Add check_1d_arrays decorator to various signal processing functions for input validation

Author: PierreRaybaut

sigima/tools/signal/dynamic.py

@@ -15,6 +15,8 @@
 import numpy as np
 import scipy.optimize
 
+from sigima.tools.checks import check_1d_arrays
+
 
 def sinusoidal_model(
     x: np.ndarray, a: float, f: float, phi: float, offset: float
@@ -23,6 +25,7 @@ def sinusoidal_model(
     return a * np.sin(2 * np.pi * f * x + phi) + offset
 
 
+@check_1d_arrays(x_evenly_spaced=True, x_sorted=True)
 def sinusoidal_fit(
     x: np.ndarray, y: np.ndarray
 ) -> tuple[tuple[float, float, float, float], float]:
@@ -63,6 +66,7 @@ def optfunc(fitparams: np.ndarray, x: np.ndarray, y: np.ndarray) -> np.ndarray:
     return fitparams, residuals
 
 
+@check_1d_arrays(x_evenly_spaced=True, x_sorted=True)
 def sinus_frequency(x: np.ndarray, y: np.ndarray) -> float:
     """Compute the frequency of a sinusoidal signal.
 
@@ -77,6 +81,7 @@ def sinus_frequency(x: np.ndarray, y: np.ndarray) -> float:
     return fitparams[1]
 
 
+@check_1d_arrays(x_evenly_spaced=True, x_sorted=True)
 def enob(x: np.ndarray, y: np.ndarray, full_scale: float = 1.0) -> float:
     """Compute Effective Number of Bits (ENOB).
 
@@ -92,6 +97,7 @@ def enob(x: np.ndarray, y: np.ndarray, full_scale: float = 1.0) -> float:
     return -np.log2(residuals * np.sqrt(12) / full_scale)
 
 
+@check_1d_arrays(x_evenly_spaced=True, x_sorted=True)
 def sinad(
     x: np.ndarray,
     y: np.ndarray,
@@ -119,6 +125,7 @@ def sinad(
     return 20 * np.log10(powf / residuals)
 
 
+@check_1d_arrays(x_evenly_spaced=True, x_sorted=True)
 def thd(
     x: np.ndarray,
     y: np.ndarray,
@@ -159,6 +166,7 @@ def thd(
     return 20 * np.log10(sumharm / powfund)
 
 
+@check_1d_arrays(x_evenly_spaced=True, x_sorted=True)
 def sfdr(
     x: np.ndarray,
     y: np.ndarray,
@@ -189,6 +197,7 @@ def sfdr(
     return 20 * np.log10(powfund / maxspike)
 
 
+@check_1d_arrays(x_evenly_spaced=True, x_sorted=True)
 def snr(
     x: np.ndarray,
     y: np.ndarray,

sigima/tools/signal/features.py

@@ -8,6 +8,8 @@
 
 import numpy as np
 
+from sigima.tools.checks import check_1d_arrays
+
 
 def find_zero_crossings(y: np.ndarray) -> np.ndarray:
     """
@@ -23,6 +25,7 @@ def find_zero_crossings(y: np.ndarray) -> np.ndarray:
     return zero_crossing_indices
 
 
+@check_1d_arrays(x_sorted=True)
 def find_first_x_at_y_value(x: np.ndarray, y: np.ndarray, y_value: float) -> float:
     """Find the first x value where the signal reaches a given y value (interpolated).
 
@@ -52,6 +55,7 @@ def find_first_x_at_y_value(x: np.ndarray, y: np.ndarray, y_value: float) -> flo
     return np.nan  # not found
 
 
+@check_1d_arrays(x_sorted=True)
 def find_y_at_x_value(x: np.ndarray, y: np.ndarray, x_value: float) -> float:
     """Find the y value at a given x value using linear interpolation.
 
@@ -77,6 +81,7 @@ def find_y_at_x_value(x: np.ndarray, y: np.ndarray, x_value: float) -> float:
     return float(np.interp(x_value, x_valid, y_valid))
 
 
+@check_1d_arrays(x_sorted=True)
 def find_x_at_value(x: np.ndarray, y: np.ndarray, value: float) -> np.ndarray:
     """Find the x values where the y value is the closest to the given value using
     linear interpolation to deduce the precise x value.

sigima/tools/signal/interpolation.py

@@ -12,7 +12,10 @@
 import numpy as np
 import scipy.interpolate
 
+from sigima.tools.checks import check_1d_arrays
 
+
+@check_1d_arrays(x_sorted=True)
 def interpolate(
     x: np.ndarray,
     y: np.ndarray,

sigima/tools/signal/peakdetection.py

@@ -9,6 +9,8 @@
 
 import numpy as np
 
+from sigima.tools.checks import check_1d_arrays
+
 
 def peak_indices(
     y, thres: float = 0.3, min_dist: int = 1, thres_abs: bool = False
@@ -107,6 +109,7 @@ def peak_indices(
     return peaks
 
 
+@check_1d_arrays
 def xpeak(x: np.ndarray, y: np.ndarray) -> float:
     """Return default peak X-position (assuming a single peak).
 

sigima/tools/signal/pulse.py

@@ -14,11 +14,13 @@
 import numpy as np
 import scipy.optimize
 
+from sigima.tools.checks import check_1d_arrays
 from sigima.tools.signal import features, fitmodels, peakdetection
 
 # MARK: Pulse analysis -----------------------------------------------------------------
 
 
+@check_1d_arrays(x_sorted=True)
 def full_width_at_y(
     data: np.ndarray, level: float
 ) -> tuple[float, float, float, float]:
@@ -38,6 +40,7 @@ def full_width_at_y(
     return crossings[0], level, crossings[-1], level
 
 
+@check_1d_arrays(x_sorted=True)
 def fwhm(
     data: np.ndarray,
     method: Literal["zero-crossing", "gauss", "lorentz", "voigt"] = "zero-crossing",
@@ -101,6 +104,7 @@ def func(params):
     return fit_model_class.half_max_segment(amp, sigma, mu, base)
 
 
+@check_1d_arrays(x_sorted=True)
 def fw1e2(data: np.ndarray) -> tuple[float, float, float, float]:
     """Compute Full Width at 1/e² of the input data (using a Gaussian model fitting).
 

sigima/tools/signal/stability.py

@@ -9,7 +9,10 @@
 
 import numpy as np
 
+from sigima.tools.checks import check_1d_arrays
 
+
+@check_1d_arrays(x_evenly_spaced=True)
 def allan_variance(x: np.ndarray, y: np.ndarray, tau_values: np.ndarray) -> np.ndarray:
     """
     Calculate the Allan variance for given time and measurement values at specified
@@ -63,6 +66,7 @@ def allan_variance(x: np.ndarray, y: np.ndarray, tau_values: np.ndarray) -> np.n
     return np.array(allan_var)
 
 
+@check_1d_arrays(x_evenly_spaced=True)
 def allan_deviation(x: np.ndarray, y: np.ndarray, tau_values: np.ndarray) -> np.ndarray:
     """
     Calculate the Allan deviation for given time and measurement values at specified
@@ -79,6 +83,7 @@ def allan_deviation(x: np.ndarray, y: np.ndarray, tau_values: np.ndarray) -> np.
     return np.sqrt(allan_variance(x, y, tau_values))
 
 
+@check_1d_arrays(x_evenly_spaced=True)
 def overlapping_allan_variance(
     x: np.ndarray, y: np.ndarray, tau_values: np.ndarray
 ) -> np.ndarray:
@@ -117,6 +122,7 @@ def overlapping_allan_variance(
     return np.array(overlapping_var)
 
 
+@check_1d_arrays(x_evenly_spaced=True)
 def modified_allan_variance(
     x: np.ndarray, y: np.ndarray, tau_values: np.ndarray
 ) -> np.ndarray:
@@ -157,6 +163,7 @@ def modified_allan_variance(
     return np.array(mod_allan_var)
 
 
+@check_1d_arrays(x_evenly_spaced=True)
 def hadamard_variance(
     x: np.ndarray, y: np.ndarray, tau_values: np.ndarray
 ) -> np.ndarray:
@@ -195,6 +202,7 @@ def hadamard_variance(
     return np.array(hadamard_var)
 
 
+@check_1d_arrays(x_evenly_spaced=True)
 def total_variance(x: np.ndarray, y: np.ndarray, tau_values: np.ndarray) -> np.ndarray:
     """
     Calculate the Total variance for given time and measurement values.
@@ -233,6 +241,7 @@ def total_variance(x: np.ndarray, y: np.ndarray, tau_values: np.ndarray) -> np.n
     return np.array(total_var)
 
 
+@check_1d_arrays(x_evenly_spaced=True)
 def time_deviation(x: np.ndarray, y: np.ndarray, tau_values: np.ndarray) -> np.ndarray:
     """
     Calculate the Time Deviation (TDEV) for given time and measurement values.