# default_exp corr
⚠️ The writing is a work in progress. The functions work but text retouching⚠️
!pip install geopandas !pip install VitalSigns#export import numpy as np import pandas as pd import matplotlib.pyplot as plt import networkx as nx import warnings warnings.filterwarnings('ignore')#hide t = """ """ !pip install nbdev from google.colab import drive drive.mount('/content/drive') %cd /content/drive/My Drive/'Software Development Documents'/dataplay/ # !pip install dataplayls[0m[01;34mbuild[0m/ [01;34mdataplay[0m/ [01;34mdocs[0m/ Makefile [01;34mnotebooks[0m/ settings.ini CONTRIBUTING.md [01;34mdist[0m/ LICENSE MANIFEST.in README.md setup.py #export from dataplay import merge from dataplay import intaker from VitalSigns import acsDownload#export # this example is from the Geek for Geeks link above def average_clustering(G, trials=1000): """Estimates the average clustering coefficient of G. The local clustering of each node in `G` is the fraction of triangles that actually exist over all possible triangles in its neighborhood. The average clustering coefficient of a graph `G` is the mean of local clusterings. This function finds an approximate average clustering coefficient for G by repeating `n` times (defined in `trials`) the following experiment: choose a node at random, choose two of its neighbors at random, and check if they are connected. The approximate coefficient is the fraction of triangles found over the number of trials [1]_. Parameters ---------- G : NetworkX graph trials : integer Number of trials to perform (default 1000). Returns ------- c : float Approximated average clustering coefficient. """ n = len(G) triangles = 0 nodes = G.nodes() for i in [int(random.random() * n) for i in range(trials)]: nbrs = list(G[nodes[i]]) if len(nbrs) < 2: continue u, v = random.sample(nbrs, 2) if u in G[v]: triangles += 1 return triangles / float(trials) G=nx.erdos_renyi_graph(10,0.4) cc=nx.average_clustering(G) c=nx.clustering(G) c nx.draw(G)In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established between two nodes (Holland and Leinhardt, 1971; Watts and Strogatz, 1998).
Two versions of this measure exist: the global and the local. The global version was designed to give an overall indication of the clustering in the network, whereas the local gives an indication of the embeddedness of single nodes. - Geek for Geeks
CSA2010 | variable | value | |
---|---|---|---|
0 | Allendale/Irvington/S. Hilton | hhchpov15 | 38.931487 |
1 | Beechfield/Ten Hills/West Hills | hhchpov15 | 19.416892 |
2 | Belair-Edison | hhchpov15 | 36.882547 |
3 | Brooklyn/Curtis Bay/Hawkins Point | hhchpov15 | 45.008801 |
4 | Canton | hhchpov15 | 5.490849 |
CSA2010 | Allendale/Irvington/S. Hilton | Beechfield/Ten Hills/West Hills | Belair-Edison | Brooklyn/Curtis Bay/Hawkins Point | Canton | Cedonia/Frankford | Cherry Hill | Chinquapin Park/Belvedere | Claremont/Armistead | Clifton-Berea | Cross-Country/Cheswolde | Dickeyville/Franklintown | Dorchester/Ashburton | Downtown/Seton Hill | Edmondson Village | Fells Point | Forest Park/Walbrook | Glen-Fallstaff | Greater Charles Village/Barclay | Greater Govans | Greater Mondawmin | Greater Roland Park/Poplar Hill | Greater Rosemont | Greenmount East | Hamilton | Harbor East/Little Italy | Harford/Echodale | Highlandtown | Howard Park/West Arlington | Inner Harbor/Federal Hill | Lauraville | Loch Raven | Madison/East End | Medfield/Hampden/Woodberry/Remington | Midtown | Midway/Coldstream | Morrell Park/Violetville | Mount Washington/Coldspring | North Baltimore/Guilford/Homeland | Northwood | Oldtown/Middle East | Orangeville/East Highlandtown | Patterson Park North & East | Penn North/Reservoir Hill | Pimlico/Arlington/Hilltop | Poppleton/The Terraces/Hollins Market | Sandtown-Winchester/Harlem Park | South Baltimore | Southeastern | Southern Park Heights | Southwest Baltimore | The Waverlies | Upton/Druid Heights | Washington Village/Pigtown | Westport/Mount Winans/Lakeland |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
variable | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
hhchpov15 | 38.931487 | 19.416892 | 36.882547 | 45.008801 | 5.490849 | 23.345482 | 62.515723 | 16.493874 | 32.883093 | 34.932280 | 10.342224 | 28.861004 | 34.596982 | 11.666667 | 40.469062 | 13.196481 | 46.385224 | 27.512978 | 42.048055 | 39.080000 | 28.571429 | 7.652733 | 43.860132 | 37.705789 | 13.201821 | 48.872786 | 17.117117 | 15.205725 | 30.994475 | 18.129032 | 14.863103 | 17.322624 | 59.304511 | 14.421177 | 8.195021 | 48.240964 | 13.084746 | 8.096085 | 5.391433 | 13.901663 | 60.271572 | 24.084178 | 42.457923 | 42.544197 | 39.779513 | 65.723270 | 59.821650 | 3.237858 | 51.587302 | 51.345415 | 50.533420 | 27.025090 | 60.262829 | 36.734694 | 40.281974 |
hhchpov16 | 34.727823 | 21.220847 | 36.126984 | 46.447508 | 2.994012 | 20.574534 | 60.918367 | 18.154463 | 34.492519 | 47.579864 | 9.850668 | 29.828851 | 37.873874 | 13.079019 | 27.287762 | 6.785714 | 44.330814 | 30.681548 | 44.968732 | 42.551253 | 28.767123 | 8.474576 | 46.378322 | 38.253012 | 15.762813 | 40.180995 | 18.549157 | 21.197719 | 33.410006 | 9.171598 | 16.026993 | 17.197964 | 58.773982 | 11.312790 | 18.000000 | 44.501134 | 17.697729 | 5.918728 | 6.335549 | 11.769006 | 60.285320 | 21.852277 | 37.150556 | 40.456081 | 41.500853 | 65.254237 | 54.812174 | 0.123001 | 51.501416 | 50.180151 | 48.027102 | 37.340153 | 63.693865 | 42.672919 | 38.505747 |
hhchpov17 | 32.770530 | 23.916752 | 34.555625 | 46.409959 | 4.017217 | 19.963812 | 56.787964 | 23.162194 | 34.615385 | 47.924528 | 13.025459 | 34.451902 | 43.492770 | 13.372093 | 25.280599 | 7.336343 | 47.650417 | 27.400642 | 40.369231 | 47.240705 | 37.469136 | 3.792415 | 43.246623 | 36.447734 | 13.077790 | 33.364574 | 18.230648 | 14.610674 | 21.644774 | 8.212928 | 16.561656 | 6.473641 | 56.125654 | 8.452088 | 16.191562 | 49.336166 | 15.354018 | 3.796376 | 4.497258 | 9.751037 | 55.195090 | 22.486360 | 40.903427 | 46.640000 | 38.960245 | 57.445307 | 56.704165 | 0.118906 | 52.896725 | 48.490108 | 49.225293 | 44.224037 | 64.863124 | 34.242094 | 40.186480 |
hhchpov18 | 35.273791 | 21.904105 | 39.735752 | 39.891754 | 4.608939 | 15.529640 | 58.823529 | 12.376238 | 33.538251 | 50.104058 | 15.060064 | 20.465686 | 37.679149 | 8.074534 | 23.536100 | 11.256281 | 40.463918 | 26.230303 | 33.080070 | 44.286840 | 45.763994 | 2.800546 | 47.002221 | 39.011567 | 7.554380 | 33.402923 | 14.364641 | 15.151515 | 18.334235 | 7.267645 | 11.578509 | 12.104813 | 55.329405 | 6.997972 | 11.945813 | 40.830636 | 11.454905 | 4.645048 | 3.443223 | 9.154013 | 55.508637 | 18.710191 | 50.312695 | 42.607527 | 37.264151 | 59.105099 | 58.755090 | 0.371287 | 45.880612 | 41.626984 | 45.773875 | 42.046719 | 60.711887 | 28.032129 | 44.811801 |
hhchpov19 | 32.601602 | 15.384615 | 41.038850 | 41.388479 | 4.834254 | 17.905647 | 58.080194 | 11.683168 | 31.844888 | 39.654296 | 15.152378 | 12.348668 | 35.002158 | 6.169297 | 21.624650 | 8.425721 | 37.659381 | 28.945343 | 25.609096 | 46.453089 | 44.152047 | 2.256186 | 46.772865 | 45.657826 | 6.794589 | 32.803632 | 9.240100 | 21.975806 | 18.680710 | 7.024169 | 13.777219 | 17.239165 | 55.864324 | 6.446701 | 13.172043 | 35.834208 | 18.294271 | 2.288732 | 4.072398 | 9.772423 | 49.527559 | 13.424771 | 55.013459 | 38.382353 | 32.449903 | 51.431049 | 61.071093 | 0.000000 | 42.322457 | 30.489939 | 50.904137 | 37.535982 | 66.545718 | 38.116592 | 43.192272 |
cor_matrix contains the full correlation matrix. The table below shows a snapshot of the first 5 rows.
#craetes a correlation matrix cor_matrix = vs10to17IndComm.iloc[:,:].corr() #shows the first 5 rows cor_matrix.head(1)CSA2010 | Allendale/Irvington/S. Hilton | Beechfield/Ten Hills/West Hills | Belair-Edison | Brooklyn/Curtis Bay/Hawkins Point | Canton | Cedonia/Frankford | Cherry Hill | Chinquapin Park/Belvedere | Claremont/Armistead | Clifton-Berea | Cross-Country/Cheswolde | Dickeyville/Franklintown | Dorchester/Ashburton | Downtown/Seton Hill | Edmondson Village | Fells Point | Forest Park/Walbrook | Glen-Fallstaff | Greater Charles Village/Barclay | Greater Govans | Greater Mondawmin | Greater Roland Park/Poplar Hill | Greater Rosemont | Greenmount East | Hamilton | Harbor East/Little Italy | Harford/Echodale | Highlandtown | Howard Park/West Arlington | Inner Harbor/Federal Hill | Lauraville | Loch Raven | Madison/East End | Medfield/Hampden/Woodberry/Remington | Midtown | Midway/Coldstream | Morrell Park/Violetville | Mount Washington/Coldspring | North Baltimore/Guilford/Homeland | Northwood | Oldtown/Middle East | Orangeville/East Highlandtown | Patterson Park North & East | Penn North/Reservoir Hill | Pimlico/Arlington/Hilltop | Poppleton/The Terraces/Hollins Market | Sandtown-Winchester/Harlem Park | South Baltimore | Southeastern | Southern Park Heights | Southwest Baltimore | The Waverlies | Upton/Druid Heights | Washington Village/Pigtown | Westport/Mount Winans/Lakeland |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CSA2010 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
Allendale/Irvington/S. Hilton | 1.0 | 0.014863 | -0.119179 | 0.095415 | 0.470058 | 0.548944 | 0.88326 | -0.104335 | -0.102976 | -0.503038 | -0.560746 | 0.245054 | -0.513196 | 0.219368 | 0.902809 | 0.824206 | 0.349305 | -0.225704 | 0.455862 | -0.95513 | -0.55844 | 0.61754 | -0.240447 | -0.401832 | 0.301535 | 0.894259 | 0.356956 | -0.399832 | 0.575175 | 0.894022 | -0.128303 | 0.422069 | 0.688614 | 0.817967 | -0.709692 | 0.407924 | -0.593179 | 0.931204 | 0.337036 | 0.812526 | 0.721937 | 0.590431 | -0.283071 | 0.036479 | 0.480814 | 0.771909 | 0.15346 | 0.920392 | 0.34112 | 0.555418 | 0.0125 | -0.808695 | -0.872919 | -0.041575 | -0.163644 |
⚠️ The writing is a work in progress. The functions work but text retouching⚠️
!pip install geopandas !pip install VitalSigns#export import numpy as np import pandas as pd import matplotlib.pyplot as plt import networkx as nx import warnings warnings.filterwarnings('ignore')#hide t = """ """ !pip install nbdev from google.colab import drive drive.mount('/content/drive') %cd /content/drive/My Drive/'Software Development Documents'/dataplay/ # !pip install dataplayls[0m[01;34mbuild[0m/ [01;34mdataplay[0m/ [01;34mdocs[0m/ Makefile [01;34mnotebooks[0m/ settings.ini CONTRIBUTING.md [01;34mdist[0m/ LICENSE MANIFEST.in README.md setup.py #export from dataplay import merge from dataplay import intaker from VitalSigns import acsDownload#export # this example is from the Geek for Geeks link above def average_clustering(G, trials=1000): """Estimates the average clustering coefficient of G. The local clustering of each node in `G` is the fraction of triangles that actually exist over all possible triangles in its neighborhood. The average clustering coefficient of a graph `G` is the mean of local clusterings. This function finds an approximate average clustering coefficient for G by repeating `n` times (defined in `trials`) the following experiment: choose a node at random, choose two of its neighbors at random, and check if they are connected. The approximate coefficient is the fraction of triangles found over the number of trials [1]_. Parameters ---------- G : NetworkX graph trials : integer Number of trials to perform (default 1000). Returns ------- c : float Approximated average clustering coefficient. """ n = len(G) triangles = 0 nodes = G.nodes() for i in [int(random.random() * n) for i in range(trials)]: nbrs = list(G[nodes[i]]) if len(nbrs) < 2: continue u, v = random.sample(nbrs, 2) if u in G[v]: triangles += 1 return triangles / float(trials) G=nx.erdos_renyi_graph(10,0.4) cc=nx.average_clustering(G) c=nx.clustering(G) c nx.draw(G)In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established between two nodes (Holland and Leinhardt, 1971; Watts and Strogatz, 1998).
Two versions of this measure exist: the global and the local. The global version was designed to give an overall indication of the clustering in the network, whereas the local gives an indication of the embeddedness of single nodes. - Geek for Geeks
CSA2010 | variable | value | |
---|---|---|---|
0 | Allendale/Irvington/S. Hilton | hhchpov15 | 38.931487 |
1 | Beechfield/Ten Hills/West Hills | hhchpov15 | 19.416892 |
2 | Belair-Edison | hhchpov15 | 36.882547 |
3 | Brooklyn/Curtis Bay/Hawkins Point | hhchpov15 | 45.008801 |
4 | Canton | hhchpov15 | 5.490849 |
CSA2010 | Allendale/Irvington/S. Hilton | Beechfield/Ten Hills/West Hills | Belair-Edison | Brooklyn/Curtis Bay/Hawkins Point | Canton | Cedonia/Frankford | Cherry Hill | Chinquapin Park/Belvedere | Claremont/Armistead | Clifton-Berea | Cross-Country/Cheswolde | Dickeyville/Franklintown | Dorchester/Ashburton | Downtown/Seton Hill | Edmondson Village | Fells Point | Forest Park/Walbrook | Glen-Fallstaff | Greater Charles Village/Barclay | Greater Govans | Greater Mondawmin | Greater Roland Park/Poplar Hill | Greater Rosemont | Greenmount East | Hamilton | Harbor East/Little Italy | Harford/Echodale | Highlandtown | Howard Park/West Arlington | Inner Harbor/Federal Hill | Lauraville | Loch Raven | Madison/East End | Medfield/Hampden/Woodberry/Remington | Midtown | Midway/Coldstream | Morrell Park/Violetville | Mount Washington/Coldspring | North Baltimore/Guilford/Homeland | Northwood | Oldtown/Middle East | Orangeville/East Highlandtown | Patterson Park North & East | Penn North/Reservoir Hill | Pimlico/Arlington/Hilltop | Poppleton/The Terraces/Hollins Market | Sandtown-Winchester/Harlem Park | South Baltimore | Southeastern | Southern Park Heights | Southwest Baltimore | The Waverlies | Upton/Druid Heights | Washington Village/Pigtown | Westport/Mount Winans/Lakeland |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
variable | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
hhchpov15 | 38.931487 | 19.416892 | 36.882547 | 45.008801 | 5.490849 | 23.345482 | 62.515723 | 16.493874 | 32.883093 | 34.932280 | 10.342224 | 28.861004 | 34.596982 | 11.666667 | 40.469062 | 13.196481 | 46.385224 | 27.512978 | 42.048055 | 39.080000 | 28.571429 | 7.652733 | 43.860132 | 37.705789 | 13.201821 | 48.872786 | 17.117117 | 15.205725 | 30.994475 | 18.129032 | 14.863103 | 17.322624 | 59.304511 | 14.421177 | 8.195021 | 48.240964 | 13.084746 | 8.096085 | 5.391433 | 13.901663 | 60.271572 | 24.084178 | 42.457923 | 42.544197 | 39.779513 | 65.723270 | 59.821650 | 3.237858 | 51.587302 | 51.345415 | 50.533420 | 27.025090 | 60.262829 | 36.734694 | 40.281974 |
hhchpov16 | 34.727823 | 21.220847 | 36.126984 | 46.447508 | 2.994012 | 20.574534 | 60.918367 | 18.154463 | 34.492519 | 47.579864 | 9.850668 | 29.828851 | 37.873874 | 13.079019 | 27.287762 | 6.785714 | 44.330814 | 30.681548 | 44.968732 | 42.551253 | 28.767123 | 8.474576 | 46.378322 | 38.253012 | 15.762813 | 40.180995 | 18.549157 | 21.197719 | 33.410006 | 9.171598 | 16.026993 | 17.197964 | 58.773982 | 11.312790 | 18.000000 | 44.501134 | 17.697729 | 5.918728 | 6.335549 | 11.769006 | 60.285320 | 21.852277 | 37.150556 | 40.456081 | 41.500853 | 65.254237 | 54.812174 | 0.123001 | 51.501416 | 50.180151 | 48.027102 | 37.340153 | 63.693865 | 42.672919 | 38.505747 |
hhchpov17 | 32.770530 | 23.916752 | 34.555625 | 46.409959 | 4.017217 | 19.963812 | 56.787964 | 23.162194 | 34.615385 | 47.924528 | 13.025459 | 34.451902 | 43.492770 | 13.372093 | 25.280599 | 7.336343 | 47.650417 | 27.400642 | 40.369231 | 47.240705 | 37.469136 | 3.792415 | 43.246623 | 36.447734 | 13.077790 | 33.364574 | 18.230648 | 14.610674 | 21.644774 | 8.212928 | 16.561656 | 6.473641 | 56.125654 | 8.452088 | 16.191562 | 49.336166 | 15.354018 | 3.796376 | 4.497258 | 9.751037 | 55.195090 | 22.486360 | 40.903427 | 46.640000 | 38.960245 | 57.445307 | 56.704165 | 0.118906 | 52.896725 | 48.490108 | 49.225293 | 44.224037 | 64.863124 | 34.242094 | 40.186480 |
hhchpov18 | 35.273791 | 21.904105 | 39.735752 | 39.891754 | 4.608939 | 15.529640 | 58.823529 | 12.376238 | 33.538251 | 50.104058 | 15.060064 | 20.465686 | 37.679149 | 8.074534 | 23.536100 | 11.256281 | 40.463918 | 26.230303 | 33.080070 | 44.286840 | 45.763994 | 2.800546 | 47.002221 | 39.011567 | 7.554380 | 33.402923 | 14.364641 | 15.151515 | 18.334235 | 7.267645 | 11.578509 | 12.104813 | 55.329405 | 6.997972 | 11.945813 | 40.830636 | 11.454905 | 4.645048 | 3.443223 | 9.154013 | 55.508637 | 18.710191 | 50.312695 | 42.607527 | 37.264151 | 59.105099 | 58.755090 | 0.371287 | 45.880612 | 41.626984 | 45.773875 | 42.046719 | 60.711887 | 28.032129 | 44.811801 |
hhchpov19 | 32.601602 | 15.384615 | 41.038850 | 41.388479 | 4.834254 | 17.905647 | 58.080194 | 11.683168 | 31.844888 | 39.654296 | 15.152378 | 12.348668 | 35.002158 | 6.169297 | 21.624650 | 8.425721 | 37.659381 | 28.945343 | 25.609096 | 46.453089 | 44.152047 | 2.256186 | 46.772865 | 45.657826 | 6.794589 | 32.803632 | 9.240100 | 21.975806 | 18.680710 | 7.024169 | 13.777219 | 17.239165 | 55.864324 | 6.446701 | 13.172043 | 35.834208 | 18.294271 | 2.288732 | 4.072398 | 9.772423 | 49.527559 | 13.424771 | 55.013459 | 38.382353 | 32.449903 | 51.431049 | 61.071093 | 0.000000 | 42.322457 | 30.489939 | 50.904137 | 37.535982 | 66.545718 | 38.116592 | 43.192272 |
cor_matrix contains the full correlation matrix. The table below shows a snapshot of the first 5 rows.
#craetes a correlation matrix cor_matrix = vs10to17IndComm.iloc[:,:].corr() #shows the first 5 rows cor_matrix.head(1)CSA2010 | Allendale/Irvington/S. Hilton | Beechfield/Ten Hills/West Hills | Belair-Edison | Brooklyn/Curtis Bay/Hawkins Point | Canton | Cedonia/Frankford | Cherry Hill | Chinquapin Park/Belvedere | Claremont/Armistead | Clifton-Berea | Cross-Country/Cheswolde | Dickeyville/Franklintown | Dorchester/Ashburton | Downtown/Seton Hill | Edmondson Village | Fells Point | Forest Park/Walbrook | Glen-Fallstaff | Greater Charles Village/Barclay | Greater Govans | Greater Mondawmin | Greater Roland Park/Poplar Hill | Greater Rosemont | Greenmount East | Hamilton | Harbor East/Little Italy | Harford/Echodale | Highlandtown | Howard Park/West Arlington | Inner Harbor/Federal Hill | Lauraville | Loch Raven | Madison/East End | Medfield/Hampden/Woodberry/Remington | Midtown | Midway/Coldstream | Morrell Park/Violetville | Mount Washington/Coldspring | North Baltimore/Guilford/Homeland | Northwood | Oldtown/Middle East | Orangeville/East Highlandtown | Patterson Park North & East | Penn North/Reservoir Hill | Pimlico/Arlington/Hilltop | Poppleton/The Terraces/Hollins Market | Sandtown-Winchester/Harlem Park | South Baltimore | Southeastern | Southern Park Heights | Southwest Baltimore | The Waverlies | Upton/Druid Heights | Washington Village/Pigtown | Westport/Mount Winans/Lakeland |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CSA2010 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
Allendale/Irvington/S. Hilton | 1.0 | 0.014863 | -0.119179 | 0.095415 | 0.470058 | 0.548944 | 0.88326 | -0.104335 | -0.102976 | -0.503038 | -0.560746 | 0.245054 | -0.513196 | 0.219368 | 0.902809 | 0.824206 | 0.349305 | -0.225704 | 0.455862 | -0.95513 | -0.55844 | 0.61754 | -0.240447 | -0.401832 | 0.301535 | 0.894259 | 0.356956 | -0.399832 | 0.575175 | 0.894022 | -0.128303 | 0.422069 | 0.688614 | 0.817967 | -0.709692 | 0.407924 | -0.593179 | 0.931204 | 0.337036 | 0.812526 | 0.721937 | 0.590431 | -0.283071 | 0.036479 | 0.480814 | 0.771909 | 0.15346 | 0.920392 | 0.34112 | 0.555418 | 0.0125 | -0.808695 | -0.872919 | -0.041575 | -0.163644 |