whenxuan: black init for the repo

Author: wwhenxuan

docs/ipynb_to_gallery.py

@@ -200,7 +200,9 @@ def _convert_table(lines: list[str]) -> str:
 def _convert_blockquote(lines: list[str]) -> str:
     body = [re.sub(r"^\s*>\s?", "", line) for line in lines]
     converted = _convert_inline("\n".join(body)).strip()
-    indented = ["    " + line if line.strip() else "" for line in converted.splitlines()]
+    indented = [
+        "    " + line if line.strip() else "" for line in converted.splitlines()
+    ]
     return "\n\n.. epigraph::\n\n" + "\n".join(indented) + "\n"
 
 
@@ -256,12 +258,18 @@ def _stash_display(match: re.Match[str]) -> str:
             out.append("")
             continue
 
-        if stripped.startswith("|") and i + 1 < n and (
-            TABLE_SEP_RE.match(lines[i + 1]) or lines[i + 1].strip().startswith("|")
+        if (
+            stripped.startswith("|")
+            and i + 1 < n
+            and (
+                TABLE_SEP_RE.match(lines[i + 1]) or lines[i + 1].strip().startswith("|")
+            )
         ):
             table_lines = [line]
             i += 1
-            while i < n and (lines[i].strip().startswith("|") or TABLE_SEP_RE.match(lines[i])):
+            while i < n and (
+                lines[i].strip().startswith("|") or TABLE_SEP_RE.match(lines[i])
+            ):
                 table_lines.append(lines[i])
                 i += 1
             out.append(_convert_table(table_lines))
@@ -312,7 +320,9 @@ def _stash_display(match: re.Match[str]) -> str:
 
     text = "\n".join(out)
     for idx, body in enumerate(display_blocks):
-        text = text.replace(f"@@DISPLAYMATH{idx}@@", _rst_display_math(body).strip("\n"))
+        text = text.replace(
+            f"@@DISPLAYMATH{idx}@@", _rst_display_math(body).strip("\n")
+        )
     text = re.sub(r"\n{3,}", "\n\n", text)
     return text.strip() + "\n"
 
@@ -336,11 +346,19 @@ def strip_boilerplate(code: str) -> str:
     code = BOILERPLATE_RE.sub("", code)
     code = PYSDKIT_PATH_PRINT_RE.sub("", code)
     leftover = code
-    if "sys." not in leftover and not re.search(r"\bsys\b", leftover.replace("import sys", "")):
-        leftover = re.sub(r"^import sys\s*\n", "", leftover, count=1, flags=re.MULTILINE)
-    uses_path = bool(re.search(r"\bPath\b", leftover.replace("from pathlib import Path", "")))
+    if "sys." not in leftover and not re.search(
+        r"\bsys\b", leftover.replace("import sys", "")
+    ):
+        leftover = re.sub(
+            r"^import sys\s*\n", "", leftover, count=1, flags=re.MULTILINE
+        )
+    uses_path = bool(
+        re.search(r"\bPath\b", leftover.replace("from pathlib import Path", ""))
+    )
     if not uses_path:
-        leftover = re.sub(r"^from pathlib import Path\s*\n", "", leftover, count=1, flags=re.MULTILINE)
+        leftover = re.sub(
+            r"^from pathlib import Path\s*\n", "", leftover, count=1, flags=re.MULTILINE
+        )
     leftover = re.sub(r"\n{3,}", "\n\n", leftover)
     return leftover.strip() + ("\n" if leftover.strip() else "")
 
@@ -403,7 +421,9 @@ def convert_notebook(ipynb_path: Path) -> str:
 
 def write_gallery_header(path: Path, title: str, body: str) -> None:
     underline = "=" * len(title)
-    path.write_text(f"{title}\n{underline}\n\n{body.rstrip()}\n", encoding="utf-8", newline="\n")
+    path.write_text(
+        f"{title}\n{underline}\n\n{body.rstrip()}\n", encoding="utf-8", newline="\n"
+    )
 
 
 def convert_all(*, delete_ipynb: bool = True) -> list[Path]:

docs/source/about/index.rst

@@ -47,12 +47,13 @@ Cite **two things** when a result depends on this library.
    Replace ``X.Y.Z`` with the installed version.  If a software paper
    (for example JOSS) appears later, prefer that citation.
 
-People
-------
+Developers
+----------
 
 PySDKit is an open-source project started in April 2024.  Releases,
-review, and the documentation are handled by the maintainers; algorithms
-and fixes come from a wider set of contributors.
+review, and the documentation are handled by the maintainers; academic
+guidance is listed as project **Support**.  Algorithms and fixes come
+from a wider set of contributors.
 
 .. list-table::
    :header-rows: 1
@@ -66,7 +67,10 @@ and fixes come from a wider set of contributors.
      - wwhenxuan@gmail.com
    * - `RuiZhe Wang <https://github.com/changewam>`_
      - Maintainer
-     - 3133986068@qq.com
+     - changewam6@gmail.com
+   * - `Dan Wang <https://web.xidian.edu.cn/danwang/>`_
+     - Support
+     - danwang@xidian.edu.cn
 
 Package metadata also lists Rongkun Zhu, Kai Wu, Lei Wang, josefinez,
 Deeksha Manjunath, Yuan Feng, WenTong Zhao, and JacktheFowler.  The

docs/source/index.rst

@@ -56,7 +56,7 @@ Quick links
       :link: about/index
       :link-type: doc
 
-      License, how to cite, maintainers, and related projects.
+      License, how to cite, people, and related projects.
 
 .. toctree::
    :hidden:

examples/acmd/acmd.py

@@ -15,8 +15,8 @@
 
 .. epigraph::
 
-    S. Chen, Y. Yang, Z. Peng, *et al.*  
-    *Detection of Rub-Impact Fault for Rotor-Stator Systems: A Novel Method Based on Adaptive Chirp Mode Decomposition.*  
+    S. Chen, Y. Yang, Z. Peng, *et al.*
+    *Detection of Rub-Impact Fault for Rotor-Stator Systems: A Novel Method Based on Adaptive Chirp Mode Decomposition.*
     Journal of Sound and Vibration, 2018.
 
 MATLAB code: `File Exchange 69128 <https://www.mathworks.com/matlabcentral/fileexchange/69128-adaptive-chirp-mode-decomposition>`_.

examples/acmd/ba_acmd.py

@@ -17,9 +17,9 @@
 
 .. epigraph::
 
-    S. Chen, L. Guo, J. Fan, C. Yi, K. Wang, W. Zhai.  
-    *Bandwidth-aware adaptive chirp mode decomposition for railway bearing fault diagnosis.*  
-    Structural Health Monitoring, 23(2):876–902, 2024.  
+    S. Chen, L. Guo, J. Fan, C. Yi, K. Wang, W. Zhai.
+    *Bandwidth-aware adaptive chirp mode decomposition for railway bearing fault diagnosis.*
+    Structural Health Monitoring, 23(2):876–902, 2024.
     DOI: `10.1177/14759217231174699 <https://doi.org/10.1177/14759217231174699>`_
 
 MATLAB code: `File Exchange 132792 <https://www.mathworks.com/matlabcentral/fileexchange/132792>`_.

examples/acmd/dd_acmd.py

@@ -10,9 +10,9 @@
 
 .. epigraph::
 
-    H. Wang, S. Chen, W. Zhai.  
-    *Data-driven adaptive chirp mode decomposition with application to machine fault diagnosis under non-stationary conditions.*  
-    Mechanical Systems and Signal Processing, 2023.  
+    H. Wang, S. Chen, W. Zhai.
+    *Data-driven adaptive chirp mode decomposition with application to machine fault diagnosis under non-stationary conditions.*
+    Mechanical Systems and Signal Processing, 2023.
     DOI / MSSP: `S0888327022010652 <https://www.sciencedirect.com/science/article/abs/pii/S0888327022010652>`_
 
 MATLAB code: `File Exchange 121373 <https://www.mathworks.com/matlabcentral/fileexchange/121373>`_.
@@ -25,7 +25,7 @@
 # .. list-table::
 #    :header-rows: 1
 #
-#    * -  
+#    * -
 #      - **ACMD**
 #      - **DD-ACMD**
 #    * - Role
@@ -314,6 +314,7 @@
 imfs3, ini3, eif3, eia3 = dd3.fit_transform(sig3, return_all=True)
 print(f"Extracted K = {imfs3.shape[0]} modes")
 
+
 # Match each true chirp to the nearest estimated IF (skip near-zero trend if present)
 def _match_mode(true_if, eifs):
     errs = [
@@ -322,6 +323,7 @@ def _match_mode(true_if, eifs):
     ]
     return int(np.argmin(errs))
 
+
 fig, ax = plt.subplots(figsize=(8, 4))
 colors = ["C0", "C1", "C2"]
 for k, (true_if, true_m) in enumerate(zip(ifs3_true, modes3)):

examples/deconvolution/acycbd.py

@@ -33,7 +33,7 @@
 .. list-table::
    :header-rows: 1
 
-   * -  
+   * -
      - **CYCBD**
      - **ACYCBD** (this notebook)
    * - Cyclic frequency

examples/deconvolution/imckd.py

@@ -34,7 +34,7 @@
 .. list-table::
    :header-rows: 1
 
-   * -  
+   * -
      - **MCKD**
      - **IMCKD** (this notebook)
    * - Period :math:`T`

examples/demo.py

@@ -199,6 +199,7 @@
 #
 # Dragomiretskiy & Zosso extend VMD to 2D: each mode is compact around an unknown center frequency :math:`\boldsymbol{\omega}_k\in\mathbb{R}^2`, which suits textures and directional oscillations. Below we use ``test_grayscale()`` (paper-style synthetic texture) and ``test_univariate_image`` for a quick demo; full experiments live in ``examples/image/vmd2d.py``.
 
+
 def show_image_modes(img, modes, title=""):
     """modes: (H, W, K)"""
     K = modes.shape[2]
@@ -216,6 +217,7 @@ def show_image_modes(img, modes, title=""):
     plt.tight_layout()
     plt.show()
 
+
 img = test_grayscale()
 print("grayscale texture shape:", img.shape)
 

examples/emd/emd.py

@@ -12,16 +12,16 @@
 
 This notebook walks through the **complete sifting pipeline** with visualizations at every stage:
 
-#. Motivation and IMF definition  
-#. Algorithm overview (paper flow diagram)  
-#. Demo signal  
-#. Extremum detection  
-#. Upper / lower envelope construction  
-#. Local mean (low-frequency trend)  
-#. One sifting step: subtract the mean  
-#. Multiple sifting iterations until an IMF  
-#. Full EMD with PySDKit  
-#. Completeness and reconstruction  
+#. Motivation and IMF definition
+#. Algorithm overview (paper flow diagram)
+#. Demo signal
+#. Extremum detection
+#. Upper / lower envelope construction
+#. Local mean (low-frequency trend)
+#. One sifting step: subtract the mean
+#. Multiple sifting iterations until an IMF
+#. Full EMD with PySDKit
+#. Completeness and reconstruction
 #. Summary
 """
 
@@ -39,7 +39,7 @@
 #
 # An IMF :math:`x_n(t)` must satisfy (Zeiler et al. / Huang et al.):
 #
-# #. **Extremum–zero-crossing balance**: between two successive zero crossings there is only one extremum — equivalently, the numbers of local maxima and minima differ by at most one.  
+# #. **Extremum–zero-crossing balance**: between two successive zero crossings there is only one extremum — equivalently, the numbers of local maxima and minima differ by at most one.
 # #. **Zero local mean**: the mean of the upper and lower envelopes is (approximately) zero.
 #
 # An IMF may still have **amplitude and frequency modulation**; the zero-mean condition makes subsequent Hilbert analysis physically meaningful.
@@ -59,23 +59,23 @@
 #
 # Paper §II / Fig. 1. To extract the :math:`n`-th IMF from the current residue :math:`r_{n-1}(t)`:
 #
-# #. Initialize :math:`h_0(t):=r_{n-1}(t)`, :math:`k:=1`.  
-# #. **Find extrema** of :math:`h_{k-1}(t)`.  
-# #. **Interpolate** cubic-spline envelopes :math:`U_{k-1}(t)` (through maxima) and :math:`L_{k-1}(t)` (through minima).  
+# #. Initialize :math:`h_0(t):=r_{n-1}(t)`, :math:`k:=1`.
+# #. **Find extrema** of :math:`h_{k-1}(t)`.
+# #. **Interpolate** cubic-spline envelopes :math:`U_{k-1}(t)` (through maxima) and :math:`L_{k-1}(t)` (through minima).
 # #. **Local mean** (low-frequency trend):
 #
 # .. math::
 #
 #    m_{k-1}(t)=\frac{U_{k-1}(t)+L_{k-1}(t)}{2}.
 #
-#    (Some texts misprint this as a difference; the physically correct trend estimate is the **average** of the envelopes.)  
+#    (Some texts misprint this as a difference; the physically correct trend estimate is the **average** of the envelopes.)
 # #. **Sift**:
 #
 # .. math::
 #
 #    h_k(t)=h_{k-1}(t)-m_{k-1}(t).
 #
-# #. If :math:`h_k` is not yet an IMF, set :math:`k\leftarrow k+1` and repeat from step 1; otherwise set :math:`x_n:=h_k` and :math:`r_n:=r_{n-1}-x_n`.  
+# #. If :math:`h_k` is not yet an IMF, set :math:`k\leftarrow k+1` and repeat from step 1; otherwise set :math:`x_n:=h_k` and :math:`r_n:=r_{n-1}-x_n`.
 # #. Stop when :math:`r_n` is a monotonic / low-variation residuum; otherwise continue with :math:`n\leftarrow n+1`.
 #
 # The next cells implement and visualize steps 1–5 explicitly before calling the full PySDKit ``EMD`` class.
@@ -98,11 +98,13 @@
     }
 )
 
+
 def relative_error(ref, est):
     ref = np.asarray(ref, dtype=float)
     est = np.asarray(est, dtype=float)
     return np.linalg.norm(est - ref) / (np.linalg.norm(ref) + 1e-16)
 
+
 def find_extrema_simple(t, x):
     # Discrete extrema via first differences (same idea as EMD extrema_detection='simple').
     dx = np.diff(x)
@@ -121,6 +123,7 @@ def find_extrema_simple(t, x):
             min_idx = np.unique(np.r_[min_idx, len(x) - 1])
     return max_idx, min_idx
 
+
 def cubic_envelope(t, t_ext, x_ext):
     # Build a cubic spline through extrema; require >= 2 points.
     if len(t_ext) < 2:
@@ -135,6 +138,7 @@ def cubic_envelope(t, t_ext, x_ext):
     cs = CubicSpline(t_ext, x_ext, bc_type="not-a-knot", extrapolate=True)
     return cs(t)
 
+
 def one_sift_step(t, h):
     # One complete sifting iteration: extrema -> envelopes -> mean -> h - m.
     max_idx, min_idx = find_extrema_simple(t, h)
@@ -151,6 +155,7 @@ def one_sift_step(t, h):
         "h_new": h_new,
     }
 
+
 # %%
 # 3. Demo signal
 # --------------
@@ -289,8 +294,8 @@ def one_sift_step(t, h):
 #
 # We repeat Steps A–D for several iterations and watch:
 #
-# * envelopes become more symmetric;  
-# * the local mean shrinks toward zero;  
+# * envelopes become more symmetric;
+# * the local mean shrinks toward zero;
 # * the prototype becomes a zero-mean oscillation (IMF candidate).
 
 n_show = 4
@@ -431,9 +436,9 @@ def one_sift_step(t, h):
 #
 # **Key takeaways**
 #
-# #. EMD is **data-driven**: no preset frequencies or mother wavelet.  
-# #. Sifting is a local, iterative high-pass operation guided by envelopes.  
-# #. Completeness: :math:`x=\sum\mathrm{IMFs}+r`.  
+# #. EMD is **data-driven**: no preset frequencies or mother wavelet.
+# #. Sifting is a local, iterative high-pass operation guided by envelopes.
+# #. Completeness: :math:`x=\sum\mathrm{IMFs}+r`.
 # #. IMFs enable the **Hilbert–Huang Transform** (next example: ``hht.py``).
 #
 # One-liner