add cubic spline calibration model

tupak/gw/calibration.py

+""" Functions for adding calibration factors to waveform templates.
+"""
+
+import numpy as np
+from scipy.interpolate import UnivariateSpline
+
+
+class Recalibrate(object):
+
+    name = 'none'
+
+    def __init__(self, prefix='recalib_'):
+        """
+        Base calibration object. This applies no transformation
+
+        Parameters
+        ----------
+        prefix: str
+            Prefix on parameters relating to the calibration.
+        """
+        self.params = dict()
+        self.prefix = prefix
+        pass
+
+    def get_calibration_factor(self, frequency_array, **params):
+        """Apply calibration model
+
+        This method should be overwritten by subclasses
+
+        Parameters
+        ----------
+        frequency_array: array
+            The frequency values to calculate the calibration factor for.
+        params : dict
+            Dictionary of sampling parameters which includes
+            calibration parameters.
+
+        Returns
+        -------
+        calibration_factor : array
+            The factor to multiply the strain by.
+        """
+        self.set_calibration_parameters(**params)
+        return np.ones_like(frequency_array)
+
+    def set_calibration_parameters(self, **params):
+        self.params.update({key[len(self.prefix):]: params[key] for key in params
+                            if self.prefix in key})
+
+
+class CubicSpline(Recalibrate):
+
+    name = 'cubic_spline'
+
+    def __init__(self, prefix, minimum_frequency, maximum_frequency, n_points):
+        """
+        Cubic spline recalibration
+
+        see https://dcc.ligo.org/DocDB/0116/T1400682/001/calnote.pdf
+
+        This assumes the spline points follow
+        np.logspace(np.log(minimum_frequency), np.log(maximum_frequency), n_points)
+
+        Parameters
+        ----------
+        prefix: str
+            Prefix on parameters relating to the calibration.
+        minimum_frequency: float
+            minimum frequency of spline points
+        maximum_frequency: float
+            maximum frequency of spline points
+        n_points: int
+            number of spline points
+        """
+        Recalibrate.__init__(self, prefix=prefix)
+        self.n_points = n_points
+        self.spline_points = np.logspace(np.log(minimum_frequency), np.log(maximum_frequency), n_points)
+
+    def get_calibration_factor(self, frequency_array, **params):
+        """Apply calibration model
+
+        Parameters
+        ----------
+        frequency_array: array
+            The frequency values to calculate the calibration factor for.
+        prefix: str
+            Prefix for calibration parameter names
+        params : dict
+            Dictionary of sampling parameters which includes
+            calibration parameters.
+
+        Returns
+        -------
+        calibration_factor : array
+            The factor to multiply the strain by.
+        """
+        self.set_calibration_parameters(**params)
+        amplitude_parameters = [self.params['amplitude_{}'.format(ii)] for ii in range(self.n_points)]
+        amplitude_spline = UnivariateSpline(self.spline_points, amplitude_parameters)
+        delta_amplitude = amplitude_spline(frequency_array)
+
+        phase_parameters = [self.params['phase_{}'.format(ii)] for ii in range(self.n_points)]
+        phase_spline = UnivariateSpline(self.spline_points, phase_parameters)
+        delta_phase = phase_spline(frequency_array)
+
+        calibration_factor = (1 + delta_amplitude) * (2 + 1j * delta_phase) / (2 - 1j * delta_phase)
+
+        return calibration_factor
+