Integrate canns-ripser with progress bar support (#24)

* Switch to canns-ripser and add progress bar support

Replaced ripser with canns-ripser for persistent homology computations and added progress bar configuration throughout TDA analysis functions. Updated dependencies in pyproject.toml and improved logging for analysis steps. Also updated example usage and made related dependency and lock file changes.

* Remove old ripser dependency in favor of canns-ripser

This completes the migration from the original ripser package to canns-ripser,
eliminating potential conflicts and ensuring consistent behavior.

Author: Routhleck

examples/experimental_cann2d_analysis.py

@@ -60,9 +60,11 @@
 tda_config = TDAConfig(
     maxdim=1,
     do_shuffle=False,
+    # num_shuffles=10,
     show=True,
     dim=6,
-    n_points=1200
+    n_points=1200,
+    progress_bar=True,
 )
 
 persistence_result = tda_vis(

pyproject.toml

@@ -39,10 +39,10 @@ classifiers = [
 
 dependencies = [
     "BrainX[cpu]",
+    "canns-ripser>=0.4.3",
     "furo>=2025.7.19",
     "notebook>=7.4.4",
     "ratinabox>=1.15.3",
-    "ripser>=0.6.12",
     "tqdm",
 ]
 

src/canns/analyzer/experimental_data/cann2d.py

@@ -1,3 +1,4 @@
+import logging
 import multiprocessing as mp
 import numbers
 import os
@@ -8,7 +9,8 @@
 import numpy as np
 from matplotlib import animation, cm, gridspec
 from numpy.exceptions import AxisError
-from ripser import ripser
+from canns_ripser import ripser
+# from ripser import ripser
 from scipy import signal
 from scipy.ndimage import (
     _nd_image,
@@ -58,6 +60,7 @@ class TDAConfig:
     show: bool = True
     do_shuffle: bool = False
     num_shuffles: int = 1000
+    progress_bar: bool = True
 
 
 @dataclass
@@ -465,6 +468,7 @@ def tda_vis(embed_data: np.ndarray, config: TDAConfig | None = None, **kwargs) -
             show=kwargs.get("show", True),
             do_shuffle=kwargs.get("do_shuffle", False),
             num_shuffles=kwargs.get("num_shuffles", 1000),
+            progress_bar=kwargs.get("progress_bar", True),
         )
 
     try:
@@ -496,31 +500,29 @@ def tda_vis(embed_data: np.ndarray, config: TDAConfig | None = None, **kwargs) -
 def _compute_real_persistence(embed_data: np.ndarray, config: TDAConfig) -> dict[str, Any]:
     """Compute persistent homology for real data with progress tracking."""
 
-    with tqdm(total=5, desc="Processing real data") as pbar:
-        # Step 1: Time point downsampling
-        pbar.set_description("Time point downsampling")
-        times_cube = _downsample_timepoints(embed_data, config.num_times)
-        pbar.update(1)
+    logging.info("Processing real data - Starting TDA analysis (5 steps)")
+    
+    # Step 1: Time point downsampling
+    logging.info("Step 1/5: Time point downsampling")
+    times_cube = _downsample_timepoints(embed_data, config.num_times)
 
-        # Step 2: Select most active time points
-        pbar.set_description("Selecting active time points")
-        movetimes = _select_active_timepoints(embed_data, times_cube, config.active_times)
-        pbar.update(1)
+    # Step 2: Select most active time points
+    logging.info("Step 2/5: Selecting active time points")
+    movetimes = _select_active_timepoints(embed_data, times_cube, config.active_times)
 
-        # Step 3: PCA dimensionality reduction
-        pbar.set_description("PCA dimensionality reduction")
-        dimred = _apply_pca_reduction(embed_data, movetimes, config.dim)
-        pbar.update(1)
+    # Step 3: PCA dimensionality reduction
+    logging.info("Step 3/5: PCA dimensionality reduction")
+    dimred = _apply_pca_reduction(embed_data, movetimes, config.dim)
 
-        # Step 4: Point cloud sampling (denoising)
-        pbar.set_description("Point cloud denoising")
-        indstemp = _apply_denoising(dimred, config)
-        pbar.update(1)
+    # Step 4: Point cloud sampling (denoising)
+    logging.info("Step 4/5: Point cloud denoising")
+    indstemp = _apply_denoising(dimred, config)
 
-        # Step 5: Compute persistent homology
-        pbar.set_description("Computing persistent homology")
-        persistence = _compute_persistence_homology(dimred, indstemp, config)
-        pbar.update(1)
+    # Step 5: Compute persistent homology
+    logging.info("Step 5/5: Computing persistent homology")
+    persistence = _compute_persistence_homology(dimred, indstemp, config)
+    
+    logging.info("TDA analysis completed successfully")
 
     # Return all necessary data in dictionary format
     return {
@@ -573,7 +575,12 @@ def _compute_persistence_homology(
     np.fill_diagonal(d, 0)
 
     return ripser(
-        d, maxdim=config.maxdim, coeff=config.coeff, do_cocycles=True, distance_matrix=True
+        d,
+        maxdim=config.maxdim,
+        coeff=config.coeff,
+        do_cocycles=True,
+        distance_matrix=True,
+        progress_bar=config.progress_bar
     )
 
 
@@ -598,6 +605,7 @@ def _perform_shuffle_analysis(embed_data: np.ndarray, config: TDAConfig) -> dict
         embed_data,
         num_shuffles=config.num_shuffles,
         num_cores=Constants.MULTIPROCESSING_CORES,
+        progress_bar=config.progress_bar,
         **shuffle_params,
     )
 
@@ -907,6 +915,7 @@ def _compute_persistence(
     nbs=800,
     maxdim=1,
     coeff=47,
+    progress_bar=True,
 ):
     # Time point downsampling
     times_cube = np.arange(0, sspikes.shape[0], num_times)
@@ -927,7 +936,7 @@ def _compute_persistence(
     np.fill_diagonal(d, 0)
 
     # Compute persistent homology
-    persistence = ripser(d, maxdim=maxdim, coeff=coeff, do_cocycles=True, distance_matrix=True)
+    persistence = ripser(d, maxdim=maxdim, coeff=coeff, do_cocycles=True, distance_matrix=True, progress_bar=progress_bar)
 
     return persistence
 
@@ -1302,32 +1311,28 @@ def _second_build(data, indstemp, nbs=800, metric="cosine"):
     return d
 
 
-def _run_shuffle_analysis(sspikes, num_shuffles=1000, num_cores=4, **kwargs):
+def _run_shuffle_analysis(sspikes, num_shuffles=1000, num_cores=4, progress_bar=True, **kwargs):
     """Perform shuffle analysis with optimized computation."""
-    return _run_shuffle_analysis_multiprocessing(sspikes, num_shuffles, num_cores, **kwargs)
+    return _run_shuffle_analysis_multiprocessing(sspikes, num_shuffles, num_cores, progress_bar, **kwargs)
 
 
-def _run_shuffle_analysis_multiprocessing(sspikes, num_shuffles=1000, num_cores=4, **kwargs):
+def _run_shuffle_analysis_multiprocessing(sspikes, num_shuffles=1000, num_cores=4, progress_bar=True, **kwargs):
     """Original multiprocessing implementation for fallback."""
     max_lifetimes = {0: [], 1: [], 2: []}
 
     # Estimate runtime with a test iteration
-    print("Running test iteration to estimate runtime...")
+    logging.info("Running test iteration to estimate runtime...")
 
     _ = _process_single_shuffle((0, sspikes, kwargs))
 
     # Prepare task list
     tasks = [(i, sspikes, kwargs) for i in range(num_shuffles)]
+    logging.info(f"Starting shuffle analysis with {num_shuffles} iterations using {num_cores} cores...")
 
     # Use multiprocessing pool for parallel processing
     with mp.Pool(processes=num_cores) as pool:
-        results = list(
-            tqdm(
-                pool.imap(_process_single_shuffle, tasks),
-                total=num_shuffles,
-                desc="Running shuffle analysis",
-            )
-        )
+        results = list(pool.imap(_process_single_shuffle, tasks))
+        logging.info("Shuffle analysis completed")
 
     # Collect results
     for res in results:
@@ -1798,7 +1803,6 @@ def plot_3d_bump_on_torus(
     frame_data = []
     prev_m = None
 
-    print("Preparing animation data...")
     for frame_idx in tqdm(range(n_frames), desc="Processing frames"):
         start_idx = frame_idx * frame_step
         end_idx = start_idx + window_size
@@ -2102,8 +2106,8 @@ def _smooth_image(img, sigma):
     # reduce_func = reducer.fit_transform
     #
     # plot_projection(reduce_func=reduce_func, embed_data=spikes, show=True)
-    # results = tda_vis(
-    #     embed_data=spikes, maxdim=2, do_shuffle=False, show=True
-    # )
+    results = tda_vis(
+        embed_data=spikes, maxdim=1, do_shuffle=False, show=True
+    )
 
-    results = tda_vis(embed_data=spikes, maxdim=1, do_shuffle=True, num_shuffles=10, show=True)
+    # results = tda_vis(embed_data=spikes, maxdim=1, do_shuffle=True, num_shuffles=10, show=True)