Run ruff again...

Author: jdehning

mrestimator/input_output.py

@@ -4,6 +4,9 @@
 import re
 
 from mrestimator import utility as ut
+from mrestimator._version import __version__
+from mrestimator.coefficients import CoefficientResult
+from mrestimator.fit import FitResult
 
 log = ut.log
 import matplotlib

mrestimator/utility.py

@@ -1,14 +1,17 @@
 import logging
 import logging.handlers
 import math
+from typing import TYPE_CHECKING
 
 import numpy as np
 import scipy
 import scipy.stats
 
-__version__ = "unknown"
 from ._version import __version__
 
+if TYPE_CHECKING:
+    from mrestimator.coefficients import CoefficientResult
+
 log = logging.getLogger("mrestimator")
 _log_locals = False
 
@@ -17,12 +20,13 @@
 # ------------------------------------------------------------------ #
 
 
-def _c_rk_greater_zero(data, plim=0.1):
+def _c_rk_greater_zero(data: "CoefficientResult", plim=0.1):
     """
     check rk are signigicantly larger than 0
 
     returns True if the test passed (and the null hypothesis was rejected)
     """
+    from mrestimator.coefficients import CoefficientResult
     if not isinstance(data, CoefficientResult):
         log.exception("_c_rk_greater_zero needs a CoefficientResult")
         raise TypeError
@@ -36,12 +40,13 @@ def _c_rk_greater_zero(data, plim=0.1):
     return passed, t, p
 
 
-def _c_rk_smaller_one(data, plim=0.1):
+def _c_rk_smaller_one(data: "CoefficientResult", plim=0.1):
     """
     check rk are signigicantly smaller than 1, this should fail if m>1
 
     returns True if the test passed (and the null hypothesis was rejected)
     """
+    from mrestimator.coefficients import CoefficientResult
     if not isinstance(data, CoefficientResult):
         log.exception("_c_rk_smaller_one needs a CoefficientResult")
         raise TypeError

tests/test_mr_estimator.py

@@ -209,16 +209,20 @@ def test_stationary_mean(self):
 
                         # Since the fits use different algorithms and starting points,
                         # we should be more lenient with the comparison
-                        # Some datasets may converge to very different solutions due to numerical issues
-                        # Check if the fits look reasonable (not extreme values) before comparing
+                        # Some datasets may converge to very different solutions due to
+                        # numerical issues
+                        # Check if the fits look reasonable (not extreme values) before
+                        # comparing
 
-                        # Skip comparison if either fit resulted in extreme values (likely convergence failure)
+                        # Skip comparison if either fit resulted in extreme values
+                        # (likely convergence failure)
                         extreme_manual = any(abs(val) > 1e6 for val in popt)
                         extreme_mre = any(abs(val) > 1e6 for val in res_mre.popt)
 
                         if extreme_manual or extreme_mre:
                             print(
-                                f"Skipping comparison for {name_data} due to extreme parameter values"
+                                f"Skipping comparison for {name_data} due to "
+                                "extreme parameter values"
                             )
                             continue
 
@@ -231,15 +235,17 @@ def test_stationary_mean(self):
                                 # For small values, use absolute difference
                                 self.assertTrue(
                                     abs(manual_val - mre_val) < 1e-2,
-                                    f"Parameter {i}: {manual_val} vs {mre_val} (absolute diff too large)",
+                                    f"Parameter {i}: {manual_val} vs {mre_val} "
+                                    "(absolute diff too large)",
                                 )
                             else:
                                 # For larger values, use relative difference
                                 self.assertTrue(
                                     check_similarity(
                                         manual_val, mre_val, ratio_different=2e-1
                                     ),
-                                    f"Parameter {i}: {manual_val} vs {mre_val} (relative diff too large)",
+                                    f"Parameter {i}: {manual_val} vs {mre_val} "
+                                    "(relative diff too large)",
                                 )
                         # plt.plot(k_arr, corr_arr)
                         # plt.plot(k_arr, fitfunction_complex(k_arr, *popt))
@@ -267,16 +273,20 @@ def test_stationary_mean(self):
 
                         # Since the fits use different algorithms and starting points,
                         # we should be more lenient with the comparison
-                        # Some datasets may converge to very different solutions due to numerical issues
-                        # Check if the fits look reasonable (not extreme values) before comparing
+                        # Some datasets may converge to very different solutions due to
+                        # numerical issues
+                        # Check if the fits look reasonable (not extreme values) before
+                        # comparing
 
-                        # Skip comparison if either fit resulted in extreme values (likely convergence failure)
+                        # Skip comparison if either fit resulted in extreme values
+                        # (likely convergence failure)
                         extreme_manual = any(abs(val) > 1e6 for val in popt)
                         extreme_mre = any(abs(val) > 1e6 for val in res_mre.popt)
 
                         if extreme_manual or extreme_mre:
                             print(
-                                f"Skipping comparison for {name_data} due to extreme parameter values"
+                                f"Skipping comparison for {name_data} due to "
+                                "extreme parameter values"
                             )
                             continue
 
@@ -289,13 +299,15 @@ def test_stationary_mean(self):
                                 # For small values, use absolute difference
                                 self.assertTrue(
                                     abs(manual_val - mre_val) < 1e-2,
-                                    f"Parameter {i}: {manual_val} vs {mre_val} (absolute diff too large)",
+                                    f"Parameter {i}: {manual_val} vs {mre_val} "
+                                    "(absolute diff too large)",
                                 )
                             else:
                                 # For larger values, use relative difference
                                 self.assertTrue(
                                     check_similarity(
                                         manual_val, mre_val, ratio_different=2e-1
                                     ),
-                                    f"Parameter {i}: {manual_val} vs {mre_val} (relative diff too large)",
+                                    f"Parameter {i}: {manual_val} vs {mre_val} "
+                                    "(relative diff too large)",
                                 )