use rich instead of preogressbar

examples/JSS_example.py

@@ -2,6 +2,7 @@
 from pathlib import Path
 
 import matplotlib.pyplot as plt
+from rich import print
 
 import neurodesign
 

examples/compare_designs.py

@@ -1,3 +1,5 @@
+from rich import print
+
 import neurodesign
 from neurodesign import generate
 

examples/comparison_neurodesign.ipynb

@@ -30,6 +30,7 @@
     "import numpy as np\n",
     "import seaborn as sns\n",
     "from scipy.stats import t\n",
+    "from rich import print\n",
     "\n",
     "from neurodesign import Experiment, Optimisation\n",
     "\n",

neurodesign/classes.py

@@ -10,16 +10,39 @@
 from pathlib import Path
 
 import numpy as np
-import progressbar
 import scipy
 import scipy.linalg
 import sklearn.cluster
 from numpy import transpose as t
+from rich import print
+from rich.progress import (
+    BarColumn,
+    MofNCompleteColumn,
+    Progress,
+    SpinnerColumn,
+    TaskProgressColumn,
+    TextColumn,
+    TimeElapsedColumn,
+    TimeRemainingColumn,
+)
 from scipy.special import gamma
 
 from neurodesign import generate, report
 
 
+def progress_bar(text: str, color: str = "green") -> Progress:
+    """Return a rich progress bar instance."""
+    return Progress(
+        TextColumn(f"[{color}]{text}"),
+        SpinnerColumn("dots"),
+        TimeElapsedColumn(),
+        BarColumn(),
+        MofNCompleteColumn(),
+        TaskProgressColumn(),
+        TimeRemainingColumn(),
+    )
+
+
 class Design:
     """
     This class represents an experimental design for an fMRI experiment.
@@ -982,41 +1005,58 @@ def optimise(self):
             # add new designs
             self.clear()
             self.add_new_designs(weights=[1, 0, 0, 0])
+
             # loop
-            bar = progressbar.ProgressBar()
-            for _ in bar(range(self.preruncycles)):
-                self.to_next_generation(seed=self.seed, weights=[1, 0, 0, 0])
-                if self.finished:
-                    continue
+            with progress_bar(text="Optimizing") as progress:
+                task = progress.add_task(
+                    description="optimize", total=len(range(self.preruncycles))
+                )
+                for _ in range(self.preruncycles):
+                    self.to_next_generation(seed=self.seed, weights=[1, 0, 0, 0])
+                    progress.update(task, advance=1)
+                    if self.finished:
+                        continue
+
             self.exp.FeMax = np.max(self.bestdesign.F)
 
         if self.exp.FdMax == 1 and self.weights[1] > 0:
             self.clear()
             self.add_new_designs(weights=[0, 1, 0, 0])
+
             # loop
-            bar = progressbar.ProgressBar()
-            for _ in bar(range(self.preruncycles)):
-                self.to_next_generation(seed=self.seed, weights=[0, 1, 0, 0])
-                if self.finished:
-                    continue
+            with progress_bar(text="Optimizing") as progress:
+                task = progress.add_task(
+                    description="optimize", total=len(range(self.preruncycles))
+                )
+                for _ in range(self.preruncycles):
+                    self.to_next_generation(seed=self.seed, weights=[0, 1, 0, 0])
+                    progress.update(task, advance=1)
+                    if self.finished:
+                        continue
+
             self.exp.FdMax = np.max(self.bestdesign.F)
 
         # clear all attributes
         self.clear()
         self.add_new_designs()
+
         # loop
-        bar = progressbar.ProgressBar()
-        for _ in bar(range(self.cycles)):
-            self.to_next_generation(seed=self.seed)
-            if self.finished:
-                continue
+        with progress_bar(text="Optimizing") as progress:
+            task = progress.add_task(
+                description="optimize", total=len(range(self.cycles))
+            )
+            for _ in range(self.cycles):
+                self.to_next_generation(seed=self.seed)
+                progress.update(task, advance=1)
+                if self.finished:
+                    continue
 
         return self
 
     def evaluate(self):
         # select designs: best from k-means clusters
         shape = self.bestdesign.Xconv.shape
-        des = np.zeros([np.product(shape), len(self.designs)])
+        des = np.zeros([np.prod(shape), len(self.designs)])
         efficiencies = np.array([x.F for x in self.designs])
         for d in range(len(self.designs)):
             hrf = []

neurodesign/msequence.py

@@ -8,6 +8,7 @@
 import math
 
 import numpy as np
+from rich import print
 
 
 class Msequence:

neurodesign/report.py

@@ -22,12 +22,10 @@
     Table,
 )
 
-from neurodesign import Optimisation
-
 plt.switch_backend("agg")
 
 
-def make_report(population: Optimisation, outfile: str | Path = "NeuroDesign.pdf"):
+def make_report(population, outfile: str | Path = "NeuroDesign.pdf"):
     """Create a report of a finished design optimisation."""
     if not isinstance(population.cov, np.ndarray):
         population.evaluate()

pyproject.toml

@@ -23,7 +23,7 @@ classifiers = [
 ]
 dependencies = [
     "scikit-learn>0.15.0",
-    "progressbar>2.0",
+    "rich",
     "reportlab",
     "pdfrw",
     "matplotlib"