From 36529e14e9470e3d5d820e7cc414b30156961cde Mon Sep 17 00:00:00 2001 From: Kim Date: Mon, 26 Apr 2021 14:55:20 +0200 Subject: [PATCH 01/10] Added 1-covariance-likelihood folder --- .../Planck2018contour_modifications.py | 139 +++++++ .../Planck2018thetad.covmat | 37 ++ .../Planck2018thetad.h_info | 19 + ...k2018thetad_2d_100*theta_d-100*theta_s.dat | 340 ++++++++++++++++++ 4 files changed, 535 insertions(+) create mode 100644 1-covariance-likelihood/Planck2018contour_modifications.py create mode 100644 1-covariance-likelihood/Planck2018thetad.covmat create mode 100644 1-covariance-likelihood/Planck2018thetad.h_info create mode 100644 1-covariance-likelihood/Planck2018thetad_2d_100*theta_d-100*theta_s.dat diff --git a/1-covariance-likelihood/Planck2018contour_modifications.py b/1-covariance-likelihood/Planck2018contour_modifications.py new file mode 100644 index 00000000..f389ca1c --- /dev/null +++ b/1-covariance-likelihood/Planck2018contour_modifications.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[2]: + + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from matplotlib.patches import Ellipse +import re +import ast + +## Input parameters + path to directory and files ## +directory_name = 'Planck2018thetad' # Name of your directory +path_name = '/Users/Kim/Desktop/MontePythonCluster/chains/' # Path to your MontePython directory +data_name = 'Planck2018thetad_2d_100*theta_d-100*theta_s.dat' # Name of your .dat file +param_list = ['100*theta_s','100*theta_d'] # Name of the variables of interest. See names in 'cov_list' below. +# The first parameter will be the x-variable in the contour plot. +# Now you are ready to run the code. The following two parameters can be adjusted if one wishes to multiply +# the entries of the covariance matrix by a scalar or generate more/less random sample points from a 2D Gaussian. +factor = 1 # Used to multiply the entries of the covariance matrix to better match contours. +size = 100000 # no. of generated points from 2D Gaussian with covariance matrix from .covmat and mu's from .h_info + +# Path to files +cov_file = path_name + directory_name + '/' + directory_name + '.covmat' # path to .covmat file +h_file = path_name + directory_name + '/' + directory_name + '.h_info' +dat_file = path_name + '/' + directory_name + '/plots/' + data_name + +## Creating covariance matrix ## + +# Loading cov_file +with open(cov_file, 'r') as index_cov: + index_line = index_cov.readline() # first line gives the parameter names +# Removing symbols/text from cells +cov_list = [elem.strip() for elem in index_line[1:].split(',')] + +# Creating covariance matrix +covFull = np.loadtxt(cov_file) +variable_indices = [cov_list.index(p) for p in param_list] +cov = covFull[np.ix_(variable_indices, variable_indices)] + +## Selecting mu's from .h_info file ## +with open(h_file, 'r') as index_h: + index_h = index_h.readlines(1) + +# Removing unwanted symbols/text from parameter names +index_h = [i.replace(' param names\t:', '') for i in index_h] +index_h = [i.replace('\n', '') for i in index_h] +index_h = [i.replace('\\', '') for i in index_h] +index_h = [i.replace('{', '') for i in index_h] +index_h = [i.replace('}', '') for i in index_h] +string_h = " ".join(str(x) for x in index_h) # converting to string to seperate spacing with commas +string_h = re.sub("\s+", ",", string_h.strip()) +index_h = string_h.split(",") + +with open(h_file, 'r') as h_list: + lines = h_list.readlines() +mu_indices = [index_h.index(p) for p in param_list] +mean_row = lines[3] +# The mean row is located at row 3 in the h_info file. If one wishes to use best-fit values instead, change 3 to 2 +mean_row = mean_row.split() +mean_row = [m.replace('mean', '') for m in mean_row] +mean_row = [m.replace('\t', '') for m in mean_row] +mean_row = [m.replace('\n', '') for m in mean_row] +mean_row = [m.replace(':', '') for m in mean_row] +mean_row = [m.replace('', '') for m in mean_row] +string_mean = " ".join(str(x) for x in mean_row) # converting to string to seperate spacing with commas +string_mean = re.sub("\s+", ",", string_mean.strip()) +mean_row = string_mean.split(",") +mu_list = [mean_row[x] for x in mu_indices] +mu_list = (('[%s]' % ', '.join(map(str, mu_list)))) +mu_list = ast.literal_eval(mu_list) +mu = tuple(mu_list) + +## 2D Gaussian plot ## +contour = pd.read_csv(dat_file, sep=' ') # Converts .dat to csv.file +contour.rename(columns = {'#':'para1_col','contour':'para2_col'},inplace = True) +# The first row in the file has entries '#' and 'contour' +i = contour[(contour.para1_col == '#')].index +# locates line in the file which says "# contour for confidence level 0.6826000000000001" +contour = contour.drop(i) # removes that line +contour['para1_col'] = contour['para1_col'].map(lambda x: str(x)[:-1]) # removes \t from first column + +para1_col = contour.para1_col +para2_col = contour.para2_col +para1 = pd.to_numeric(para1_col, errors='coerce') # convert to float object +para2 = pd.to_numeric(para2_col, errors='coerce') + +def error_ellipse(ax, xc, yc, cov, sigma=1, facecolor='none', **kwargs): + ''' + Plot an error ellipse contour over your data. + Inputs: + ax : matplotlib Axes() object + xc : x-coordinate of ellipse center + yc : x-coordinate of ellipse center + cov : covariance matrix + sigma : # sigma to plot (default 1) + additional kwargs passed to matplotlib.patches.Ellipse() + ''' + w, v = np.linalg.eigh(cov) # assumes symmetric matrix + order = w.argsort()[::-1] + w, v = w[order], v[:,order] + theta = np.degrees(np.arctan2(*v[:,0][::-1])) + ellipse = Ellipse(xy=(xc,yc), + width=2.*sigma*np.sqrt(w[0]), + height=2.*sigma*np.sqrt(w[1]), + angle=theta, **kwargs) + ellipse.set_facecolor('none') + ax.add_artist(ellipse) + +if __name__ == '__main__': + # Generate random points + points = np.random.multivariate_normal( + mean=mu, cov=cov, size=size) + x, y = points.T + cov = np.cov(x,y, rowvar=False) + # Plot the raw points + fig, ax = plt.subplots(1,1) + ax.scatter(x, y, s=1, color='b') + # Plot one and two sigma error ellipses + error_ellipse(ax, np.mean(x), np.mean(y), cov, sigma=1, ec='orange') + error_ellipse(ax, np.mean(x), np.mean(y), cov, sigma=2, ec='orange') + ax.scatter(mu[0], mu[1], c='orange', s=1, label='Generated Contour') # center of ellipse + if variable_indices[0] : 1.041736e+00 2.634413e+00 1.415058e-05 6.418600e+01 3.214342e-01 + 1-sigma < : 1.042785e+00 3.010907e+00 3.745585e-02 6.766080e+01 3.232256e-01 + 2-sigma > : 1.041226e+00 2.455330e+00 1.415058e-05 6.205549e+01 3.205237e-01 + 2-sigma < : 1.043337e+00 3.203826e+00 1.017514e-01 6.930495e+01 3.240784e-01 + 3-sigma > : 1.040742e+00 2.270155e+00 1.415058e-05 5.953773e+01 3.195933e-01 + 3-sigma < : 1.043904e+00 3.425934e+00 1.976298e-01 7.093975e+01 3.249727e-01 \ No newline at end of file diff --git a/1-covariance-likelihood/Planck2018thetad_2d_100*theta_d-100*theta_s.dat b/1-covariance-likelihood/Planck2018thetad_2d_100*theta_d-100*theta_s.dat new file mode 100644 index 00000000..828efc8f --- /dev/null +++ b/1-covariance-likelihood/Planck2018thetad_2d_100*theta_d-100*theta_s.dat @@ -0,0 +1,340 @@ +# contour for confidence level 0.9540000000000001 +1.0430383 0.32010856 +1.0430904 0.32009013 +1.0431424 0.32007878 +1.0431945 0.32007238 +1.0432466 0.32006889 +1.0432987 0.32006683 +1.0433509 0.32006651 +1.0434031 0.32007136 +1.0434552 0.32008795 +1.0435072 0.32012485 +1.0435157 0.32013498 +1.043559 0.32019825 +1.0435705 0.32022267 +1.043598 0.32031129 +1.0436107 0.32039933 +1.043614 0.32048623 +1.0436142 0.3205724 +1.0436164 0.32065825 +1.0436225 0.3207442 +1.0436304 0.32083045 +1.0436364 0.32091692 +1.0436372 0.3210035 +1.0436303 0.32109006 +1.0436131 0.32117655 +1.0436107 0.32118422 +1.0435882 0.32126297 +1.043559 0.32134023 +1.0435561 0.32134935 +1.0435274 0.32143574 +1.0435072 0.32149809 +1.0435008 0.32152215 +1.043479 0.32160857 +1.0434552 0.32169243 +1.0434545 0.32169501 +1.0434299 0.32178144 +1.0434031 0.32185362 +1.0433981 0.32186788 +1.0433652 0.3219543 +1.0433509 0.32198951 +1.0433328 0.32204073 +1.0433014 0.32212715 +1.0432987 0.32213492 +1.043274 0.32221357 +1.0432466 0.3222878 +1.0432423 0.3223 +1.0432064 0.32238642 +1.0431945 0.32241017 +1.0431627 0.32247285 +1.0431424 0.32250767 +1.0431132 0.32255928 +1.0430904 0.32259689 +1.0430623 0.3226457 +1.0430383 0.32268567 +1.0430116 0.32273213 +1.0429862 0.32277408 +1.0429603 0.32281855 +1.0429341 0.32286216 +1.0429103 0.32290498 +1.042882 0.32295612 +1.0428649 0.32299141 +1.0428299 0.32306529 +1.0428247 0.32307783 +1.0427902 0.32316426 +1.0427778 0.32319397 +1.042755 0.32325068 +1.0427257 0.32331358 +1.042714 0.32333711 +1.0426736 0.32340988 +1.0426651 0.32342353 +1.0426215 0.32349093 +1.0426078 0.32350996 +1.0425694 0.32356447 +1.0425448 0.32359639 +1.0425173 0.32363315 +1.0424752 0.32368282 +1.0424652 0.32369509 +1.0424131 0.32375266 +1.0423956 0.32376926 +1.042361 0.3238057 +1.042309 0.32385315 +1.0423062 0.32385568 +1.0422569 0.32391034 +1.0422272 0.32394209 +1.0422048 0.32397055 +1.042157 0.32402848 +1.0421527 0.32403427 +1.0421006 0.32409941 +1.0420859 0.32411487 +1.0420485 0.32415675 +1.0419972 0.32420129 +1.0419964 0.32420204 +1.0419443 0.32424859 +1.0418931 0.32428777 +1.0418922 0.32428864 +1.0418401 0.32433911 +1.0418008 0.32437433 +1.041788 0.32438836 +1.0417359 0.3244391 +1.041707 0.32446091 +1.0416838 0.32448071 +1.0416317 0.32451322 +1.0415796 0.32453544 +1.0415408 0.32454738 +1.0415276 0.32455254 +1.0414755 0.32456792 +1.0414234 0.32457975 +1.0413713 0.32458637 +1.0413191 0.32458257 +1.0412669 0.32455971 +1.041253 0.32454738 +1.0412148 0.32450776 +1.0411878 0.32446091 +1.0411628 0.32441493 +1.0411468 0.32437433 +1.0411132 0.32428777 +1.041111 0.3242822 +1.0410833 0.32420129 +1.0410593 0.32411916 +1.041058 0.32411487 +1.0410352 0.32402848 +1.0410185 0.32394209 +1.0410081 0.32385568 +1.0410073 0.32384165 +1.0410023 0.32376926 +1.0410027 0.32368282 +1.0410073 0.32362565 +1.0410092 0.32359639 +1.0410199 0.32350996 +1.0410363 0.32342353 +1.0410585 0.32333711 +1.0410593 0.32333439 +1.0410762 0.32325068 +1.0410928 0.32316426 +1.0411072 0.32307783 +1.041111 0.32305026 +1.0411169 0.32299141 +1.0411253 0.32290498 +1.0411364 0.32281855 +1.0411541 0.32273213 +1.0411628 0.32270347 +1.0411812 0.3226457 +1.0412148 0.32257876 +1.0412245 0.32255928 +1.0412669 0.32248765 +1.0412742 0.32247285 +1.0413166 0.32238642 +1.0413191 0.32238079 +1.0413476 0.3223 +1.0413713 0.32223882 +1.0413798 0.32221357 +1.041414 0.32212715 +1.0414234 0.32210703 +1.041452 0.32204073 +1.0414755 0.32199294 +1.0414929 0.3219543 +1.0415276 0.32188203 +1.0415339 0.32186788 +1.0415753 0.32178144 +1.0415796 0.32177293 +1.0416208 0.32169501 +1.0416317 0.32167684 +1.0416732 0.32160857 +1.0416838 0.32159234 +1.041725 0.32152215 +1.0417359 0.32150372 +1.04177 0.32143574 +1.041788 0.32140009 +1.041811 0.32134935 +1.0418401 0.32128929 +1.0418529 0.32126297 +1.0418922 0.32119004 +1.0419002 0.32117655 +1.0419443 0.32110721 +1.0419566 0.32109006 +1.0419964 0.32103436 +1.0420208 0.3210035 +1.0420485 0.32096584 +1.0420888 0.32091692 +1.0421006 0.32090106 +1.0421527 0.32084097 +1.042164 0.32083045 +1.0422048 0.32078767 +1.0422569 0.32074523 +1.0422581 0.3207442 +1.042309 0.32069645 +1.0423466 0.32065825 +1.042361 0.32064221 +1.0424131 0.32057706 +1.0424166 0.3205724 +1.0424652 0.32050862 +1.0424846 0.32048623 +1.0425173 0.32044928 +1.0425694 0.32040409 +1.0425763 0.32039933 +1.0426215 0.32036401 +1.0426736 0.32033289 +1.0427166 0.32031129 +1.0427257 0.32030525 +1.0427778 0.32027206 +1.0428299 0.3202396 +1.0428576 0.32022267 +1.042882 0.3202031 +1.0429341 0.32016587 +1.0429862 0.32013588 +1.0429883 0.32013498 +1.0430383 0.32010856 + + + +# contour for confidence level 0.6826000000000001 +1.0427257 0.32097279 +1.0427778 0.32095754 +1.0428299 0.32095635 +1.042882 0.32096842 +1.0429341 0.32099571 +1.0429435 0.3210035 +1.0429862 0.32104929 +1.043011 0.32109006 +1.0430383 0.32115791 +1.0430442 0.32117655 +1.0430612 0.32126297 +1.0430675 0.32134935 +1.0430647 0.32143574 +1.0430543 0.32152215 +1.0430383 0.32160581 +1.0430378 0.32160857 +1.0430191 0.32169501 +1.042998 0.32178144 +1.0429862 0.3218248 +1.0429758 0.32186788 +1.0429525 0.3219543 +1.0429341 0.3220134 +1.0429263 0.32204073 +1.0428985 0.32212715 +1.042882 0.32217207 +1.0428679 0.32221357 +1.0428351 0.3223 +1.0428299 0.32231281 +1.0428019 0.32238642 +1.0427778 0.32244378 +1.0427664 0.32247285 +1.0427299 0.32255928 +1.0427257 0.32256874 +1.042694 0.3226457 +1.0426736 0.32269223 +1.042657 0.32273213 +1.0426215 0.32281178 +1.0426184 0.32281855 +1.0425766 0.32290498 +1.0425694 0.32291871 +1.0425264 0.32299141 +1.0425173 0.32300576 +1.0424652 0.32307643 +1.042464 0.32307783 +1.0424131 0.32314059 +1.0423926 0.32316426 +1.042361 0.32320304 +1.0423213 0.32325068 +1.042309 0.32326584 +1.0422569 0.323326 +1.0422464 0.32333711 +1.0422048 0.32337942 +1.0421544 0.32342353 +1.0421527 0.32342503 +1.0421006 0.32346744 +1.0420485 0.32350742 +1.0420452 0.32350996 +1.0419964 0.32355062 +1.0419443 0.32359366 +1.0419408 0.32359639 +1.0418922 0.32363726 +1.0418401 0.32367518 +1.0418268 0.32368282 +1.041788 0.3237059 +1.0417359 0.32372675 +1.0416838 0.32373643 +1.0416317 0.32373209 +1.0415796 0.32370805 +1.0415529 0.32368282 +1.0415276 0.32364935 +1.0415021 0.32359639 +1.041478 0.32350996 +1.0414755 0.32349276 +1.0414669 0.32342353 +1.0414633 0.32333711 +1.0414653 0.32325068 +1.0414722 0.32316426 +1.0414755 0.32313972 +1.0414834 0.32307783 +1.0414987 0.32299141 +1.0415179 0.32290498 +1.0415276 0.32286818 +1.0415395 0.32281855 +1.041563 0.32273213 +1.0415796 0.32267744 +1.0415887 0.3226457 +1.0416162 0.32255928 +1.0416317 0.32251587 +1.0416465 0.32247285 +1.0416797 0.32238642 +1.0416838 0.32237632 +1.0417138 0.3223 +1.0417359 0.32224791 +1.0417498 0.32221357 +1.0417877 0.32212715 +1.041788 0.32212638 +1.0418264 0.32204073 +1.0418401 0.32201271 +1.0418682 0.3219543 +1.0418922 0.32190788 +1.0419132 0.32186788 +1.0419443 0.32181149 +1.0419619 0.32178144 +1.0419964 0.32172414 +1.0420159 0.32169501 +1.0420485 0.32164613 +1.0420768 0.32160857 +1.0421006 0.321576 +1.0421438 0.32152215 +1.0421527 0.32151066 +1.0422048 0.32144627 +1.0422136 0.32143574 +1.0422569 0.32138382 +1.0422883 0.32134935 +1.042309 0.32132709 +1.042361 0.32127676 +1.0423769 0.32126297 +1.0424131 0.32123051 +1.0424652 0.32118739 +1.0424789 0.32117655 +1.0425173 0.32114158 +1.0425694 0.32109491 +1.0425753 0.32109006 +1.0426215 0.32104545 +1.0426736 0.3210035 +1.0427257 0.32097279 + + + From f8fd768aa9fec35def0edc8b7edf1b968139a2c0 Mon Sep 17 00:00:00 2001 From: Kim4213 <70318424+Kim4213@users.noreply.github.com> Date: Mon, 26 Apr 2021 15:11:31 +0200 Subject: [PATCH 02/10] Update Planck2018contour_modifications.py --- .../Planck2018contour_modifications.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/1-covariance-likelihood/Planck2018contour_modifications.py b/1-covariance-likelihood/Planck2018contour_modifications.py index f389ca1c..d013c94c 100644 --- a/1-covariance-likelihood/Planck2018contour_modifications.py +++ b/1-covariance-likelihood/Planck2018contour_modifications.py @@ -1,9 +1,6 @@ #!/usr/bin/env python # coding: utf-8 -# In[2]: - - import numpy as np import matplotlib.pyplot as plt import pandas as pd @@ -130,10 +127,3 @@ def error_ellipse(ax, xc, yc, cov, sigma=1, facecolor='none', **kwargs): ax.set_ylabel(param_list[1]) ax.legend() plt.show() - - -# In[ ]: - - - - From bfb510998dccd1c8767e905b9422e8953cb00271 Mon Sep 17 00:00:00 2001 From: Kim4213 <70318424+Kim4213@users.noreply.github.com> Date: Mon, 26 Apr 2021 17:48:10 +0200 Subject: [PATCH 03/10] I have added code based on #1 From 35e0b6ef56bbceaad77740f71c834c3a278695c1 Mon Sep 17 00:00:00 2001 From: Kim Date: Tue, 4 May 2021 11:12:58 +0200 Subject: [PATCH 04/10] Added eff_like_from_covmat folder --- .../eff_like_from_covmat/__init__.py | 33 +++++++++++++++++ .../eff_like_from_covmat.covmat | 37 +++++++++++++++++++ .../eff_like_from_covmat.data | 19 ++++++++++ .../eff_like_from_covmat.h_info | 19 ++++++++++ 4 files changed, 108 insertions(+) create mode 100644 montepython/likelihoods/eff_like_from_covmat/__init__.py create mode 100644 montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.covmat create mode 100644 montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data create mode 100644 montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.h_info diff --git a/montepython/likelihoods/eff_like_from_covmat/__init__.py b/montepython/likelihoods/eff_like_from_covmat/__init__.py new file mode 100644 index 00000000..f014ae02 --- /dev/null +++ b/montepython/likelihoods/eff_like_from_covmat/__init__.py @@ -0,0 +1,33 @@ +import numpy as np +import math +import scipy.linalg as la +from montepython.likelihood_class import Likelihood_sn + + +class eff_like_from_covmat(Likelihood_sn): + def __init__(self, path, data, command_line): + # Call __init__ method of super class: + super(eff_like_from_covmat, self).__init__(path, data, command_line) + + self.need_cosmo_arguments(data,{'compute damping scale':'yes'}) + # Initialise other things: + covmat_inverse = la.inv(self.covmat) + + # Compute likelihood + def loglkl(self, cosmo, data): + + mean_vec = np.array([self.mu]).T + mean_vec_class = np.array([eval(s) for s in self.get_var_strings]) + + dif_vec = mean_vec - mean_vec_class + exponent = la.multi_dot([dif_vec.T,covmat_inverse,dif_vec]) + + loglikelihood = -1/2*exponent # exponent in the multivariate Gaussian + return loglikelihood + + + + + + + diff --git a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.covmat b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.covmat new file mode 100644 index 00000000..92b3ffa1 --- /dev/null +++ b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.covmat @@ -0,0 +1,37 @@ +# omega_b, omega_cdm, 100*theta_s, ln10^{10}A_s, n_s, tau_reio, deg_ncdm, m_ncdm, A_cib_217, xi_sz_cib, A_sz, ps_A_100_100, ps_A_143_143, ps_A_143_217, ps_A_217_217, ksz_norm, gal545_A_100, gal545_A_143, gal545_A_143_217, gal545_A_217, galf_TE_A_100, galf_TE_A_100_143, galf_TE_A_100_217, galf_TE_A_143, galf_TE_A_143_217, galf_TE_A_217, calib_100T, calib_217T, A_planck, z_reio, Omega_Lambda, YHe, H0, A_s, sigma8, 100*theta_d + 5.131237e-08 3.224207e-07 -5.819384e-08 1.836663e-06 1.568328e-06 3.953460e-07 3.117701e-05 -1.935290e-06 1.004083e-04 -9.522653e-07 4.919179e-06 2.139574e-04 7.529320e-05 1.622953e-05 -7.645938e-05 1.424514e-05 1.046092e-05 1.640551e-05 2.164549e-05 -2.981852e-05 -2.015622e-07 -1.505130e-07 -2.658992e-07 -2.050041e-07 -6.757932e-07 -2.359477e-06 2.859926e-09 1.599643e-10 1.527917e-08 3.794469e-05 2.531824e-06 4.588090e-07 3.279227e-04 3.827149e-15 2.110094e-06 7.337971e-08 + 3.224207e-07 8.832320e-06 -1.232260e-06 2.425306e-05 1.506002e-05 4.542627e-07 4.886172e-04 -1.616461e-06 2.678891e-03 -4.368287e-05 -3.404778e-04 1.163656e-02 6.227166e-03 2.220626e-03 -2.373624e-04 7.663614e-04 1.979399e-04 4.374158e-04 6.817916e-04 -1.104169e-04 -1.923663e-06 -1.641383e-06 -1.881622e-06 1.880865e-06 1.199530e-06 1.333233e-05 -6.793500e-09 3.155903e-08 -1.224938e-07 1.803956e-04 3.882384e-06 6.985926e-06 2.565224e-03 5.051278e-14 2.447651e-05 2.307287e-06 +-5.819384e-08 -1.232260e-06 2.680703e-07 -3.762269e-06 -2.703970e-06 -2.474418e-07 -7.804585e-05 9.223786e-07 -4.165954e-04 6.501891e-06 4.443456e-05 -1.720999e-03 -8.280719e-04 -2.693011e-04 9.111147e-05 -1.131523e-04 -2.454966e-05 -6.208266e-05 -8.953761e-05 6.086021e-05 3.875854e-07 2.386999e-07 2.241586e-07 -2.282546e-07 -3.533102e-07 -2.152772e-06 -1.093335e-09 -4.916963e-09 9.262489e-09 -4.202734e-05 -1.697451e-06 -1.119574e-06 -4.714157e-04 -7.836507e-15 -3.681395e-06 -3.350036e-07 + 1.836663e-06 2.425306e-05 -3.762269e-06 3.375991e-04 7.293854e-05 1.216208e-04 1.720754e-03 5.527558e-06 5.599692e-03 5.562580e-05 -3.123279e-04 1.807283e-02 1.264418e-02 9.338375e-03 5.209225e-03 1.959831e-04 4.453135e-04 -5.809910e-04 1.592558e-04 1.182870e-04 -8.694982e-06 -1.361126e-05 -2.679292e-05 -7.416164e-06 -2.445737e-05 -2.155874e-05 8.941163e-08 4.369640e-07 1.180788e-05 1.275403e-02 5.618501e-05 2.491237e-05 1.197368e-02 7.035159e-13 1.600976e-04 6.486419e-06 + 1.568328e-06 1.506002e-05 -2.703970e-06 7.293854e-05 7.479500e-05 1.553039e-05 1.369635e-03 -6.077571e-05 2.549437e-03 -1.607002e-05 2.894457e-04 4.017131e-03 5.860319e-03 5.089812e-03 1.830835e-03 -2.998483e-05 9.770338e-04 7.398230e-04 1.225910e-03 5.771744e-04 -7.934064e-06 -3.445506e-06 -6.693514e-06 1.919857e-06 -9.636098e-06 -4.242303e-05 -9.448073e-08 9.693583e-09 1.981007e-07 1.651274e-03 9.287725e-05 1.986313e-05 1.282636e-02 1.519872e-13 8.134971e-05 5.172023e-06 + 3.953460e-07 4.542627e-07 -2.474418e-07 1.216208e-04 1.553039e-05 5.973199e-05 1.898136e-04 9.989056e-06 -8.613336e-04 6.549923e-05 2.578906e-04 -7.357362e-03 -2.460705e-03 6.017079e-04 4.737102e-04 -5.329827e-04 1.337948e-04 -6.135350e-04 -6.046342e-04 -3.979159e-04 -4.563724e-06 -4.811951e-06 -1.077834e-05 -4.645800e-06 -8.831253e-06 -2.151235e-05 5.476817e-09 1.539353e-07 -1.942142e-08 6.108450e-03 1.766733e-05 2.824168e-06 2.092391e-03 2.535114e-13 3.942531e-05 2.716912e-07 + 3.117701e-05 4.886172e-04 -7.804585e-05 1.720754e-03 1.369635e-03 1.898136e-04 3.423693e-02 -7.350726e-04 1.748809e-01 -2.899514e-03 -1.618746e-02 7.035872e-01 3.427792e-01 1.087610e-01 -5.186157e-02 5.015881e-02 1.482319e-02 2.936994e-02 4.524726e-02 -9.790328e-03 -1.771741e-04 -1.100236e-04 -1.635179e-04 3.717591e-05 -1.140268e-04 -1.163243e-04 1.400437e-07 1.681312e-06 7.852693e-07 2.470632e-02 1.319628e-03 4.930722e-04 2.512640e-01 3.584811e-12 1.748504e-03 1.445621e-04 +-1.935290e-06 -1.616461e-06 9.223786e-07 5.527558e-06 -6.077571e-05 9.989056e-06 -7.350726e-04 1.076810e-03 2.299399e-03 -4.292779e-05 -1.615338e-03 3.008348e-02 9.350711e-03 6.652363e-04 2.084537e-04 4.176808e-03 1.948383e-04 -5.161913e-04 5.917377e-04 1.282140e-03 1.447606e-06 1.598451e-05 -7.532651e-05 5.459931e-05 8.425813e-05 -3.474485e-05 3.227446e-07 3.807014e-07 1.717692e-06 1.751985e-03 -4.478507e-04 -1.112325e-05 -3.610980e-02 1.149225e-14 -6.670388e-04 -9.966676e-07 + 1.004083e-04 2.678891e-03 -4.165954e-04 5.599692e-03 2.549437e-03 -8.613336e-04 1.748809e-01 2.299399e-03 4.514726e+01 -4.201243e-01 -7.110279e-01 3.975066e+01 2.898003e+00 -1.231209e+01 -5.468924e+01 6.208935e-03 3.648324e-01 1.006689e+00 -5.232690e+00 -3.297501e+01 -4.939509e-04 -5.609017e-03 -9.419962e-03 -1.938389e-03 -6.636149e-03 -1.362025e-02 -1.601600e-04 6.265679e-04 9.508892e-04 -4.449956e-02 3.225973e-03 2.491030e-03 1.012849e+00 1.164510e-11 3.444173e-03 8.948334e-04 +-9.522653e-07 -4.368287e-05 6.501891e-06 5.562580e-05 -1.607002e-05 6.549923e-05 -2.899514e-03 -4.292779e-05 -4.201243e-01 8.058549e-02 -3.006146e-02 -7.752667e-01 8.484025e-01 1.684186e+00 6.701657e-01 3.469684e-02 -6.428920e-03 6.625766e-03 1.040408e-01 1.448382e-01 5.956271e-05 9.737892e-05 -3.573909e-04 -2.258863e-04 -2.915062e-04 -9.896129e-04 4.365593e-06 -5.177211e-07 -2.572381e-06 5.963770e-03 -4.776080e-05 -4.094115e-05 -1.642373e-02 1.166120e-13 1.219848e-05 -1.711046e-05 + 4.919179e-06 -3.404778e-04 4.443456e-05 -3.123279e-04 2.894457e-04 2.578906e-04 -1.618746e-02 -1.615338e-03 -7.110279e-01 -3.006146e-02 3.773387e+00 -3.095472e+01 -3.604691e+00 7.271454e+00 4.679820e+00 -2.552336e+00 -2.463936e-01 1.294346e-01 2.467546e+00 2.938734e+00 6.602042e-04 4.672041e-04 3.340702e-03 1.110814e-03 1.956502e-03 4.860517e-03 8.024661e-05 -4.219112e-05 3.179199e-05 1.720414e-02 8.458201e-04 -2.250581e-04 -1.357324e-02 -6.459726e-13 4.372049e-04 -1.181833e-04 + 2.139574e-04 1.163656e-02 -1.720999e-03 1.807283e-02 4.017131e-03 -7.357362e-03 7.035872e-01 3.008348e-02 3.975066e+01 -7.752667e-01 -3.095472e+01 7.958596e+02 7.856257e+01 -5.345230e+01 -5.996219e+01 1.356285e+01 -5.936891e+00 6.317520e+00 -1.006978e+01 -2.191097e+01 -1.131577e-03 -2.340420e-02 -4.813699e-02 -2.142126e-02 -2.318519e-02 2.404666e-02 3.662030e-03 6.931787e-04 3.283097e-03 -5.188863e-01 -6.835501e-04 9.926801e-03 3.068377e+00 3.772040e-11 6.549248e-05 4.097475e-03 + 7.529320e-05 6.227166e-03 -8.280719e-04 1.264418e-02 5.860319e-03 -2.460705e-03 3.427792e-01 9.350711e-03 2.898003e+00 8.484025e-01 -3.604691e+00 7.856257e+01 6.363570e+01 3.976516e+01 1.491510e+01 -3.940535e+00 -3.057012e-01 -9.998158e-01 3.580528e+00 4.135197e+00 -2.123734e-03 9.575263e-04 -8.770112e-03 -1.055140e-03 -3.307478e-03 3.763237e-02 7.251788e-05 1.707714e-04 6.119563e-04 -1.183542e-01 -1.782000e-03 4.836605e-03 1.411154e+00 2.625295e-11 8.177586e-03 2.071305e-03 + 1.622953e-05 2.220626e-03 -2.693011e-04 9.338375e-03 5.089812e-03 6.017079e-04 1.087610e-01 6.652363e-04 -1.231209e+01 1.684186e+00 7.271454e+00 -5.345230e+01 3.976516e+01 8.373152e+01 5.113432e+01 -1.081239e+01 -8.062098e-01 -6.544744e-01 4.368546e+00 7.029795e+00 1.901507e-03 4.609135e-03 -4.992084e-03 2.813632e-03 4.925152e-03 1.038827e-02 3.448412e-04 -2.879068e-04 3.379176e-04 1.100413e-01 -1.106784e-03 1.534675e-03 4.043427e-01 1.942566e-11 7.841552e-03 6.725971e-04 +-7.645938e-05 -2.373624e-04 9.111147e-05 5.209225e-03 1.830835e-03 4.737102e-04 -5.186157e-02 2.084537e-04 -5.468924e+01 6.701657e-01 4.679820e+00 -5.996219e+01 1.491510e+01 5.113432e+01 1.013382e+02 -8.540118e+00 -8.322943e-01 -1.654593e+00 4.716851e+00 3.251167e+01 1.848454e-03 7.961594e-03 1.117364e-02 6.551264e-03 2.300106e-02 6.651186e-02 3.335501e-04 -1.139199e-03 8.961424e-04 5.657381e-02 -4.815927e-03 -7.547909e-04 -5.737403e-01 1.081066e-11 3.785543e-03 -1.570000e-04 + 1.424514e-05 7.663614e-04 -1.131523e-04 1.959831e-04 -2.998483e-05 -5.329827e-04 5.015881e-02 4.176808e-03 6.208935e-03 3.469684e-02 -2.552336e+00 1.356285e+01 -3.940535e+00 -1.081239e+01 -8.540118e+00 5.517043e+00 2.101788e-01 -1.474734e-01 -1.783672e+00 -2.003892e+00 -1.374340e-04 -3.228142e-04 8.362341e-04 -1.413543e-03 -4.409769e-03 -1.321100e-02 -2.823747e-05 -9.337632e-06 -4.040361e-05 -3.786235e-02 -3.587398e-04 7.095495e-04 1.928400e-01 4.226261e-13 -2.003275e-03 2.984949e-04 + 1.046092e-05 1.979399e-04 -2.454966e-05 4.453135e-04 9.770338e-04 1.337948e-04 1.482319e-02 1.948383e-04 3.648324e-01 -6.428920e-03 -2.463936e-01 -5.936891e+00 -3.057012e-01 -8.062098e-01 -8.322943e-01 2.101788e-01 3.412613e+00 5.182987e-01 5.968776e-02 -9.490056e-02 -1.794052e-03 -1.011854e-05 8.941677e-05 -5.428733e-04 1.175946e-03 -6.801965e-04 1.759105e-04 1.609398e-05 2.101935e-05 1.610072e-02 4.841623e-04 2.119880e-04 1.023863e-01 9.385604e-13 4.212071e-04 7.431699e-05 + 1.640551e-05 4.374158e-04 -6.208266e-05 -5.809910e-04 7.398230e-04 -6.135350e-04 2.936994e-02 -5.161913e-04 1.006689e+00 6.625766e-03 1.294346e-01 6.317520e+00 -9.998158e-01 -6.544744e-01 -1.654593e+00 -1.474734e-01 5.182987e-01 3.160032e+00 2.673398e+00 2.437018e+00 -8.224378e-05 -4.464128e-04 -1.191742e-03 -1.074668e-03 3.105385e-03 4.795649e-03 -1.917776e-04 -3.973630e-05 1.115624e-05 -5.567092e-02 9.121926e-04 4.188212e-04 1.990522e-01 -1.214688e-12 5.997705e-04 1.554790e-04 + 2.164549e-05 6.817916e-04 -8.953761e-05 1.592558e-04 1.225910e-03 -6.046342e-04 4.524726e-02 5.917377e-04 -5.232690e+00 1.040408e-01 2.467546e+00 -1.006978e+01 3.580528e+00 4.368546e+00 4.716851e+00 -1.783672e+00 5.968776e-02 2.673398e+00 1.056121e+01 1.870252e+01 8.282984e-04 1.625913e-03 -9.530550e-04 -1.579159e-03 2.969915e-03 1.440250e-02 -5.113094e-05 4.005321e-04 3.192193e-05 -5.002930e-02 9.229266e-04 6.432091e-04 2.679803e-01 3.479209e-13 5.536783e-04 2.502205e-04 +-2.981852e-05 -1.104169e-04 6.086021e-05 1.182870e-04 5.771744e-04 -3.979159e-04 -9.790328e-03 1.282140e-03 -3.297501e+01 1.448382e-01 2.938734e+00 -2.191097e+01 4.135197e+00 7.029795e+00 3.251167e+01 -2.003892e+00 -9.490056e-02 2.437018e+00 1.870252e+01 5.289176e+01 6.281433e-04 6.504719e-03 2.897754e-03 7.398015e-04 3.444662e-03 1.916481e-02 1.981721e-05 9.612049e-04 6.213590e-04 -3.850272e-02 -9.571926e-04 -1.485998e-04 -1.121567e-01 3.004532e-13 -3.516671e-04 2.968196e-05 +-2.015622e-07 -1.923663e-06 3.875854e-07 -8.694982e-06 -7.934064e-06 -4.563724e-06 -1.771741e-04 1.447606e-06 -4.939509e-04 5.956271e-05 6.602042e-04 -1.131577e-03 -2.123734e-03 1.901507e-03 1.848454e-03 -1.374340e-04 -1.794052e-03 -8.224378e-05 8.282984e-04 6.281433e-04 1.434326e-03 2.773754e-04 1.412698e-04 -3.299205e-04 -7.780210e-05 -1.855481e-05 -1.077966e-07 -2.829410e-07 2.287227e-06 -4.610417e-04 -9.421778e-06 -2.508188e-06 -1.512614e-03 -1.851330e-14 -5.959646e-06 -6.383556e-07 +-1.505130e-07 -1.641383e-06 2.386999e-07 -1.361126e-05 -3.445506e-06 -4.811951e-06 -1.100236e-04 1.598451e-05 -5.609017e-03 9.737892e-05 4.672041e-04 -2.340420e-02 9.575263e-04 4.609135e-03 7.961594e-03 -3.228142e-04 -1.011854e-05 -4.464128e-04 1.625913e-03 6.504719e-03 2.773754e-04 8.795109e-04 6.613250e-06 7.418456e-04 5.143948e-05 -7.616077e-04 -1.630891e-07 1.381449e-07 6.399337e-07 -5.068626e-04 -9.133086e-06 -1.608031e-06 -1.156178e-03 -2.823153e-14 -1.699005e-05 -3.376972e-07 +-2.658992e-07 -1.881622e-06 2.241586e-07 -2.679292e-05 -6.693514e-06 -1.077834e-05 -1.635179e-04 -7.532651e-05 -9.419962e-03 -3.573909e-04 3.340702e-03 -4.813699e-02 -8.770112e-03 -4.992084e-03 1.117364e-02 8.362341e-04 8.941677e-05 -1.191742e-03 -9.530550e-04 2.897754e-03 1.412698e-04 6.613250e-06 7.200735e-03 -3.089424e-04 5.430137e-04 4.416995e-03 -1.087102e-07 -9.082009e-07 6.414077e-08 -1.107151e-03 1.607517e-05 -2.360634e-06 4.066862e-04 -5.609285e-14 3.542981e-05 -3.080447e-07 +-2.050041e-07 1.880865e-06 -2.282546e-07 -7.416164e-06 1.919857e-06 -4.645800e-06 3.717591e-05 5.459931e-05 -1.938389e-03 -2.258863e-04 1.110814e-03 -2.142126e-02 -1.055140e-03 2.813632e-03 6.551264e-03 -1.413543e-03 -5.428733e-04 -1.074668e-03 -1.579159e-03 7.398015e-04 -3.299205e-04 7.418456e-04 -3.089424e-04 2.870755e-03 4.968183e-04 -9.509556e-04 -2.584266e-07 1.495219e-07 -8.190901e-07 -4.203024e-04 -2.731437e-05 4.316403e-07 -1.793322e-03 -1.536066e-14 -3.059999e-05 7.151632e-07 +-6.757932e-07 1.199530e-06 -3.533102e-07 -2.445737e-05 -9.636098e-06 -8.831253e-06 -1.140268e-04 8.425813e-05 -6.636149e-03 -2.915062e-04 1.956502e-03 -2.318519e-02 -3.307478e-03 4.925152e-03 2.300106e-02 -4.409769e-03 1.175946e-03 3.105385e-03 2.969915e-03 3.444662e-03 -7.780210e-05 5.143948e-05 5.430137e-04 4.968183e-04 6.374292e-03 7.904762e-03 3.322345e-07 -1.264184e-07 -3.653082e-06 -6.675732e-04 -5.327386e-05 -1.757745e-06 -4.614363e-03 -5.224697e-14 -5.818602e-05 8.984718e-07 +-2.359477e-06 1.333233e-05 -2.152772e-06 -2.155874e-05 -4.242303e-05 -2.151235e-05 -1.163243e-04 -3.474485e-05 -1.362025e-02 -9.896129e-04 4.860517e-03 2.404666e-02 3.763237e-02 1.038827e-02 6.651186e-02 -1.321100e-02 -6.801965e-04 4.795649e-03 1.440250e-02 1.916481e-02 -1.855481e-05 -7.616077e-04 4.416995e-03 -9.509556e-04 7.904762e-03 7.225136e-02 1.914233e-07 -1.282756e-06 -8.989949e-07 -1.398452e-03 -9.640480e-05 -2.371184e-06 -8.277742e-03 -4.648749e-14 4.918051e-05 5.140367e-06 + 2.859926e-09 -6.793500e-09 -1.093335e-09 8.941163e-08 -9.448073e-08 5.476817e-09 1.400437e-07 3.227446e-07 -1.601600e-04 4.365593e-06 8.024661e-05 3.662030e-03 7.251788e-05 3.448412e-04 3.335501e-04 -2.823747e-05 1.759105e-04 -1.917776e-04 -5.113094e-05 1.981721e-05 -1.077966e-07 -1.630891e-07 -1.087102e-07 -2.584266e-07 3.322345e-07 1.914233e-07 3.717094e-07 6.247513e-10 -1.940600e-08 -4.410385e-07 -2.730221e-08 2.463596e-09 -2.795297e-06 1.968436e-16 -2.567285e-07 -7.809418e-09 + 1.599643e-10 3.155903e-08 -4.916963e-09 4.369640e-07 9.693583e-09 1.539353e-07 1.681312e-06 3.807014e-07 6.265679e-04 -5.177211e-07 -4.219112e-05 6.931787e-04 1.707714e-04 -2.879068e-04 -1.139199e-03 -9.337632e-06 1.609398e-05 -3.973630e-05 4.005321e-04 9.612049e-04 -2.829410e-07 1.381449e-07 -9.082009e-07 1.495219e-07 -1.264184e-07 -1.282756e-06 6.247513e-10 3.708007e-07 2.737782e-08 1.668029e-05 -1.391974e-07 2.321727e-08 -1.736454e-06 9.121824e-16 -1.101793e-08 9.738416e-09 + 1.527917e-08 -1.224938e-07 9.262489e-09 1.180788e-05 1.981007e-07 -1.942142e-08 7.852693e-07 1.717692e-06 9.508892e-04 -2.572381e-06 3.179199e-05 3.283097e-03 6.119563e-04 3.379176e-04 8.961424e-04 -4.040361e-05 2.101935e-05 1.115624e-05 3.192193e-05 6.213590e-04 2.287227e-06 6.399337e-07 6.414077e-08 -8.190901e-07 -3.653082e-06 -8.989949e-07 -1.940600e-08 2.737782e-08 6.002864e-06 -6.026679e-06 3.966471e-07 1.825772e-08 2.785335e-05 2.460188e-14 3.228827e-06 -3.256149e-08 + 3.794469e-05 1.803956e-04 -4.202734e-05 1.275403e-02 1.651274e-03 6.108450e-03 2.470632e-02 1.751985e-03 -4.449956e-02 5.963770e-03 1.720414e-02 -5.188863e-01 -1.183542e-01 1.100413e-01 5.657381e-02 -3.786235e-02 1.610072e-02 -5.567092e-02 -5.002930e-02 -3.850272e-02 -4.610417e-04 -5.068626e-04 -1.107151e-03 -4.203024e-04 -6.675732e-04 -1.398452e-03 -4.410385e-07 1.668029e-05 -6.026679e-06 6.293621e-01 1.325270e-03 3.619976e-04 2.033571e-01 2.657303e-11 3.961715e-03 6.728645e-05 + 2.531824e-06 3.882384e-06 -1.697451e-06 5.618501e-05 9.287725e-05 1.766733e-05 1.319628e-03 -4.478507e-04 3.225973e-03 -4.776080e-05 8.458201e-04 -6.835501e-04 -1.782000e-03 -1.106784e-03 -4.815927e-03 -3.587398e-04 4.841623e-04 9.121926e-04 9.229266e-04 -9.571926e-04 -9.421778e-06 -9.133086e-06 1.607517e-05 -2.731437e-05 -5.327386e-05 -9.640480e-05 -2.730221e-08 -1.391974e-07 3.966471e-07 1.325270e-03 2.923477e-04 1.957172e-05 2.692508e-02 1.171095e-13 2.867400e-04 3.050524e-06 + 4.588090e-07 6.985926e-06 -1.119574e-06 2.491237e-05 1.986313e-05 2.824168e-06 4.930722e-04 -1.112325e-05 2.491030e-03 -4.094115e-05 -2.250581e-04 9.926801e-03 4.836605e-03 1.534675e-03 -7.547909e-04 7.095495e-04 2.119880e-04 4.188212e-04 6.432091e-04 -1.485998e-04 -2.508188e-06 -1.608031e-06 -2.360634e-06 4.316403e-07 -1.757745e-06 -2.371184e-06 2.463596e-09 2.321727e-08 1.825772e-08 3.619976e-04 1.957172e-05 7.108017e-06 3.660176e-03 5.189389e-14 2.539641e-05 2.057670e-06 + 3.279227e-04 2.565224e-03 -4.714157e-04 1.197368e-02 1.282636e-02 2.092391e-03 2.512640e-01 -3.610980e-02 1.012849e+00 -1.642373e-02 -1.357324e-02 3.068377e+00 1.411154e+00 4.043427e-01 -5.737403e-01 1.928400e-01 1.023863e-01 1.990522e-01 2.679803e-01 -1.121567e-01 -1.512614e-03 -1.156178e-03 4.066862e-04 -1.793322e-03 -4.614363e-03 -8.277742e-03 -2.795297e-06 -1.736454e-06 2.785335e-05 2.033571e-01 2.692508e-02 3.660176e-03 3.086382e+00 2.494940e-11 2.918006e-02 8.689684e-04 + 3.827149e-15 5.051278e-14 -7.836507e-15 7.035159e-13 1.519872e-13 2.535114e-13 3.584811e-12 1.149225e-14 1.164510e-11 1.166120e-13 -6.459726e-13 3.772040e-11 2.625295e-11 1.942566e-11 1.081066e-11 4.226261e-13 9.385604e-13 -1.214688e-12 3.479209e-13 3.004532e-13 -1.851330e-14 -2.823153e-14 -5.609285e-14 -1.536066e-14 -5.224697e-14 -4.648749e-14 1.968436e-16 9.121824e-16 2.460188e-14 2.657303e-11 1.171095e-13 5.189389e-14 2.494940e-11 1.466311e-21 3.336132e-13 1.350928e-14 + 2.110094e-06 2.447651e-05 -3.681395e-06 1.600976e-04 8.134971e-05 3.942531e-05 1.748504e-03 -6.670388e-04 3.444173e-03 1.219848e-05 4.372049e-04 6.549248e-05 8.177586e-03 7.841552e-03 3.785543e-03 -2.003275e-03 4.212071e-04 5.997705e-04 5.536783e-04 -3.516671e-04 -5.959646e-06 -1.699005e-05 3.542981e-05 -3.059999e-05 -5.818602e-05 4.918051e-05 -2.567285e-07 -1.101793e-08 3.228827e-06 3.961715e-03 2.867400e-04 2.539641e-05 2.918006e-02 3.336132e-13 5.179119e-04 6.585396e-06 + 7.337971e-08 2.307287e-06 -3.350036e-07 6.486419e-06 5.172023e-06 2.716912e-07 1.445621e-04 -9.966676e-07 8.948334e-04 -1.711046e-05 -1.181833e-04 4.097475e-03 2.071305e-03 6.725971e-04 -1.570000e-04 2.984949e-04 7.431699e-05 1.554790e-04 2.502205e-04 2.968196e-05 -6.383556e-07 -3.376972e-07 -3.080447e-07 7.151632e-07 8.984718e-07 5.140367e-06 -7.809418e-09 9.738416e-09 -3.256149e-08 6.728645e-05 3.050524e-06 2.057670e-06 8.689684e-04 1.350928e-14 6.585396e-06 7.713178e-07 diff --git a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data new file mode 100644 index 00000000..08617457 --- /dev/null +++ b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data @@ -0,0 +1,19 @@ +# Parameter list + +eff_like_from_covmat.data_directory = data.path['data'] +cov_list = ['100*theta_s','100*theta_d'] # variables of interest in .covmat file +eff_like_from_covmat.get_var_strings = ['cosmo.theta_s_100()', 'cosmo.theta_s_100()'] # name of the variables in CLASS +mu_list = ['100*\theta_{s }','100*\theta_{d }'] # name of the means in h_info file + +# covariance matrix + +covFull = 'eff_like_from_covmat.covmat' +cov_indices = [cov_list.index(c) for c in cov_list] +eff_like_from_covmat.covmat = covFull[np.ix_(cov_indices, cov_indices)] + +# mean list + +muFull = 'eff_like_from_covmat.h_info' +mu_indices = [cov_list.index(m) for m in mu_list] +eff_like_from_covmat.mu = muFull[np.ix_([3],mu_list)] # mean is located at row index 3 in h_info file + diff --git a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.h_info b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.h_info new file mode 100644 index 00000000..f117dad1 --- /dev/null +++ b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.h_info @@ -0,0 +1,19 @@ + param names : 100*\theta_{s }deg_{ncdm } m_{ncdm } H0 100*\theta_{d } + R-1 values : 0.000876 0.002535 0.002437 0.002875 0.002500 + Best Fit : 1.042305e+00 2.746032e+00 5.134637e-03 6.538970e+01 3.221057e-01 + mean : 1.042278e+00 2.825026e+00 3.279609e-02 6.576704e+01 3.223170e-01 + sigma : 5.246247e-04 1.882472e-01 1.872085e-02 1.737398e+00 8.956704e-04 + + 1-sigma - : -5.426182e-04 -1.906133e-01 -3.278194e-02 -1.581037e+00 -8.827746e-04 + 1-sigma + : 5.066313e-04 1.858811e-01 4.659757e-03 1.893759e+00 9.085663e-04 + 2-sigma - : -1.052337e-03 -3.696960e-01 -3.278194e-02 -3.711543e+00 -1.793280e-03 + 2-sigma + : 1.059192e-03 3.787995e-01 6.895530e-02 3.537918e+00 1.761348e-03 + 3-sigma - : -1.536302e-03 -5.548711e-01 -3.278194e-02 -6.229307e+00 -2.723714e-03 + 3-sigma + : 1.626237e-03 6.009074e-01 1.648337e-01 5.172713e+00 2.655721e-03 + + 1-sigma > : 1.041736e+00 2.634413e+00 1.415058e-05 6.418600e+01 3.214342e-01 + 1-sigma < : 1.042785e+00 3.010907e+00 3.745585e-02 6.766080e+01 3.232256e-01 + 2-sigma > : 1.041226e+00 2.455330e+00 1.415058e-05 6.205549e+01 3.205237e-01 + 2-sigma < : 1.043337e+00 3.203826e+00 1.017514e-01 6.930495e+01 3.240784e-01 + 3-sigma > : 1.040742e+00 2.270155e+00 1.415058e-05 5.953773e+01 3.195933e-01 + 3-sigma < : 1.043904e+00 3.425934e+00 1.976298e-01 7.093975e+01 3.249727e-01 \ No newline at end of file From 2ea98a1302fe15204c18ffcd70ccc9ad5f4a613f Mon Sep 17 00:00:00 2001 From: Kim Date: Tue, 25 May 2021 14:12:24 +0200 Subject: [PATCH 05/10] Added eff_like_from_covmat folder --- .../eff_like_from_covmat/__init__.py | 21 ++++++----- .../eff_like_from_covmat.covmat | 37 ------------------- .../eff_like_from_covmat.data | 19 +--------- .../eff_like_from_covmat.h_info | 19 ---------- 4 files changed, 14 insertions(+), 82 deletions(-) delete mode 100644 montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.covmat delete mode 100644 montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.h_info diff --git a/montepython/likelihoods/eff_like_from_covmat/__init__.py b/montepython/likelihoods/eff_like_from_covmat/__init__.py index f014ae02..91e7a8a8 100644 --- a/montepython/likelihoods/eff_like_from_covmat/__init__.py +++ b/montepython/likelihoods/eff_like_from_covmat/__init__.py @@ -1,27 +1,30 @@ import numpy as np import math import scipy.linalg as la -from montepython.likelihood_class import Likelihood_sn +from montepython.likelihood_class import Likelihood_prior +from numpy.linalg import multi_dot -class eff_like_from_covmat(Likelihood_sn): +class eff_like_from_covmat(Likelihood_prior): def __init__(self, path, data, command_line): # Call __init__ method of super class: super(eff_like_from_covmat, self).__init__(path, data, command_line) self.need_cosmo_arguments(data,{'compute damping scale':'yes'}) - # Initialise other things: - covmat_inverse = la.inv(self.covmat) # Compute likelihood def loglkl(self, cosmo, data): - - mean_vec = np.array([self.mu]).T - mean_vec_class = np.array([eval(s) for s in self.get_var_strings]) + covmat = self.covmat + mu = self.mu + mean_vec = np.array(mu) + covmat_inverse = la.inv(covmat) + mean_vec_class = [1.042195411207378,0.324186976388346] # 100*theta_s and 100*theta_d from CLASS dif_vec = mean_vec - mean_vec_class - exponent = la.multi_dot([dif_vec.T,covmat_inverse,dif_vec]) - + dif_vec_T = dif_vec.T + #exponent = la.multi_dot([dif_vec.T,covmat_inverse,dif_vec]) + #covmat_inverse = la.inv(self.covmat) # might delete covmat_inverse above + exponent = dif_vec_T.dot(covmat_inverse).dot(dif_vec) loglikelihood = -1/2*exponent # exponent in the multivariate Gaussian return loglikelihood diff --git a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.covmat b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.covmat deleted file mode 100644 index 92b3ffa1..00000000 --- a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.covmat +++ /dev/null @@ -1,37 +0,0 @@ -# omega_b, omega_cdm, 100*theta_s, ln10^{10}A_s, n_s, tau_reio, deg_ncdm, m_ncdm, A_cib_217, xi_sz_cib, A_sz, ps_A_100_100, ps_A_143_143, ps_A_143_217, ps_A_217_217, ksz_norm, gal545_A_100, gal545_A_143, gal545_A_143_217, gal545_A_217, galf_TE_A_100, galf_TE_A_100_143, galf_TE_A_100_217, galf_TE_A_143, galf_TE_A_143_217, galf_TE_A_217, calib_100T, calib_217T, A_planck, z_reio, Omega_Lambda, YHe, H0, A_s, sigma8, 100*theta_d - 5.131237e-08 3.224207e-07 -5.819384e-08 1.836663e-06 1.568328e-06 3.953460e-07 3.117701e-05 -1.935290e-06 1.004083e-04 -9.522653e-07 4.919179e-06 2.139574e-04 7.529320e-05 1.622953e-05 -7.645938e-05 1.424514e-05 1.046092e-05 1.640551e-05 2.164549e-05 -2.981852e-05 -2.015622e-07 -1.505130e-07 -2.658992e-07 -2.050041e-07 -6.757932e-07 -2.359477e-06 2.859926e-09 1.599643e-10 1.527917e-08 3.794469e-05 2.531824e-06 4.588090e-07 3.279227e-04 3.827149e-15 2.110094e-06 7.337971e-08 - 3.224207e-07 8.832320e-06 -1.232260e-06 2.425306e-05 1.506002e-05 4.542627e-07 4.886172e-04 -1.616461e-06 2.678891e-03 -4.368287e-05 -3.404778e-04 1.163656e-02 6.227166e-03 2.220626e-03 -2.373624e-04 7.663614e-04 1.979399e-04 4.374158e-04 6.817916e-04 -1.104169e-04 -1.923663e-06 -1.641383e-06 -1.881622e-06 1.880865e-06 1.199530e-06 1.333233e-05 -6.793500e-09 3.155903e-08 -1.224938e-07 1.803956e-04 3.882384e-06 6.985926e-06 2.565224e-03 5.051278e-14 2.447651e-05 2.307287e-06 --5.819384e-08 -1.232260e-06 2.680703e-07 -3.762269e-06 -2.703970e-06 -2.474418e-07 -7.804585e-05 9.223786e-07 -4.165954e-04 6.501891e-06 4.443456e-05 -1.720999e-03 -8.280719e-04 -2.693011e-04 9.111147e-05 -1.131523e-04 -2.454966e-05 -6.208266e-05 -8.953761e-05 6.086021e-05 3.875854e-07 2.386999e-07 2.241586e-07 -2.282546e-07 -3.533102e-07 -2.152772e-06 -1.093335e-09 -4.916963e-09 9.262489e-09 -4.202734e-05 -1.697451e-06 -1.119574e-06 -4.714157e-04 -7.836507e-15 -3.681395e-06 -3.350036e-07 - 1.836663e-06 2.425306e-05 -3.762269e-06 3.375991e-04 7.293854e-05 1.216208e-04 1.720754e-03 5.527558e-06 5.599692e-03 5.562580e-05 -3.123279e-04 1.807283e-02 1.264418e-02 9.338375e-03 5.209225e-03 1.959831e-04 4.453135e-04 -5.809910e-04 1.592558e-04 1.182870e-04 -8.694982e-06 -1.361126e-05 -2.679292e-05 -7.416164e-06 -2.445737e-05 -2.155874e-05 8.941163e-08 4.369640e-07 1.180788e-05 1.275403e-02 5.618501e-05 2.491237e-05 1.197368e-02 7.035159e-13 1.600976e-04 6.486419e-06 - 1.568328e-06 1.506002e-05 -2.703970e-06 7.293854e-05 7.479500e-05 1.553039e-05 1.369635e-03 -6.077571e-05 2.549437e-03 -1.607002e-05 2.894457e-04 4.017131e-03 5.860319e-03 5.089812e-03 1.830835e-03 -2.998483e-05 9.770338e-04 7.398230e-04 1.225910e-03 5.771744e-04 -7.934064e-06 -3.445506e-06 -6.693514e-06 1.919857e-06 -9.636098e-06 -4.242303e-05 -9.448073e-08 9.693583e-09 1.981007e-07 1.651274e-03 9.287725e-05 1.986313e-05 1.282636e-02 1.519872e-13 8.134971e-05 5.172023e-06 - 3.953460e-07 4.542627e-07 -2.474418e-07 1.216208e-04 1.553039e-05 5.973199e-05 1.898136e-04 9.989056e-06 -8.613336e-04 6.549923e-05 2.578906e-04 -7.357362e-03 -2.460705e-03 6.017079e-04 4.737102e-04 -5.329827e-04 1.337948e-04 -6.135350e-04 -6.046342e-04 -3.979159e-04 -4.563724e-06 -4.811951e-06 -1.077834e-05 -4.645800e-06 -8.831253e-06 -2.151235e-05 5.476817e-09 1.539353e-07 -1.942142e-08 6.108450e-03 1.766733e-05 2.824168e-06 2.092391e-03 2.535114e-13 3.942531e-05 2.716912e-07 - 3.117701e-05 4.886172e-04 -7.804585e-05 1.720754e-03 1.369635e-03 1.898136e-04 3.423693e-02 -7.350726e-04 1.748809e-01 -2.899514e-03 -1.618746e-02 7.035872e-01 3.427792e-01 1.087610e-01 -5.186157e-02 5.015881e-02 1.482319e-02 2.936994e-02 4.524726e-02 -9.790328e-03 -1.771741e-04 -1.100236e-04 -1.635179e-04 3.717591e-05 -1.140268e-04 -1.163243e-04 1.400437e-07 1.681312e-06 7.852693e-07 2.470632e-02 1.319628e-03 4.930722e-04 2.512640e-01 3.584811e-12 1.748504e-03 1.445621e-04 --1.935290e-06 -1.616461e-06 9.223786e-07 5.527558e-06 -6.077571e-05 9.989056e-06 -7.350726e-04 1.076810e-03 2.299399e-03 -4.292779e-05 -1.615338e-03 3.008348e-02 9.350711e-03 6.652363e-04 2.084537e-04 4.176808e-03 1.948383e-04 -5.161913e-04 5.917377e-04 1.282140e-03 1.447606e-06 1.598451e-05 -7.532651e-05 5.459931e-05 8.425813e-05 -3.474485e-05 3.227446e-07 3.807014e-07 1.717692e-06 1.751985e-03 -4.478507e-04 -1.112325e-05 -3.610980e-02 1.149225e-14 -6.670388e-04 -9.966676e-07 - 1.004083e-04 2.678891e-03 -4.165954e-04 5.599692e-03 2.549437e-03 -8.613336e-04 1.748809e-01 2.299399e-03 4.514726e+01 -4.201243e-01 -7.110279e-01 3.975066e+01 2.898003e+00 -1.231209e+01 -5.468924e+01 6.208935e-03 3.648324e-01 1.006689e+00 -5.232690e+00 -3.297501e+01 -4.939509e-04 -5.609017e-03 -9.419962e-03 -1.938389e-03 -6.636149e-03 -1.362025e-02 -1.601600e-04 6.265679e-04 9.508892e-04 -4.449956e-02 3.225973e-03 2.491030e-03 1.012849e+00 1.164510e-11 3.444173e-03 8.948334e-04 --9.522653e-07 -4.368287e-05 6.501891e-06 5.562580e-05 -1.607002e-05 6.549923e-05 -2.899514e-03 -4.292779e-05 -4.201243e-01 8.058549e-02 -3.006146e-02 -7.752667e-01 8.484025e-01 1.684186e+00 6.701657e-01 3.469684e-02 -6.428920e-03 6.625766e-03 1.040408e-01 1.448382e-01 5.956271e-05 9.737892e-05 -3.573909e-04 -2.258863e-04 -2.915062e-04 -9.896129e-04 4.365593e-06 -5.177211e-07 -2.572381e-06 5.963770e-03 -4.776080e-05 -4.094115e-05 -1.642373e-02 1.166120e-13 1.219848e-05 -1.711046e-05 - 4.919179e-06 -3.404778e-04 4.443456e-05 -3.123279e-04 2.894457e-04 2.578906e-04 -1.618746e-02 -1.615338e-03 -7.110279e-01 -3.006146e-02 3.773387e+00 -3.095472e+01 -3.604691e+00 7.271454e+00 4.679820e+00 -2.552336e+00 -2.463936e-01 1.294346e-01 2.467546e+00 2.938734e+00 6.602042e-04 4.672041e-04 3.340702e-03 1.110814e-03 1.956502e-03 4.860517e-03 8.024661e-05 -4.219112e-05 3.179199e-05 1.720414e-02 8.458201e-04 -2.250581e-04 -1.357324e-02 -6.459726e-13 4.372049e-04 -1.181833e-04 - 2.139574e-04 1.163656e-02 -1.720999e-03 1.807283e-02 4.017131e-03 -7.357362e-03 7.035872e-01 3.008348e-02 3.975066e+01 -7.752667e-01 -3.095472e+01 7.958596e+02 7.856257e+01 -5.345230e+01 -5.996219e+01 1.356285e+01 -5.936891e+00 6.317520e+00 -1.006978e+01 -2.191097e+01 -1.131577e-03 -2.340420e-02 -4.813699e-02 -2.142126e-02 -2.318519e-02 2.404666e-02 3.662030e-03 6.931787e-04 3.283097e-03 -5.188863e-01 -6.835501e-04 9.926801e-03 3.068377e+00 3.772040e-11 6.549248e-05 4.097475e-03 - 7.529320e-05 6.227166e-03 -8.280719e-04 1.264418e-02 5.860319e-03 -2.460705e-03 3.427792e-01 9.350711e-03 2.898003e+00 8.484025e-01 -3.604691e+00 7.856257e+01 6.363570e+01 3.976516e+01 1.491510e+01 -3.940535e+00 -3.057012e-01 -9.998158e-01 3.580528e+00 4.135197e+00 -2.123734e-03 9.575263e-04 -8.770112e-03 -1.055140e-03 -3.307478e-03 3.763237e-02 7.251788e-05 1.707714e-04 6.119563e-04 -1.183542e-01 -1.782000e-03 4.836605e-03 1.411154e+00 2.625295e-11 8.177586e-03 2.071305e-03 - 1.622953e-05 2.220626e-03 -2.693011e-04 9.338375e-03 5.089812e-03 6.017079e-04 1.087610e-01 6.652363e-04 -1.231209e+01 1.684186e+00 7.271454e+00 -5.345230e+01 3.976516e+01 8.373152e+01 5.113432e+01 -1.081239e+01 -8.062098e-01 -6.544744e-01 4.368546e+00 7.029795e+00 1.901507e-03 4.609135e-03 -4.992084e-03 2.813632e-03 4.925152e-03 1.038827e-02 3.448412e-04 -2.879068e-04 3.379176e-04 1.100413e-01 -1.106784e-03 1.534675e-03 4.043427e-01 1.942566e-11 7.841552e-03 6.725971e-04 --7.645938e-05 -2.373624e-04 9.111147e-05 5.209225e-03 1.830835e-03 4.737102e-04 -5.186157e-02 2.084537e-04 -5.468924e+01 6.701657e-01 4.679820e+00 -5.996219e+01 1.491510e+01 5.113432e+01 1.013382e+02 -8.540118e+00 -8.322943e-01 -1.654593e+00 4.716851e+00 3.251167e+01 1.848454e-03 7.961594e-03 1.117364e-02 6.551264e-03 2.300106e-02 6.651186e-02 3.335501e-04 -1.139199e-03 8.961424e-04 5.657381e-02 -4.815927e-03 -7.547909e-04 -5.737403e-01 1.081066e-11 3.785543e-03 -1.570000e-04 - 1.424514e-05 7.663614e-04 -1.131523e-04 1.959831e-04 -2.998483e-05 -5.329827e-04 5.015881e-02 4.176808e-03 6.208935e-03 3.469684e-02 -2.552336e+00 1.356285e+01 -3.940535e+00 -1.081239e+01 -8.540118e+00 5.517043e+00 2.101788e-01 -1.474734e-01 -1.783672e+00 -2.003892e+00 -1.374340e-04 -3.228142e-04 8.362341e-04 -1.413543e-03 -4.409769e-03 -1.321100e-02 -2.823747e-05 -9.337632e-06 -4.040361e-05 -3.786235e-02 -3.587398e-04 7.095495e-04 1.928400e-01 4.226261e-13 -2.003275e-03 2.984949e-04 - 1.046092e-05 1.979399e-04 -2.454966e-05 4.453135e-04 9.770338e-04 1.337948e-04 1.482319e-02 1.948383e-04 3.648324e-01 -6.428920e-03 -2.463936e-01 -5.936891e+00 -3.057012e-01 -8.062098e-01 -8.322943e-01 2.101788e-01 3.412613e+00 5.182987e-01 5.968776e-02 -9.490056e-02 -1.794052e-03 -1.011854e-05 8.941677e-05 -5.428733e-04 1.175946e-03 -6.801965e-04 1.759105e-04 1.609398e-05 2.101935e-05 1.610072e-02 4.841623e-04 2.119880e-04 1.023863e-01 9.385604e-13 4.212071e-04 7.431699e-05 - 1.640551e-05 4.374158e-04 -6.208266e-05 -5.809910e-04 7.398230e-04 -6.135350e-04 2.936994e-02 -5.161913e-04 1.006689e+00 6.625766e-03 1.294346e-01 6.317520e+00 -9.998158e-01 -6.544744e-01 -1.654593e+00 -1.474734e-01 5.182987e-01 3.160032e+00 2.673398e+00 2.437018e+00 -8.224378e-05 -4.464128e-04 -1.191742e-03 -1.074668e-03 3.105385e-03 4.795649e-03 -1.917776e-04 -3.973630e-05 1.115624e-05 -5.567092e-02 9.121926e-04 4.188212e-04 1.990522e-01 -1.214688e-12 5.997705e-04 1.554790e-04 - 2.164549e-05 6.817916e-04 -8.953761e-05 1.592558e-04 1.225910e-03 -6.046342e-04 4.524726e-02 5.917377e-04 -5.232690e+00 1.040408e-01 2.467546e+00 -1.006978e+01 3.580528e+00 4.368546e+00 4.716851e+00 -1.783672e+00 5.968776e-02 2.673398e+00 1.056121e+01 1.870252e+01 8.282984e-04 1.625913e-03 -9.530550e-04 -1.579159e-03 2.969915e-03 1.440250e-02 -5.113094e-05 4.005321e-04 3.192193e-05 -5.002930e-02 9.229266e-04 6.432091e-04 2.679803e-01 3.479209e-13 5.536783e-04 2.502205e-04 --2.981852e-05 -1.104169e-04 6.086021e-05 1.182870e-04 5.771744e-04 -3.979159e-04 -9.790328e-03 1.282140e-03 -3.297501e+01 1.448382e-01 2.938734e+00 -2.191097e+01 4.135197e+00 7.029795e+00 3.251167e+01 -2.003892e+00 -9.490056e-02 2.437018e+00 1.870252e+01 5.289176e+01 6.281433e-04 6.504719e-03 2.897754e-03 7.398015e-04 3.444662e-03 1.916481e-02 1.981721e-05 9.612049e-04 6.213590e-04 -3.850272e-02 -9.571926e-04 -1.485998e-04 -1.121567e-01 3.004532e-13 -3.516671e-04 2.968196e-05 --2.015622e-07 -1.923663e-06 3.875854e-07 -8.694982e-06 -7.934064e-06 -4.563724e-06 -1.771741e-04 1.447606e-06 -4.939509e-04 5.956271e-05 6.602042e-04 -1.131577e-03 -2.123734e-03 1.901507e-03 1.848454e-03 -1.374340e-04 -1.794052e-03 -8.224378e-05 8.282984e-04 6.281433e-04 1.434326e-03 2.773754e-04 1.412698e-04 -3.299205e-04 -7.780210e-05 -1.855481e-05 -1.077966e-07 -2.829410e-07 2.287227e-06 -4.610417e-04 -9.421778e-06 -2.508188e-06 -1.512614e-03 -1.851330e-14 -5.959646e-06 -6.383556e-07 --1.505130e-07 -1.641383e-06 2.386999e-07 -1.361126e-05 -3.445506e-06 -4.811951e-06 -1.100236e-04 1.598451e-05 -5.609017e-03 9.737892e-05 4.672041e-04 -2.340420e-02 9.575263e-04 4.609135e-03 7.961594e-03 -3.228142e-04 -1.011854e-05 -4.464128e-04 1.625913e-03 6.504719e-03 2.773754e-04 8.795109e-04 6.613250e-06 7.418456e-04 5.143948e-05 -7.616077e-04 -1.630891e-07 1.381449e-07 6.399337e-07 -5.068626e-04 -9.133086e-06 -1.608031e-06 -1.156178e-03 -2.823153e-14 -1.699005e-05 -3.376972e-07 --2.658992e-07 -1.881622e-06 2.241586e-07 -2.679292e-05 -6.693514e-06 -1.077834e-05 -1.635179e-04 -7.532651e-05 -9.419962e-03 -3.573909e-04 3.340702e-03 -4.813699e-02 -8.770112e-03 -4.992084e-03 1.117364e-02 8.362341e-04 8.941677e-05 -1.191742e-03 -9.530550e-04 2.897754e-03 1.412698e-04 6.613250e-06 7.200735e-03 -3.089424e-04 5.430137e-04 4.416995e-03 -1.087102e-07 -9.082009e-07 6.414077e-08 -1.107151e-03 1.607517e-05 -2.360634e-06 4.066862e-04 -5.609285e-14 3.542981e-05 -3.080447e-07 --2.050041e-07 1.880865e-06 -2.282546e-07 -7.416164e-06 1.919857e-06 -4.645800e-06 3.717591e-05 5.459931e-05 -1.938389e-03 -2.258863e-04 1.110814e-03 -2.142126e-02 -1.055140e-03 2.813632e-03 6.551264e-03 -1.413543e-03 -5.428733e-04 -1.074668e-03 -1.579159e-03 7.398015e-04 -3.299205e-04 7.418456e-04 -3.089424e-04 2.870755e-03 4.968183e-04 -9.509556e-04 -2.584266e-07 1.495219e-07 -8.190901e-07 -4.203024e-04 -2.731437e-05 4.316403e-07 -1.793322e-03 -1.536066e-14 -3.059999e-05 7.151632e-07 --6.757932e-07 1.199530e-06 -3.533102e-07 -2.445737e-05 -9.636098e-06 -8.831253e-06 -1.140268e-04 8.425813e-05 -6.636149e-03 -2.915062e-04 1.956502e-03 -2.318519e-02 -3.307478e-03 4.925152e-03 2.300106e-02 -4.409769e-03 1.175946e-03 3.105385e-03 2.969915e-03 3.444662e-03 -7.780210e-05 5.143948e-05 5.430137e-04 4.968183e-04 6.374292e-03 7.904762e-03 3.322345e-07 -1.264184e-07 -3.653082e-06 -6.675732e-04 -5.327386e-05 -1.757745e-06 -4.614363e-03 -5.224697e-14 -5.818602e-05 8.984718e-07 --2.359477e-06 1.333233e-05 -2.152772e-06 -2.155874e-05 -4.242303e-05 -2.151235e-05 -1.163243e-04 -3.474485e-05 -1.362025e-02 -9.896129e-04 4.860517e-03 2.404666e-02 3.763237e-02 1.038827e-02 6.651186e-02 -1.321100e-02 -6.801965e-04 4.795649e-03 1.440250e-02 1.916481e-02 -1.855481e-05 -7.616077e-04 4.416995e-03 -9.509556e-04 7.904762e-03 7.225136e-02 1.914233e-07 -1.282756e-06 -8.989949e-07 -1.398452e-03 -9.640480e-05 -2.371184e-06 -8.277742e-03 -4.648749e-14 4.918051e-05 5.140367e-06 - 2.859926e-09 -6.793500e-09 -1.093335e-09 8.941163e-08 -9.448073e-08 5.476817e-09 1.400437e-07 3.227446e-07 -1.601600e-04 4.365593e-06 8.024661e-05 3.662030e-03 7.251788e-05 3.448412e-04 3.335501e-04 -2.823747e-05 1.759105e-04 -1.917776e-04 -5.113094e-05 1.981721e-05 -1.077966e-07 -1.630891e-07 -1.087102e-07 -2.584266e-07 3.322345e-07 1.914233e-07 3.717094e-07 6.247513e-10 -1.940600e-08 -4.410385e-07 -2.730221e-08 2.463596e-09 -2.795297e-06 1.968436e-16 -2.567285e-07 -7.809418e-09 - 1.599643e-10 3.155903e-08 -4.916963e-09 4.369640e-07 9.693583e-09 1.539353e-07 1.681312e-06 3.807014e-07 6.265679e-04 -5.177211e-07 -4.219112e-05 6.931787e-04 1.707714e-04 -2.879068e-04 -1.139199e-03 -9.337632e-06 1.609398e-05 -3.973630e-05 4.005321e-04 9.612049e-04 -2.829410e-07 1.381449e-07 -9.082009e-07 1.495219e-07 -1.264184e-07 -1.282756e-06 6.247513e-10 3.708007e-07 2.737782e-08 1.668029e-05 -1.391974e-07 2.321727e-08 -1.736454e-06 9.121824e-16 -1.101793e-08 9.738416e-09 - 1.527917e-08 -1.224938e-07 9.262489e-09 1.180788e-05 1.981007e-07 -1.942142e-08 7.852693e-07 1.717692e-06 9.508892e-04 -2.572381e-06 3.179199e-05 3.283097e-03 6.119563e-04 3.379176e-04 8.961424e-04 -4.040361e-05 2.101935e-05 1.115624e-05 3.192193e-05 6.213590e-04 2.287227e-06 6.399337e-07 6.414077e-08 -8.190901e-07 -3.653082e-06 -8.989949e-07 -1.940600e-08 2.737782e-08 6.002864e-06 -6.026679e-06 3.966471e-07 1.825772e-08 2.785335e-05 2.460188e-14 3.228827e-06 -3.256149e-08 - 3.794469e-05 1.803956e-04 -4.202734e-05 1.275403e-02 1.651274e-03 6.108450e-03 2.470632e-02 1.751985e-03 -4.449956e-02 5.963770e-03 1.720414e-02 -5.188863e-01 -1.183542e-01 1.100413e-01 5.657381e-02 -3.786235e-02 1.610072e-02 -5.567092e-02 -5.002930e-02 -3.850272e-02 -4.610417e-04 -5.068626e-04 -1.107151e-03 -4.203024e-04 -6.675732e-04 -1.398452e-03 -4.410385e-07 1.668029e-05 -6.026679e-06 6.293621e-01 1.325270e-03 3.619976e-04 2.033571e-01 2.657303e-11 3.961715e-03 6.728645e-05 - 2.531824e-06 3.882384e-06 -1.697451e-06 5.618501e-05 9.287725e-05 1.766733e-05 1.319628e-03 -4.478507e-04 3.225973e-03 -4.776080e-05 8.458201e-04 -6.835501e-04 -1.782000e-03 -1.106784e-03 -4.815927e-03 -3.587398e-04 4.841623e-04 9.121926e-04 9.229266e-04 -9.571926e-04 -9.421778e-06 -9.133086e-06 1.607517e-05 -2.731437e-05 -5.327386e-05 -9.640480e-05 -2.730221e-08 -1.391974e-07 3.966471e-07 1.325270e-03 2.923477e-04 1.957172e-05 2.692508e-02 1.171095e-13 2.867400e-04 3.050524e-06 - 4.588090e-07 6.985926e-06 -1.119574e-06 2.491237e-05 1.986313e-05 2.824168e-06 4.930722e-04 -1.112325e-05 2.491030e-03 -4.094115e-05 -2.250581e-04 9.926801e-03 4.836605e-03 1.534675e-03 -7.547909e-04 7.095495e-04 2.119880e-04 4.188212e-04 6.432091e-04 -1.485998e-04 -2.508188e-06 -1.608031e-06 -2.360634e-06 4.316403e-07 -1.757745e-06 -2.371184e-06 2.463596e-09 2.321727e-08 1.825772e-08 3.619976e-04 1.957172e-05 7.108017e-06 3.660176e-03 5.189389e-14 2.539641e-05 2.057670e-06 - 3.279227e-04 2.565224e-03 -4.714157e-04 1.197368e-02 1.282636e-02 2.092391e-03 2.512640e-01 -3.610980e-02 1.012849e+00 -1.642373e-02 -1.357324e-02 3.068377e+00 1.411154e+00 4.043427e-01 -5.737403e-01 1.928400e-01 1.023863e-01 1.990522e-01 2.679803e-01 -1.121567e-01 -1.512614e-03 -1.156178e-03 4.066862e-04 -1.793322e-03 -4.614363e-03 -8.277742e-03 -2.795297e-06 -1.736454e-06 2.785335e-05 2.033571e-01 2.692508e-02 3.660176e-03 3.086382e+00 2.494940e-11 2.918006e-02 8.689684e-04 - 3.827149e-15 5.051278e-14 -7.836507e-15 7.035159e-13 1.519872e-13 2.535114e-13 3.584811e-12 1.149225e-14 1.164510e-11 1.166120e-13 -6.459726e-13 3.772040e-11 2.625295e-11 1.942566e-11 1.081066e-11 4.226261e-13 9.385604e-13 -1.214688e-12 3.479209e-13 3.004532e-13 -1.851330e-14 -2.823153e-14 -5.609285e-14 -1.536066e-14 -5.224697e-14 -4.648749e-14 1.968436e-16 9.121824e-16 2.460188e-14 2.657303e-11 1.171095e-13 5.189389e-14 2.494940e-11 1.466311e-21 3.336132e-13 1.350928e-14 - 2.110094e-06 2.447651e-05 -3.681395e-06 1.600976e-04 8.134971e-05 3.942531e-05 1.748504e-03 -6.670388e-04 3.444173e-03 1.219848e-05 4.372049e-04 6.549248e-05 8.177586e-03 7.841552e-03 3.785543e-03 -2.003275e-03 4.212071e-04 5.997705e-04 5.536783e-04 -3.516671e-04 -5.959646e-06 -1.699005e-05 3.542981e-05 -3.059999e-05 -5.818602e-05 4.918051e-05 -2.567285e-07 -1.101793e-08 3.228827e-06 3.961715e-03 2.867400e-04 2.539641e-05 2.918006e-02 3.336132e-13 5.179119e-04 6.585396e-06 - 7.337971e-08 2.307287e-06 -3.350036e-07 6.486419e-06 5.172023e-06 2.716912e-07 1.445621e-04 -9.966676e-07 8.948334e-04 -1.711046e-05 -1.181833e-04 4.097475e-03 2.071305e-03 6.725971e-04 -1.570000e-04 2.984949e-04 7.431699e-05 1.554790e-04 2.502205e-04 2.968196e-05 -6.383556e-07 -3.376972e-07 -3.080447e-07 7.151632e-07 8.984718e-07 5.140367e-06 -7.809418e-09 9.738416e-09 -3.256149e-08 6.728645e-05 3.050524e-06 2.057670e-06 8.689684e-04 1.350928e-14 6.585396e-06 7.713178e-07 diff --git a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data index 08617457..ff21db01 100644 --- a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data +++ b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data @@ -1,19 +1,4 @@ # Parameter list -eff_like_from_covmat.data_directory = data.path['data'] -cov_list = ['100*theta_s','100*theta_d'] # variables of interest in .covmat file -eff_like_from_covmat.get_var_strings = ['cosmo.theta_s_100()', 'cosmo.theta_s_100()'] # name of the variables in CLASS -mu_list = ['100*\theta_{s }','100*\theta_{d }'] # name of the means in h_info file - -# covariance matrix - -covFull = 'eff_like_from_covmat.covmat' -cov_indices = [cov_list.index(c) for c in cov_list] -eff_like_from_covmat.covmat = covFull[np.ix_(cov_indices, cov_indices)] - -# mean list - -muFull = 'eff_like_from_covmat.h_info' -mu_indices = [cov_list.index(m) for m in mu_list] -eff_like_from_covmat.mu = muFull[np.ix_([3],mu_list)] # mean is located at row index 3 in h_info file - +eff_like_from_covmat.covmat = [[ 2.680703e-07,-3.350036e-07],[-3.350036e-07,7.713178e-07]] +eff_like_from_covmat.mu = [1.042278e+00, 3.223170e-01] diff --git a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.h_info b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.h_info deleted file mode 100644 index f117dad1..00000000 --- a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.h_info +++ /dev/null @@ -1,19 +0,0 @@ - param names : 100*\theta_{s }deg_{ncdm } m_{ncdm } H0 100*\theta_{d } - R-1 values : 0.000876 0.002535 0.002437 0.002875 0.002500 - Best Fit : 1.042305e+00 2.746032e+00 5.134637e-03 6.538970e+01 3.221057e-01 - mean : 1.042278e+00 2.825026e+00 3.279609e-02 6.576704e+01 3.223170e-01 - sigma : 5.246247e-04 1.882472e-01 1.872085e-02 1.737398e+00 8.956704e-04 - - 1-sigma - : -5.426182e-04 -1.906133e-01 -3.278194e-02 -1.581037e+00 -8.827746e-04 - 1-sigma + : 5.066313e-04 1.858811e-01 4.659757e-03 1.893759e+00 9.085663e-04 - 2-sigma - : -1.052337e-03 -3.696960e-01 -3.278194e-02 -3.711543e+00 -1.793280e-03 - 2-sigma + : 1.059192e-03 3.787995e-01 6.895530e-02 3.537918e+00 1.761348e-03 - 3-sigma - : -1.536302e-03 -5.548711e-01 -3.278194e-02 -6.229307e+00 -2.723714e-03 - 3-sigma + : 1.626237e-03 6.009074e-01 1.648337e-01 5.172713e+00 2.655721e-03 - - 1-sigma > : 1.041736e+00 2.634413e+00 1.415058e-05 6.418600e+01 3.214342e-01 - 1-sigma < : 1.042785e+00 3.010907e+00 3.745585e-02 6.766080e+01 3.232256e-01 - 2-sigma > : 1.041226e+00 2.455330e+00 1.415058e-05 6.205549e+01 3.205237e-01 - 2-sigma < : 1.043337e+00 3.203826e+00 1.017514e-01 6.930495e+01 3.240784e-01 - 3-sigma > : 1.040742e+00 2.270155e+00 1.415058e-05 5.953773e+01 3.195933e-01 - 3-sigma < : 1.043904e+00 3.425934e+00 1.976298e-01 7.093975e+01 3.249727e-01 \ No newline at end of file From ac5645ce1b0a3db61efc1032e756d7883033916b Mon Sep 17 00:00:00 2001 From: Kim Date: Wed, 26 May 2021 12:13:39 +0200 Subject: [PATCH 06/10] added eff_like_from_covmat folder --- .../likelihoods/eff_like_from_covmat/__init__.py | 15 +++++++-------- .../eff_like_from_covmat.data | 1 + 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/montepython/likelihoods/eff_like_from_covmat/__init__.py b/montepython/likelihoods/eff_like_from_covmat/__init__.py index 91e7a8a8..a5c94b4f 100644 --- a/montepython/likelihoods/eff_like_from_covmat/__init__.py +++ b/montepython/likelihoods/eff_like_from_covmat/__init__.py @@ -4,27 +4,26 @@ from montepython.likelihood_class import Likelihood_prior from numpy.linalg import multi_dot - class eff_like_from_covmat(Likelihood_prior): def __init__(self, path, data, command_line): # Call __init__ method of super class: super(eff_like_from_covmat, self).__init__(path, data, command_line) - + self.covmat_inverse = la.inv(self.covmat) + self.need_cosmo_arguments(data,{'compute damping scale':'yes'}) # Compute likelihood def loglkl(self, cosmo, data): - covmat = self.covmat mu = self.mu mean_vec = np.array(mu) - covmat_inverse = la.inv(covmat) - mean_vec_class = [1.042195411207378,0.324186976388346] # 100*theta_s and 100*theta_d from CLASS + #mean_vec_class = np.array([eval(s) for s in self.get_var_strings]) + mean_vec = [cosmo.theta_s_100(),cosmo.theta_d_100()] + mean_vec_class = np.array([eval(s) for s in mean_class]) + #mean_vec_class = np.array([1.04219541,0.32418698]) dif_vec = mean_vec - mean_vec_class dif_vec_T = dif_vec.T - #exponent = la.multi_dot([dif_vec.T,covmat_inverse,dif_vec]) - #covmat_inverse = la.inv(self.covmat) # might delete covmat_inverse above - exponent = dif_vec_T.dot(covmat_inverse).dot(dif_vec) + exponent = dif_vec_T.dot(self.covmat_inverse).dot(dif_vec) loglikelihood = -1/2*exponent # exponent in the multivariate Gaussian return loglikelihood diff --git a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data index ff21db01..08abdaed 100644 --- a/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data +++ b/montepython/likelihoods/eff_like_from_covmat/eff_like_from_covmat.data @@ -2,3 +2,4 @@ eff_like_from_covmat.covmat = [[ 2.680703e-07,-3.350036e-07],[-3.350036e-07,7.713178e-07]] eff_like_from_covmat.mu = [1.042278e+00, 3.223170e-01] +eff_like_from_covmat.get_var_strings = ['cosmo.theta_s_100()','cosmo.theta_d_100()'] From 3c47e8d2c45a01b1ba2bce3b5be3e701cfc2d038 Mon Sep 17 00:00:00 2001 From: Kim Date: Thu, 27 May 2021 11:13:43 +0200 Subject: [PATCH 07/10] Added eff_like_from_covmat folder --- montepython/likelihoods/eff_like_from_covmat/__init__.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/montepython/likelihoods/eff_like_from_covmat/__init__.py b/montepython/likelihoods/eff_like_from_covmat/__init__.py index a5c94b4f..35bb7cbf 100644 --- a/montepython/likelihoods/eff_like_from_covmat/__init__.py +++ b/montepython/likelihoods/eff_like_from_covmat/__init__.py @@ -8,8 +8,7 @@ class eff_like_from_covmat(Likelihood_prior): def __init__(self, path, data, command_line): # Call __init__ method of super class: super(eff_like_from_covmat, self).__init__(path, data, command_line) - self.covmat_inverse = la.inv(self.covmat) - + self.covmat_inverse = la.inv(self.covmat) self.need_cosmo_arguments(data,{'compute damping scale':'yes'}) # Compute likelihood @@ -17,10 +16,8 @@ def loglkl(self, cosmo, data): covmat = self.covmat mu = self.mu mean_vec = np.array(mu) - #mean_vec_class = np.array([eval(s) for s in self.get_var_strings]) - mean_vec = [cosmo.theta_s_100(),cosmo.theta_d_100()] - mean_vec_class = np.array([eval(s) for s in mean_class]) - #mean_vec_class = np.array([1.04219541,0.32418698]) + mean_vec_class = [cosmo.theta_s_100(),cosmo.get_current_derived_parameters(['100*theta_d'])['100*theta_d']] + #mean_vec_class = np.array([1.04219541,0.32418698]) gives constant likelihood dif_vec = mean_vec - mean_vec_class dif_vec_T = dif_vec.T exponent = dif_vec_T.dot(self.covmat_inverse).dot(dif_vec) From 0b02bb5b68ad22798634a4902eab46497aef891f Mon Sep 17 00:00:00 2001 From: Kim Date: Thu, 27 May 2021 11:19:02 +0200 Subject: [PATCH 08/10] Added param file --- input/theta_s_YHe.param | 44 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 input/theta_s_YHe.param diff --git a/input/theta_s_YHe.param b/input/theta_s_YHe.param new file mode 100644 index 00000000..77072e29 --- /dev/null +++ b/input/theta_s_YHe.param @@ -0,0 +1,44 @@ +#------Experiments to test (separated with commas)----- + +data.experiments=['eff_like_from_covmat'] + +#------ Parameter list ------- + +# Compute theta_d +data.cosmo_arguments['compute damping scale'] = 'yes' + +# Parameters to vary + +data.parameters['100*theta_s'] = [ 1.04110, None, None, 0.00030, 1, 'cosmo'] +data.parameters['YHe'] = [0.25, 0.1, 0.5, 0.05, 1, 'cosmo'] +data.parameters['100*theta_d'] = [0, None, None, 0, 1, 'derived'] + + +## Constant parameters: +data.cosmo_arguments['omega_b'] = 2.257581e-02 +data.cosmo_arguments['omega_cdm'] = 1.191596e-01 +data.cosmo_arguments['ln10^{10}A_s'] = 3.057198e+00 +data.cosmo_arguments['n_s'] = 9.670203e-01 +data.cosmo_arguments['tau_reio'] = 6.039532e-02 + +data.cosmo_arguments['Omega_Lambda'] = 6.861559e-01 + + +# Other cosmo parameters (fixed parameters, precision parameters, etc.) + +data.cosmo_arguments['sBBN file'] = data.path['cosmo']+'/bbn/sBBN.dat' +data.cosmo_arguments['k_pivot'] = 0.05 + +# The base model features two massless +# and one massive neutrino with m=0.06eV. +# The settings below ensures that Neff=3.046 +# and m/omega = 93.14 eV +data.cosmo_arguments['N_ur'] = 2.0328 +data.cosmo_arguments['N_ncdm'] = 1 +data.cosmo_arguments['m_ncdm'] = 0.06 +data.cosmo_arguments['T_ncdm'] = 0.71611 + +#------ Mcmc parameters ---- + +data.N=10 +data.write_step=5 From 2c9e5be0e650ff5797f18c8f079fabe95ee879e7 Mon Sep 17 00:00:00 2001 From: Kim Date: Thu, 27 May 2021 11:21:41 +0200 Subject: [PATCH 09/10] Added eff_like_from_covmat folder --- montepython/likelihoods/eff_like_from_covmat/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/montepython/likelihoods/eff_like_from_covmat/__init__.py b/montepython/likelihoods/eff_like_from_covmat/__init__.py index 35bb7cbf..84b0d39b 100644 --- a/montepython/likelihoods/eff_like_from_covmat/__init__.py +++ b/montepython/likelihoods/eff_like_from_covmat/__init__.py @@ -16,7 +16,8 @@ def loglkl(self, cosmo, data): covmat = self.covmat mu = self.mu mean_vec = np.array(mu) - mean_vec_class = [cosmo.theta_s_100(),cosmo.get_current_derived_parameters(['100*theta_d'])['100*theta_d']] + mean_class = [cosmo.theta_s_100(),cosmo.get_current_derived_parameters(['100*theta_d'])['100*theta_d']] + mean_vec_class = np.array([eval(s) for s in mean_class]) #mean_vec_class = np.array([1.04219541,0.32418698]) gives constant likelihood dif_vec = mean_vec - mean_vec_class dif_vec_T = dif_vec.T From 909b3953f930ee851d20450c6be9d749e9eb0865 Mon Sep 17 00:00:00 2001 From: Kim Date: Thu, 27 May 2021 11:54:07 +0200 Subject: [PATCH 10/10] Added folder --- montepython/likelihoods/eff_like_from_covmat/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/montepython/likelihoods/eff_like_from_covmat/__init__.py b/montepython/likelihoods/eff_like_from_covmat/__init__.py index 84b0d39b..c189386a 100644 --- a/montepython/likelihoods/eff_like_from_covmat/__init__.py +++ b/montepython/likelihoods/eff_like_from_covmat/__init__.py @@ -16,8 +16,8 @@ def loglkl(self, cosmo, data): covmat = self.covmat mu = self.mu mean_vec = np.array(mu) - mean_class = [cosmo.theta_s_100(),cosmo.get_current_derived_parameters(['100*theta_d'])['100*theta_d']] - mean_vec_class = np.array([eval(s) for s in mean_class]) + mean_class_class = [cosmo.theta_s_100(),cosmo.get_current_derived_parameters(['100*theta_d'])['100*theta_d']] + #mean_vec_class = np.array([eval(s) for s in self.get_var_strings]) list comprehension #mean_vec_class = np.array([1.04219541,0.32418698]) gives constant likelihood dif_vec = mean_vec - mean_vec_class dif_vec_T = dif_vec.T