Merge branch 'pre-commit-ci-update-config' of https://github.com/swagner-astro/lightcurves into pre-commit-ci-update-config

precommit merge

Author: Sarah Wagner

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: "v5.0.0"
+    rev: "v6.0.0"
     hooks:
       - id: check-added-large-files
       - id: check-case-conflict
@@ -16,7 +16,7 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.12.5"
+    rev: "v0.12.9"
     hooks:
       #  id: ruff-check would go here if using both
       - id: ruff-format

illustration_lightcurve.ipynb

@@ -2,14 +2,13 @@
 
 import logging
 import pickle
+from pathlib import Path
 
 import astropy
 import astropy.stats.bayesian_blocks as bblocks
 import numpy as np
 from matplotlib import pyplot as plt
 from matplotlib.axes import Axes  # for type hints only
-from pathlib import Path
-
 
 # https://docs.astropy.org/en/stable/api/astropy.stats.bayesian_blocks.html
 import lightcurves.HopFinder as hf
@@ -37,7 +36,8 @@ def load_lc(path: str | Path) -> LightCurve:
     with path.open("rb") as f:
         return pickle.load(f)
 
-''' 
+
+'''
 this following is exactly like the previous one.. might not be necessary
 def load_lc_npy(path: str | Path) -> LightCurve:
     """
@@ -52,6 +52,7 @@ def load_lc_npy(path: str | Path) -> LightCurve:
         return pickle.load(f)
 '''
 
+
 def load_lc_csv(path: str) -> LightCurve:
     """
     Load a pickled LightCurve instance from a CSV file saved with `save_csv()`.
@@ -153,8 +154,8 @@ def clean_data(
         ts_ = ts[nan_mask]
         ts_clean = ts_[time_unique_id]
         return (time_unique, flux_clean, flux_error_clean, ts_clean)
-    else:
-        return (time_unique, flux_clean, flux_error_clean, None)
+    return (time_unique, flux_clean, flux_error_clean, None)
+
 
 def get_gti_iis(
     time: np.ndarray, n_gaps: int, n_pick: int | None
@@ -316,7 +317,7 @@ def __init__(
             friendly_error = "Input arrays do not have same length"
             raise ValueError(friendly_error)
         if len(flux[np.isnan(flux)]) > 0 or len(flux_error[np.isnan(flux_error)]) > 0:
-            friendly_error = "flux or flux_error contain np.nan values" 
+            friendly_error = "flux or flux_error contain np.nan values"
             raise TypeError(friendly_error)
         if len(time) != len(np.unique(time)):
             friendly_error = "time contains duplicate values"
@@ -422,7 +423,7 @@ def save_npy(self, path: str) -> None:
         Use `load_lc_npy()` to read this file.
         This does not update `LC.py`, it saves current object state.
         TBD: actaully since this is an object it just saves a pickle that could be called .npy
-             the save npy business needs to be revisited I think it might be nonesense 
+             the save npy business needs to be revisited I think it might be nonesense
         """
         path = Path(path)
         with path.open("wb") as pickle_file:

src/lightcurves/LC.py

@@ -91,8 +91,8 @@ def clean(self, peaks, starts, ends, lc):
             logging.info("not variable enough, no peak found")
             return (None, None, None)
         if self.lc_edges == "neglect" and (len(starts) < 1 or len(ends) < 1):
-                logging.info("not variable enough, missing start or end")
-                return (None, None, None)
+            logging.info("not variable enough, missing start or end")
+            return (None, None, None)
         if self.lc_edges == "add":
             if len(starts) < 1:
                 starts = np.insert(starts, 0, lc.edges[0])
@@ -199,8 +199,8 @@ def find_peaks(self, lc):
         for i in range(1, len(diff)):
             # if previous rising; this falling
             if (diff[i - 1] > 0 and diff[i] < 0) and (lc.block_val[i] > lc.baseline):
-                    # peak_time = middle of peak block
-                    peaks.append(lc.edges[i] + (lc.edges[i + 1] - lc.edges[i]) / 2)
+                # peak_time = middle of peak block
+                peaks.append(lc.edges[i] + (lc.edges[i + 1] - lc.edges[i]) / 2)
         return peaks
 
     def find_start_end(self, lc):