On this tutorial, we construct an end-to-end cognitive complexity evaluation workflow utilizing complexipy. We begin by measuring complexity immediately from uncooked code strings, then scale the identical evaluation to particular person information and a whole undertaking listing. Alongside the way in which, we generate machine-readable reviews, normalize them into structured DataFrames, and visualize complexity distributions to grasp how resolution depth accumulates throughout capabilities. By treating cognitive complexity as a measurable engineering sign, we present how it may be built-in naturally into on a regular basis Python growth and high quality checks. Take a look at the FULL CODES right here.
!pip -q set up complexipy pandas matplotlib
import os
import json
import textwrap
import subprocess
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
from complexipy import code_complexity, file_complexity
print("✅ Put in complexipy and dependencies")We arrange the surroundings by putting in the required libraries and importing all dependencies wanted for evaluation and visualization. We make sure the pocket book is totally self-contained and able to run in Google Colab with out exterior setup. It varieties the spine of execution for every part that follows.
snippet = """
def score_orders(orders):
whole = 0
for o in orders:
if o.get("legitimate"):
if o.get("precedence"):
if o.get("quantity", 0) > 100:
whole += 3
else:
whole += 2
else:
if o.get("quantity", 0) > 100:
whole += 2
else:
whole += 1
else:
whole -= 1
return whole
"""
res = code_complexity(snippet)
print("=== Code string complexity ===")
print("Total complexity:", res.complexity)
print("Capabilities:")
for f in res.capabilities:
print(f" - {f.title}: {f.complexity} (strains {f.line_start}-{f.line_end})")We start by analyzing a uncooked Python code string to grasp cognitive complexity on the operate degree. We immediately examine how nested conditionals and management circulate contribute to complexity. It helps us validate the core conduct of complexipy earlier than scaling to actual information.
root = Path("toy_project")
src = root / "src"
exams = root / "exams"
src.mkdir(dad and mom=True, exist_ok=True)
exams.mkdir(dad and mom=True, exist_ok=True)
(src / "__init__.py").write_text("")
(exams / "__init__.py").write_text("")
(src / "easy.py").write_text(textwrap.dedent("""
def add(a, b):
return a + b
def safe_div(a, b):
if b == 0:
return None
return a / b
""").strip() + "n")
(src / "legacy_adapter.py").write_text(textwrap.dedent("""
def legacy_adapter(x, y):
if x and y:
if x > 0:
if y > 0:
return x + y
else:
return x - y
else:
if y > 0:
return y - x
else:
return -(x + y)
return 0
""").strip() + "n")
(src / "engine.py").write_text(textwrap.dedent("""
def route_event(occasion):
variety = occasion.get("variety")
payload = occasion.get("payload", {})
if variety == "A":
if payload.get("x") and payload.get("y"):
return _handle_a(payload)
return None
elif variety == "B":
if payload.get("flags"):
return _handle_b(payload)
else:
return None
elif variety == "C":
for merchandise in payload.get("gadgets", []):
if merchandise.get("enabled"):
if merchandise.get("mode") == "quick":
_do_fast(merchandise)
else:
_do_safe(merchandise)
return True
else:
return None
def _handle_a(p):
whole = 0
for v in p.get("vals", []):
if v > 10:
whole += 2
else:
whole += 1
return whole
def _handle_b(p):
rating = 0
for f in p.get("flags", []):
if f == "x":
rating += 1
elif f == "y":
rating += 2
else:
rating -= 1
return rating
def _do_fast(merchandise):
return merchandise.get("id")
def _do_safe(merchandise):
if merchandise.get("id") is None:
return None
return merchandise.get("id")
""").strip() + "n")
(exams / "test_engine.py").write_text(textwrap.dedent("""
from src.engine import route_event
def test_route_event_smoke():
assert route_event({"variety": "A", "payload": {"x": 1, "y": 2, "vals": [1, 20]}}) == 3
""").strip() + "n")
print(f"✅ Created undertaking at: {root.resolve()}")We programmatically assemble a small however real looking Python undertaking with a number of modules and check information. We deliberately embody various control-flow patterns to create significant variations in complexity. Take a look at the FULL CODES right here.
engine_path = src / "engine.py"
file_res = file_complexity(str(engine_path))
print("n=== File complexity (Python API) ===")
print("Path:", file_res.path)
print("File complexity:", file_res.complexity)
for f in file_res.capabilities:
print(f" - {f.title}: {f.complexity} (strains {f.line_start}-{f.line_end})")
MAX_ALLOWED = 8
def run_complexipy_cli(project_dir: Path, max_allowed: int = 8):
cmd = [
"complexipy",
".",
"--max-complexity-allowed", str(max_allowed),
"--output-json",
"--output-csv",
]
proc = subprocess.run(cmd, cwd=str(project_dir), capture_output=True, textual content=True)
preferred_csv = project_dir / "complexipy.csv"
preferred_json = project_dir / "complexipy.json"
csv_candidates = []
json_candidates = []
if preferred_csv.exists():
csv_candidates.append(preferred_csv)
if preferred_json.exists():
json_candidates.append(preferred_json)
csv_candidates += checklist(project_dir.glob("*.csv")) + checklist(project_dir.glob("**/*.csv"))
json_candidates += checklist(project_dir.glob("*.json")) + checklist(project_dir.glob("**/*.json"))
def uniq(paths):
seen = set()
out = []
for p in paths:
p = p.resolve()
if p not in seen and p.is_file():
seen.add(p)
out.append(p)
return out
csv_candidates = uniq(csv_candidates)
json_candidates = uniq(json_candidates)
def pick_best(paths):
if not paths:
return None
paths = sorted(paths, key=lambda p: p.stat().st_mtime, reverse=True)
return paths[0]
return proc.returncode, pick_best(csv_candidates), pick_best(json_candidates)
rc, csv_report, json_report = run_complexipy_cli(root, MAX_ALLOWED)We analyze an actual supply file utilizing the Python API, then run the complexipy CLI on your complete undertaking. We run the CLI from the right working listing to reliably generate reviews. This step bridges native API utilization with production-style static evaluation workflows.
df = None
if csv_report and csv_report.exists():
df = pd.read_csv(csv_report)
elif json_report and json_report.exists():
knowledge = json.hundreds(json_report.read_text())
if isinstance(knowledge, checklist):
df = pd.DataFrame(knowledge)
elif isinstance(knowledge, dict):
if "information" in knowledge and isinstance(knowledge["files"], checklist):
df = pd.DataFrame(knowledge["files"])
elif "outcomes" in knowledge and isinstance(knowledge["results"], checklist):
df = pd.DataFrame(knowledge["results"])
else:
df = pd.json_normalize(knowledge)
if df is None:
increase RuntimeError("No report produced")
def explode_functions_table(df_in):
if "capabilities" in df_in.columns:
tmp = df_in.explode("capabilities", ignore_index=True)
if tmp["functions"].notna().any() and isinstance(tmp["functions"].dropna().iloc[0], dict):
fn = pd.json_normalize(tmp["functions"])
base = tmp.drop(columns=["functions"])
return pd.concat([base.reset_index(drop=True), fn.reset_index(drop=True)], axis=1)
return tmp
return df_in
fn_df = explode_functions_table(df)
col_map = {}
for c in fn_df.columns:
lc = c.decrease()
if lc in ("path", "file", "filename", "module"):
col_map[c] = "path"
if ("operate" in lc and "title" in lc) or lc in ("operate", "func", "function_name"):
col_map[c] = "operate"
if lc == "title" and "operate" not in fn_df.columns:
col_map[c] = "operate"
if "complexity" in lc and "allowed" not in lc and "max" not in lc:
col_map[c] = "complexity"
if lc in ("line_start", "linestart", "start_line", "startline"):
col_map[c] = "line_start"
if lc in ("line_end", "lineend", "end_line", "endline"):
col_map[c] = "line_end"
fn_df = fn_df.rename(columns=col_map)We load the generated complexity reviews into pandas and normalize them right into a function-level desk. We deal with a number of potential report schemas to maintain the workflow sturdy. This structured illustration permits us to motive about complexity utilizing commonplace knowledge evaluation instruments.
if "complexity" in fn_df.columns:
fn_df["complexity"] = pd.to_numeric(fn_df["complexity"], errors="coerce")
plt.determine()
fn_df["complexity"].dropna().plot(variety="hist", bins=20)
plt.title("Cognitive Complexity Distribution (capabilities)")
plt.xlabel("complexity")
plt.ylabel("depend")
plt.present()
def refactor_hints(complexity):
if complexity >= 20:
return [
"Split into smaller pure functions",
"Replace deep nesting with guard clauses",
"Extract complex boolean predicates"
]
if complexity >= 12:
return [
"Extract inner logic into helpers",
"Flatten conditionals",
"Use dispatch tables"
]
if complexity >= 8:
return [
"Reduce nesting",
"Early returns"
]
return ["Acceptable complexity"]
if "complexity" in fn_df.columns and "operate" in fn_df.columns:
for _, r in fn_df.sort_values("complexity", ascending=False).head(8).iterrows():
cx = float(r["complexity"]) if pd.notna(r["complexity"]) else None
if cx is None:
proceed
print(r["function"], cx, refactor_hints(cx))
print("✅ Tutorial full.")We visualize the distribution of cognitive complexity and derive refactoring steering from numeric thresholds. We translate summary complexity scores into concrete engineering actions. It closes the loop by connecting measurement on to maintainability choices.
In conclusion, we introduced a sensible, reproducible pipeline for auditing cognitive complexity in Python initiatives utilizing complexipy. We demonstrated how we will transfer from advert hoc inspection to data-driven reasoning about code construction, establish high-risk capabilities, and supply actionable refactoring steering primarily based on quantified thresholds. The workflow permits us to motive about maintainability early, implement complexity budgets constantly, and evolve codebases with readability and confidence, slightly than relying solely on instinct.
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