Source code for getdist.covmat

from __future__ import print_function
import numpy as np
import io

[docs]class CovMat(object): """ Class holding a covariance matrix for some named parameters :ivar matrix: the covariance matrix (square numpy array) :ivar paramNames: list of parameter name strings """ def __init__(self, filename='', matrix=None, paramNames=None): """ :param filename: optionally, a file name to load from """ if not paramNames: paramNames = [] self.matrix = matrix self.paramNames = paramNames self.size = 0 if matrix is not None: self.size = matrix.shape[0] if filename != '': self.loadFromFile(filename) def paramNameString(self): return " ".join(self.paramNames) def loadFromFile(self, filename): with open(filename) as f: first = f.readline().strip() if first.startswith('#'): self.paramNames = first[1:].split() self.size = len(self.paramNames) else: raise Exception('.covmat must now have parameter names header') self.matrix = np.loadtxt(f)
[docs] def saveToFile(self, filename): """ Save the covariance matrix to a text file, with comment header listing the parameter names :param filename: name of file to save to (.covmat) """ with, 'wb') as fout: fout.write(('# ' + self.paramNameString() + '\n').encode('UTF-8')) np.savetxt(fout, self.matrix, '%15.7E')
[docs] def rescaleParameter(self, name, scale): """ Used to rescale a covariance if a parameter is renormalized :param name: parameter name to rescale :param scale: value to rescale by """ if name in self.paramNames: i = self.paramNames.index(name) self.matrix[:, i] = self.matrix[:, i] * scale self.matrix[i, :] = self.matrix[i, :] * scale else: print('Not in covmat: ' + name)
def mergeCovmatWhereNew(self, cov2): params1 = self.paramNames params2 = cov2.paramNames C = CovMat() C.paramNames.extend(params1) for param in cov2.paramNames: if param not in C.paramNames: C.paramNames.append(param) l1 = len(params1) l2 = len(params2) l = len(C.paramNames) map1 = dict(list(zip(params1, list(range(0, l1))))) map2 = dict(list(zip(params2, list(range(0, l2))))) covmap = dict(list(zip(list(range(0, l)), C.paramNames))) C.matrix = np.zeros((l, l)) for i in range(0, l): for j in range(0, l): if C.paramNames[i] in params1 and C.paramNames[j] in params1: C.matrix[i, j] = self.matrix[map1[covmap[i]], map1[covmap[j]]] elif C.paramNames[i] in params2 and C.paramNames[j] in params2: C.matrix[i, j] = cov2.matrix[map2[covmap[i]], map2[covmap[j]]] return C
[docs] def correlation(self): """ Get the correlation matrix :return: numpy array giving the correlation matrix """ m = self.matrix.copy() for i in range(self.size): s = np.sqrt(self.matrix[i, i]) m[i, :] /= s m[:, i] /= s return m
[docs] def plot(self): """ Plot the correlation matrix as grid of colored squares """ import matplotlib.pyplot as plt plt.pcolor(self.correlation()) plt.colorbar() sz = self.size plt.yticks(np.arange(0.5, sz + .5), list(range(1, sz + 1))) plt.gca().set_yticklabels(self.paramNames) plt.xticks(np.arange(0.5, sz + .5), list(range(1, sz + 1))) plt.xlim([0, sz]) plt.ylim([0, sz])