Update the pre-commit hooks

.github/workflows/python-publish.yml

@@ -9,37 +9,37 @@
 name: Release Package
 
 on:
-  push:
-    tags:
-      - '*'
+    push:
+        tags:
+            - '*'
 
 jobs:
-  release-tag:
-    name: "Tagged Release"
-    runs-on: "ubuntu-latest"
-    steps:
-      - name: Create a new GitHub release
-        uses: "marvinpinto/action-automatic-releases@latest"
-        with:
-          repo_token: "${{ secrets.GITHUB_TOKEN }}"
-          prerelease: false
+    release-tag:
+        name: 'Tagged Release'
+        runs-on: 'ubuntu-latest'
+        steps:
+            - name: Create a new GitHub release
+              uses: 'marvinpinto/action-automatic-releases@latest'
+              with:
+                  repo_token: '${{ secrets.GITHUB_TOKEN }}'
+                  prerelease: false
 
-  release-to-pypi:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python
-      uses: actions/setup-python@v2
-      with:
-        python-version: '3.x'
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install build
-    - name: Build package
-      run: python -m build
-    - name: Publish package
-      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
-      with:
-        user: __token__
-        password: ${{ secrets.PYPI_API_TOKEN }}
+    release-to-pypi:
+        runs-on: ubuntu-latest
+        steps:
+            - uses: actions/checkout@v2
+            - name: Set up Python
+              uses: actions/setup-python@v2
+              with:
+                  python-version: '3.x'
+            - name: Install dependencies
+              run: |
+                  python -m pip install --upgrade pip
+                  pip install build
+            - name: Build package
+              run: python -m build
+            - name: Publish package
+              uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+              with:
+                  user: __token__
+                  password: ${{ secrets.PYPI_API_TOKEN }}

.pre-commit-config.yaml

@@ -1,42 +1,26 @@
 repos:
     - repo: https://github.com/pre-commit/pre-commit-hooks
-      rev: v4.0.1
+      rev: v4.4.0
       hooks:
           - id: trailing-whitespace
           - id: end-of-file-fixer
           - id: check-yaml
           - id: check-added-large-files
           - id: check-ast
-    - repo: https://gitlab.com/pycqa/flake8
-      rev: 3.9.2
+    - repo: https://github.com/psf/black
+      rev: 23.3.0
       hooks:
-          - id: flake8
-            additional_dependencies:
-                [
-                    flake8-docstrings,
-                    flake8-mutable,
-                    flake8-debugger,
-                    flake8-pytest-style,
-                    pep8-naming,
-                    flake8-simplify,
-                    darglint,
-                ]
+          - id: black
+    - repo: https://github.com/astral-sh/ruff-pre-commit
+      rev: v0.0.270
+      hooks:
+          - id: ruff
+            args: [--fix, --exit-non-zero-on-fix]
     - repo: https://github.com/pre-commit/mirrors-prettier
-      rev: v2.3.2
+      rev: v3.0.0-alpha.9-for-vscode
       hooks:
           - id: prettier
     - repo: https://github.com/pre-commit/mirrors-mypy
-      rev: 'v0.910'
+      rev: 'v1.3.0'
       hooks:
           - id: mypy
-    - repo: https://github.com/pycqa/isort
-      rev: 5.9.3
-      hooks:
-          - id: isort
-            name: isort (python)
-          - id: isort
-            name: isort (cython)
-            types: [cython]
-          - id: isort
-            name: isort (pyi)
-            types: [pyi]

pyforc/core/__init__.py

@@ -1 +0,0 @@
-from . import config, coordinates, forc, forcdata, ingester, ops, plot

pyforc/core/config.py

@@ -40,7 +40,7 @@ class Config:
 
     file_name: Optional[str] = None
     step: Optional[float] = None
-    interpolation: str = 'cubic'
+    interpolation: str = "cubic"
     drift_correction: bool = True
     drift_kernel_size: int = 4
     drift_density: int = 3

pyforc/core/forc.py

@@ -5,7 +5,7 @@
 from .ingester import IngesterBase
 
 
-class Forc():
+class Forc:
     """
     Generic for storing, extracting, and processing FORC data.
 

pyforc/core/forcdata.py

@@ -1,5 +1,6 @@
 """Containers for holding FORC data as arrays."""
 from __future__ import annotations
+from typing import Optional
 
 import numpy as np
 
@@ -11,11 +12,11 @@ class ForcData:
 
     Parameters
     ----------
-    h_raw : list[np.ndarray]
+    h_raw : Optional[list[np.ndarray]]
         Raw h data
-    m_raw : list[np.ndarray]
+    m_raw : Optional[list[np.ndarray]]
         Raw m data
-    t_raw : list[np.ndarray]
+    t_raw : Optional[list[np.ndarray]]
         Raw t data
     m_drift : np.ndarray
         Values of the magnetization at the drift points.
@@ -33,9 +34,9 @@ class ForcData:
 
     def __init__(
         self,
-        h_raw: list[np.ndarray] = None,
-        m_raw: list[np.ndarray] = None,
-        t_raw: list[np.ndarray] = None,
+        h_raw: Optional[list[np.ndarray]] = None,
+        m_raw: Optional[list[np.ndarray]] = None,
+        t_raw: Optional[list[np.ndarray]] = None,
         m_drift: np.ndarray = None,
         h: np.ndarray = None,
         hr: np.ndarray = None,
@@ -68,9 +69,9 @@ def get_step(self) -> float:
     @staticmethod
     def from_existing(
         data: ForcData,
-        h_raw: list[np.ndarray] = None,
-        m_raw: list[np.ndarray] = None,
-        t_raw: list[np.ndarray] = None,
+        h_raw: Optional[list[np.ndarray]] = None,
+        m_raw: Optional[list[np.ndarray]] = None,
+        t_raw: Optional[list[np.ndarray]] = None,
         m_drift: np.ndarray = None,
         h: np.ndarray = None,
         hr: np.ndarray = None,
@@ -84,11 +85,11 @@ def from_existing(
         ----------
         data : ForcData
             Data which is to be copied over to the new instance
-        h_raw : list[np.ndarray]
+        h_raw : Optional[list[np.ndarray]]
             Raw magnetization data
-        m_raw : list[np.ndarray]
+        m_raw : Optional[list[np.ndarray]]
             Raw magnetization data
-        t_raw : list[np.ndarray]
+        t_raw : Optional[list[np.ndarray]]
             Raw temperature data
         m_drift : np.ndarray
             Drift magnetization measurements
@@ -190,7 +191,7 @@ def get_limits(
             (x_min, x_max), (y_min, y_max)
         """
         if mask:
-            data_mask = (self.h >= self.hr)
+            data_mask = self.h >= self.hr
             h = self.h[data_mask].flatten()
             hr = self.hr[data_mask].flatten()
         else:

pyforc/core/ingester.py

@@ -60,9 +60,9 @@ class PMCIngester(IngesterBase):
     """Ingester for data measured by Princeton Measurements Corporation (now Lakeshore) VSMs."""
 
     pattern = (
-        r'(?P<h>([+-]\d+\.\d+(E[+-]\d+)?)),'
-        r'(?P<m>([+-]\d+\.\d+(E[+-]\d+)?))'
-        r'(,(?P<t>([+-]\d+\.\d+(E[+-]\d+)?)))?'
+        r"(?P<h>([+-]\d+\.\d+(E[+-]\d+)?)),"
+        r"(?P<m>([+-]\d+\.\d+(E[+-]\d+)?))"
+        r"(,(?P<t>([+-]\d+\.\d+(E[+-]\d+)?)))?"
     )
 
     def ingest(self) -> ForcData:
@@ -76,12 +76,12 @@ def ingest(self) -> ForcData:
         if not self.config.file_name:
             raise ValueError("No file name specified.")
 
-        with open(self.config.file_name, 'r') as f:
+        with open(self.config.file_name, "r") as f:
             lines = f.readlines()
 
         # Find first data line
         i = 0
-        while i < len(lines) and lines[i][0] not in '-+':
+        while i < len(lines) and lines[i][0] not in "-+":
             i += 1
 
         header = lines[:i]
@@ -114,9 +114,9 @@ def ingest_curve(self, lines: list[str], i: int) -> tuple[np.ndarray, ...]:
 
             if match:
                 groups = match.groupdict()
-                h_buf.append(float(groups['h']))
-                m_buf.append(float(groups['m']))
-                t_buf.append(float(groups['t']) if groups['t'] else np.nan)
+                h_buf.append(float(groups["h"]))
+                m_buf.append(float(groups["m"]))
+                t_buf.append(float(groups["t"]) if groups["t"] else np.nan)
 
             else:
                 # End of the reversal curve
@@ -148,12 +148,11 @@ def ingest_from_hchb(self, lines: list[str], i: int) -> ForcData:
             if match:
                 # Handle drift point
                 groups = match.groupdict()
-                m_drift.append(float(groups['m']))
+                m_drift.append(float(groups["m"]))
 
                 # Next line should be blank; line after is the start of the reversal curve data
-                if (
-                    re.search(self.pattern, lines[i + 1])
-                    or not re.search(self.pattern, lines[i + 2])
+                if re.search(self.pattern, lines[i + 1]) or not re.search(
+                    self.pattern, lines[i + 2]
                 ):
                     raise ValueError(f"Unexpected data format starting on line {i}")
 
@@ -228,4 +227,4 @@ def is_hhr(header: list[str]) -> bool:
         bool
             True if the header comes from a file measured across h/hr space, False otherwise.
         """
-        return not any(re.match('(Hc1|Hc2|Hb1|Hb2).*', line) for line in header)
+        return not any(re.match("(Hc1|Hc2|Hb1|Hb2).*", line) for line in header)

pyforc/core/ops.py

@@ -52,10 +52,11 @@ def interpolate(
         np.linspace(hr_min, hr_max, int((hr_max - hr_min) // step) + 1),
     )
 
-    hhr_vals = np.concatenate(
-        (np.reshape(h_vals, (-1, 1)), np.reshape(hr_vals, (-1, 1))),
-        axis=1
-    ),
+    hhr_vals = (
+        np.concatenate(
+            (np.reshape(h_vals, (-1, 1)), np.reshape(hr_vals, (-1, 1))), axis=1
+        ),
+    )
 
     m = si.griddata(hhr_vals, m_vals, (h, hr), method=config.interpolation)
 
@@ -117,15 +118,11 @@ def correct_drift(data: ForcData, config: Config) -> ForcData:
     m_sat_avg = np.mean(data.m_drift)
 
     m_sat_mov = decimate(
-        moving_average(data.m_drift, config.drift_kernel_size),
-        config.drift_density
-    )
-    index_mov = decimate(
-        np.arange(0, len(data.m_drift)),
-        config.drift_density
+        moving_average(data.m_drift, config.drift_kernel_size), config.drift_density
     )
+    index_mov = decimate(np.arange(0, len(data.m_drift)), config.drift_density)
 
-    m_sat_interp = si.interp1d(index_mov, m_sat_mov, kind='cubic')
+    m_sat_interp = si.interp1d(index_mov, m_sat_mov, kind="cubic")
 
     m_raw = []
     for i, curve in enumerate(data.m_raw):
@@ -179,7 +176,7 @@ def moving_average(data: np.ndarray, kernel_size: int) -> np.ndarray:
     return sn.convolve(
         data,
         np.ones(window_size) / window_size,
-        mode='nearest',
+        mode="nearest",
     )
 
 
@@ -215,7 +212,8 @@ def normalize(data: ForcData, _) -> ForcData:
     """
     return ForcData.from_existing(
         data=data,
-        m=1 - 2 * (np.nanmax(data.m) - data.m) / (np.nanmax(data.m) - np.nanmin(data.m))
+        m=1
+        - 2 * (np.nanmax(data.m) - data.m) / (np.nanmax(data.m) - np.nanmin(data.m)),
     )
 
 
@@ -244,19 +242,16 @@ def correct_slope(data: ForcData, config: Config) -> ForcData:
     ForcData
         Data with the background subtracted out.
     """
-    fit_region = (np.abs(data.h) > config.h_sat) & (~np.isnan(data.m)) & (data.h >= data.hr)
+    fit_region = (
+        (np.abs(data.h) > config.h_sat) & (~np.isnan(data.m)) & (data.h >= data.hr)
+    )
 
     h = data.h[fit_region].flatten()
     m = data.m[fit_region].flatten()
 
-    (a, b1, b2), _ = so.curve_fit(
-        generate_fit_func(h, config.h_sat), h, m
-    )
+    (a, b1, b2), _ = so.curve_fit(generate_fit_func(h, config.h_sat), h, m)
 
-    return ForcData.from_existing(
-        data=data,
-        m=data.m - line(data.h, a, 0)
-    )
+    return ForcData.from_existing(data=data, m=data.m - line(data.h, a, 0))
 
 
 def generate_fit_func(
@@ -289,6 +284,7 @@ def fit_func(h: np.ndarray, a: float, b1: float, b2: float) -> np.ndarray:
         y[i_upper_saturation] = line(h[i_upper_saturation], a, b1)
         y[~i_upper_saturation] = line(h[~i_upper_saturation], a, b2)
         return y
+
     return fit_func
 
 
@@ -340,12 +336,13 @@ def compute_forc_distribution(data: ForcData, config: Config) -> ForcData:
     step = data.get_step()
     return ForcData.from_existing(
         data=data,
-        rho=-0.5 * sn.convolve(
+        rho=-0.5
+        * sn.convolve(
             input=data.m,
             weights=compute_sg_kernel(config.smoothing_factor, step, step),
-            mode='constant',
+            mode="constant",
             cval=np.nan,
-        )
+        ),
     )
 
 
@@ -370,12 +367,14 @@ def compute_sg_kernel(sf: int, step_x: float, step_y: float) -> np.ndarray:
     """
     xx, yy = np.meshgrid(
         np.linspace(sf * step_x, -sf * step_x, 2 * sf + 1),
-        np.linspace(sf * step_y, -sf * step_y, 2 * sf + 1)
+        np.linspace(sf * step_y, -sf * step_y, 2 * sf + 1),
     )
 
     xx = np.reshape(xx, (-1, 1))
     yy = np.reshape(yy, (-1, 1))
 
-    coefficients = np.linalg.pinv(np.hstack((np.ones_like(xx), xx, xx ** 2, yy, yy ** 2, xx * yy)))
+    coefficients = np.linalg.pinv(
+        np.hstack((np.ones_like(xx), xx, xx**2, yy, yy**2, xx * yy))
+    )
 
     return np.reshape(coefficients[5, :], (2 * sf + 1, 2 * sf + 1))