Linting.

pyproject.toml

@@ -44,9 +44,6 @@ extend-select = [
   "D",   # pydocstyle
 ]
 
-[tool.ruff.lint.per-file-ignores]
-"__init__.py" = ["F401"]
-
 [tool.ruff.lint.pydocstyle]
 convention = "google"
 

src/quackseq/measurement.py

@@ -338,6 +338,7 @@ class T2StarFit(Fit):
         """Initial guess for the T2* fit."""
         return [1, 1]
 
+
 class LorentzianFit(Fit):
     """Lorentzian fit for measurement data."""
 

src/quackseq/pulseparameters.py

@@ -10,7 +10,12 @@ import logging
 from numpy.core.multiarray import array as array
 
 from quackseq.options import BooleanOption, FunctionOption, NumericOption, Option
-from quackseq.functions import RectFunction, SincFunction, GaussianFunction, CustomFunction
+from quackseq.functions import (
+    RectFunction,
+    SincFunction,
+    GaussianFunction,
+    CustomFunction,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -71,7 +76,6 @@ class PulseParameter:
         raise ValueError(f"Option with name {name} not found")
 
 
-
 class TXPulse(PulseParameter):
     """Basic TX Pulse Parameter. It includes options for the relative amplitude, the phase and the pulse shape.
 
@@ -104,12 +108,22 @@ class TXPulse(PulseParameter):
         self.add_option(NumericOption(self.TX_PHASE, 0))
         self.add_option(
             NumericOption(
-                self.N_PHASE_CYCLES, 1, is_float=False, min_value=1, max_value=360, slider=False
+                self.N_PHASE_CYCLES,
+                1,
+                is_float=False,
+                min_value=1,
+                max_value=360,
+                slider=False,
             )
         )
         self.add_option(
             NumericOption(
-                self.PHASE_CYCLE_LEVEL, 0, is_float=False, min_value=0, max_value=10, slider=False
+                self.PHASE_CYCLE_LEVEL,
+                0,
+                is_float=False,
+                min_value=0,
+                max_value=10,
+                slider=False,
             )
         )
         self.add_option(
@@ -124,6 +138,7 @@ class TXPulse(PulseParameter):
             ),
         )
 
+
 class RXReadout(PulseParameter):
     """Basic PulseParameter for the RX Readout. It includes an option for the RX Readout state.
 
@@ -144,6 +159,7 @@ class RXReadout(PulseParameter):
         super().__init__(name)
         self.add_option(BooleanOption(self.RX, False))
 
+
 class Gate(PulseParameter):
     """Basic PulseParameter for the Gate. It includes an option for the Gate state.
 

src/quackseq/signalprocessing.py

@@ -1,4 +1,5 @@
 """Helper used for signal processing."""
+
 import logging
 from scipy.fft import fft, fftfreq, fftshift
 import numpy as np
@@ -6,11 +7,14 @@ import sympy
 
 logger = logging.getLogger(__name__)
 
+
 class SignalProcessing:
     """This class provides various signal processing methods that can then be used by nqrduck modules."""
 
     @classmethod
-    def fft(cls, tdx : np.array, tdy: np.array, freq_shift : float = 0, zero_padding = 1000) -> tuple[np.array, np.array]:
+    def fft(
+        cls, tdx: np.array, tdy: np.array, freq_shift: float = 0, zero_padding=1000
+    ) -> tuple[np.array, np.array]:
         """This method calculates the FFT of the time domain data.
 
         Args:
@@ -23,7 +27,7 @@ class SignalProcessing:
             np.array: Frequency domain x data in MHz.
             np.array: Frequency domain magnitude y data.
         """
-        dwell_time = (tdx[1] - tdx[0])
+        dwell_time = tdx[1] - tdx[0]
 
         N = len(tdx) + zero_padding
 
@@ -57,7 +61,9 @@ class SignalProcessing:
         pass
 
     @classmethod
-    def apodization(cls, tdx : np.array, tdy : np.array, apodization_function : sympy.Expr) -> np.array:
+    def apodization(
+        cls, tdx: np.array, tdy: np.array, apodization_function: sympy.Expr
+    ) -> np.array:
         """This method calculates the apodization of the time domain data.
 
         Args:
@@ -72,7 +78,9 @@ class SignalProcessing:
         return tdy * weight
 
     @classmethod
-    def peak_picking(cls, fdx: np.array, fdy: np.array, threshold : float = 0.05) -> tuple[np.array, np.array]:
+    def peak_picking(
+        cls, fdx: np.array, fdy: np.array, threshold: float = 0.05
+    ) -> tuple[np.array, np.array]:
         """This method calculates the peak picking of the frequency domain data.
 
         Args: