterMillNight/Data_hard/depth_rcam_fish.zip 12.892363761 G
+WaterMillNight/Data_hard/depth_rcam_front.zip 1.870104687 G
+WaterMillNight/Data_hard/depth_rcam_left.zip 1.798105519 G
+WaterMillNight/Data_hard/depth_rcam_right.zip 1.793440608 G
+WaterMillNight/Data_hard/depth_rcam_top.zip 1.577238952 G
+WaterMillNight/Data_hard/flow_lcam_front.zip 3.053287192 G
+WaterMillNight/Data_hard/image_lcam_back.zip 1.947517036 G
+WaterMillNight/Data_hard/image_lcam_bottom.zip 1.394350755 G
+WaterMillNight/Data_hard/image_lcam_equirect.zip 10.975812224 G
+WaterMillNight/Data_hard/image_lcam_fish.zip 5.029848852 G
+WaterMillNight/Data_hard/image_lcam_front.zip 1.829000804 G
+WaterMillNight/Data_hard/image_lcam_left.zip 1.853559861 G
+WaterMillNight/Data_hard/image_lcam_right.zip 1.936459298 G
+WaterMillNight/Data_hard/image_lcam_top.zip 2.184272334 G
+WaterMillNight/Data_hard/image_rcam_back.zip 1.948469581 G
+WaterMillNight/Data_hard/image_rcam_bottom.zip 1.392886805 G
+WaterMillNight/Data_hard/image_rcam_equirect.zip 10.979373572 G
+WaterMillNight/Data_hard/image_rcam_fish.zip 5.029573014 G
+WaterMillNight/Data_hard/image_rcam_front.zip 1.827401103 G
+WaterMillNight/Data_hard/image_rcam_left.zip 1.858794449 G
+WaterMillNight/Data_hard/image_rcam_right.zip 1.933246852 G
+WaterMillNight/Data_hard/image_rcam_top.zip 2.185352623 G
+WaterMillNight/Data_hard/imu.zip 0.026994935 G
+WaterMillNight/Data_hard/lidar.zip 2.6267899 G
+WaterMillNight/Data_hard/seg_lcam_back.zip 0.15785726 G
+WaterMillNight/Data_hard/seg_lcam_bottom.zip 0.121203654 G
+WaterMillNight/Data_hard/seg_lcam_equirect.zip 0.612394055 G
+WaterMillNight/Data_hard/seg_lcam_fish.zip 0.341618724 G
+WaterMillNight/Data_hard/seg_lcam_front.zip 0.162231355 G
+WaterMillNight/Data_hard/seg_lcam_left.zip 0.151100983 G
+WaterMillNight/Data_hard/seg_lcam_right.zip 0.147178928 G
+WaterMillNight/Data_hard/seg_lcam_top.zip 0.105781318 G
+WaterMillNight/Data_hard/seg_rcam_back.zip 0.157966031 G
+WaterMillNight/Data_hard/seg_rcam_bottom.zip 0.121437923 G
+WaterMillNight/Data_hard/seg_rcam_equirect.zip 0.61306157 G
+WaterMillNight/Data_hard/seg_rcam_fish.zip 0.341978542 G
+WaterMillNight/Data_hard/seg_rcam_front.zip 0.162494567 G
+WaterMillNight/Data_hard/seg_rcam_left.zip 0.152433276 G
+WaterMillNight/Data_hard/seg_rcam_right.zip 0.146139403 G
+WaterMillNight/Data_hard/seg_rcam_top.zip 0.105840575 G
+WesternDesertTown/Data_easy/depth_lcam_back.zip 5.813273313 G
+WesternDesertTown/Data_easy/depth_lcam_bottom.zip 6.622946944 G
+WesternDesertTown/Data_easy/depth_lcam_equirect.zip 129.491749286 G
+WesternDesertTown/Data_easy/depth_lcam_fish.zip 52.087994326 G
+WesternDesertTown/Data_easy/depth_lcam_front.zip 6.337070739 G
+WesternDesertTown/Data_easy/depth_lcam_left.zip 6.282888967 G
+WesternDesertTown/Data_easy/depth_lcam_right.zip 6.292598463 G
+WesternDesertTown/Data_easy/depth_lcam_top.zip 3.78276734 G
+WesternDesertTown/Data_easy/depth_rcam_back.zip 5.816924811 G
+WesternDesertTown/Data_easy/depth_rcam_bottom.zip 6.631699076 G
+WesternDesertTown/Data_easy/depth_rcam_equirect.zip 129.504105203 G
+WesternDesertTown/Data_easy/depth_rcam_fish.zip 52.091527405 G
+WesternDesertTown/Data_easy/depth_rcam_front.zip 6.341551547 G
+WesternDesertTown/Data_easy/depth_rcam_left.zip 6.324213323 G
+WesternDesertTown/Data_easy/depth_rcam_right.zip 6.254219837 G
+WesternDesertTown/Data_easy/depth_rcam_top.zip 3.783194286 G
+WesternDesertTown/Data_easy/flow_lcam_front.zip 9.793439953 G
+WesternDesertTown/Data_easy/image_lcam_back.zip 10.389829088 G
+WesternDesertTown/Data_easy/image_lcam_bottom.zip 9.871650466 G
+WesternDesertTown/Data_easy/image_lcam_equirect.zip 53.5796654 G
+WesternDesertTown/Data_easy/image_lcam_fish.zip 24.966958967 G
+WesternDesertTown/Data_easy/image_lcam_front.zip 10.671001073 G
+WesternDesertTown/Data_easy/image_lcam_left.zip 10.539336745 G
+WesternDesertTown/Data_easy/image_lcam_right.zip 10.671804225 G
+WesternDesertTown/Data_easy/image_lcam_top.zip 8.311357727 G
+WesternDesertTown/Data_easy/image_rcam_back.zip 10.40660596 G
+WesternDesertTown/Data_easy/image_rcam_bottom.zip 9.876290635 G
+WesternDesertTown/Data_easy/image_rcam_equirect.zip 53.586904255 G
+WesternDesertTown/Data_easy/image_rcam_fish.zip 24.967750233 G
+WesternDesertTown/Data_easy/image_rcam_front.zip 10.668321988 G
+WesternDesertTown/Data_easy/image_rcam_left.zip 10.621180431 G
+WesternDesertTown/Data_easy/image_rcam_right.zip 10.607391191 G
+WesternDesertTown/Data_easy/image_rcam_top.zip 8.309950908 G
+WesternDesertTown/Data_easy/imu.zip 0.111567098 G
+WesternDesertTown/Data_easy/lidar.zip 13.130230607 G
+WesternDesertTown/Data_easy/seg_lcam_back.zip 0.148439368 G
+WesternDesertTown/Data_easy/seg_lcam_bottom.zip 0.096793086 G
+WesternDesertTown/Data_easy/seg_lcam_equirect.zip 0.523701916 G
+WesternDesertTown/Data_easy/seg_lcam_fish.zip 0.419120876 G
+WesternDesertTown/Data_easy/seg_lcam_front.zip 0.154822592 G
+WesternDesertTown/Data_easy/seg_lcam_left.zip 0.159559443 G
+WesternDesertTown/Data_easy/seg_lcam_right.zip 0.162978745 G
+WesternDesertTown/Data_easy/seg_lcam_top.zip 0.057120458 G
+WesternDesertTown/Data_easy/seg_rcam_back.zip 0.148388958 G
+WesternDesertTown/Data_easy/seg_rcam_bottom.zip 0.097233115 G
+WesternDesertTown/Data_easy/seg_rcam_equirect.zip 0.524302657 G
+WesternDesertTown/Data_easy/seg_rcam_fish.zip 0.419412298 G
+WesternDesertTown/Data_easy/seg_rcam_front.zip 0.154994032 G
+WesternDesertTown/Data_easy/seg_rcam_left.zip 0.162023351 G
+WesternDesertTown/Data_easy/seg_rcam_right.zip 0.160626467 G
+WesternDesertTown/Data_easy/seg_rcam_top.zip 0.05700627 G
+WesternDesertTown/Data_hard/depth_lcam_back.zip 3.581502193 G
+WesternDesertTown/Data_hard/depth_lcam_bottom.zip 4.081707706 G
+WesternDesertTown/Data_hard/depth_lcam_equirect.zip 78.049821612 G
+WesternDesertTown/Data_hard/depth_lcam_fish.zip 31.240924587 G
+WesternDesertTown/Data_hard/depth_lcam_front.zip 3.766234264 G
+WesternDesertTown/Data_hard/depth_lcam_left.zip 3.762042959 G
+WesternDesertTown/Data_hard/depth_lcam_right.zip 3.669201549 G
+WesternDesertTown/Data_hard/depth_lcam_top.zip 2.646923782 G
+WesternDesertTown/Data_hard/depth_rcam_back.zip 3.582647456 G
+WesternDesertTown/Data_hard/depth_rcam_bottom.zip 4.080673199 G
+WesternDesertTown/Data_hard/depth_rcam_equirect.zip 78.057237936 G
+WesternDesertTown/Data_hard/depth_rcam_fish.zip 31.240083028 G
+WesternDesertTown/Data_hard/depth_rcam_front.zip 3.763738069 G
+WesternDesertTown/Data_hard/depth_rcam_left.zip 3.779022462 G
+WesternDesertTown/Data_hard/depth_rcam_right.zip 3.648943412 G
+WesternDesertTown/Data_hard/depth_rcam_top.zip 2.647717928 G
+WesternDesertTown/Data_hard/flow_lcam_front.zip 7.671602065 G
+WesternDesertTown/Data_hard/image_lcam_back.zip 6.109049391 G
+WesternDesertTown/Data_hard/image_lcam_bottom.zip 5.608394892 G
+WesternDesertTown/Data_hard/image_lcam_equirect.zip 31.360967972 G
+WesternDesertTown/Data_hard/image_lcam_fish.zip 14.373199783 G
+WesternDesertTown/Data_hard/image_lcam_front.zip 5.881987838 G
+WesternDesertTown/Data_hard/image_lcam_left.zip 5.931262831 G
+WesternDesertTown/Data_hard/image_lcam_right.zip 6.0592456 G
+WesternDesertTown/Data_hard/image_lcam_top.zip 5.236535728 G
+WesternDesertTown/Data_hard/image_rcam_back.zip 6.112282823 G
+WesternDesertTown/Data_hard/image_rcam_bottom.zip 5.617730118 G
+WesternDesertTown/Data_hard/image_rcam_equirect.zip 31.374949325 G
+WesternDesertTown/Data_hard/image_rcam_fish.zip 14.380939461 G
+WesternDesertTown/Data_hard/image_rcam_front.zip 5.887985924 G
+WesternDesertTown/Data_hard/image_rcam_left.zip 5.982615115 G
+WesternDesertTown/Data_hard/image_rcam_right.zip 6.003242953 G
+WesternDesertTown/Data_hard/image_rcam_top.zip 5.243743745 G
+WesternDesertTown/Data_hard/imu.zip 0.067059941 G
+WesternDesertTown/Data_hard/lidar.zip 7.655103731 G
+WesternDesertTown/Data_hard/seg_lcam_back.zip 0.09011898 G
+WesternDesertTown/Data_hard/seg_lcam_bottom.zip 0.070554822 G
+WesternDesertTown/Data_hard/seg_lcam_equirect.zip 0.330510097 G
+WesternDesertTown/Data_hard/seg_lcam_fish.zip 0.252750114 G
+WesternDesertTown/Data_hard/seg_lcam_front.zip 0.088927328 G
+WesternDesertTown/Data_hard/seg_lcam_left.zip 0.094775644 G
+WesternDesertTown/Data_hard/seg_lcam_right.zip 0.091503239 G
+WesternDesertTown/Data_hard/seg_lcam_top.zip 0.056477629 G
+WesternDesertTown/Data_hard/seg_rcam_back.zip 0.089990442 G
+WesternDesertTown/Data_hard/seg_rcam_bottom.zip 0.070632948 G
+WesternDesertTown/Data_hard/seg_rcam_equirect.zip 0.330435 G
+WesternDesertTown/Data_hard/seg_rcam_fish.zip 0.252751366 G
+WesternDesertTown/Data_hard/seg_rcam_front.zip 0.088892175 G
+WesternDesertTown/Data_hard/seg_rcam_left.zip 0.096678479 G
+WesternDesertTown/Data_hard/seg_rcam_right.zip 0.089685183 G
+WesternDesertTown/Data_hard/seg_rcam_top.zip 0.056369289 G
\ No newline at end of file
diff --git a/tartanair/downloader.py b/tartanair/downloader.py
index 5c12ecc..fd5d8a3 100644
--- a/tartanair/downloader.py
+++ b/tartanair/downloader.py
@@ -12,18 +12,8 @@
 import yaml
 
 # Local imports.
-from .tartanair_module import TartanAirModule
+from .tartanair_module import TartanAirModule, print_error, print_highlight, print_warn
 from os.path import isdir, isfile, join
-from botocore.exceptions import NoCredentialsError
-
-def print_error(msg):
-    print(Fore.RED + msg + Style.RESET_ALL)
-
-def print_warn(msg):
-    print(Fore.YELLOW + msg + Style.RESET_ALL)
-
-def print_highlight(msg):
-    print(Fore.GREEN + msg + Style.RESET_ALL)
 
 class AirLabDownloader(object):
     def __init__(self, bucket_name = 'tartanair2') -> None:
@@ -77,6 +67,7 @@ def download(self, filelist, destination_path):
         - str: A message indicating success or failure.
         """
 
+        from botocore.exceptions import NoCredentialsError
         target_filelist = []
         for source_file_name in filelist:
             target_file_name = join(destination_path, source_file_name.replace('/', '_'))
@@ -115,64 +106,8 @@ class TartanAirDownloader(TartanAirModule):
     def __init__(self, tartanair_data_root):
         super().__init__(tartanair_data_root)
 
-        # The modalities that have a camera associated with them and that we'll download the pose file along with.
-        self.cam_modalities = ['image', 'depth', 'seg'] # the modalities that support all camera names
-        self.flow_camlist = ['lcam_front'] # valid camera name for the flow modality
-
         self.downloader = CloudFlareDownloader()
 
-    def check_env_valid(self, envlist):
-        invalid_list = []
-        for env in envlist:
-            if not env in self.env_names:
-                invalid_list.append(env)
-        
-        if len(invalid_list) == 0:
-            return True
-        
-        print_error(f"The following envs are invalid: {invalid_list}")
-        print_warn(f"The available envs are: {self.env_names}")
-        return False
-
-    def check_modality_valid(self, modlist):
-        invalid_list = []
-        for mod in modlist:
-            if not mod in self.modality_names:
-                invalid_list.append(mod)
-        
-        if len(invalid_list) == 0:
-            return True
-        
-        print_error(f"The following modalities are invalid: {invalid_list}")
-        print_warn(f"The available modalities are: {self.modality_names}")
-        return False
-
-    def check_camera_valid(self, camlist):
-        invalid_list = []
-        for cam in camlist:
-            if not cam in self.camera_names:
-                invalid_list.append(cam)
-        
-        if len(invalid_list) == 0:
-            return True
-        
-        print_error(f"The following camera names are invalid: {invalid_list}")
-        print_warn(f"The available camera names are: {self.camera_names}")
-        return False
-
-    def check_difficulty_valid(self, difflist):
-        invalid_list = []
-        for diff in difflist:
-            if not diff in self.difficulty_names:
-                invalid_list.append(diff)
-        
-        if len(invalid_list) == 0:
-            return True
-        
-        print_error(f"The following difficulties are invalid: {invalid_list}")
-        print_warn(f"The available difficulties are: {self.difficulty_names}")
-        return False
-
     def generate_filelist(self, envs, difficulties, modalities, camera_names): 
         '''
         Return a list of zipfiles to be downloaded
@@ -189,21 +124,10 @@ def generate_filelist(self, envs, difficulties, modalities, camera_names):
             envstr = env + '/'
             for difficulty in difficulties:
                 diffstr = envstr + 'Data_' + difficulty + '/'
-                for mod in modalities:
-                    if mod in self.cam_modalities:
-                        for camname in camera_names:
-                            zipfile = diffstr + mod + '_' + camname + '.zip'
-                            zipfilelist.append(zipfile)
-                    elif mod == 'flow':
-                        for camname in camera_names:
-                            if camname in self.flow_camlist:
-                                zipfile = diffstr + mod + '_' + camname + '.zip'
-                                zipfilelist.append(zipfile)
-                            else:
-                                print_warn("Warn: flow modality doesn't have {}! We only have flow for {}".format(camname, self.flow_camlist))
-                    else: # for lidar and imu
-                        zipfile = diffstr + mod + '.zip'
-                        zipfilelist.append(zipfile)
+                folderlist = self.compile_modality_and_cameraname(modalities, camera_names)
+                zipfiles = [diffstr + fl + '.zip' for fl in folderlist]
+                zipfilelist.extend(zipfiles)
+
         return zipfilelist
 
     def doublecheck_filelist(self, filelist, gtfile=''):
diff --git a/tartanair/evaluator.py b/tartanair/evaluator.py
index 0bb17a9..331335e 100644
--- a/tartanair/evaluator.py
+++ b/tartanair/evaluator.py
@@ -44,7 +44,6 @@ def evaluate_traj(self,
             if gt_traj is None:
                 raise ValueError("Please pass a ground truth trajectory or a trajectory specification (env, difficulty, trajectory_id, camera_name) to the evaluation method.")
 
-
         # Make sure that the input is a numpy array with a correct shape.
         if isinstance(est_traj, list):
             print(Fore.CYAN + "Warning: Converting the estimated trajectory (list) to a numpy array." + Style.RESET_ALL)
diff --git a/tartanair/image_resampling/image_sampler b/tartanair/image_resampling/image_sampler
index aca2b45..620f75b 160000
--- a/tartanair/image_resampling/image_sampler
+++ b/tartanair/image_resampling/image_sampler
@@ -1 +1 @@
-Subproject commit aca2b45cc6b9d04a6c9c45bbc9af9311d5f164e3
+Subproject commit 620f75b06b83a43a426e01fa7922fb14a4ccd584
diff --git a/tartanair/lister.py b/tartanair/lister.py
index 19442a9..6e48e5f 100644
--- a/tartanair/lister.py
+++ b/tartanair/lister.py
@@ -11,6 +11,7 @@
 
 # Local imports.
 from .tartanair_module import TartanAirModule
+from os.path import isdir, join
 
 class TartanAirLister(TartanAirModule):
     def __init__(self, tartanair_data_root):
@@ -22,6 +23,7 @@ def list_envs(self, verbose = True):
         '''
         # Get the local environments.
         local_envs = os.listdir(self.tartanair_data_root)
+        local_envs = [pp for pp in local_envs if isdir(join(self.tartanair_data_root, pp))]
 
         # Get the remote environments.
         remote_envs = self.env_names
diff --git a/tartanair/reader.py b/tartanair/reader.py
index 9eec446..6cb2a52 100644
--- a/tartanair/reader.py
+++ b/tartanair/reader.py
@@ -88,7 +88,7 @@ def read_flow(self, flowpath, scale = 1):
         return flownp
 
     def read_dist(self, fn ): # read a depth image and convert it to distance
-        depth = self.read_dep(fn)
+        depth = self.read_depth(fn)
         return self.depth_to_dist(depth)
 
     def depth_to_dist(self, depth):
@@ -109,6 +109,78 @@ def depth_to_dist(self, depth):
         disp = self.conv_matrix * depth
         return disp
 
+
+    ###############################
+    # Data reading, writing, and processing.
+    ###############################
+    def depth_to_dist(self, depth):
+        '''
+        assume: fov = 90 on both x and y axes, and optical center is at image center.
+        '''
+        # import ipdb;ipdb.set_trace()
+        if self.depth_shape is None or \
+            depth.shape != self.depth_shape or \
+            self.conv_matrix is None: # only calculate once if the depth shape has not changed
+            hh, ww = depth.shape
+            f = ww/2
+            wIdx = np.linspace(0, ww - 1, ww, dtype=np.float32) + 0.5 - ww/2 # put the optical center at the middle of the image
+            hIdx = np.linspace(0, hh - 1, hh, dtype=np.float32) + 0.5 - hh/2 # put the optical center at the middle of the image
+            u, v = np.meshgrid(wIdx, hIdx)
+            dd = np.sqrt(u * u + v * v + f * f)/f
+            self.conv_matrix = dd
+        self.depth_shape = depth.shape
+        disp = self.conv_matrix * depth
+        return disp
+
+    # def ocv_read(self, fn ):
+    #     image = cv2.imread(fn, cv2.IMREAD_UNCHANGED)
+    #     assert image is not None, \
+    #         f'{fn} read error. '
+    #     return image
+
+    # def read_rgb(self, fn ):
+    #     return self.ocv_read(fn)
+
+    # def read_dist(self, fn ): # read a depth image and convert it to distance
+    #     depth = self.read_dep(fn)
+    #     return self.depth_to_dist(depth)
+
+    # def read_dep(self, fn ):
+    #     image = self.ocv_read(fn)
+    #     depth = np.squeeze( image.view('<f4'), axis=-1 )
+    #     return depth
+
+    # def vis_dep(self, fn):
+    #     depth = self.read_dep(fn)
+    #     depthvis = np.clip(1/depth * 400, 0, 255).astype(np.uint8)
+    #     return cv2.applyColorMap(depthvis, cv2.COLORMAP_JET)
+
+    # def read_seg(self, fn ):
+    #     image = self.ocv_read(fn)
+    #     return image.astype(np.uint8)
+
+    def ocv_write(self, fn, image ):
+        cv2.imwrite(fn, image)
+    
+    def write_as_is(self, fn, image ):
+        self.ocv_write( fn, image )
+    
+    def write_float_compressed(self, fn, image):
+        assert(image.ndim == 2), 'image.ndim = {}'.format(image.ndim)
+    
+        # Check if the input array is contiguous.
+        if ( not image.flags['C_CONTIGUOUS'] ):
+            image = np.ascontiguousarray(image)
+
+        dummy = np.expand_dims( image, 2 )
+        self.ocv_write( fn, dummy )
+        
+    def write_float_depth(self, fn, image):
+        if len(image.shape) == 2:
+            image = image[...,np.newaxis]
+        depth_rgba = image.view("<u1")
+        self.ocv_write(fn, depth_rgba)
+
 class TartanAirTrajectoryReader(TartanAirModule):
     '''
     Load a trajectory from TartanAir dataset.
diff --git a/tartanair/seg_rgbs.txt b/tartanair/seg_rgbs.txt
new file mode 100644
index 0000000..b210208
--- /dev/null
+++ b/tartanair/seg_rgbs.txt
@@ -0,0 +1,256 @@
+0, 0, 0
+153, 108, 6
+112, 105, 191
+89, 121, 72
+190, 225, 64
+206, 190, 59
+81, 13, 36
+115, 176, 195
+161, 171, 27
+135, 169, 180
+29, 26, 199
+102, 16, 239
+242, 107, 146
+156, 198, 23
+49, 89, 160
+68, 218, 116
+11, 236, 9
+196, 30, 8
+121, 67, 28
+0, 53, 65
+146, 52, 70
+226, 149, 143
+151, 126, 171
+194, 39, 7
+205, 120, 161
+212, 51, 60
+211, 80, 208
+189, 135, 188
+54, 72, 205
+103, 252, 157
+124, 21, 123
+19, 132, 69
+195, 237, 132
+94, 253, 175
+182, 251, 87
+90, 162, 242
+199, 29, 1
+254, 12, 229
+35, 196, 244
+220, 163, 49
+86, 254, 214
+152, 3, 129
+92, 31, 106
+207, 229, 90
+125, 75, 48
+98, 55, 74
+126, 129, 238
+222, 153, 109
+85, 152, 34
+173, 69, 31
+37, 128, 125
+58, 19, 33
+134, 57, 119
+218, 124, 115
+120, 0, 200
+225, 131, 92
+246, 90, 16
+51, 155, 241
+202, 97, 155
+184, 145, 182
+96, 232, 44
+133, 244, 133
+180, 191, 29
+1, 222, 192
+99, 242, 104
+91, 168, 219
+65, 54, 217
+148, 66, 130
+203, 102, 204
+216, 78, 75
+234, 20, 250
+109, 206, 24
+164, 194, 17
+157, 23, 236
+158, 114, 88
+245, 22, 110
+67, 17, 35
+181, 213, 93
+170, 179, 42
+52, 187, 148
+247, 200, 111
+25, 62, 174
+100, 25, 240
+191, 195, 144
+252, 36, 67
+241, 77, 149
+237, 33, 141
+119, 230, 85
+28, 34, 108
+78, 98, 254
+114, 161, 30
+75, 50, 243
+66, 226, 253
+46, 104, 76
+8, 234, 216
+15, 241, 102
+93, 14, 71
+192, 255, 193
+253, 41, 164
+24, 175, 120
+185, 243, 231
+169, 233, 97
+243, 215, 145
+72, 137, 21
+160, 113, 101
+214, 92, 13
+167, 140, 147
+101, 109, 181
+53, 118, 126
+3, 177, 32
+40, 63, 99
+186, 139, 153
+88, 207, 100
+71, 146, 227
+236, 38, 187
+215, 4, 215
+18, 211, 66
+113, 49, 134
+47, 42, 63
+219, 103, 127
+57, 240, 137
+227, 133, 211
+145, 71, 201
+217, 173, 183
+250, 40, 113
+208, 125, 68
+224, 186, 249
+69, 148, 46
+239, 85, 20
+108, 116, 224
+56, 214, 26
+179, 147, 43
+48, 188, 172
+221, 83, 47
+155, 166, 218
+62, 217, 189
+198, 180, 122
+201, 144, 169
+132, 2, 14
+128, 189, 114
+163, 227, 112
+45, 157, 177
+64, 86, 142
+118, 193, 163
+14, 32, 79
+200, 45, 170
+74, 81, 2
+59, 37, 212
+73, 35, 225
+95, 224, 39
+84, 170, 220
+159, 58, 173
+17, 91, 237
+31, 95, 84
+34, 201, 248
+63, 73, 209
+129, 235, 107
+231, 115, 40
+36, 74, 95
+238, 228, 154
+61, 212, 54
+13, 94, 165
+141, 174, 0
+140, 167, 255
+117, 93, 91
+183, 10, 186
+165, 28, 61
+144, 238, 194
+12, 158, 41
+76, 110, 234
+150, 9, 121
+142, 1, 246
+230, 136, 198
+5, 60, 233
+232, 250, 80
+143, 112, 56
+187, 70, 156
+2, 185, 62
+138, 223, 226
+122, 183, 222
+166, 245, 3
+175, 6, 140
+240, 59, 210
+248, 44, 10
+83, 82, 52
+223, 248, 167
+87, 15, 150
+111, 178, 117
+197, 84, 22
+235, 208, 124
+9, 76, 45
+176, 24, 50
+154, 159, 251
+149, 111, 207
+168, 231, 15
+209, 247, 202
+80, 205, 152
+178, 221, 213
+27, 8, 38
+244, 117, 51
+107, 68, 190
+23, 199, 139
+171, 88, 168
+136, 202, 58
+6, 46, 86
+105, 127, 176
+174, 249, 197
+172, 172, 138
+228, 142, 81
+7, 204, 185
+22, 61, 247
+233, 100, 78
+127, 65, 105
+33, 87, 158
+139, 156, 252
+42, 7, 136
+20, 99, 179
+79, 150, 223
+131, 182, 184
+110, 123, 37
+60, 138, 96
+210, 96, 94
+123, 48, 18
+137, 197, 162
+188, 18, 5
+39, 219, 151
+204, 143, 135
+249, 79, 73
+77, 64, 178
+41, 246, 77
+16, 154, 4
+116, 134, 19
+4, 122, 235
+177, 106, 230
+21, 119, 12
+104, 5, 98
+50, 130, 53
+30, 192, 25
+26, 165, 166
+10, 160, 82
+106, 43, 131
+44, 216, 103
+255, 101, 221
+32, 151, 196
+213, 220, 89
+70, 209, 228
+97, 184, 83
+82, 239, 232
+251, 164, 128
+193, 11, 245
+38, 27, 159
+229, 141, 203
+130, 56, 55
+147, 210, 11
+162, 203, 118
+255, 255, 255
\ No newline at end of file
diff --git a/tartanair/tartanair.py b/tartanair/tartanair.py
index dd450d2..e547bea 100644
--- a/tartanair/tartanair.py
+++ b/tartanair/tartanair.py
@@ -8,7 +8,7 @@
 from .evaluator import TartanAirEvaluator
 from .reader import TartanAirTrajectoryReader
 from .dataloader import TartanAirDataLoader
-from .random_accessor import TartanAirRandomAccessor
+# from .random_accessor import TartanAirRandomAccessor
 
 print("TartanAir toolbox initialized.")
 
@@ -41,6 +41,7 @@ def init(tartanair_root):
     # If a token is provided, use it. Otherwise, let the downloader use self.azure_token from the parent class.
     downloader = TartanAirDownloader(tartanair_data_root)
 
+    # TODO: 
     global dataset
     dataset = TartanAirDataset(tartanair_data_root)
 
@@ -50,29 +51,34 @@ def init(tartanair_root):
     except:
         print("Could not initialize customizer.")
 
+    # TODO: 
     global lister
     lister = TartanAirLister(tartanair_data_root)
 
+    # TODO: 
     global visualizer
     visualizer = TartanAirVisualizer(tartanair_data_root)
 
     global iterator
     iterator = TartanAirIterator(tartanair_data_root)
 
+    # TODO: 
     global traj_reader
     traj_reader = TartanAirTrajectoryReader(tartanair_data_root)
 
+    # TODO: 
     global evaluator
     evaluator = TartanAirEvaluator(tartanair_data_root)
 
-    global dataloader
-    dataloader = TartanAirDataLoader(tartanair_data_root)
+    global data_loader
+    data_loader = TartanAirDataLoader(tartanair_data_root)
 
+    # TODO: 
     global slowloader
     slowloader = TartanAirSlowLoaderCreator(tartanair_data_root)
 
-    global random_accessor
-    random_accessor = TartanAirRandomAccessor(tartanair_data_root, trajectory_reader=traj_reader)
+    # global random_accessor
+    # random_accessor = TartanAirRandomAccessor(tartanair_data_root, trajectory_reader=traj_reader)
 
     global is_init 
     is_init = True
@@ -148,8 +154,8 @@ def dataloader(env,
             modality = [], 
             camera_name = [], 
             new_image_shape_hw = [640, 640], 
-            subset_framenum = 360, 
             seq_length = 1, 
+            subset_framenum = 360, 
             seq_stride = 1, 
             frame_skip = 0, 
             batch_size = 8, 
@@ -193,16 +199,16 @@ def dataloader(env,
     :param verbose: Whether to print information regarding memory usage and trajectory loading. If empty, no verbose information will be printed.
     :type verbose: bool
     """
-    global dataloader
+    global data_loader
     check_init()
-    return dataloader.get_data_cacher(env = env, 
+    return data_loader.get_data_cacher(env = env, 
             difficulty = difficulty, 
             trajectory_id = trajectory_id, 
             modality = modality, 
             camera_name = camera_name, 
             new_image_shape_hw = new_image_shape_hw, 
-            subset_framenum = subset_framenum, 
             seq_length = seq_length, 
+            subset_framenum = subset_framenum, 
             seq_stride = seq_stride, 
             frame_skip = frame_skip, 
             batch_size = batch_size, 
@@ -312,7 +318,7 @@ def visualize(env, difficulty, trajectory_id, modality, camera_name = None):
 def check_init():
     global is_init
     if not is_init:
-            raise Exception("TartanAir toolbox not initialized. Please call tartanair.init(tartanair_data_root) first.")
+        raise Exception("TartanAir toolbox not initialized. Please call tartanair.init(tartanair_data_root) first.")
 
 def iterator( env = None, difficulty = None, trajectory_id = None, modality = None, camera_name = None):
     """
@@ -394,7 +400,6 @@ def evaluate_traj(est_traj,
 
     :return: A dictionary containing the evaluation metrics, which include ATE, RPE, the ground truth trajectory, and the estimated trajectory after alignment and scaling if those were requested
     :rtype: dict
-
     """
     global evaluator    
     check_init()
@@ -410,13 +415,13 @@ def evaluate_traj(est_traj,
                                    do_scale, 
                                    do_align)
 
-def get_random_accessor():
-    """
-    Returns the random accessor object for the TartanAir dataset. The random accessor allows for random access to the dataset, and is useful for sampling new correspondences.
+# def get_random_accessor():
+#     """
+#     Returns the random accessor object for the TartanAir dataset. The random accessor allows for random access to the dataset, and is useful for sampling new correspondences.
 
-    :return: The random accessor object.
-    :rtype: TartanAirRandomAccessor
-    """
-    global random_accessor
-    check_init()
-    return random_accessor
+#     :return: The random accessor object.
+#     :rtype: TartanAirRandomAccessor
+#     """
+#     global random_accessor
+#     check_init()
+#     return random_accessor
diff --git a/tartanair/tartanair_module.py b/tartanair/tartanair_module.py
index a6622a7..6efba8f 100644
--- a/tartanair/tartanair_module.py
+++ b/tartanair/tartanair_module.py
@@ -5,6 +5,16 @@
 
 # General imports.
 import os
+from colorama import Fore, Style
+
+def print_error(msg):
+    print(Fore.RED + msg + Style.RESET_ALL)
+
+def print_warn(msg):
+    print(Fore.YELLOW + msg + Style.RESET_ALL)
+
+def print_highlight(msg):
+    print(Fore.GREEN + msg + Style.RESET_ALL)
 
 class TartanAirModule():
     '''
@@ -36,7 +46,11 @@ def __init__(self, tartanair_data_root):
 
         self.camera_directions = ["front", "right", "back", "left", "top", "bottom"]
 
-        self.modality_names = ['image', 'depth', 'seg', 'imu', 'lidar', 'flow']
+        self.modality_names = ['image', 'depth', 'seg', 'imu', 'lidar', 'flow', 'pose']
+
+        self.cam_modalities = ['image', 'depth', 'seg'] # the modalities that support all camera names
+
+        self.flow_camlist = ['lcam_front'] # valid camera name for the flow modality
 
         self.env_names = [
             'AbandonedCable', 
@@ -116,3 +130,104 @@ def __init__(self, tartanair_data_root):
         ]
 
         self.difficulty_names = ['easy', 'hard']
+
+    ###############################
+    # Data enumeration.
+    ###############################
+    def enumerate_trajs(self, data_folders = ['Data_easy','Data_hard']):
+        '''
+        Return a dict:
+            res['env0']: ['Data_easy/P000', 'Data_easy/P001', ...], 
+            res['env1']: ['Data_easy/P000', 'Data_easy/P001', ...], 
+        '''
+        env_folders = os.listdir(self.tartanair_data_root)    
+        env_folders = [ee for ee in env_folders if os.path.isdir(os.path.join(self.tartanair_data_root, ee))]
+        env_folders.sort()
+        trajlist = {}
+        for env_folder in env_folders:
+            env_dir = os.path.join(self.tartanair_data_root, env_folder)
+            trajlist[env_folder] = []
+            for data_folder in data_folders:
+                datapath = os.path.join(env_dir, data_folder)
+                if not os.path.isdir(datapath):
+                    continue
+
+                trajfolders = os.listdir(datapath)
+                trajfolders = [ os.path.join(data_folder, tf) for tf in trajfolders if tf[0]=='P' ]
+                trajfolders.sort()
+                trajlist[env_folder].extend(trajfolders)
+        return trajlist
+
+    def check_env_valid(self, envlist):
+        invalid_list = []
+        for env in envlist:
+            if not env in self.env_names:
+                invalid_list.append(env)
+        
+        if len(invalid_list) == 0:
+            return True
+        
+        print_error(f"The following envs are invalid: {invalid_list}")
+        print_warn(f"The available envs are: {self.env_names}")
+        return False
+
+    def check_modality_valid(self, modlist):
+        invalid_list = []
+        for mod in modlist:
+            if not mod in self.modality_names:
+                invalid_list.append(mod)
+        
+        if len(invalid_list) == 0:
+            return True
+        
+        print_error(f"The following modalities are invalid: {invalid_list}")
+        print_warn(f"The available modalities are: {self.modality_names}")
+        return False
+
+    def check_camera_valid(self, camlist):
+        invalid_list = []
+        for cam in camlist:
+            if not cam in self.camera_names:
+                invalid_list.append(cam)
+        
+        if len(invalid_list) == 0:
+            return True
+        
+        print_error(f"The following camera names are invalid: {invalid_list}")
+        print_warn(f"The available camera names are: {self.camera_names}")
+        return False
+
+    def check_difficulty_valid(self, difflist):
+        invalid_list = []
+        for diff in difflist:
+            if not diff in self.difficulty_names:
+                invalid_list.append(diff)
+        
+        if len(invalid_list) == 0:
+            return True
+        
+        print_error(f"The following difficulties are invalid: {invalid_list}")
+        print_warn(f"The available difficulties are: {self.difficulty_names}")
+        return False
+
+    def compile_modality_and_cameraname(self, modalities, camera_names):
+        folderlist = []
+        for mod in modalities:
+            if mod in self.cam_modalities:
+                for camname in camera_names:
+                    folderstr =  mod + '_' + camname 
+                    folderlist.append(folderstr)
+            elif mod == 'flow':
+                for camname in camera_names:
+                    if camname in self.flow_camlist:
+                        folderstr =  mod + '_' + camname 
+                        folderlist.append(folderstr)
+                    else:
+                        print_warn("Warn: flow modality doesn't have {}! We only have flow for {}".format(camname, self.flow_camlist))
+            elif mod == 'lidar' or mod == 'imu': # for lidar and imu
+                folderstr = mod
+                folderlist.append(folderstr)
+            else:
+                print_warn("Warn: note modality {} needs to be processed separately".format(mod))
+                
+        return folderlist
diff --git a/tartanair/visualizer.py b/tartanair/visualizer.py
index 9ed8886..c0ffee8 100644
--- a/tartanair/visualizer.py
+++ b/tartanair/visualizer.py
@@ -14,12 +14,15 @@
 from .tartanair_module import TartanAirModule
 from .iterator import TartanAirIterator
 
+_CURRENT_PATH = os.path.dirname(os.path.realpath(__file__))
+
 class TartanAirVisualizer(TartanAirModule):
     def __init__(self, tartanair_data_root, azure_token = None):
         super().__init__(tartanair_data_root)
 
         # Modality mapped to a reader.
         self.modality_to_vis_func = {'image': self.visimage, 'depth': self.visdepth, 'seg': self.visseg}
+        self.seg_colors = np.loadtxt(_CURRENT_PATH + '/seg_rgbs.txt', delimiter=',',dtype=np.uint8)
 
     def visualize(self, env, difficulty = ['easy'], trajectory_id = ['P000'], modality = [], camera_name = []):
         """
@@ -100,7 +103,7 @@ def visualize(self, env, difficulty = ['easy'], trajectory_id = ['P000'], modali
             cv2.imshow(window_name, black_image)
 
             # Wait for the user to press a key.
-            cv2.waitKey(5)
+            cv2.waitKey(10)
 
 
     def visimage(self, image):
@@ -117,12 +120,17 @@ def visimage(self, image):
 
 
     def visdepth(self, depth):
-        depthvis = np.clip(400/depth ,0 ,255)
+        depthvis = np.clip(200/depth ,0 ,255)
         depthvis = depthvis.astype(np.uint8)
         depthvis = cv2.applyColorMap(depthvis, cv2.COLORMAP_JET)
-
+        depthvis = cv2.cvtColor(depthvis, cv2.COLOR_RGB2BGR)
         return depthvis
 
 
     def visseg(self, seg):
-        return seg
\ No newline at end of file
+        segvis = np.zeros(seg.shape+(3,), dtype=np.uint8)
+
+        segvis = self.seg_colors[ seg, : ]
+        segvis = segvis.reshape( seg.shape+(3,) )
+
+        return segvis