STY: Avoid using package name for variable or parameter names

README.md

@@ -33,10 +33,10 @@ pip install .
 from trx.io import load, save
 
 # Load a tractogram (supports .trx, .trk, .tck, .vtk, .fib, .dpy)
-trx = load("tractogram.trx")
+tgm = load("tractogram.trx")
 
 # Save to a different format
-save(trx, "output.trk")
+save(tgm, "output.trk")
 ```
 
 ### Command-Line Interface

docs/source/dev.md

@@ -251,7 +251,7 @@ def load(filename, reference=None):
     Examples
     --------
     >>> from trx.io import load
-    >>> trx = load("tractogram.trx")
+    >>> tgm = load("tractogram.trx")
     """
     pass
 ```

examples/plot_dps_dpv.py

@@ -52,22 +52,22 @@
 trx_path = os.path.join(trx_home, "gold_standard", "gs.trx")
 
 # Load the TRX file
-trx = load(trx_path)
+tgm = load(trx_path)
 
-print(f"Loaded TRX with {len(trx)} streamlines")
-print(f"Total vertices: {trx.header['NB_VERTICES']}")
+print(f"Loaded TRX with {len(tgm)} streamlines")
+print(f"Total vertices: {tgm.header['NB_VERTICES']}")
 
 # %%
 # Exploring Data Per Vertex (dpv)
 # -------------------------------
 #
 # Let's see what dpv data is available.
 
-print("Data Per Vertex keys:", list(trx.data_per_vertex.keys()))
+print("Data Per Vertex keys:", list(tgm.data_per_vertex.keys()))
 
 # Examine each dpv field
-for key in trx.data_per_vertex:
-    data = trx.data_per_vertex[key]
+for key in tgm.data_per_vertex:
+    data = tgm.data_per_vertex[key]
     print(f"\n  {key}:")
     print(f"    Shape: {data._data.shape}")
     print(f"    Dtype: {data._data.dtype}")
@@ -80,9 +80,9 @@
 # The dpv data is organized to match the streamlines. You can access
 # the dpv values for a specific streamline using the same indices.
 
-if len(trx.data_per_vertex) > 0:
-    first_dpv_key = list(trx.data_per_vertex.keys())[0]
-    dpv_data = trx.data_per_vertex[first_dpv_key]
+if len(tgm.data_per_vertex) > 0:
+    first_dpv_key = list(tgm.data_per_vertex.keys())[0]
+    dpv_data = tgm.data_per_vertex[first_dpv_key]
 
     # Get dpv values for the first streamline
     first_streamline_dpv = dpv_data[0]
@@ -96,11 +96,11 @@
 #
 # Now let's examine the dps data.
 
-print("Data Per Streamline keys:", list(trx.data_per_streamline.keys()))
+print("Data Per Streamline keys:", list(tgm.data_per_streamline.keys()))
 
 # Examine each dps field
-for key in trx.data_per_streamline:
-    data = trx.data_per_streamline[key]
+for key in tgm.data_per_streamline:
+    data = tgm.data_per_streamline[key]
     print(f"\n  {key}:")
     print(f"    Shape: {data.shape}")
     print(f"    Dtype: {data.dtype}")
@@ -113,10 +113,10 @@
 # A common use case is filtering streamlines based on dps values.
 # For example, selecting streamlines with high FA values.
 
-if len(trx.data_per_streamline) > 0:
+if len(tgm.data_per_streamline) > 0:
     # Use the first dps key for demonstration
-    first_dps_key = list(trx.data_per_streamline.keys())[0]
-    dps_data = trx.data_per_streamline[first_dps_key]
+    first_dps_key = list(tgm.data_per_streamline.keys())[0]
+    dps_data = tgm.data_per_streamline[first_dps_key]
 
     # Calculate some statistics
     print(f"\nStatistics for '{first_dps_key}':")
@@ -156,14 +156,14 @@
 print("\nDemonstrating multi-dimensional data:")
 
 # Check for any multi-dimensional dpv
-for key in trx.data_per_vertex:
-    data = trx.data_per_vertex[key]
+for key in tgm.data_per_vertex:
+    data = tgm.data_per_vertex[key]
     if len(data._data.shape) > 1 and data._data.shape[1] > 1:
         print(f"  {key}: {data._data.shape[1]}D data per vertex")
 
 # Check for any multi-dimensional dps
-for key in trx.data_per_streamline:
-    data = trx.data_per_streamline[key]
+for key in tgm.data_per_streamline:
+    data = tgm.data_per_streamline[key]
     if len(data.shape) > 1 and data.shape[1] > 1:
         print(f"  {key}: {data.shape[1]}D data per streamline")
 
@@ -177,23 +177,23 @@
 
 # Get vertex counts for first few streamlines
 print("\nVertex distribution for first 5 streamlines:")
-for i in range(min(5, len(trx))):
-    streamline = trx.streamlines[i]
+for i in range(min(5, len(tgm))):
+    streamline = tgm.streamlines[i]
     print(f"  Streamline {i}: {len(streamline)} vertices")
 
 # Total vertices should match
-total_from_streamlines = sum(len(trx.streamlines[i]) for i in range(len(trx)))
+total_from_streamlines = sum(len(tgm.streamlines[i]) for i in range(len(tgm)))
 print(f"\nTotal vertices from streamlines: {total_from_streamlines}")
-print(f"Total vertices in header: {trx.header['NB_VERTICES']}")
+print(f"Total vertices in header: {tgm.header['NB_VERTICES']}")
 
 # %%
 # Summary
 # -------
 #
 # In this tutorial, you learned how to:
 #
-# - Access dpv data using ``trx.data_per_vertex[key]``
-# - Access dps data using ``trx.data_per_streamline[key]``
+# - Access dpv data using ``tgm.data_per_vertex[key]``
+# - Access dps data using ``tgm.data_per_streamline[key]``
 # - Understand the shape conventions for scalar and vector data
 # - Use metadata for statistical analysis
 # - Understand the file structure for dpv and dps

examples/plot_groups.py

@@ -48,9 +48,9 @@
 trx_path = os.path.join(trx_home, "gold_standard", "gs.trx")
 
 # Load the TRX file
-trx = load(trx_path)
+tgm = load(trx_path)
 
-print(f"Loaded TRX with {len(trx)} streamlines")
+print(f"Loaded TRX with {len(tgm)} streamlines")
 
 # %%
 # Accessing groups
@@ -59,15 +59,15 @@
 # Groups are stored as a dictionary where keys are group names and values
 # are numpy arrays of streamline indices.
 
-print(f"Available groups: {list(trx.groups.keys())}")
+print(f"Available groups: {list(tgm.groups.keys())}")
 
 # Check the number of groups
-print(f"Number of groups: {len(trx.groups)}")
+print(f"Number of groups: {len(tgm.groups)}")
 
 # %%
 # Let's examine the groups in more detail:
 
-for group_name, indices in trx.groups.items():
+for group_name, indices in tgm.groups.items():
     print(f"  {group_name}: {len(indices)} streamlines")
 
 # %%
@@ -77,16 +77,16 @@
 # You can extract all streamlines belonging to a specific group using
 # the ``get_group()`` method.
 
-if len(trx.groups) > 0:
+if len(tgm.groups) > 0:
     # Get the first group name
-    first_group = list(trx.groups.keys())[0]
+    first_group = list(tgm.groups.keys())[0]
 
     # Extract the group as a new TrxFile
-    group_trx = trx.get_group(first_group)
-    print(f"Extracted group '{first_group}' with {len(group_trx)} streamlines")
+    group_tgm = tgm.get_group(first_group)
+    print(f"Extracted group '{first_group}' with {len(group_tgm)} streamlines")
 
     # You can also access the raw indices
-    group_indices = trx.groups[first_group]
+    group_indices = tgm.groups[first_group]
     print(f"Raw indices (first 10): {group_indices[:10]}")
 else:
     print("No groups available in this file")
@@ -98,12 +98,12 @@
 # You can use group indices to select streamlines directly with the
 # ``select()`` method.
 
-if len(trx.groups) > 0:
-    first_group = list(trx.groups.keys())[0]
-    indices = trx.groups[first_group]
+if len(tgm.groups) > 0:
+    first_group = list(tgm.groups.keys())[0]
+    indices = tgm.groups[first_group]
 
     # Select streamlines using indices
-    selected = trx.select(indices[:5])  # Select first 5 from the group
+    selected = tgm.select(indices[:5])  # Select first 5 from the group
     print(f"Selected {len(selected)} streamlines from group '{first_group}'")
 
 # %%
@@ -114,11 +114,11 @@
 # This is useful for storing group-level statistics like mean FA, volume,
 # or color codes.
 
-print(f"Data per group keys: {list(trx.data_per_group.keys())}")
+print(f"Data per group keys: {list(tgm.data_per_group.keys())}")
 
 # Check what metadata is available for each group
-for group_name in trx.data_per_group:
-    dpg_keys = list(trx.data_per_group[group_name].keys())
+for group_name in tgm.data_per_group:
+    dpg_keys = list(tgm.data_per_group[group_name].keys())
     print(f"  {group_name}: {dpg_keys}")
 
 # %%
@@ -169,18 +169,18 @@
 # A common workflow is to filter streamlines based on group membership
 # and then analyze or visualize specific bundles.
 
-if len(trx.groups) > 0:
+if len(tgm.groups) > 0:
     # Get all group names
-    group_names = list(trx.groups.keys())
+    group_names = list(tgm.groups.keys())
 
     # Report statistics for each group
     print("Group statistics:")
     for group_name in group_names:
-        group_trx = trx.get_group(group_name)
-        total_points = len(group_trx.streamlines._data)
-        avg_length = total_points / len(group_trx) if len(group_trx) > 0 else 0
+        group_tgm = tgm.get_group(group_name)
+        total_points = len(group_tgm.streamlines._data)
+        avg_length = total_points / len(group_tgm) if len(group_tgm) > 0 else 0
         print(f"  {group_name}:")
-        print(f"    - Streamlines: {len(group_trx)}")
+        print(f"    - Streamlines: {len(group_tgm)}")
         print(f"    - Total points: {total_points}")
         print(f"    - Avg points per streamline: {avg_length:.1f}")
 
@@ -190,7 +190,7 @@
 #
 # In this tutorial, you learned how to:
 #
-# - Access groups using ``trx.groups``
+# - Access groups using ``tgm.groups``
 # - Extract group streamlines using ``get_group()``
 # - Work with ``data_per_group`` (dpg) metadata
 # - Understand that groups can overlap

examples/plot_read_write_trx.py

@@ -36,7 +36,7 @@
 trx_path = os.path.join(trx_home, "gold_standard", "gs.trx")
 
 # Load the TRX file
-trx = load(trx_path)
+tgm = load(trx_path)
 
 print("TRX file loaded successfully!")
 
@@ -48,16 +48,16 @@
 # Let's look at what's inside our loaded file.
 
 # Print a summary of the TRX file
-print(trx)
+print(tgm)
 
 # %%
 # The header contains essential metadata about the tractogram:
 
 print("Header information:")
-print(f"  Number of streamlines: {trx.header['NB_STREAMLINES']}")
-print(f"  Number of vertices: {trx.header['NB_VERTICES']}")
-print(f"  Image dimensions: {trx.header['DIMENSIONS']}")
-print(f"  Voxel to RASMM affine:\n{trx.header['VOXEL_TO_RASMM']}")
+print(f"  Number of streamlines: {tgm.header['NB_STREAMLINES']}")
+print(f"  Number of vertices: {tgm.header['NB_VERTICES']}")
+print(f"  Image dimensions: {tgm.header['DIMENSIONS']}")
+print(f"  Voxel to RASMM affine:\n{tgm.header['VOXEL_TO_RASMM']}")
 
 # %%
 # Accessing streamlines
@@ -66,11 +66,11 @@
 # Streamlines are the core data in a TRX file. Each streamline is a sequence
 # of 3D points representing a fiber tract in the brain.
 
-print(f"Number of streamlines: {len(trx)}")
-print(f"Total number of vertices: {len(trx.streamlines._data)}")
+print(f"Number of streamlines: {len(tgm)}")
+print(f"Total number of vertices: {len(tgm.streamlines._data)}")
 
 # Access the first streamline
-first_streamline = trx.streamlines[0]
+first_streamline = tgm.streamlines[0]
 print(f"\nFirst streamline has {len(first_streamline)} points")
 print(f"First 3 points of the first streamline:\n{first_streamline[:3]}")
 
@@ -80,9 +80,9 @@
 #
 # TRX files can contain additional data per vertex (dpv) and per streamline (dps).
 
-print("Data per vertex (dpv) keys:", list(trx.data_per_vertex.keys()))
-print("Data per streamline (dps) keys:", list(trx.data_per_streamline.keys()))
-print("Groups:", list(trx.groups.keys()))
+print("Data per vertex (dpv) keys:", list(tgm.data_per_vertex.keys()))
+print("Data per streamline (dps) keys:", list(tgm.data_per_streamline.keys()))
+print("Groups:", list(tgm.groups.keys()))
 
 # %%
 # Selecting a subset of streamlines
@@ -91,13 +91,13 @@
 # You can easily select a subset of streamlines using indices or slicing.
 
 # Select first 5 streamlines
-subset = trx[:5]
+subset = tgm[:5]
 print(f"Subset has {len(subset)} streamlines")
 
 # Select specific streamlines by indices (ensure indices are valid)
-max_idx = len(trx) - 1
+max_idx = len(tgm) - 1
 indices = [0, min(2, max_idx), min(5, max_idx)]
-selected = trx.select(indices)
+selected = tgm.select(indices)
 print(f"Selected {len(selected)} streamlines")
 
 # %%
@@ -110,7 +110,7 @@
 with tempfile.TemporaryDirectory() as tmpdir:
     # Save as TRX file (zip archive)
     output_path = os.path.join(tmpdir, "output.trx")
-    save(trx, output_path)
+    save(tgm, output_path)
     print(f"Saved TRX file to: {output_path}")
     print(f"File size: {os.path.getsize(output_path)} bytes")
 
@@ -126,10 +126,10 @@
 # preserving the spatial reference information.
 
 # Create a deepcopy of the loaded TRX file
-trx_copy = trx.deepcopy()
+tgm_copy = tgm.deepcopy()
 
-print(f"Created copy with {len(trx_copy)} streamlines")
-print(f"Header preserved: DIMENSIONS = {trx_copy.header['DIMENSIONS']}")
+print(f"Created copy with {len(tgm_copy)} streamlines")
+print(f"Header preserved: DIMENSIONS = {tgm_copy.header['DIMENSIONS']}")
 
 # %%
 # Summary

trx/cli.py

@@ -233,15 +233,15 @@ def concatenate_tractograms(
             has_group = True
         trx_list.append(tractogram_obj)
 
-    trx = concatenate(
+    tgm = concatenate(
         trx_list,
         delete_dpv=delete_dpv,
         delete_dps=delete_dps,
         delete_groups=delete_groups or not has_group,
         check_space_attributes=True,
         preallocation=False,
     )
-    save(trx, str(out_tractogram))
+    save(tgm, str(out_tractogram))
 
     typer.echo(
         typer.style(
@@ -1002,8 +1002,8 @@ def info(
     typer.echo("")
 
     # Show TRX content info
-    trx = load_trx(str(in_tractogram))
-    typer.echo(trx)
+    tgm = load_trx(str(in_tractogram))
+    typer.echo(tgm)
 
     # Show file listing (unzip -l style)
     typer.echo("\nArchive contents:")
@@ -1021,7 +1021,7 @@ def info(
         typer.echo("---------                     -------")
         typer.echo(f"{total_uncompressed:>9}                     {num_files} files")
 
-    trx.close()
+    tgm.close()
 
 
 def main():