# 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 |
⚠ 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 |


Deborah Isabell Faust
Unafffiliated
Mortgage Loan Originator, The Mortgage Network Public Education and Business Coalition, Executive Director of Residency Program and Senior Director of Evaluation
PhD, Quantitative Research Methods, University of Denver MA, Cultural Anthropology, University of Denver BBA, Business Administration, University of Denver
81147
Although I have followed politics carefully in the past, I have not actively participated in campaigns.
Vice President, Board of Directors for High Point Academy in Aurora, Colorado Carnegie Foundation Improvement Science American Association of Leadership Educators ASCD American Evaluation Association
For the past several years, my advocacy has been primarily through my professional work in an attempt to ensure that each child has a great teacher with access and opportunity to learn. An important part of this work involved studying and advocating for extended use of culturally responsive/culturally relevant pedagogy and restorative justice strategies, which were important to our work with school districts, as well as my work as a board member at High Point Academy.
A number of years ago, I became disillusioned with how partisan our political systems and structures were becoming, and I changed my voter affiliation to independent. Part of this was done to remind myself to think critically about and listen to all aspects of various arguments before making decisions. Since that time, it seems that the partisan politics has increased and people are no longer listening to one another or working together for the betterment of our society and communities. I believe that this has, in part, been fueled by failed structures which have led to inequities in our political system. Through living and working in Denver, as well as my liaison work with rural school districts across the state, I came to appreciate the differences, as well as some of the similarities, in the needs of rural and urban communities. These experiences led me to move to a rural community in Southwest Colorado, and through this experience I learned even more about the needs of rural communities. I believe that the work on fair maps is crucial in order to increase the focus on serving the members of our communities and working together, while de-emphasizing somewhat the focus on partisan politics. My doctorate degree is in Quantitative Research Methods from the University of Denver.
When working with groups or teams, I believe it is important to incorporate and attend to norms of collaboration which foster listening to one another and respect for differences of opinion. Several years ago, the organization I worked with received a grant to extend residency teacher preparation to rural areas. We had a preliminary strategic plan for rolling out the program over the years of the grant. However, almost immediately, as I began talking with school superintendents from across the state, I realized that the needs of rural school districts were very different from the needs in the front range urban centers. Moreover, it turned out that the needs in different rural regions of the state were also very different and that a model that would work in one area created substantial difficulties in another. As a result, we ended up developing three different program options, rather than a one-size fits all model.
Through my work on the grant mentioned above, as well as other administrative work I have engaged in, I have extensive experience in budgeting and strategic planning. I have also studied extensively the work of the Carnegie Foundation in Improvement Science. Although I have a degree in Quantitative Research Methods, the majority of my work has been in administration and program evaluation, as opposed to the implementation of research studies. However, I also have a master’s degree in cultural anthropology, which focused on qualitative research methods. Through my experiences I believe it is important to understand and use the data relevant to the task at hand, but it is important to pay attention to the big picture and remember that the emphasis is on individuals and communities.
Female


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[1]
air con was quiet and the hotel principe.
[2]
air con was quiet and the room completely peaceful.


Are you planning your vacation closer to winter or summer months?
Summer
Winter
During your ideal vacation, would you be more likely to spend time exploring a city or relaxing on the beach?
Exploring a city
Relaxing on the beach
When planning your vacation, are you considering a budget to get the most value out of your trip?
Or is money not an object for this trip?
Following strict budget
Money is no object
When you travel do you prefer to be an outsider and explore the lives of native people?
Or would you rather travel with a tourist group, surrounded by people like you?
I like to be the outsider
I prefer to be with people like me
Do you prefer to have fine dining options when you travel?
Or do you just need food to fuel your exploration?
Wine me and dine me!
Just the basics, I need fuel to explore!
Submit
Congratulations! You're going to Puerto Vallarta, Mexico!
Congratulations! You're going to San Francisco, California!
Congratulations! You're going to Paris, France!
sorry, but it's hard to relax on a beach in winter,try again


Access Websites' Data
You can explore the added websites by clicking on the tab (Websites). You can also access some of these websites by checking our demos. Once you choose one fo these websites it will direct you to the page where yoy can manage the data you're intrested in accessing from this website.
Manage Websits' Data
When you choose a website that you're intertsted in its data (e.g. YouTube), you will be able to search for your desired data, filter, save, and download: (1) Search for the data (e.g. Funny Cats), filter the data based on the parameters that this sepcific websites allow you to do (e.g. Order By: relevance, Safe Search: moderate, etc). (2) Once you click "enter" or submit button, you will view your result in the middle of the page in a table format (default view). (3) You can choose the exact fields that you are intersted in by checking off the ones that you don't want.
Available Functionalities
Other than being able to filter the data (as explained above), You can alos view the data not only in table view but also in a tree view. You can also download the data in CSV and JSON format, and save it to your account return back to it later (more details about saving your data below). In addition, you can control the pagination, by interacting with pagination section in footer of the table.
Save Your Dataset and Share them Publically
You can save your dataset to your account, to return back to it later. And you can also choose to share your search result (your dataset) publically. Once you check on the "Make Your Dataset Public", you will be able to find it under the Datsets page
Search Datasets
You can search for publically shared datasets by clicking on Datasets tab. You can search datasets, by source (the website in which the data belongs to) or by the datasets itself (e.g. I want to find all datasets that people have searched for about the subreddit AskReddit on Reddit)
You can download (in CSV or JSON format) thess publically shared datasets, and you can also get a copy of a previously acquired datasets and make changes to it!
Data Extraction DEMO
API Integration Process
To access and add\publish web APIs into the system, you will have to go through a number of steps, but first you need to look for the API documentation! For example, if I wanted to access New York Times articles, I would look for the API documenation for NYT artcile search API. Now, you can go to Integrate page, where you will be asked to follow some stepps, and each step you will provide information that you can find in the API documentation.
While going through the steps, you can validate if you are doing the right thing or if you're missing something by checking the pink box "Validate Your API Integration". The color of the box will change to green once you were able to successfully access the API.
API URL
Inthe API url field, you can provide the API url (e.g. https://api.nytimes.com/svc/search/v2/articlesearch.json), and you can also provide a URL with paramerters (e.g. https://api.nytimes.com/svc/search/v2/articlesearch.json?q=election&api-key=yourkey), the= attached paramertes (q and api-key) will be added in the next step "Request Paramerters", read more about this step below.
Request Parameters
What Fields to Add for Each Parameter
Every API has required and optional parameters. You need to add the required parameters and assign them values. The values can be provided by you (e.g. q=election) or provided in the API doc (e.g. sort=newest). For the optional parameters, it is recommended that you add values, since some API might not give you results if you did not assign default values to some of the parameters. If you are planning to make some\all parameters displayed for the users so they can control their values, you should add a readable name to that parameter in the dispalyed name field, and you can leave the 'displayed?' checkbox checked, and if you do not want to show some parameters (e.g. key), then simply uncheck the 'displayed?' checkbox. By default the displayed name will be the bane of the parameter if you did not assign it a name and the 'displayed?' checkbox is checked.
Authentication
To access web APIs, sometimes we need to be authorized to access its data. ScrAPIr so far supports a number of authentication methods: - API keys as a Query Parameter or Header - Authorization header as Bearer Token or Basic Auth
Some APIs might ask you to add header values to acces their data (e.g. In GitHub search code API, you can get the code snippets and highlights in search results by adding 'Accept' header as the key and assign it a value of 'application/vnd.github.v3.text-match+json'). Other APIs only required their authentication to be passed as a header parameter.
Response Parameters (Result Fields)
In this step, scrAPIr allows to choose specific fields that you are intersted in as opposed to getting all the result returned by the API. You can view a sample fo the response (left), in which you can search for specific feilds that you are intersted in, and\or browse the response tree view. When you click on the desired fields, they will be added to table on the right, where you can change their names that will be displayed on the API access web page
Pagination
(1) The default number of results that each API gives is different. For example, YouTube search returns 5 videos by default, but the maximum number of results (or videos in the case of YouTube) is 50 videos. So you should set the MAX NUMBER OF RESULTS PER PAGE field to 50. (2) If the API specifies a parameter that you can assign the maximum results to, please provide that in the RESULT PER PAGE PARAMETER field (e.g. maxResults in YouTube Search).(3) If you wanted to get more results than the max number of results per page allowed by the API, you should loop through the other pages and get more results. There are two different ways to loop through pages: 1- Some APIs use index or offset to go through multiple pages (e.g. In Wikipedia search API, they use sroffset as the page parameter. sroffset=0 is the first page, offset=1 is the second page, etc). 2- Other APIs will provide parameters that will indicate the current page, in which you can assign it to the next page parameter to get more results (e.g. in YouTube search API, the current page parameter is pageToken and to go to the next page we assign pageToken to the nextPageToken). If the API you are trying to access has both types "index" and "current\next page", you can just specify the index parameter.
 You can go back to previous steps and change things.
Manage API's Data
When are happy with your API integration, you can clikc on Publish button, which will direct you to the API management page, in which you can search and filter the data on that API, you can save and download them as CSV\JSON.
Demo of the API Integration Process
This is a demo of adding Yelp Search API using scrAPIr. It shows how to go through the process wizard to fill out the required information to access this API using the information available in the API documentation.


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An API is the important contract between a client and a service. Therefore API changes must be tracked using version numbers. The version numbering must reflect the changes of the service and provide information about breaking API changes that might affect a client. The version numbering must ensure that every breaking API change is tracked.
We track API changes via a 4 digit version pattern with a qualifier segment.
+- facade segment | +- major segment | | +- minor segment | | | +- service segment | | | | +- qualifier segment | | | | | V V V V V <IP_ADDRESS>-SNAPSHOT
- API package - a package that contains at least one API class or API interface. The names of API packages are advertised in the Component documentation. These names will appear in Client code; the names of non-API packages should never appear in Client code. Note that Clients must be prohibited from declaring their code in Component packages (API or otherwise).
- API class - a public class in an API package, or a public or protected class member declared in, or inherited by, some other API class or interface. The names of API classes appear in Client code.
- API interface - a public interface in an API package, or a public or protected interface member declared in, or inherited by, some other API class or interface. The names of API interfaces appear in Client code.
- API method - a public or protected method either declared in, or inherited by, an API class or interface. The names of API methods appear in Client code.
- API constructor - a public or protected constructor of an API class. The names of API constructors appear in Client code.
The following elements are not considered API:
- Any package that is not advertised in the Component documentation as an API package.
- All classes and interfaces declared in non-API packages. However, when API classes and interface extend or implement non-API classes, the non-API classes and interface may contribute API elements nevertheless.
- Non-public classes and interfaces in API packages.
- Default access and private methods, constructors, fields, and type members declared in, or inherited by, API classes and interfaces.
- protected methods, constructors, fields, and type members declared in, or inherited by, final API classes, including enums (which are implicitly final). With no ability to declare subclasses, these cannot be referenced by Client code.
A package defines the boundary of classes and interfaces that belong
together to fulfill some special purpose. In contrast to classes and
interfaces a package is a coarse-grained perspective. A package can be
named and therefore give a set of classes and interfaces a meaning.
The name should always be chosen in order to increase the clarity of
understanding of a system and should not give any technical or
implementation hints. It's bad practice to separate interfaces from
their implementation by putting them in sub-packages named
impl
. For details see
://www.link-intersystems.com
/bin/view/Blog/Wise+separation+of+api+and+implementation
.
Packages might contain sub-packages. A sub-package should
be seen as a
"is part of"
relationship. E.g if you have a package called
building
the
floor
package should be a sub package with the full qualified name
building.floor
.
Packages are named after the singular form of a substantive that
describes the package. So a packages should be named
floor
instead of
floors
.
A package might be a complex system that is hard to understand, to
configure and to manage. Therefore a package can provide a high level
abstraction of the common use cases that hides the complexity of
interactions of it's classes from clients. E.g. a http protocol
package might contain a get object a http connection manager, a proxy
configuration, a content object and or transformer and so on. While
these fine-grained components are useful for a good design,
maintainability and professional users they are too complex for easy
use cases like most clients are interested in. E.g.
String content = new
URL("http://www.somesite.com").getContent();
. For those use cases the package should provide a facade that
encapsulates the common use cases in an easy way. This facade should
be named after the package and postfixed with
Facade
. E.g.
ReflectionFacade
. The facade must be an interface and it's implementation is available
through the service provider specification as defined in the jar file
specification (META-INF/services). For this purposes clients can use
an instance of the
com.link_intersystems.lang.services.JarFileSpecificationServiceLocator
that implements the
com.link_intersystems.lang.services.ClassServiceLocator
interface. All facades must be stateless which means that the state of
the facade must not be tied to a client's call history. Nevertheless a
facade can act as a factory for objects that are stateful.
An API is the significant contract between a client and a service. Therefore API changes must be tracked using version numbers. The version numbering must reflect the changes of the service and provide information about breaking API changes that might affect a client. The version numbering must ensure that every breaking API change is tracked. To learn more about API and breaking API changes take a look at http://wiki.eclipse.org/Evolving_Java-based_APIs.
null
.
null
it must be explicitly documented in the parameter's javadoc. Every
method or constructor should only define parameters that it needs.
Parameters that can be
null
confuse the client and make the implementation code harder to
understand, because it must handle a lot more possible pre-conditions.
If a parameter is optional the method or constructor should be
overloaded to provide a clear API for the client and separate the
different pre-condition constraint validations.
public void doSomething(Range range){ if(range == null){ throw new IllegalArgumentException("range must not be null"); } ... }The Range parameter object does enforces your API constraints.
public class Range { private final int start; private final int end; public Range(int start, int end) { if (start > end) { throw new IllegalArgumentException( "start must be less than end, but was " + start + " > " + end); } this.end = end; this.start = start; } public boolean within(int value) { return start <= value && value <= end; } public int getStart() { return start; } public int getEnd() { return end; } }
null
. Methods that return a
Collection
type must return an empty
Collection
instead of
null
. If a method does not comply to this contract it must be documented in
the method's javadoc. In some cases the returned object might be used
by the client and in other situations it might not be used. If it is an
expensive operation to construct the return object and the method can
not know if it will ever by used, consider to return a
lazy proxy
or
Reference
instead.


This inspection only reports if the project or module is configured to use a language level of 5.0 or higher.
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Nordstrom.
Full-stack UX Design
for PRICE TOOLS team
Full Price Coming Soon!
- UX Design
- Product Design
- UI Development
The problem you are trying to solve
— What can’t they do now or
can’t do
well that your design is meant
to
address?


No special activated events
2016.01.01~ 2016.12.31
No special activated events since professor visited University of California San Diego (UCSD) to conduct one-year research cooperating between Chonnam National University and UCSD.
--- layout: index title: News group: navigation tags : [blog, posts, post] author : Pakyoungno css: js: keywords: bootstrap4, UI kit, jekyll canonical: https://pakyoungno.github.io/News --- {% include JB/setup %}
No special activated events since professor visited University of California San Diego (UCSD) to conduct one-year research cooperating between Chonnam National University and UCSD.


Match Play. Red trails 2-4 to 5 (post-Crawford). How should Red play 43?
pips Red=161 White=184
(CorrectAnswer= 0/0, ErrorScore= 0)
Despite posting the wrong diagram (my apologies) and score most people go t this correct!
A gammon is very useful to Red so despite the seven (!) blots the right play is the double hitting 13/9*, 6/3*. Any other move is a blunder. He can’t execute a full blitz but there are plenty of gammons to be won even without a full closeout.
A gammon is very useful to Red so despite the seven (!) blots the right play is the double hitting 13/9*, 6/3*. Any other move is a blunder. He can’t execute a full blitz but there are plenty of gammons to be won even without a full closeout.
1. Cubeful 2-ply 13/9* 6/3* Eq.: +0.718 0.614 0.302 0.051 - 0.386 0.098 0.007 2. Cubeful 2-ply 22/15 Eq.: +0.549 (-0.169) 0.601 0.221 0.018 - 0.399 0.093 0.003 3. Cubeful 2-ply 15/11 6/3* Eq.: +0.537 (-0.181) 0.578 0.243 0.032 - 0.422 0.108 0.006 4. Cubeful 0-ply 22/18 21/18 Eq.: +0.488 (-0.230) 0.583 0.211 0.016 - 0.417 0.100 0.003 5. Cubeful 0-ply 22/18 6/3* Eq.: +0.426 (-0.292) 0.562 0.219 0.027 - 0.438 0.126 0.009


--- layout: post title: no title date: '2016-03-06T18:30:41+01:00' categories: - Sketches - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/140597726772 ---


|
The Court
Practicing lawyer shows are
perennially popular, and series featuring trial court judges
come and go, but in ABC's The Court, this year's television
docket brings us a second series specifically devoted to Supreme
Court Justices. The show stars Oscar winner Sally Field as a
nominee to the Court, whose unanimous Senate confirmation comes
during the course of the premiere.
Like
CBS' First Monday (Fridays, 9-10 p.m., ET/PT), The
Court introduces us to a justice who will be a "swing
vote" on the Nation's highest bench. Ohio Governor Kathleen
Nolan (Field) is a practicing Catholic who seems to be both tough
on crime and supportive of women's issues (including abortion)
although she declines to declare herself on either subject. Nominated
by the President to fill an unexpected vacancy when a powerful
woman Justice is killed in a car accident, she represents the
focus for any number of legal, social and political debates that
will play out during the series.
Contrast to the sometimes unreal
activity of the Court and Capitol Hill is provided by her former
law student and current gadfly journalist Harlan Brandt. Also
like First Monday, The Court features a strong
Chief, CJ Townsend (Pat Hingle), active, committed, opinionated
law clerks (Nicole DeHuff, Hill Harper, and Josh Radnor), and
Associate Justices of all persuasions, including an Hispanic
(Miguel Sandoval), a black woman (Diahann Carroll) and a traditional
Kennedy liberal (Chris Sarandon).
En banc review of The Court
begins with the same slate that heard oral argument on First
Monday. As usual we invite reader response about the show,
our reviews, and the course of television justice.
The Court airs on ABC Tuesdays at 10 pm. (ET/PT).
Website: http://abc.abcnews.go.com/primetime/thecourt/index.html
The Court update [May 10, 2002]:
The Court has been placed on "broadcast hiatus."
According to the ABC Entertainment Publicity Department, this
means that the three produced-but-unaired shows will probably
be shown at some time in the future. There was no available
information about when the shows might air or whether any others
will be produced.
Posted March, 2002
|


As I felt the disturb in the Force by the prank of this challenge 😜, the game actually does:
The inputs from the formulary will be used to populate the table bellow. And the table records are automagically sorted alphabetically by the lastName field.
Generates an username using the firstname and lastname fields concatenated with a randon number within the range from 1 to 9


Nathan Stankowski
About Me
About Me
NorCal Native
About Me
Campaign Organizing
About Me
Project Manager
Vinfolio is a wine and tech company that provides services for high end wine collectors.
$12M-$14M in yearly revenue
500k bottles stored
10k processed a month
20k Active Users
30 Employees
Marketplace:
A Pivot Story
Warehouse Zoning:
Aligning Processses for Scaling
iOS App:
Re-tooling continues
My Interest in Working with
Thank you!
←
→
/
Go to slide:


Analog In: Attach the wiper of a 10kΩ potentiometer to AIN6 (P9_36). Attach the other ends to VDD_ACD (P9_32) and GNDA_ADC (P9_34).
gpio: Attach a switch between gpio0_7 (P9_42) and GND (P9_1).
i2c: Attach a TMP101 temperature sensor. Pin 1 (SCL) goes to P9_19 (i2c2_scl), Pin 2 (GND) to P9_2 (DGND), Pin 3 (ALERT) no connection, Pin 4 ((SDA) to P9_20 (i2c2_sda), Pin 5 (ADD0) to Pin P9_2 (GND) so address will be 0x48, Pin 6 (V+) to P9_3 (VDD_3V3).
LED Brightnes: Attach an LED in series with a 220Ω resistor between P9_1 (DGND) and P9_14 (ehrpwm1A).


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Model fitting in Python
Contents
Model fitting in Python#
Introduction#
Python offers a wide range of tools for fitting mathematical models to data. Here we will look at using Python to fit non-linear models to data using Least Squares (NLLS).
You may want to have a look at this Chapter, and in particular, it NLLS section, and the lectures on Model fitting and NLLS before proceeding.
WE will use the Lmfit
package, which provides a high-level interface to non-linear optimization and curve fitting problems for Python.
There are three main things that one needs in order to perform NLLS fitting succesfully in Python.
Data
Model specification
Initial values for parameters in the model
The image below summarizes how NLLS fitting works with these 3 entities.
We will use population growth rates as an example, as we did the model fitting chapter (where we used R).
First let’s import the necessary packages (you may need to install lmfit
first).
from lmfit import Minimizer, Parameters, report_fit
import numpy as np
import matplotlib.pylab as plt
Now, create some artificial data:
t = np.arange(0, 24, 2)
N = np.array([32500, 33000, 38000, 105000, 445000, 1430000, 3020000, 4720000, 5670000, 5870000, 5930000, 5940000])
np.random.seed(1234) #Set random seed for reproducibility
N_rand = N*(1 + np.random.normal(scale = 0.1, size = len(N))) #Add some error to data
Here’s what the data look like:
plt.plot(t, N_rand, 'r+', markersize = 15, markeredgewidth = 2, label = 'Data')
plt.xlabel('t', fontsize = 20)
plt.ylabel(r'$N$', fontsize = 20)
plt.ticklabel_format(style='scientific', scilimits=[0,3])
plt.yscale('log')
Fitting a Linear model to the data#
Lets start with the simplest case; a linear model.
where \(a\), \(b\), \(c\) and \(d\), are phenomenological parameters.
Using NLLS#
#Create object for storing parameters
params_linear = Parameters()
#Add parameters and initial values to it
params_linear.add('a', value = 1)
params_linear.add('b', value = 1)
params_linear.add('c', value = 1)
params_linear.add('d', value = 1)
#Write down the objective function that we want to minimize, i.e., the residuals
def residuals_linear(params, t, data):
"""Calculate cubic growth and subtract data"""
#Get an ordered dictionary of parameter values
v = params.valuesdict()
#Cubic model
model = v['a']*t**3 + v['b']*t**2 + v['c']*t + v['d']
return model - data #Return residuals
#Create a Minimizer object
minner = Minimizer(residuals_linear, params_linear, fcn_args=(t, np.log(N_rand)))
#Perform the minimization
fit_linear_NLLS = minner.minimize()
The variable fit_linear
belongs to a class called MinimizerResult
, which include data such as status and error messages, fit statistics, and the updated (i.e., best-fit) parameters themselves in the params attribute.
Now get the summary of the fit:
report_fit(fit_linear_NLLS)
[[Fit Statistics]]
# fitting method = leastsq
# function evals = 10
# data points = 12
# variables = 4
chi-square = 1.68617886
reduced chi-square = 0.21077236
Akaike info crit = -15.5493005
Bayesian info crit = -13.6096739
[[Variables]]
a: -0.00147820 +/- 5.3322e-04 (36.07%) (init = 1)
b: 0.03687009 +/- 0.01787451 (48.48%) (init = 1)
c: 0.14339899 +/- 0.16467922 (114.84%) (init = 1)
d: 10.0545124 +/- 0.39977042 (3.98%) (init = 1)
[[Correlations]] (unreported correlations are < 0.100)
C(a, b) = -0.984
C(b, c) = -0.960
C(a, c) = 0.900
C(c, d) = -0.779
C(b, d) = 0.621
C(a, d) = -0.528
Using OLS#
lmfit
’s purpose is not to fit linear models, although it is general enough to do so. Recall that a 3rd degree polynomial is a Lienar model, and it can be fitted using Ordinary Least Squares. You can easily do this with the function polyfit
.
Let’s try this for the same data.
fit_linear_OLS = np.polyfit(t, np.log(N_rand), 3) # degree = 3 as this is a cubic
Now check the fitted coefficients:
print(fit_linear_OLS)
[-1.47819938e-03 3.68700893e-02 1.43398989e-01 1.00545124e+01]
Comparing the NLLS and OLS fits#
Let’s compare the the coefficients obtained using NLLS (using lmfit
) vs using OLS (with polyfit
) above.
First have a another look at the NLLS result:
fit_linear_NLLS.params
| name | value | standard error | relative error | initial value | min | max | vary |
|---|---|---|---|---|---|---|---|
| a | -0.00147820 | 5.3322e-04 | (36.07%) | 1 | -inf | inf | True |
| b | 0.03687009 | 0.01787451 | (48.48%) | 1 | -inf | inf | True |
| c | 0.14339899 | 0.16467922 | (114.84%) | 1 | -inf | inf | True |
| d | 10.0545124 | 0.39977042 | (3.98%) | 1 | -inf | inf | True |
Now extract the just the estimated parameter values obtained with lmfit
(it takes some effort as these are saved in a Python dictionary within the fitted model object):
par_dict = fit_linear_NLLS.params.valuesdict().values()
#Transform these into an array
par = np.array(list(par_dict))
#Check the difefrences in the the parameter values obtained with lmfit and polyfit
print(fit_linear_OLS - par)
[ 2.18245647e-12 2.18892265e-12 7.72548692e-13 -1.61293201e-11]
There are differences because NLLS is not an exact method. But these differences are tiny, showing that NLLS converges on pretty much the same solution as the (exact) OLS method.
Next, you can calculate the residuals of the fit:
Calculating the residuals, etc.#
Once you have done the model fitting you can calculate the residuals:
#Construct the fitted polynomial equation
my_poly = np.poly1d(fit_linear_OLS)
#Compute predicted values
ypred = my_poly(t)
#Calculate residuals
residuals = ypred - np.log(N_rand)
The residuals can be then used to calculate BIC, AIC, etc, as you learned previously.
Exercise#
We calculated the residuals above using the OLS fit of the cubic model to the data. Calculate the residuals for the NLLS fit of the same model to the data (i.e., using the
fit_linear_NLLS
object).Hint: To do this, you will need to first extract the coefficients, and then use the
residuals_linear
function that we created above.
Fitting Non-linear models to the data#
Now let’s use lmfit
to do what it was actually designed for: fitting non-linear mathematical models to data. We will fit the same two models that we fitted in the Model Fitting Chapter (where we used R).
Logistic model fit#
A classical, somewhat mechanistic model is the logistic growth equation:
Here \(N_t\) is population size at time \(t\), \(N_0\) is initial population size, \(r\) is maximum growth rate (AKA \(r_{max}\)), and \(N_{max}\) is carrying capacity (commonly denoted by \(K\) in the ecological literature).
#Create object for parameter storing
params_logistic = Parameters()
params_logistic.add('N_0', value = N_rand[0])
params_logistic.add('N_max', value = N_rand[-1])
#Recall the value for growth rate obtained from a linear fit
params_logistic.add('r', value = 0.62)
#Write down the objective function that we want to minimize, i.e., the residuals
def residuals_logistic(params, t, data):
'''Model a logistic growth and subtract data'''
#Get an ordered dictionary of parameter values
v = params.valuesdict()
#Logistic model
model = np.log(v['N_0'] * v['N_max'] * np.exp(v['r']*t) / \
(v['N_max'] + v['N_0'] * ( np.exp(v['r']*t) - 1 )))
#Return residuals
return model - data
#Create a Minimizer object
minner = Minimizer(residuals_logistic, params_logistic, fcn_args=(t, np.log(N_rand)))#Plug in the logged data.
#Perform the minimization
fit_logistic = minner.minimize(method = 'leastsq')
Note that I am choosing the least squares as the optimization method. A table with alternative fitting algorithms offered by lmfit can be found here.
#Get summary of the fit
report_fit(fit_logistic)
[[Fit Statistics]]
# fitting method = leastsq
# function evals = 34
# data points = 12
# variables = 3
chi-square = 2.19693074
reduced chi-square = 0.24410342
Akaike info crit = -14.3741446
Bayesian info crit = -12.9194246
[[Variables]]
N_0: 13998.5739 +/- 4769.60185 (34.07%) (init = 34032.16)
N_max: 7108406.32 +/- 2165947.53 (30.47%) (init = 6529216)
r: 0.44848151 +/- 0.05161938 (11.51%) (init = 0.62)
[[Correlations]] (unreported correlations are < 0.100)
C(N_0, r) = -0.807
C(N_max, r) = -0.448
C(N_0, N_max) = 0.239
Where function evals, is the number of iterations needed to reach the minimum. The report_fit
function also offers estimations of goodness of fit such as \(\chi^2\), BIC and AIC. These are calculated as specified here. Finally, uncertainties of each parameter estimate, correlation levels between them are also calculated. Details and caveats about these values can be found here.
Gompertz model#
Another alternative is the Gompertz model, which has been used frequently in the literature to model bacterial growth
Here maximum growth rate (\(r_{max}\)) is the tangent to the inflection point, \(t_{lag}\) is the x-axis intercept to this tangent (duration of the delay before the population starts growing exponentially) and \(\log\left(\frac{N_{max}}{N_0}\right)\) is the asymptote of the log-transformed population growth trajectory, i.e., the log ratio of maximum population density \(N_{max}\) (aka “carrying capacity”) and initial cell (Population) \(N_0\) density.
#Create object for parameter storing
params_gompertz = Parameters()
# add with tuples: (NAME VALUE VARY MIN MAX EXPR BRUTE_STEP)
params_gompertz.add_many(('N_0', np.log(N_rand)[0] , True, 0, None, None, None),
('N_max', np.log(N_rand)[-1], True, 0, None, None, None),
('r_max', 0.62, True, None, None, None, None),
('t_lag', 5, True, 0, None, None, None))#I see it in the graph
#Write down the objective function that we want to minimize, i.e., the residuals
def residuals_gompertz(params, t, data):
'''Model a logistic growth and subtract data'''
#Get an ordered dictionary of parameter values
v = params.valuesdict()
#Logistic model
model = v['N_0'] + (v['N_max'] - v['N_0']) * \
np.exp(-np.exp(v['r_max'] * np.exp(1) * (v['t_lag'] - t) / \
((v['N_max'] - v['N_0']) * np.log(10)) + 1))
#Return residuals
return model - data
#Create a Minimizer object
minner = Minimizer(residuals_gompertz, params_gompertz, fcn_args=(t, np.log(N_rand)))
#Perform the minimization
fit_gompertz = minner.minimize()
#Sumarize results
report_fit(fit_gompertz)
[[Fit Statistics]]
# fitting method = leastsq
# function evals = 37
# data points = 12
# variables = 4
chi-square = 0.11350314
reduced chi-square = 0.01418789
Akaike info crit = -47.9299775
Bayesian info crit = -45.9903509
[[Variables]]
N_0: 10.3910132 +/- 0.07901002 (0.76%) (init = 10.43506)
N_max: 15.6520240 +/- 0.06730933 (0.43%) (init = 15.6918)
r_max: 1.74097660 +/- 0.09837840 (5.65%) (init = 0.62)
t_lag: 4.52049075 +/- 0.24729695 (5.47%) (init = 5)
[[Correlations]] (unreported correlations are < 0.100)
C(r_max, t_lag) = 0.757
C(N_0, t_lag) = 0.581
C(N_max, r_max) = -0.465
C(N_max, t_lag) = -0.318
C(N_0, N_max) = -0.132
C(N_0, r_max) = 0.115
Plot the results#
plt.rcParams['figure.figsize'] = [20, 15]
#Linear
result_linear = np.log(N_rand) + fit_linear_NLLS.residual # These points lay on top of the theoretical fitted curve
plt.plot(t, result_linear, 'y.', markersize = 15, label = 'Linear')
#Get a smooth curve by plugging a time vector to the fitted logistic model
t_vec = np.linspace(0,24,1000)
log_N_vec = np.ones(len(t_vec))#Create a vector of ones.
residual_smooth_linear = residuals_linear(fit_linear_NLLS.params, t_vec, log_N_vec)
plt.plot(t_vec, residual_smooth_linear + log_N_vec, 'orange', linestyle = '--', linewidth = 1)
#Logistic
result_logistic = np.log(N_rand) + fit_logistic.residual
plt.plot(t, result_logistic, 'b.', markersize = 15, label = 'Logistic')
#Get a smooth curve by plugging a time vector to the fitted logistic model
t_vec = np.linspace(0,24,1000)
log_N_vec = np.ones(len(t_vec))
residual_smooth_logistic = residuals_logistic(fit_logistic.params, t_vec, log_N_vec)
plt.plot(t_vec, residual_smooth_logistic + log_N_vec, 'blue', linestyle = '--', linewidth = 1)
#Gompertz
result_gompertz = np.log(N_rand) + fit_gompertz.residual
plt.plot(t, result_gompertz, 'g.', markersize = 15, label = 'Gompertz')
#Get a smooth curve by plugging a time vector to the fitted logistic model
t_vec = np.linspace(0,24,1000)
log_N_vec = np.ones(len(t_vec))
residual_smooth_gompertz = residuals_gompertz(fit_gompertz.params, t_vec, log_N_vec)
plt.plot(t_vec, residual_smooth_gompertz + log_N_vec, 'green', linestyle = '--', linewidth = 1)
#Plot data points
plt.plot(t, np.log(N_rand), 'r+', markersize = 15,markeredgewidth = 2, label = 'Data')
#Plot legend
plt.legend(fontsize = 20)
plt.xlabel('t', fontsize = 20)
plt.ylabel(r'$\log(N_t)$', fontsize = 20)
plt.ticklabel_format(style='scientific', scilimits=[0,3])
Exercise#
The generalized Logistic model (also known as Richards’ curve) is an extension of the logistic or sigmoid functions, allowing for more flexible S-shaped curves:
Where \(A\) is the lower asymptote, \(K\) is the higher asymptote. If \(A=0\) then \(K\) is the carrying capacity. \(B\) is the growth rate, \(\mu >0\) affects near which asymptote maximum growth occurs. \(Q\) is related to the value \(N(0)\)
Fit this model to the data using as initial values for the parameters: \(A = 10\), \(K = 16\), \(Q = 0.5\), \(B = 1\), \(\mu = 0.1\), \(T = 7.5\)
#Define the parameter object
params_genlogistic = Parameters()
#Add parameters and initial values
params_genlogistic.add('A', value = 10, min = 0)
params_genlogistic.add('K', value = 16, min = 0)
params_genlogistic.add('Q', value = 0.5, min = 0)
params_genlogistic.add('B', value = 1, min = 0)
params_genlogistic.add('mu', value = 0.1, min = 0)
params_genlogistic.add('T', value = 7.5, min = 0)
#Define the model
def residuals_genlogistic(params, t, data):
'''Model a logistic growth and subtract data'''
#Get an ordered dictionary of parameter values
v = params.valuesdict()
#Logistic model
model = v['A'] + (v['K'] - v['A']) / \
(1 + v['Q'] * np.exp(-v['B']*(t-v['T'])))**(1/v['mu'])
#Return residuals
return model - data
#Perform the fit
#Create a Minimizer object
minner = Minimizer(residuals_genlogistic, params_genlogistic, fcn_args=(t, np.log(N_rand)))
#Perform the minimization
fit_genlogistic = minner.minimize()
#Overlay the fit with the others
plt.rcParams['figure.figsize'] = [20, 15]
#Linear
result_linear = np.log(N_rand) + fit_linear_NLLS.residual
plt.plot(t, result_linear, 'y.', markersize = 15, label = 'Linear')
#Get a smooth curve by plugging a time vector to the fitted logistic model
t_vec = np.linspace(0,24,1000)
log_N_vec = np.ones(len(t_vec))
residual_smooth_linear = residuals_linear(fit_linear_NLLS.params, t_vec, log_N_vec)
plt.plot(t_vec, residual_smooth_linear + log_N_vec, 'orange', linestyle = '--', linewidth = 1)
#Logistic
result_logistic = np.log(N_rand) + fit_logistic.residual
plt.plot(t, result_logistic, 'b.', markersize = 15, label = 'Logistic')
#Get a smooth curve by plugging a time vector to the fitted logistic model
t_vec = np.linspace(0,24,1000)
log_N_vec = np.ones(len(t_vec))
residual_smooth_logistic = residuals_logistic(fit_logistic.params, t_vec, log_N_vec)
plt.plot(t_vec, residual_smooth_logistic + log_N_vec, 'blue', linestyle = '--', linewidth = 1)
#Gompertz
result_gompertz = np.log(N_rand) + fit_gompertz.residual
plt.plot(t, result_gompertz, 'g.', markersize = 15, label = 'Gompertz')
#Get a smooth curve by plugging a time vector to the fitted logistic model
t_vec = np.linspace(0,24,1000)
log_N_vec = np.ones(len(t_vec))
residual_smooth_gompertz = residuals_gompertz(fit_gompertz.params, t_vec, log_N_vec)
plt.plot(t_vec, residual_smooth_gompertz + log_N_vec, 'green', linestyle = '--', linewidth = 1)
#Generalized logistic
result_genlogistic = np.log(N_rand) + fit_genlogistic.residual
plt.plot(t, result_genlogistic, '.', markerfacecolor = 'magenta',
markeredgecolor = 'magenta', markersize = 15, label = 'Generalized Logistic')
#Get a smooth curve by plugging a time vector to the fitted logistic model
t_vec = np.linspace(0,24,1000)
log_N_vec = np.ones(len(t_vec))
residual_smooth_genlogistic = residuals_genlogistic(fit_genlogistic.params, t_vec, log_N_vec)
plt.plot(t_vec, residual_smooth_genlogistic + log_N_vec, 'magenta', linestyle = '--', linewidth = 1)
#Plot data points
plt.plot(t, np.log(N_rand), 'r+', markersize = 15,markeredgewidth = 2, label = 'Data')
#Plot legend
plt.legend(fontsize = 20)
plt.xlabel('t', fontsize = 20)
plt.ylabel(r'$\log(N_t)$', fontsize = 20)
plt.ticklabel_format(style='scientific', scilimits=[0,3])
We can visually tell that the model that best fits the data is the Generalized Logistic model, closely followed by the Gompertz model. Does this mean that the Generalized Logistic model is a better model? No. Generally, better models will be those that fit the data well, have less parameters, and these can be interpreted mechanistically. In these case, the Gomperz model fits the data almost as good as the Generalized Logistic (GL), but has less parameters, and these are more ‘mechanistic’ than the ones in GL, so it is clearly better.
Readings & Resources#
If you choose Python for the model fitting component of your workflow, you will probably want to use
lmfit
. Look up documentation/help on submodulesminimize
,Parameters
,Parameter
, andreport_fit
in particular.Also Have a look through http://lmfit.github.io/lmfit-py, especially http://lmfit.github.io/lmfit-py/fitting.html#minimize .
You will have to install
lmfit
usingpip
oreasy_install
(use sudo mode). Lots of examples of using lmfit can be found online (for example, by searching for “lmfit examples”!).


Former Left Front minister Kshiti Goswami passes away
He died in a private hospital in Chennai due to old-age-related illness
by PTISenior Revolutionary Socialist Party (RSP) leader and former West Bengal minister Kshiti Goswami died at a private hospital in Chennai due to old-age-related illness on Sunday morning, family sources said.
He was 77 and is survived by his wife and daughter.
Apart from old-age-related illness, Goswami was also suffering from a lung infection for which he was admitted to the Chennai hospital where he breathed his last, the family sources said.
The leader of RSP, which was a constituent of the Left Front, was the State PWD minister for more than two decades since the late eighties till 2011 when the regime was voted out of power.
He was also the State secretary of the RSP for a few years since 2012.


--- layout: post authors: ["Greg Wilson"] title: "Adding a Contributor Covenant" date: 2015-03-02 time: "19:30:00" tags: ["Community"] ---
The Software Carpentry Foundation's Steering Committee has voted to add a Contributor Covenant to our lessons and other repositories. Like the Code of Conduct for our workshops, the Contributor Covenant's aim is to ensure that participation in Software Carpentry is a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.


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Desarrollador: Juan Esteban Cepeda.
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
| Statistic | Value |
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
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CyberSource Key Files
1. CyberSource requires that you
generate a series of key files for your store in order to process transactions.
Please use the ECert Application to generate these
keys and place them in a folder of your choice and reference this folder as
explained in Step 5 of Configuring the
Processor. The latest version of this application can be located here: http://apps.cybersource.com/cgi-bin/pages/additional.cgi?kit=Update_to_ECert_Application
2. For instructions on
generating the CyberSource Key Files please see
Creating a Certificate and Private Key:
http://apps.cybersource.com/library/documentation/dev_guides/Microsoft_.NET/html/ch02.htm#ch02_6699_1015313
3. For best results place the
“Keys” Folder in your store BIN folder. This folder should contain at minimum
(Replace “YourStoreID” with your account): YourStoreID.crt, CyberSource_SJC_US.crt,
and YourStoreID.pvt. These files should have been
generated when you created your .crt file.
4. Note: Thhese
links are included for your convenience but please refer to CyberSource
for updated information.
Example Processor Setup in the BV Admin would look like:
Store: YourStoreID
KeyFile: C:\YourFilePath\YourBVStoreRoot\BIN\Keys
Test URL: ics2test.ic3.com or Blank
Live URL: ics2.ic3.com or Blank


Paper-Rock-Scissors with SPOCK AND LIZARD
Rules:
[Rock
]
Wins
[
and
]
[Paper
]
Wins
[
and
]
[Scissors
]
Wins
[
and
]
[Lizard
]
Wins
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and
]
[Spock
]
Wins
[
and
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Your Score:
0
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