Filter run_ribasim terminal output in docs and increase node icon size

docs/run_ribasim.py

@@ -13,14 +13,57 @@
 That way we can run this version on CI but it looks like we run the Ribasim Python version.
 """
 
+import os
+import re
+import sys
+import tempfile
 from pathlib import Path
 
 from juliacall import Main as jl
 
 jl.seval("import Ribasim")
 
+_ANSI_RE = re.compile(r"\x1b\[[0-9;]*[a-zA-Z]")
+
+
+def _filter_output(raw: str) -> str:
+    """Remove blank lines and progress bar repetitions from Ribasim output."""
+    lines = raw.splitlines()
+    filtered = []
+    for line in lines:
+        plain = _ANSI_RE.sub("", line)
+        # Skip lines that are only whitespace or invisible braille blanks (U+2800)
+        if all(c in " \t\u2800" for c in plain):
+            continue
+        # Skip progress bar log lines from OrdinaryDiffEqCore
+        if "@ OrdinaryDiffEqCore" in plain:
+            continue
+        if plain.lstrip().startswith("\u250c") and "Simulating" in plain:
+            continue
+        filtered.append(line)
+    return "\n".join(filtered)
+
 
 def run_ribasim(toml_path: str | Path) -> None:
     """Run a Ribasim simulation via juliacall."""
-    retcode = jl.Ribasim.main(str(toml_path))
+    # Redirect stderr at the fd level to capture Julia output
+    # (logo, log messages, progress bar) which all go to stderr.
+    sys.stderr.flush()
+    old_fd = os.dup(2)
+    try:
+        with tempfile.TemporaryFile(mode="w+b") as tmp:
+            os.dup2(tmp.fileno(), 2)
+            try:
+                retcode = jl.Ribasim.main(str(toml_path))
+            finally:
+                os.dup2(old_fd, 2)
+            tmp.seek(0)
+            raw = tmp.read().decode("utf-8", errors="replace")
+    finally:
+        os.close(old_fd)
+
+    output = _filter_output(raw)
+    if output:
+        print(output, file=sys.stderr)
+
     assert retcode == 0, f"Simulation failed: {toml_path}"

python/ribasim/ribasim/geometry/node.py

@@ -143,7 +143,7 @@ def plot(self, ax=None, zorder=None) -> plt.Axes:
         if self.df is None:
             return ax
 
-        NODE_ICON_SIZE = 17.0  # display-points per icon
+        NODE_ICON_SIZE = 25.0  # display-points per icon
         for nodetype, df in self.df.groupby("node_type"):
             assert isinstance(nodetype, str)
             for x, y in zip(df.geometry.x, df.geometry.y, strict=True):