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| -rw-r--r-- | spline.py | 102 |
1 files changed, 102 insertions, 0 deletions
diff --git a/spline.py b/spline.py new file mode 100644 index 0000000..3470a1a --- /dev/null +++ b/spline.py @@ -0,0 +1,102 @@ +# -*- mode: python -*- +# +# Aaron LI +# Created: 2016-07-10 +# Updated: 2016-07-10 +# + +""" +This module implements the spline functions used to interpolate +and/or extrapolate a group of discrete data. + +* smoothing spline: R's mgcv::gam() +""" + +import numpy as np +import rpy2.robjects as ro +from rpy2.robjects.packages import importr + + +class Spline: + """ + Meta class to fit a spline to the input data. + """ + # input data + x = None + y = None + # weights of each data point when fitting the spline + weights = None + # specifies which axis should be transformed to logarithmic scale + log10 = None + # fitted spline for interpolation and extrapolation + spline = None + + def __init__(self, x, y, weights=None): + self.x = np.array(x) + self.y = np.array(y) + if weights is None: + self.weights = np.ones(self.x.shape) + else: + self.weights = np.array(weights) + self.log10 = [] + + def fit(self, log10=[]): + """ + The parameter `log10` specifies the axis to be transformed + to the logarithmic scale before fitted by the spline, e.g., + `log10=["x"]`, `log10=["x", "y"]`. + """ + raise NotImplementedError + + def eval(self, x): + """ + Evaluate the specified spline at the supplied positions. + Also check whether the spline was fitted in the log-scale space, + and transform the evaluated values if necessary. + """ + raise NotImplementedError + + +class SmoothSpline(Spline): + """ + Fit a smoothing spline to the data. + + Currently, the penalized smoothing spline from R's `mgcv::gam()` + is employed. In addition, the fitted spline allows extrapolation. + """ + mgcv = importr("mgcv") + + def fit(self, log10=[]): + self.log10 = list(map(str.upper, log10)) + if "X" in self.log10: + x = ro.FloatVector(np.log10(self.x)) + else: + x = ro.FloatVector(self.x) + if "Y" in self.log10: + y = ro.FloatVector(np.log10(self.y)) + else: + y = ro.FloatVector(self.y) + weights = ro.FloatVector(self.weights) + self.spline = self.mgcv.gam( + ro.Formula("y ~ s(x, bs='ps')"), + data=ro.DataFrame({"x": x, "y": y}), + weights=weights, method="REML") + + def eval(self, x): + x = np.array(x) + if x.shape == (): + x = x.reshape((1,)) + if "X" in self.log10: + x_new = ro.ListVector({"x": ro.FloatVector(np.log10(x))}) + else: + x_new = ro.ListVector({"x": ro.FloatVector(x)}) + y_pred = self.mgcv.predict_gam(self.spline, newdata=x_new) + if "Y" in self.log10: + y_pred = 10 ** np.array(y_pred) + else: + y_pred = np.array(y_pred) + # + if len(y_pred) == 1: + return y_pred[0] + else: + return y_pred |