code clean up

CHANGELOG.md

@@ -15,7 +15,7 @@ The rules for this file:
     * YYYY-MM-DD date format (following ISO 8601)
   * accompany each entry with github issue/PR number (Issue #xyz)
 -->
-## [1.1.3] - UNRELEASES
+## [1.1.3] - 2025-09-11
 
 ### Authors
 * @orbeckst
@@ -28,11 +28,15 @@ The rules for this file:
   (Issue #48)
 
 ### Changed
+The following changes do not functionally change the code but users relying
+on default values need to be aware of the changes to gibbs.Gibbs.
+
 * Default kwargs for the skipping in the Gibbs sampler are now
   gibbs.Gibbs(g=100, gskip=1) (used to be g=50, gskip=2) but for most users
   gskip for processing data is not important and it makes more sense to focus 
   on g as the stride at which we sample AND process data (#48, PR #49)
-
+* internal code refactor and clean-up: moved util.run_residue() to
+  gibbs.run_residue() and use direct assignment instead of setattr() (PR #50)
 
 ## [1.1.2] - 2025-07-22
 

basicrta/cluster.py

@@ -119,10 +119,10 @@ def reprocess(self, nproc=1):
 
         taus = np.array(taus)
         bars = get_bars(taus)
-        setattr(self, 'taus', taus[:, 1])
-        setattr(self, 'bars', bars)
-        setattr(self, 'residues', np.array(residues))
-        setattr(self, 'files', np.array(results))
+        self.taus = taus[:, 1]
+        self.bars = bars
+        self.residues = np.array(residues)
+        self.files = np.array(results)
 
     def get_taus(self, nproc=1):
         r"""Get :math:`\tau` and 95\% confidence interval bounds for the slowest
@@ -158,10 +158,10 @@ def get_taus(self, nproc=1):
 
         taus = np.array(taus)
         bars = get_bars(taus)
-        setattr(self, 'taus', taus[:, 1])
-        setattr(self, 'bars', bars)
-        setattr(self, 'residues', np.array(residues))
-        setattr(self, 'files', np.array(results))
+        self.taus = taus[:, 1]
+        self.bars = bars
+        self.residues = np.array(residues)
+        self.files = np.array(results)
         return taus[:, 1], bars
 
     def write_data(self, fname='tausout'):

basicrta/gibbs.py

@@ -8,6 +8,7 @@
 from tqdm import tqdm
 from MDAnalysis.analysis.base import Results
 from basicrta.util import confidence_interval
+import multiprocessing
 from multiprocessing import Pool, Lock
 import MDAnalysis as mda
 from basicrta import istarmap
@@ -47,8 +48,6 @@ def run(self, run_resids=None, g=100):
         :param run_resids: Resid(s) for which to run a Gibbs sampler. 
         :type run_resids: int or list, optional
         """
-        from basicrta.util import run_residue
-
         with open(self.contacts, 'r+b') as f:
             contacts = pickle.load(f)
 
@@ -96,6 +95,38 @@ def run(self, run_resids=None, g=100):
                 pass
 
 
+def run_residue(residue, time, proc, ncomp, niter, cutoff, g, from_combined=False):
+    """Run Gibbs sampler for a single residue.
+
+    :param residue: Residue name
+    :type residue: str
+    :param time: Residence times data
+    :type time: array-like
+    :param proc: Process number for progress bar positioning
+    :type proc: int
+    :param ncomp: Number of mixture components
+    :type ncomp: int
+    :param niter: Number of iterations
+    :type niter: int
+    :param cutoff: Cutoff value used in contact analysis
+    :type cutoff: float
+    :param g: Gibbs skip parameter
+    :type g: int
+    :param from_combined: Whether data comes from combined contacts
+    :type from_combined: bool
+    """
+    x = np.array(time)
+    if len(x) != 0:
+        try:
+            proc = int(multiprocessing.current_process().name.split('-')[-1])
+        except ValueError:
+            proc = 1
+
+    gib = Gibbs(times=x, residue=residue, loc=proc, ncomp=ncomp, niter=niter, cutoff=cutoff, g=g)
+    gib._from_combined_contacts = from_combined
+    gib.run()
+
+
 class Gibbs(object):
     r"""Gibbs sampler to estimate parameters of an exponential mixture for a set
     of data. Results are stored in :class:`gibbs.results`, which uses
@@ -301,8 +332,8 @@ def cluster(self, method="GaussianMixture", **kwargs):
                 pindicator[tmpind, mapinds[i]] += 1
 
         pindicator = (pindicator.T / pindicator.sum(axis=1)).T
-        setattr(self.processed_results, 'indicator', pindicator)
-        setattr(self.processed_results, 'labels', all_labels)
+        self.processed_results.indicator = pindicator
+        self.processed_results.labels = all_labels
 
     def process_gibbs(self, show=True):
         r"""
@@ -329,9 +360,8 @@ def process_gibbs(self, show=True):
 
         self.cluster(n_init=117, n_components=lmode)
         labels, presorts = mixture_and_plot(self, show=show)
-        setattr(self.processed_results, 'labels', labels)
-        setattr(self.processed_results, 'indicator',
-                self.processed_results.indicator[:, presorts])
+        self.processed_results.labels = labels
+        self.processed_results.indicator = self.processed_results.indicator[:, presorts]
 
         attrs = ["weights", "rates", "ncomp", "residue", "iteration", "niter"]
         values = [fweights, frates, lmode, self.residue, indices, self.niter]
@@ -364,7 +394,7 @@ def _sample_indicator(self):
             # sample indicator
             s = np.argmax(rng.multinomial(1, z), axis=1)
             indicator[i] = s
-        setattr(self, 'indicator', indicator)
+        self.indicator = indicator
         return indicator[burnin_ind::self.gskip]
 
     def save(self):
@@ -719,8 +749,8 @@ def _estimate_params(self):
         params = np.array([[wh[1][np.argmax(wh[0])], rh[1][np.argmax(rh[0])]]
                            for wh, rh in zip(whists, rhists)])
 
-        setattr(rp, 'parameters', params)
-        setattr(rp, 'intervals', np.array([wbounds, rbounds]))
+        rp.parameters = params
+        rp.intervals = np.array([wbounds, rbounds])
 
     def estimate_tau(self):
         r"""

basicrta/util.py

@@ -472,19 +472,6 @@ def plot_protein(residues, t_slow, bars, prot=None, label_cutoff=3, ylim=None,
 #     gib.run()
 
 
-def run_residue(residue, time, proc, ncomp, niter, cutoff, g, from_combined=False):
-    from basicrta.gibbs import Gibbs
-    x = np.array(time)
-    if len(x) != 0:
-        try:
-            proc = int(multiprocessing.current_process().name.split('-')[-1])
-        except ValueError:
-            proc = 1
-
-    gib = Gibbs(x, residue, proc, ncomp=ncomp, niter=niter, cutoff=cutoff)
-    gib._from_combined_contacts = from_combined
-    gib.run()
-
 
 def check_results(residues, times, ts):
     if not os.path.exists('result_check'):