First pass at linting changes based on ruff

caliscope/__init__.py

@@ -40,9 +40,9 @@
     # default to storing projects in user/__package_name__
     USER_SETTINGS = {"recent_projects":[],
                      "last_project_parent":str(__user_dir__) # default initially to home...this will be where the 'New' folder dialog starts
-                     } 
+                     }
+
 
-
     with open(__settings_path__, "a") as f:
         rtoml.dump(USER_SETTINGS, f)
 
@@ -68,9 +68,9 @@
     with open(test_file, 'w') as f:
         f.write('Test')
     print(f"Test file created at: {test_file}")
-    
-    
+
+
     import os
     parent = r'C:\Users\Mac Prible\AppData\Local'
     print(os.path.exists(parent))
-    print(os.listdir(parent))
+    print(os.listdir(parent))

caliscope/calibration/capture_volume/capture_volume.py

@@ -31,7 +31,7 @@ class CaptureVolume:
 
     def __post__init__():
         logger.info("Creating capture volume from estimated camera array and stereotriangulated points...")
-        
+
     def _save(self, directory: Path, descriptor: str = None):
         if descriptor is None:
             pkl_name = "capture_volume_stage_" + str(self.stage) + ".pkl"
@@ -80,7 +80,7 @@ def get_rmse_summary(self):
                 rmse_string+=f"    {key: >9}: {round(float(value),2)}\n"
 
         return rmse_string
-        
+
     def get_xy_reprojection_error(self):
         vectorized_params = self.get_vectorized_params()
         error = xy_reprojection_error(vectorized_params, self)
@@ -195,7 +195,7 @@ def xy_reprojection_error(current_param_estimates, capture_volume: CaptureVolume
     # iterate across cameras...while this injects a loop in the residual function
     # it should scale linearly with the number of cameras...a tradeoff for stable
     # and explicit calculations...
-    
+
     for port, cam in capture_volume.camera_array.cameras.items():
         cam_points = np.where(capture_volume.point_estimates.camera_indices == port)
         object_points = points_3d_and_2d[cam_points][:, 1:4]
@@ -250,9 +250,9 @@ def rms_reproj_error(xy_reproj_error, camera_indices):
 
 #     camera_array = get_camera_array(config)
 #     point_estimates = load_point_estimates(config)
-    
-#     capture_volume = CaptureVolume(camera_array, point_estimates)    
+
+#     capture_volume = CaptureVolume(camera_array, point_estimates)
 #     capture_volume.stage = config["capture_volume"]["stage"]
 #     # capture_volume.origin_sync_index = config["capture_volume"]["origin_sync_index"]
 
-#     return capture_volume
+#     return capture_volume

caliscope/calibration/capture_volume/helper_functions/get_point_estimates.py

@@ -84,11 +84,11 @@ def get_point_estimates(camera_array:CameraArray, point_data_path: Path) -> Poin
     data structured in a way that is amenable to bundle adjustment
     """
 
-    
+
     logger.info("Creating point history object based on camera_array and stereotriangulated_table")
     stereotriangulated_points = get_stereotriangulated_table(camera_array, point_data_path)
-    
-    
+
+
     points_3d_df = get_points_3d_df(stereotriangulated_points)
     merged_point_data = get_merged_2d_3d(stereotriangulated_points)
 

caliscope/calibration/capture_volume/point_estimates.py

@@ -6,14 +6,11 @@
 import caliscope.logger
 logger = caliscope.logger.get(__name__)
 
-from pathlib import Path
 
 from scipy.sparse import lil_matrix
 
-import pandas as pd
 import numpy as np
 from dataclasses import dataclass
-from caliscope.calibration.capture_volume.helper_functions.get_stereotriangulated_table import get_stereotriangulated_table
 
 CAMERA_PARAM_COUNT = 6  # this will evolve when moving from extrinsic to intrinsic
 
@@ -24,7 +21,7 @@ class PointEstimates:
     Initialized from triangulated_points.csv to provide the formatting of data required for bundle adjustment
     "full" is used here because there is currently a method to filter the data based on reprojection error
     Not sure if it will be used going forward, but it remains here if so.
-    """ 
+    """
 
     sync_indices: np.ndarray  # the sync_index from when the image was taken
     camera_indices: np.ndarray  # camera id associated with the img point
@@ -41,7 +38,7 @@ def __post_init__(self):
         self.point_id = self.point_id.astype(np.uint16)
         self.img = self.img.astype(np.float64)
         self.obj_indices = self.obj_indices.astype(np.int32)
-        self.obj = self.obj.astype(np.float64)    
+        self.obj = self.obj.astype(np.float64)
 
     @property
     def n_cameras(self):
@@ -84,15 +81,15 @@ def update_obj_xyz(self, least_sq_result_x):
         Provided with the least_squares estimate of the best fit of model parameters (including camera 6DoF)
         parse out the x,y,z object positions and update self.obj
         """
-        
+
         xyz = least_sq_result_x[self.n_cameras * CAMERA_PARAM_COUNT :]
         xyz = xyz.reshape(-1, 3)
 
         self.obj = xyz
-        
-        
-        
- 
+
+
+
+
 def load_point_estimates(config:dict)->PointEstimates:
     point_estimates_dict = config["point_estimates"]