black (still without mortimer)

turbox/__init__.py

@@ -1,2 +1,7 @@
-from .turbox import  get_pspec, get_pspec_slope, build_suite, apply_rule_pdf, span_resolution
-
+from .turbox import (
+    get_pspec,
+    get_pspec_slope,
+    build_suite,
+    apply_rule_pdf,
+    span_resolution,
+)

turbox/turbox.py

@@ -81,14 +81,14 @@ def get_pspec_slope(pp, field: str, resol: int, plotdebug: bool = False):
         f"Fit results in get_slope({field}, {resol}):  slope:{results.slope:.2f}"
         + f", b:{results.intercept:.2f}, R2:{results.rvalue**2:.2f}"
     )
-    if results.rvalue ** 2 < 0.8:
+    if results.rvalue**2 < 0.8:
         pp.logger.warning(
             f"Bad fit in get_slope({field}, {resol}) with {logkmin} <= logk < {logkmax}"
         )
         pp.logger.warning(f"log(k) is \n {logk[mask]}")
         pp.logger.warning(f"log(power) is \n {logpower[mask]}")
 
-    return results.slope, results.intercept, results.rvalue ** 2
+    return results.slope, results.intercept, results.rvalue**2
 
 
 def build_suite(pl, redo=False, cs0=0.28834810480560674):
@@ -155,7 +155,7 @@ def build_suite(pl, redo=False, cs0=0.28834810480560674):
         )
     )
 
-    df["sigma"] = list(map(lambda l: np.mean(l), df["sigma_all"].values))
+    df["sigma"] = list(map(lambda sig_list: np.mean(sig_list), df["sigma_all"].values))
     df["Mach"] = df["sigma"] / cs0
     df["turnover"] = (df["L"] * U.pc.express(U.km) / (2 * df["sigma"])) * U.s.express(
         U.Myr