pre-commit update

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 - repo: https://github.com/psf/black
-  rev: 22.12.0
+  rev: 23.1.0
   hooks:
   - id: black
     args: [--safe, --quiet, --line-length=100]
@@ -12,7 +12,7 @@ repos:
   - id: check-yaml
   - id: debug-statements
 - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
-  rev: v2.5.0
+  rev: v2.7.0
   hooks:
   - id: pretty-format-yaml
     args: [--preserve-quotes, --autofix, --indent, '2']
@@ -40,7 +40,7 @@ repos:
   hooks:
   - id: setup-cfg-fmt
 - repo: https://github.com/tdegeus/conda_envfile
-  rev: v0.4.1
+  rev: v0.4.2
   hooks:
   - id: conda_envfile_parse
     files: environment.yaml

enstat/__init__.py

@@ -23,13 +23,11 @@ class scalar:
     """
 
     def __init__(self):
-
         self.first = 0.0
         self.second = 0.0
         self.norm = 0.0
 
     def __iter__(self):
-
         yield "first", self.first
         yield "second", self.second
         yield "norm", self.norm
@@ -51,7 +49,6 @@ def restore(cls, first: float = 0, second: float = 0, norm: float = 0):
         return ret
 
     def __add__(self, datum: float):
-
         self.add_sample(datum)
         return self
 
@@ -143,7 +140,6 @@ def __init__(
         shape: tuple[int] = None,
         dtype: DTypeLike = np.float64,
     ):
-
         self.compute_variance = compute_variance
         self.norm = None
         self.first = None
@@ -158,7 +154,6 @@ def __init__(
             self.second = np.zeros(shape, dtype)
 
     def __iter__(self):
-
         yield "first", self.first
         yield "second", self.second
         yield "norm", self.norm
@@ -179,7 +174,6 @@ def restore(
         """
 
         if norm is not None:
-
             assert first is not None
             assert first.shape == norm.shape
 
@@ -193,7 +187,6 @@ def restore(
         return ret
 
     def _allocate(self, shape, dtype):
-
         self.norm = np.zeros(shape, np.int64)
         self.first = np.zeros(shape, dtype)
         if self.compute_variance:
@@ -234,7 +227,6 @@ def ravel(self) -> scalar:
         )
 
     def __add__(self, data: ArrayLike):
-
         self.add_sample(data)
         return self
 
@@ -340,7 +332,6 @@ def std(self, min_norm: int = 2) -> ArrayLike:
 
 
 def _expand_array1d(data, size):
-
     tmp = np.zeros((size), data.dtype)
     tmp[: data.size] = data
     return tmp
@@ -382,7 +373,6 @@ def __init__(
         )
 
     def _expand(self, size: int):
-
         if size <= self.first.size:
             return
 
@@ -393,12 +383,10 @@ def _expand(self, size: int):
             self.second = _expand_array1d(self.second, size)
 
     def __add__(self, data: ArrayLike):
-
         self.add_sample(data)
         return self
 
     def add_sample(self, data: ArrayLike):
-
         assert data.ndim == 1
 
         if self.first is None:
@@ -413,7 +401,6 @@ def add_sample(self, data: ArrayLike):
             self.second[: data.size] += data**2
 
     def add_point(self, datum: float | int, index: int):
-
         if self.first is None:
             self._allocate(index + 1, type(datum))
         else:
@@ -442,7 +429,6 @@ def __init__(
         right: bool = False,
         bound_error: str = "raise",
     ):
-
         assert np.all(np.diff(bin_edges) > 0) or np.all(np.diff(bin_edges) < 0)
 
         self.right = right
@@ -455,7 +441,6 @@ def __init__(
         self.count = np.zeros((len(bin_edges) - 1), np.uint64)
 
     def __iter__(self):
-
         yield "bin_edges", self.bin_edges
         yield "count", self.count
         yield "count_left", self.count_left
@@ -709,7 +694,6 @@ def as_integer(self):
         self.right = False
 
     def __add__(self, data: ArrayLike):
-
         self.add_sample(data)
         return self
 

enstat/detail.py

@@ -27,7 +27,6 @@ def histogram_bin_edges(data: ArrayLike, bins: int, mode: str, min_count: int =
         return bin_edges
 
     if mode == "uniform":
-
         if min_count and bins is None:
             if not isinstance(min_count, int):
                 raise OSError('"min_count" must be an integer number')
@@ -44,7 +43,6 @@ def histogram_bin_edges(data: ArrayLike, bins: int, mode: str, min_count: int =
         return bin_edges
 
     if mode == "voronoi":
-
         mid_points = np.unique(data)
         diff = np.diff(mid_points)
         bin_edges = mid_points + np.hstack((0.5 * diff, 0.5 * diff[-1]))
@@ -63,7 +61,6 @@ def histogram_bin_edges_minwidth(bin_edges: ArrayLike, min_width: int) -> ArrayL
     """
 
     while True:
-
         idx = np.where(np.diff(bin_edges) < min_width)[0]
 
         if len(idx) == 0:
@@ -94,7 +91,6 @@ def histogram_bin_edges_mincount(
     assert isinstance(min_count, int)
 
     while True:
-
         count, _ = np.histogram(data, bins=bin_edges, density=False)
 
         idx = np.where(count < min_count)[0]

enstat/mean.py

@@ -19,7 +19,6 @@ class Scalar:
     """
 
     def __init__(self, first=0, second=0, norm=0):
-
         warnings.warn("Deprecated. Use: enstat.scalar.", DeprecationWarning)
 
         self.m_first = first
@@ -136,7 +135,6 @@ def __init__(
         second=None,
         norm=None,
     ):
-
         warnings.warn("Deprecated. Use: enstat.static.", DeprecationWarning)
 
         self.m_compute_variance = compute_variance
@@ -147,7 +145,6 @@ def __init__(
         self.m_dtype = dtype
 
     def _allocate(self, data):
-
         if self.m_first is not None:
             assert data.shape == self.m_shape
             return
@@ -267,7 +264,6 @@ def second(self):
 
 
 def _expand_array1d(data, size):
-
     tmp = np.zeros((size), data.dtype)
     tmp[: data.size] = data
     return tmp
@@ -308,7 +304,6 @@ def __init__(
         second=None,
         norm=None,
     ):
-
         warnings.warn("Deprecated. Use: enstat.dynamic1d.", DeprecationWarning)
 
         self.m_compute_variance = compute_variance
@@ -338,7 +333,6 @@ def _allocate(self, data):
             self.m_second = np.zeros((self.m_size), self.m_dtype)
 
     def _expand(self, data):
-
         if data.size <= self.m_size:
             return