Improve linting ruleset, upgrade ruff to 0.3.0

pyproject.toml

@@ -14,21 +14,21 @@ packages = [{ include = "qudi_hira_analysis" }]
 [tool.poetry.dependencies]
 python = ">=3.10,<3.13"
 lmfit = "^1.1.0"
-matplotlib = "^3.6.2"
+matplotlib = "^3.8.2"
 numpy = "^1.26.0"
 pandas = "^2.0.0"
 xlrd = "^2.0.1"
 openpyxl = "^3.0.10"
 tqdm = "^4.64.1"
-pyspm = "^0.5.0"
+pyspm = "^0.6.0"
 
 [tool.poetry.group.dev]
 optional = true
 
 [tool.poetry.group.dev.dependencies]
 jupyterlab = "^3.6.2"
 notebook = "^6.5.4"
-ruff = "^0.1.0"
+ruff = "^0.3.0"
 ruff-lsp = "^0.0.39"
 seaborn = "^0.13.0"
 
@@ -49,12 +49,21 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.ruff]
+target-version = "py310"
+
+[tool.ruff.lint]
 extend-select = [
+    "E", # pycodestyle-error
+    "W", # pycodestyle-warning
+    "F", # pyflakes
+    "N", # pep8-naming
     "B", # flake8-bugbear
+    "I", # isort
+    "ASYNC", # flake8-async
+    "BLE", # flake8-blind-except
+    "A", # flake8-builtins
     "C4", # flake8-comprehensions
     "ERA", # flake8-eradicate/eradicate
-    "I", # isort
-    "N", # pep8-naming
     "PIE", # flake8-pie
     "PGH", # pygrep
     "RUF", # ruff checks
@@ -63,3 +72,6 @@ extend-select = [
     "TID", # flake8-tidy-imports
     "UP", # pyupgrade
 ]
+
+[tool.ruff.format]
+docstring-code-format = true

qudi_hira_analysis/__init__.py

@@ -16,13 +16,13 @@
 from qudi_hira_analysis import DataHandler
 
 dh = DataHandler(
-    data_folder=Path("C:/Data"), # Path to the data folder
-    figure_folder=Path("C:/QudiHiraAnalysis"), # Path to the figure folder
-    measurement_folder=Path("20230101_NV1") # Name of the measurement folder
- )
- # Output:
- # qudi_hira_analysis.data_handler :: INFO :: Data folder path is C:/Data/20230101_NV1
- # qudi_hira_analysis.data_handler :: INFO :: Figure folder path is C:/QudiHiraAnalysis/20230101_NV1
+    data_folder=Path("C:/Data"),  # Path to the data folder
+    figure_folder=Path("C:/QudiHiraAnalysis"),  # Path to the figure folder
+    measurement_folder=Path("20230101_NV1"),  # Name of the measurement folder
+)
+# Output:
+# qudi_hira_analysis.data_handler :: INFO :: Data folder path is C:/Data/20230101_NV1
+# qudi_hira_analysis.data_handler :: INFO :: Figure folder path is C:/QudiHiraAnalysis/20230101_NV1
 ```
 
 ### Loading data
@@ -90,12 +90,17 @@
 
 
 ```python
-x_fit, y_fit, result = dh.fit(x="Controlled variable(Hz)", y="Signal",
-                              fit_function=dh.fit_function.lorentziandouble,
-                              data=odmr.data)
+x_fit, y_fit, result = dh.fit(
+    x="Controlled variable(Hz)",
+    y="Signal",
+    fit_function=dh.fit_function.lorentziandouble,
+    data=odmr.data,
+)
 
 # Plot the data and the fit
-ax = sns.scatterplot(x="Controlled variable(Hz)", y="Signal", data=odmr.data, label="Data")
+ax = sns.scatterplot(
+    x="Controlled variable(Hz)", y="Signal", data=odmr.data, label="Data"
+)
 sns.lineplot(x=x_fit, y=y_fit, ax=ax, label="Fit")
 ```
 
@@ -143,8 +148,9 @@
 
 ```python
 # Save the figure to the figure folder specified earlier
-dh.save_figures(filepath=Path("odmr"), fig=ax.get_figure(),
-                only_pdf=True, bbox_inches="tight")
+dh.save_figures(
+    filepath=Path("odmr"), fig=ax.get_figure(), only_pdf=True, bbox_inches="tight"
+)
 
 # The figure is saved to C:/QudiHiraAnalysis/20230101_NV1/odmr.pdf
 ```
@@ -167,11 +173,13 @@
 image = np.zeros((pixels, pixels))
 
 for idx, odmr in enumerate(odmr_measurements.values()):
-  row, col = odmr.xy_position
-  if len(odmr.fit_model.params) > 6:
-    # Calculate double Lorentzian splitting
-    image[row, col] = np.abs(odmr.fit_model.best_values["l1_center"]
-                             - odmr.fit_model.best_values["l0_center"])
+    row, col = odmr.xy_position
+    if len(odmr.fit_model.params) > 6:
+        # Calculate double Lorentzian splitting
+        image[row, col] = np.abs(
+            odmr.fit_model.best_values["l1_center"]
+            - odmr.fit_model.best_values["l0_center"]
+        )
 
 
 map = sns.heatmap(image, cbar_kws={"label": "Delta E (MHz)"})
@@ -249,9 +257,12 @@
     # Plot the data
     sns.lineplot(data=autocorrelation.data, x="Time (ns)", y="g2(t) norm", ax=ax)
     # Fit the data using the antibunching function
-    fit_x, fit_y, result = dh.fit(x="Time (ns)", y="g2(t) norm",
-                                  data=autocorrelation.data,
-                                  fit_function=dh.fit_function.antibunching)
+    fit_x, fit_y, result = dh.fit(
+        x="Time (ns)",
+        y="g2(t) norm",
+        data=autocorrelation.data,
+        fit_function=dh.fit_function.antibunching,
+    )
     # Plot the fit
     sns.lineplot(x=fit_x, y=fit_y, ax=ax, color="C1")
 
@@ -269,9 +280,12 @@
 
 for odmr in odmr_measurements.values():
     sns.scatterplot(data=odmr.data, x="Controlled variable(Hz)", y="Signal", ax=ax)
-    fit_x, fit_y, result = dh.fit(x="Controlled variable(Hz)", y="Signal",
-                                  data=odmr.data,
-                                  fit_function=dh.fit_function.lorentziandouble)
+    fit_x, fit_y, result = dh.fit(
+        x="Controlled variable(Hz)",
+        y="Signal",
+        data=odmr.data,
+        fit_function=dh.fit_function.lorentziandouble,
+    )
     sns.lineplot(x=fit_x, y=fit_y, ax=ax, color="C1")
 
 dh.save_figures(filepath="odmr_variation", fig=fig)
@@ -287,9 +301,12 @@
 
 for rabi in rabi_measurements.values():
     sns.scatterplot(data=rabi.data, x="Controlled variable(s)", y="Signal", ax=ax)
-    fit_x, fit_y, result = dh.fit(x="Controlled variable(s)", y="Signal",
-                                  data=rabi.data,
-                                  fit_function=dh.fit_function.sineexponentialdecay)
+    fit_x, fit_y, result = dh.fit(
+        x="Controlled variable(s)",
+        y="Signal",
+        data=rabi.data,
+        fit_function=dh.fit_function.sineexponentialdecay,
+    )
     sns.lineplot(x=fit_x, y=fit_y, ax=ax, color="C1")
 
 dh.save_figures(filepath="rabi_variation", fig=fig)

qudi_hira_analysis/_fitmethods/gaussianlikemethods.py

@@ -908,7 +908,7 @@ def estimate_twoDgaussian(self, x_axis, y_axis, data, params):
     for var in parameters:
         # FIXME: Why don't you check earlier?
         # FIXME: Check for 1D array, 2D
-        if not isinstance(var, (frozenset, list, set, tuple, np.ndarray)):
+        if not isinstance(var, frozenset | list | set | tuple | np.ndarray):
             self.log.error("Given parameter is not an array.")
             amplitude = 0.0
             center_x = 0.0
@@ -989,7 +989,7 @@ def estimate_twoDgaussian_MLE(self, x_axis, y_axis, data, params):
     for var in parameters:
         # FIXME: Why don't you check earlier?
         # FIXME: Check for 1D array, 2D
-        if not isinstance(var, (frozenset, list, set, tuple, np.ndarray)):
+        if not isinstance(var, frozenset | list | set | tuple | np.ndarray):
             self.log.error("Given parameter is not an array.")
             amplitude = 0.0
             center_x = 0.0

qudi_hira_analysis/_fitmethods/generalmethods.py

@@ -586,7 +586,7 @@ def _check_1D_input(self, x_axis, data, params):
     error = 0
     parameters = [x_axis, data]
     for var in parameters:
-        if not isinstance(var, (frozenset, list, set, tuple, np.ndarray)):
+        if not isinstance(var, frozenset | list | set | tuple | np.ndarray):
             self.log.error("Given parameter is no array.")
             error = -1
         elif len(np.shape(var)) != 1:

qudi_hira_analysis/_qudi_fit_logic.py

@@ -71,7 +71,7 @@ def __init__(self):
                     f"Adding fit methods path: {self._additional_methods_import_path}"
                 )
 
-            if isinstance(self._additional_methods_import_path, (list, tuple, set)):
+            if isinstance(self._additional_methods_import_path, list | tuple | set):
                 self.log.info(
                     "Adding fit methods path list: {}".format(
                         self._additional_methods_import_path

qudi_hira_analysis/analysis_logic.py

@@ -154,7 +154,7 @@ def fit(
             dims: str = "1d"
 
         if data is None:
-            if isinstance(x, (pd.Series, pd.Index)):
+            if isinstance(x, pd.Series | pd.Index):
                 x: np.ndarray = x.to_numpy()
             if isinstance(y, pd.Series):
                 y: np.ndarray = y.to_numpy()
@@ -493,7 +493,7 @@ def fit_raster_odmr(
         x = next(iter(odmr_measurements.values())).data["Freq(MHz)"].to_numpy()
         x_fit = np.linspace(start=x[0], stop=x[-1], num=int(len(x) * 2))
 
-        for odmr, res in zip(odmr_measurements.values(), model_results):
+        for odmr, res in zip(odmr_measurements.values(), model_results, strict=False):
             if len(res.params) == 6:
                 # Evaluate a single Lorentzian
                 y_fit = model1.eval(x=x_fit, params=res.params)

qudi_hira_analysis/data_handler.py

@@ -3,7 +3,7 @@
 import datetime
 import logging
 from pathlib import Path
-from typing import TYPE_CHECKING, Callable
+from typing import TYPE_CHECKING
 
 from qudi_hira_analysis.analysis_logic import AnalysisLogic
 from qudi_hira_analysis.io_handler import IOHandler
@@ -16,6 +16,8 @@
 )
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
+
     import numpy as np
     import pandas as pd
     import pySPM
@@ -159,7 +161,7 @@ def __tree(self, dir_path: Path, prefix: str = ""):
         contents = list(dir_path.iterdir())
         # contents each get pointers that are ├── with a final └── :
         pointers = [tee] * (len(contents) - 1) + [last]
-        for pointer, path in zip(pointers, contents):
+        for pointer, path in zip(pointers, contents, strict=False):
             yield prefix + pointer + path.name
             if path.is_dir():  # extend the prefix and recurse:
                 extension = branch if pointer == tee else space

qudi_hira_analysis/io_handler.py

@@ -4,9 +4,9 @@
 import inspect
 import itertools
 import pickle
+from collections.abc import Callable
 from functools import wraps
 from pathlib import Path
-from typing import Callable, Optional
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -19,8 +19,8 @@ class IOHandler:
 
     def __init__(
         self,
-        base_read_path: Optional[Path] = None,
-        base_write_path: Optional[Path] = None,
+        base_read_path: Path | None = None,
+        base_write_path: Path | None = None,
     ):
         super().__init__()
         self.base_read_path = base_read_path
@@ -130,7 +130,7 @@ def read_qudi_parameters(self, filepath: Path) -> dict:
                             timestamp_str = "".join(line.split(":")[1:]).strip()
                             datetime_str = datetime.datetime.strptime(
                                 timestamp_str, "%d.%m.%Y %Hh%Mmin%Ss"
-                            ).replace(tzinfo=datetime.timezone.utc)
+                            ).replace(tzinfo=datetime.UTC)
                             params[label] = datetime_str
                     except Exception as _:
                         pass

qudi_hira_analysis/measurement_dataclass.py

@@ -3,13 +3,14 @@
 import logging
 import re
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Callable
+from typing import TYPE_CHECKING
 
 import pandas as pd
 from PIL import Image
 
 if TYPE_CHECKING:
     import datetime
+    from collections.abc import Callable
     from pathlib import Path
 
     import lmfit
@@ -200,19 +201,19 @@ def get_param_from_filename(self, unit: str) -> float | None:
 
         Examples:
             filename = "rabi_12dBm"
-            >>> get_param_from_filename(unit='dBm')
+            >>> get_param_from_filename(unit="dBm")
             12.0
 
             filename = "pixelscan_minus100nm"
-            >>> get_param_from_filename(unit='dBm')
+            >>> get_param_from_filename(unit="dBm")
             -100.0
 
             filename = "rabi_2e-6mbar"
-            >>> get_param_from_filename(unit='mbar')
+            >>> get_param_from_filename(unit="mbar")
             2e-6
 
             filename = "rabi_2point3uW"
-            >>> get_param_from_filename(unit='uW')
+            >>> get_param_from_filename(unit="uW")
             2.5
         """
         filename = self.filename

tools/remove_empty_data_folders.py

@@ -54,7 +54,7 @@ def dir_path(path: str) -> str:
         - use a set to store all extensions
             - if the length of the set > 2, keep folder
             - else delete folder
-            
+
     Examples
     --------
     python remove_empty_data_folders.py --path "C:\\Data"