├── utils ├── __init__.py ├── plots │ ├── __init__.py │ ├── scatter.py │ ├── bar.py │ ├── wilkinson.py │ └── line.py ├── get_data │ ├── refresh_data.py │ ├── query_forum.py │ ├── query_gwwc.py │ ├── open_phil.py │ └── data_scraping.py └── subtitle.py ├── components ├── sections │ ├── __init__.py │ ├── gwwc_donation_orgs.py │ ├── gwwc_donation_growth.py │ ├── gwwc_pledges.py │ ├── growth.py │ ├── demographics.py │ ├── geography.py │ ├── donations_sankey.py │ ├── open_phil.py │ └── forum.py ├── about.py ├── body.py ├── header.py └── sidebar.py ├── Procfile ├── eadata.png ├── assets ├── logo.png ├── favicon.ico ├── data │ ├── rp_survey_2019.xlsx │ ├── rp_survey_data_2019 │ │ ├── survey_year.csv │ │ ├── gender.csv │ │ ├── education2.csv │ │ ├── age_group.csv │ │ ├── country.csv │ │ ├── diet.csv │ │ ├── moral_view.csv │ │ ├── political_belief.csv │ │ ├── religion.csv │ │ ├── ethnicity.csv │ │ ├── education.csv │ │ ├── employment.csv │ │ ├── career_path.csv │ │ ├── subject.csv │ │ ├── lean_towards.csv │ │ ├── work_experience.csv │ │ └── country2.csv │ ├── misc.csv │ ├── is_ea_growing │ │ ├── is_ea_growing_commiting.csv │ │ ├── is_ea_growing_reading.csv │ │ ├── is_ea_growing_joining.csv │ │ └── is_ea_growing_doing.csv │ ├── gwwc │ │ ├── donations_by_year.json │ │ └── new_pledges.json │ └── ea_funds_grants.csv ├── hamburger-menu-2.svg ├── hamburger-menu.svg ├── moon.svg ├── question-mark.svg ├── sun.svg ├── main.js └── style.css ├── Pipfile ├── app.py ├── .gitignore ├── README.md ├── Pipfile.lock └── LICENSE /utils/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /utils/plots/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /components/sections/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn app:server 2 | -------------------------------------------------------------------------------- /eadata.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hamishhuggard/ea_data_viz/HEAD/eadata.png -------------------------------------------------------------------------------- /assets/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hamishhuggard/ea_data_viz/HEAD/assets/logo.png -------------------------------------------------------------------------------- /assets/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hamishhuggard/ea_data_viz/HEAD/assets/favicon.ico -------------------------------------------------------------------------------- /assets/data/rp_survey_2019.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hamishhuggard/ea_data_viz/HEAD/assets/data/rp_survey_2019.xlsx -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/survey_year.csv: -------------------------------------------------------------------------------- 1 | Survey Year Mean Median 2 | 2015 28 26 3 | 2017 32 29 4 | 2018 32 29 5 | 2019 31 28 6 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/gender.csv: -------------------------------------------------------------------------------- 1 | Gender Responses Percent 2 | Male 1343 70.9% 3 | Female 509 26.9% 4 | Other 41 2.2% 5 | Total 1893 100.0% 6 | -------------------------------------------------------------------------------- /assets/data/misc.csv: -------------------------------------------------------------------------------- 1 | Source,Cause Area,Organization,Amount 2 | Giving What We Can,Unknown,Unknowns,126751939 3 | Founders Pledge,Unknown,Unknowns,435000000 -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/education2.csv: -------------------------------------------------------------------------------- 1 | Education Responses Percent 2 | Non-College 13 0.5% 3 | Bachelor's 821 31.9% 4 | Master's 593 23.0% 5 | PhD 306 11.9% 6 | Other College 842 32.7% 7 | Total 2575 100.0% 8 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/age_group.csv: -------------------------------------------------------------------------------- 1 | Age Group Responses Percent 2 | 13-17 10 0.5% 3 | 18-24 506 26.8% 4 | 25-34 971 51.5% 5 | 35-44 233 12.4% 6 | 45-54 82 4.3% 7 | 55-64 43 2.3% 8 | 65+ 41 2.2% 9 | Total 1886 10 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/country.csv: -------------------------------------------------------------------------------- 1 | Country Responses Percent 2 | USA 754 39.3% 3 | UK 313 16.3% 4 | Germany 140 7.3% 5 | Australia 131 6.8% 6 | Canada 84 4.4% 7 | All other stated countries 496 25.9% 8 | Total respondents 1,920 9 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/diet.csv: -------------------------------------------------------------------------------- 1 | Diet Responses Percent 2 | Eat meat, but try to reduce the amount 604 31.1% 3 | Vegan 451 23.2% 4 | Vegetarian 447 23.0% 5 | Eat meat 228 11.7% 6 | Other (please specify) 108 5.6% 7 | Pescetarian 107 5.5% 8 | Total 1945 9 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/moral_view.csv: -------------------------------------------------------------------------------- 1 | Moral View Responses Percent 2 | Consequentialism (utilitarian) 947 69.6% 3 | Consequentualism (other than utilitarian) 151 11.1% 4 | Deontology 44 3.2% 5 | Virtue ethics 99 7.3% 6 | Other 119 8.8% 7 | Total 1,360 8 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/political_belief.csv: -------------------------------------------------------------------------------- 1 | Political Belief Responses Percent 2 | Left 578 31.8% 3 | Center Left 731 40.3% 4 | Center 160 8.8% 5 | Center Right 46 2.5% 6 | Right 17 0.9% 7 | Libertarian 158 8.7% 8 | Other 126 6.9% 9 | Total 1,816 10 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/religion.csv: -------------------------------------------------------------------------------- 1 | Religion Responses Percent 2 | Atheist, agnostic, or non-religious 1,625 85.9% 3 | Christian 198 10.5% 4 | Buddhist 103 5.4% 5 | Other 56 3.0% 6 | Jewish 53 2.8% 7 | Hindu 11 0.6% 8 | Muslim 4 0.2% 9 | Sikh 2 0.1% 10 | Total 1,892 11 | -------------------------------------------------------------------------------- /assets/data/is_ea_growing/is_ea_growing_commiting.csv: -------------------------------------------------------------------------------- 1 | Type of data,Jan-Dec 2014,2015,2016,2017,2018 2 | EA Survey respondents who identify as EA,1457 respondents,2240,No survey,1801,2576 3 | GWWC pledges,+396 members,642,949,909,606 4 | OFTW pledges,24,48,136,159,652 5 | Founder’s Pledge pledges,Didn’t exist,+$90M,+$120M,+$231M,+$547M 6 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/ethnicity.csv: -------------------------------------------------------------------------------- 1 | Race/Ethnicity Responses Percent 2 | White 1,621 86.9% 3 | Asian 185 9.9% 4 | Hispanic, Latino or Spanish Origin 88 4.7% 5 | Other 75 4.0% 6 | Black or African American 26 1.4% 7 | Native Hawaiian or Other Pacific Islander 5 0.3% 8 | American Indian or Alaskan Native 4 0.2% 9 | Total 1,866 10 | -------------------------------------------------------------------------------- /Pipfile: -------------------------------------------------------------------------------- 1 | [[source]] 2 | url = "https://pypi.org/simple" 3 | verify_ssl = true 4 | name = "pypi" 5 | 6 | [packages] 7 | dash = "*" 8 | pandas = "*" 9 | gunicorn = "*" 10 | countryinfo = "*" 11 | bs4 = "*" 12 | requests = "*" 13 | dash-dangerously-set-inner-html = "*" 14 | 15 | [dev-packages] 16 | 17 | [requires] 18 | python_version = "3.9" 19 | -------------------------------------------------------------------------------- /assets/hamburger-menu-2.svg: -------------------------------------------------------------------------------- 1 | Menu -------------------------------------------------------------------------------- /assets/hamburger-menu.svg: -------------------------------------------------------------------------------- 1 | 70 Basic icons by Xicons.co -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/education.csv: -------------------------------------------------------------------------------- 1 | Education Responses Percent 2 | Some high school 13 0.7% 3 | High school graduate 30 1.5% 4 | Some college, no degree 83 4.2% 5 | Associate's degree 21 1.1% 6 | Bachelor's degree 821 41.9% 7 | Professional degree 94 4.8% 8 | Master's degree 593 30.2% 9 | Doctoral degree 306 15.6% 10 | Total respondents 1,961 11 | -------------------------------------------------------------------------------- /utils/get_data/refresh_data.py: -------------------------------------------------------------------------------- 1 | import utils.get_data.data_scraping as data_scraping 2 | import os 3 | import time 4 | 5 | most_recent_refresh = None 6 | 7 | def refresh_data(): 8 | global most_recent_refresh 9 | if most_recent_refresh and time.time() - most_recent_refresh < 60*60: 10 | return 11 | most_recent_refresh = time.time() 12 | 13 | 14 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/employment.csv: -------------------------------------------------------------------------------- 1 | Employment Type Responses Percent 2 | Employed, Full-Time 970 49.7% 3 | Student, Full-Time 525 26.9% 4 | Employed, Part-Time 216 11.1% 5 | Self-Employed 184 9.4% 6 | Not employed, but looking for work 108 5.5% 7 | Student, Part-Time 88 4.5% 8 | Retired 56 2.9% 9 | Not employed, but not looking for work 32 1.6% 10 | Homemaker 11 0.6% 11 | Total 1,953 12 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/career_path.csv: -------------------------------------------------------------------------------- 1 | Career path Responses Percent 2 | For-profit (earning to give) 709 37.8% 3 | Work at a non-profit (EA organization) 688 36.6% 4 | Academia 540 28.8% 5 | Think tanks / lobbying / advocacy 423 22.5% 6 | Government 368 19.6% 7 | For-profit (not earning to give) 347 18.5% 8 | Work at a non-profit (not an EA organization) 317 16.9% 9 | Other 185 9.9% 10 | Total 1,878 11 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/subject.csv: -------------------------------------------------------------------------------- 1 | Subject Studied Responses Percent 2 | Computer Science 468 24.4% 3 | Math 335 17.5% 4 | Economics 315 16.4% 5 | Social Science 272 14.2% 6 | Philosophy 256 13.4% 7 | Arts & Humanities 251 13.1% 8 | Sciences 201 10.5% 9 | Engineering 200 10.4% 10 | Physics 164 8.6% 11 | Psychology 144 7.5% 12 | Medicine 70 3.7% 13 | Professional or vocational qualification 59 3.1% 14 | Total 1,916 15 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/lean_towards.csv: -------------------------------------------------------------------------------- 1 | Lean towards Responses Percent 2 | Strongly lean towards negative utilitarianism (reducing suffering is all that matters morally) 56 4.5% 3 | Lean towards negative utilitarianism (reducing suffering is all that matters morally) 293 23.8% 4 | Lean towards classical utilitarianism (reducing suffering and increasing happiness matter equally) 534 43.3% 5 | Strongly lean towards classical utilitarianism (reducing suffering and increasing happiness matter equally) 261 21.2% 6 | Other 89 7.2% 7 | Total 1233 8 | -------------------------------------------------------------------------------- /assets/data/is_ea_growing/is_ea_growing_reading.csv: -------------------------------------------------------------------------------- 1 | Type of data,Jan-Dec 2014,2015,2016,2017,2018 2 | Google interest in “effective altruism” (relative scoring),22,65,75,74,62 3 | Pageviews of Wikipedia article for “effective altruism”,33.9K,63.1K,71.5K,76.7K,69.5K 4 | 80K pageviews,136525,475487,889630,1583322,1634192 5 | EA Forum pageviews,No data,336175,311281,No data yet,No data yet 6 | EA Reddit Pageviews,No data,No data,No data,"~65,350",154804 7 | TLYCS web traffic (excluding adwords),154508,292092,396195,367375,279077 8 | GiveWell monthly unique visitors (excluding adwords),642252,766646,701518,698813,No data 9 | -------------------------------------------------------------------------------- /assets/data/is_ea_growing/is_ea_growing_joining.csv: -------------------------------------------------------------------------------- 1 | Type of data,Jan-Dec 2014,2015,2016,2017,2018 2 | Vox Future Perfect Newsletter sign-ups,Didn’t exist,Didn’t exist,Didn’t exist,Didn’t exist,~10K subscribers 3 | New EA Reddit subscribers,+423 subscribers,236,106,1227,1527 4 | EA Newsletter sign-ups,Didn’t exist,+ ~6522 signups,+ ~3322,"+ ~20,600",+ ~4500 5 | 80K Newsletter signups,+2125 subscribers,20810,58815,104428,62730 6 | 80K podcast listeners,Didn’t exist,Didn’t exist,Didn’t exist,"4600 subscribers",+5900 7 | Total 80K engagement hours,2793 hours,11998,28663,86179,141030 8 | EA FB “Active Users”,No data,No data,No data,No data,475 9 | EA FB membership,No data,No data,No data,+1812 members,1915 10 | EA Forum accounts,No data yet,No data yet,No data yet,No data yet,No data yet 11 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/work_experience.csv: -------------------------------------------------------------------------------- 1 | Work experience Responses Percent 2 | Software engineering 328 27.2% 3 | Management 214 17.7% 4 | Maths and statistics 202 16.7% 5 | Web development 152 12.6% 6 | Consulting 135 11.2% 7 | Operations 131 10.9% 8 | Government and policy 118 9.8% 9 | Life sciences 112 9.3% 10 | Machine learning 103 8.5% 11 | Administration and office management 92 7.6% 12 | Marketing and outreach 90 7.5% 13 | Economics 90 7.5% 14 | Movement building, public speaking and campaigning 84 7.0% 15 | Generalist research (e.g. similar to GiveWell) 76 6.3% 16 | Communications (excluding movement building and marketing) 72 6.0% 17 | Philosophy 68 5.6% 18 | International development 66 5.5% 19 | Law 57 4.7% 20 | Asset management inc. quant trading 41 3.4% 21 | Personal assistance 25 2.1% 22 | Accounting 24 2.0% 23 | AI technical safety 13 1.1% 24 | Total 1,206 25 | -------------------------------------------------------------------------------- /assets/moon.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | -------------------------------------------------------------------------------- /assets/data/is_ea_growing/is_ea_growing_doing.csv: -------------------------------------------------------------------------------- 1 | Type of data,Jan-Dec 2014,2015,2016,2017,2018 2 | Donations recorded in EA Survey,$6.8M,$6.7M,$9.9M,$18M,No data yet 3 | # donors in EA Survey,577,688,704,730,No data yet 4 | Non-OpenPhil GiveWell donations,$13.3M,$40M,$38.2M,$42.4M,No data yet 5 | OpenPhil GiveWell donations,$8M,$70M,$50M,$75.1M,No data yet 6 | Total non-OpenPhil donors to GiveWell,9044,14287,17829,23049,No data yet 7 | Total OpenPhil non-GiveWell donations,$7.84M,$7.66M,$71.94M,$187.48M,$104.84M 8 | Total recorded money actually donated (not pledges) from Giving What We Can members,$4.04M,$7.24M,$8.11M,$9.38M,$7.91M 9 | TLYCS money moved,$830K,$1.63M,$2.7M,$3.75M,$5.4M 10 | ACE money moved[x],$147K,$1.34M,$3.57M,$6.09M,No data 11 | EA Funds payouts[y],Didn’t exist,Didn’t exist,Didn’t exist,$826K,$5.5M 12 | "Number of 80,000 Hours significant plan changes (impact adjusted)",266 IASPC,614,1084,2211,~1676 13 | "Number of 80,000 Hours significant plan changes (not impact adjusted)",69 changes,158,669,685,~487 14 | -------------------------------------------------------------------------------- /assets/data/rp_survey_data_2019/country2.csv: -------------------------------------------------------------------------------- 1 | Country,Responses 2 | United States of America,754 3 | UK,313 4 | Germany,140 5 | Australia,131 6 | Canada,84 7 | New Zealand,63 8 | Norway,41 9 | Switzerland,39 10 | Netherlands,36 11 | France,33 12 | Sweden,27 13 | Czech Republic,20 14 | Denmark,17 15 | Brazil,16 16 | China,16 17 | Spain,15 18 | Israel,14 19 | Austria,13 20 | Ireland,13 21 | Finland,11 22 | India,10 23 | Poland,10 24 | Singapore,9 25 | Italy,8 26 | Russian Federation,8 27 | Belgium,7 28 | Latvia,5 29 | Colombia,4 30 | Estonia,4 31 | Greece,4 32 | Argentina,3 33 | Hungary,3 34 | Japan,3 35 | Philippines,3 36 | Portugal,3 37 | Republic of Korea,3 38 | South Africa,3 39 | Uganda,3 40 | Egypt,2 41 | Lesotho,2 42 | Lithuania,2 43 | Luxembourg,2 44 | Mexico,2 45 | Slovakia,2 46 | United Arab Emirates,2 47 | Angola,1 48 | Belarus,1 49 | Burkina Faso,1 50 | Chile,1 51 | Ecuador,1 52 | Ethiopia,1 53 | Kazakhstan,1 54 | Malaysia,1 55 | Nigeria,1 56 | Oman,1 57 | Pakistan,1 58 | Papua New Guinea,1 59 | Romania,1 60 | Tanzania,1 61 | Vietnam,1 -------------------------------------------------------------------------------- /assets/data/gwwc/donations_by_year.json: -------------------------------------------------------------------------------- 1 | {"year":{"0":2009.0,"1":2010.0,"2":2011.0,"3":2012.0,"4":2013.0,"5":2014.0,"6":2015.0,"7":2016.0,"8":2017.0,"9":2018.0,"10":2019.0,"11":2020.0,"12":2021.0,"13":2022.0},"amount_normalized":{"0":39747.75,"1":165173.04,"2":808585.15,"3":1188216.7,"4":2145120.8399999999,"5":3404558.9900000002,"6":5545259.75,"7":6354225.1799999997,"8":7680776.4199999999,"9":10900372.9499999993,"10":18555044.6099999994,"11":21729164.1799999997,"12":10706931.3000000007,"13":12000.0},"num_donors":{"0":5,"1":18,"2":106,"3":136,"4":217,"5":480,"6":845,"7":1262,"8":1415,"9":1372,"10":1424,"11":2143,"12":2253,"13":1},"amount_normalized_year_to_current_month":{"0":14077.86,"1":95976.89,"2":203166.12,"3":398242.91,"4":969777.0699999999,"5":1789929.1399999999,"6":2969511.6499999999,"7":3094835.2999999998,"8":4797081.5499999998,"9":4448694.7400000002,"10":6924164.46,"11":11786053.7400000002,"12":10230452.6400000006,"13":12000.0},"donors_year_to_current_month":{"0":2,"1":10,"2":23,"3":53,"4":141,"5":304,"6":645,"7":990,"8":1191,"9":1084,"10":1087,"11":1512,"12":2208,"13":1}} -------------------------------------------------------------------------------- /assets/question-mark.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 7 | 8 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /utils/get_data/query_forum.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import json 3 | 4 | # Queries can be tested at https://forum.effectivealtruism.org/graphiql 5 | 6 | forum_query = ''' 7 | { 8 | posts ( 9 | input: { 10 | terms: { 11 | offset:%d 12 | } 13 | } 14 | ) { 15 | results { 16 | title 17 | postedAt 18 | user { 19 | username 20 | displayName 21 | } 22 | coauthors { 23 | username 24 | displayName 25 | } 26 | pageUrl 27 | wordCount 28 | baseScore 29 | commentCount 30 | } 31 | } 32 | } 33 | ''' 34 | 35 | def get_forum_data(offset=0): 36 | print(f'Getting forum data from offset={offset}') 37 | graphql_url = 'https://forum.effectivealtruism.org/graphql?' 38 | response = requests.post(graphql_url, json={'query': forum_query % offset}) 39 | response_json = response.json() 40 | return response_json 41 | 42 | def refresh_forum_data(): 43 | 44 | offset = 0 45 | forum_data = get_forum_data(offset) 46 | 47 | # the graphql only returns 5000 results at a time 48 | # so keep increment offset by 5000 until all data collected 49 | n_results = len(forum_data['data']['posts']['results']) 50 | while n_results == 5000: 51 | offset += 5000 52 | offset_forum_data = get_forum_data(offset) 53 | n_results = len(offset_forum_data['data']['posts']['results']) 54 | 55 | forum_data['data']['posts']['results'].extend( 56 | offset_forum_data['data']['posts']['results'] 57 | ) 58 | 59 | with open('./assets/data/ea_forum.json', 'w') as f: 60 | f.write(json.dumps(forum_data)) 61 | -------------------------------------------------------------------------------- /utils/get_data/query_gwwc.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import pandas as pd 3 | 4 | NEW_PLEDGES_URL = 'https://dashboard.effectivealtruism.org/api/public/card/a8499095-be16-46fe-af1f-e3e56ee04e88/query?parameters=%5B%5D' 5 | DONATIONS_BY_YEAR_URL = 'https://dashboard.effectivealtruism.org/api/public/card/9906735e-1350-4353-9828-bb3ec16137e3/query?parameters=%5B%5D' 6 | DONATIONS_BY_ORG_URL = 'https://dashboard.effectivealtruism.org/api/public/card/b3887098-686a-491c-9f9c-9a5b0e2b7fd8/query?parameters=%5B%5D' 7 | 8 | def request_data_and_parse(url): 9 | json_response = requests.get(url).json() 10 | col_data = json_response['data']['cols'] 11 | col_names = [ col_details['name'] for col_details in col_data ] 12 | data = json_response['data']['rows'] 13 | df = pd.DataFrame(columns = col_names, data = data) 14 | return df 15 | 16 | def get_new_pledges(): 17 | df = request_data_and_parse(NEW_PLEDGES_URL) 18 | return df 19 | 20 | def get_donations_by_year(): 21 | df = request_data_and_parse(DONATIONS_BY_YEAR_URL) 22 | return df 23 | 24 | def get_donations_by_org(): 25 | df = request_data_and_parse(DONATIONS_BY_ORG_URL) 26 | return df 27 | 28 | def save_data(): 29 | print('requesting new_pledges...') 30 | new_pledges = get_new_pledges() 31 | new_pledges.to_json('./assets/data/gwwc/new_pledges.json') 32 | 33 | print('requesting donations_by_year...') 34 | donations_by_year = get_donations_by_year() 35 | donations_by_year.to_json('./assets/data/gwwc/donations_by_year.json') 36 | 37 | print('requesting donations_by_org...') 38 | donations_by_org = get_donations_by_org() 39 | donations_by_org.to_json('./assets/data/gwwc/donations_by_org.json') 40 | -------------------------------------------------------------------------------- /utils/plots/scatter.py: -------------------------------------------------------------------------------- 1 | import dash 2 | from dash import dcc 3 | import plotly.express as px 4 | 5 | class Scatter(dcc.Graph): 6 | 7 | def __init__( 8 | self, 9 | df, 10 | x='x', 11 | y='y', 12 | x_title='', 13 | y_title='', 14 | size=None, 15 | color=None, 16 | hover=None, 17 | title=None, 18 | text=None, 19 | log_y=False, 20 | transparent=True, 21 | ): 22 | 23 | fig = px.scatter( 24 | df, 25 | x = x, 26 | y = y, 27 | log_y = log_y, 28 | title = title, 29 | size = size, 30 | color = color, 31 | text = text, 32 | ) 33 | 34 | fig.update_traces( 35 | marker_color = 'rgba(12, 134, 155, 0.6)' if transparent else "#0c869b", 36 | ) 37 | 38 | if hover: 39 | fig.update_traces( 40 | hovertext = df[hover], 41 | hovertemplate = '%{hovertext}', 42 | ) 43 | 44 | fig.update_layout( 45 | margin = dict(l=0, r=0, t=30, b=0), 46 | autosize = True, 47 | xaxis = dict( 48 | title = x_title, 49 | ), 50 | yaxis = dict( 51 | title = y_title, 52 | ), 53 | title_x = 0.5, 54 | font = dict( 55 | family = "Raleway", 56 | size = 12, 57 | ) 58 | ) 59 | 60 | fig.update_traces(textposition="middle right") 61 | 62 | super().__init__( 63 | figure = fig, 64 | responsive = True, 65 | ) 66 | -------------------------------------------------------------------------------- /assets/sun.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | -------------------------------------------------------------------------------- /utils/plots/bar.py: -------------------------------------------------------------------------------- 1 | import dash 2 | from dash import dcc 3 | from dash import html 4 | import plotly.graph_objects as go 5 | import plotly.express as px 6 | 7 | class Bar(dcc.Graph): 8 | 9 | def __init__(self, df, height=None, title=None): 10 | 11 | if 'text' in df.columns: 12 | text_col = 'text' 13 | else: 14 | text_col = 'y' 15 | 16 | if 'hover' in df.columns: 17 | hover_col = 'hover' 18 | else: 19 | hover_col = 'x' 20 | 21 | self.bar = px.bar( 22 | df, 23 | y='x', 24 | x='y', 25 | text=text_col, 26 | title=title, 27 | height=height, 28 | orientation='h', 29 | ) 30 | 31 | self.bar.update_traces( 32 | marker_color="#0c869b", 33 | hovertext = df[hover_col], 34 | hovertemplate = '%{hovertext}', 35 | ) 36 | 37 | self.bar.update_xaxes(side='top') 38 | 39 | self.bar.update_layout( 40 | margin=dict(l=0, r=0, t=30, b=0), 41 | xaxis=dict( 42 | title='', 43 | fixedrange=True 44 | ), 45 | yaxis=dict( 46 | title='', 47 | # dtick=1, 48 | fixedrange=True 49 | ), 50 | title_x=0.5, 51 | font=dict( 52 | family="Raleway", 53 | size=12, 54 | ) 55 | ) 56 | 57 | super().__init__( 58 | id=title, 59 | figure=self.bar, 60 | responsive=True, 61 | config={ 62 | 'displayModeBar': False, 63 | }, 64 | ) 65 | -------------------------------------------------------------------------------- /components/about.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import dash 4 | from dash import dcc 5 | from dash import html 6 | 7 | def about_box(): 8 | return html.Div( 9 | [ 10 | html.H4("About"), 11 | html.P( 12 | [ 13 | html.A( 14 | "Effective Altruism", 15 | href="https://www.effectivealtruism.org/" 16 | ), 17 | ' (EA) is a movement that uses reason and evidence to do the most good.', 18 | ] 19 | ), 20 | html.P( 21 | 'This website aggregates and visualizes data from EA organisations, including grants, donors, and pledges.', 22 | ), 23 | html.P( 24 | [ 25 | 'More information on this website can be found ', 26 | html.A( 27 | "here", 28 | href="https://forum.effectivealtruism.org/posts/CQaNyJfsRFZhseiLZ/effectivealtruismdata-com-a-website-for-aggregating-and" 29 | ), 30 | '.', 31 | ] 32 | ), 33 | html.P( 34 | [ 35 | 'Source code is available on ', 36 | html.A( 37 | "GitHub", 38 | href="https://github.com/hamishhuggard/ea_data_viz" 39 | ), 40 | '.', 41 | ] 42 | ), 43 | html.P( 44 | [ 45 | 'Please send feedback to ', 46 | html.A( 47 | "hamish.huggard@gmail.com", 48 | href="mailto:hamish.huggard@gmail.com" 49 | ), 50 | '.', 51 | ], 52 | ), 53 | ], 54 | id = 'about-box', 55 | className = 'hidden', 56 | ) 57 | -------------------------------------------------------------------------------- /assets/main.js: -------------------------------------------------------------------------------- 1 | function toggleSidebarVisible() { 2 | 3 | const sidebar = document.getElementById("sidebar"); 4 | const buttress = document.getElementById("sidebar-buttress"); 5 | 6 | if (sidebar.classList.contains("toggled")) { 7 | sidebar.classList.remove("toggled"); 8 | buttress.classList.remove("toggled"); 9 | } else { 10 | sidebar.classList.add("toggled"); 11 | buttress.classList.add("toggled"); 12 | } 13 | 14 | } 15 | 16 | function toggleAboutVisibility() { 17 | 18 | const about = document.getElementById("about-box"); 19 | 20 | if (about.classList.contains("hidden")) { 21 | about.classList.remove("hidden"); 22 | } else { 23 | about.classList.add("hidden"); 24 | } 25 | 26 | } 27 | 28 | 29 | if (window.matchMedia("(max-media: 700px)").matches) { 30 | function mobileSidebar() { 31 | 32 | toggleSidebarVisible(); 33 | 34 | } 35 | } 36 | 37 | 38 | // NIGHT MODE AND DAY MODE 39 | 40 | function setDarkMode() { 41 | document.body.classList.add("darkmode"); 42 | const button = document.getElementById("darkmode-button"); 43 | button.src = "/assets/sun.svg" 44 | button.classList.add("noprefer"); 45 | } 46 | 47 | function setLightMode() { 48 | document.body.classList.remove("darkmode"); 49 | const button = document.getElementById("darkmode-button"); 50 | button.src = "/assets/moon.svg"; 51 | button.classList.add("noprefer"); 52 | } 53 | 54 | function toggleDarkMode() { 55 | if (document.body.classList.contains("darkmode")) 56 | setLightMode(); 57 | else 58 | setDarkMode(); 59 | } 60 | 61 | // Initial dark mode preference 62 | window.onload = () => { 63 | if (window.matchMedia && window.matchMedia('(prefers-color-scheme: dark)').matches) { 64 | setDarkMode(); 65 | } 66 | } 67 | 68 | // Detect changes in dark mode preferences 69 | window.matchMedia('(prefers-color-scheme: dark)').addListener(function (e) { 70 | if (e.matches) 71 | setDarkMode(); 72 | else 73 | setLightMode(); 74 | }); 75 | 76 | -------------------------------------------------------------------------------- /components/body.py: -------------------------------------------------------------------------------- 1 | import dash 2 | from dash import html 3 | 4 | from components.sections.forum import forum_scatter_section 5 | from components.sections.forum import forum_count_section 6 | from components.sections.forum import forum_post_wilkinson_section 7 | from components.sections.forum import forum_user_wilkinson_section 8 | 9 | from components.sections.donations_sankey import donations_sankey_section 10 | 11 | from components.sections.demographics import demographics_section 12 | from components.sections.demographics import beliefs_section 13 | from components.sections.demographics import education_section 14 | from components.sections.demographics import career_section 15 | 16 | from components.sections.gwwc_donation_growth import get_gwwc_donation_growth_section 17 | from components.sections.gwwc_pledges import get_gwwc_pledges_section 18 | from components.sections.gwwc_donation_orgs import get_gwwc_donations_orgs_section 19 | 20 | from components.sections.geography import country_total_section 21 | from components.sections.geography import country_per_capita_section 22 | 23 | from components.sections.open_phil import openphil_grants_scatter_section 24 | from components.sections.open_phil import openphil_grants_categories_section 25 | from components.sections.open_phil import openphil_line_plot_section 26 | 27 | def body(): 28 | return html.Div( 29 | [ 30 | 31 | donations_sankey_section(), 32 | 33 | openphil_grants_scatter_section(), 34 | openphil_grants_categories_section(), 35 | openphil_line_plot_section(), 36 | 37 | get_gwwc_pledges_section(), 38 | get_gwwc_donation_growth_section(), 39 | get_gwwc_donations_orgs_section(), 40 | 41 | country_total_section(), 42 | country_per_capita_section(), 43 | 44 | demographics_section(), 45 | beliefs_section(), 46 | education_section(), 47 | career_section(), 48 | 49 | forum_scatter_section(), 50 | forum_count_section(), 51 | forum_post_wilkinson_section(), 52 | forum_user_wilkinson_section(), 53 | 54 | ], 55 | className = 'content scroll-snapper', 56 | ) 57 | -------------------------------------------------------------------------------- /utils/plots/wilkinson.py: -------------------------------------------------------------------------------- 1 | from utils.plots.scatter import Scatter 2 | from collections import Counter 3 | 4 | class Wilkinson(Scatter): 5 | 6 | def __init__( 7 | self, 8 | df, 9 | value='value', 10 | bins=20, 11 | text=None, 12 | log_y=False, 13 | **kwargs, 14 | ): 15 | 16 | min_val = df[value].min() 17 | max_val = df[value].max() 18 | delta = (max_val - min_val) / bins 19 | 20 | def bin_value(value): 21 | num_deltas = round( (value - min_val) / delta ) 22 | return min_val + delta * (num_deltas + 0.5) 23 | 24 | bin_col = f'{value}_bin' 25 | df[bin_col] = df[value].apply(bin_value) 26 | 27 | bin_counter = Counter() 28 | def get_count(value): 29 | bin_counter[value] += 1 30 | return bin_counter[value] 31 | count_col = f'{value}_count' 32 | df[count_col] = df[bin_col].apply(get_count) 33 | 34 | def trim_text(text, max_len): 35 | if len(text) < max_len: 36 | return text 37 | return text[:max_len-3] + '...' 38 | 39 | def get_text(row): 40 | bin_value = row[bin_col] 41 | row_count = row[count_col] 42 | display_text = row[text] 43 | # if the dot is in a row by itself then show its text 44 | if row_count == 1 and bin_counter[bin_value] == 1: 45 | return trim_text(display_text, 40) 46 | # if there are two dots in a row, show both of their text 47 | elif row_count == 2 and bin_counter[bin_value] == 2: 48 | other_text = df.loc[ (df[bin_col]==bin_value) & (df[count_col]==1), text ].iat[0] 49 | return trim_text(other_text, 20) + ', ' + trim_text(display_text, 20) 50 | # otherwise return an empty string 51 | else: 52 | return '' 53 | 54 | if text: 55 | text_col = f'{value}_text' 56 | df[text_col] = df.apply(get_text, axis=1) 57 | else: 58 | text_col = None 59 | 60 | super().__init__( 61 | df = df, 62 | y = bin_col, 63 | x = count_col, 64 | text = text_col, 65 | transparent = False, 66 | **kwargs, 67 | ) 68 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Run this app with `python app.py` and 4 | # visit http://127.0.0.1:8050/ in your web browser. 5 | 6 | import dash 7 | from dash import html 8 | 9 | from components.header import header 10 | from components.sidebar import sidebar 11 | from components.about import about_box 12 | from components.body import body 13 | 14 | from utils.get_data.refresh_data import refresh_data 15 | from dash.dependencies import Input, Output, State 16 | import visdcc 17 | 18 | app = dash.Dash( 19 | __name__, 20 | meta_tags = [ 21 | { 22 | 'og:title': 'Effective Altruism Data', 23 | "og:url": "https://effectivealtruismdata.com", 24 | "og:site_name": "Effective Altruism Data", 25 | "og:image": "https://i.ibb.co/mqbpdXW/eadata.png", 26 | "og:image:width": "1440", 27 | "og:image:height": "630", 28 | "twitter:card": "summary_large_image", 29 | 'name': 'viewport', 30 | 'content': 'width=device-width, initial-scale=1.0', 31 | } 32 | ], 33 | ) 34 | app.title = 'Effective Altruism Data' 35 | server = app.server 36 | 37 | # refresh_data() 38 | 39 | # def serve_layout(): 40 | # return html.Div( 41 | app.layout = html.Div( 42 | [ 43 | header(), 44 | html.Div( 45 | [ 46 | html.Div( 47 | [ 48 | sidebar(), 49 | ], 50 | ), 51 | about_box(), 52 | body(), 53 | visdcc.Run_js(id='javascript-body'), 54 | ], 55 | className = 'body', 56 | id = "sidebar-visdcc", 57 | ) 58 | ], 59 | ) 60 | 61 | @app.callback( 62 | Output('javascript-body', 'run'), 63 | [Input('sidebar-visdcc', 'n_clicks')]) 64 | def sidebar(x): 65 | if x: 66 | return "document.getElementById('sidebar').setAttribute('onclick', 'mobileSidebar()')" 67 | return "" 68 | 69 | @app.callback( 70 | Output('javascript-header', 'run'), 71 | [Input('header-sidebar-visdcc', 'n_clicks')]) 72 | def sidebar(x): 73 | if x: 74 | return "document.getElementById('sidebar').setAttribute('onclick', 'mobileSidebar()')" 75 | return "" 76 | 77 | # app.layout = serve_layout 78 | 79 | if __name__ == '__main__': 80 | #app.run_server(debug=True) 81 | app.run_server(debug=False) 82 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | .prettierignore 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | pip-wheel-metadata/ 25 | share/python-wheels/ 26 | *.egg-info/ 27 | .installed.cfg 28 | *.egg 29 | MANIFEST 30 | 31 | # PyInstaller 32 | # Usually these files are written by a python script from a template 33 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 34 | *.manifest 35 | *.spec 36 | 37 | # Installer logs 38 | pip-log.txt 39 | pip-delete-this-directory.txt 40 | 41 | # Unit test / coverage reports 42 | htmlcov/ 43 | .tox/ 44 | .nox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *.cover 51 | *.py,cover 52 | .hypothesis/ 53 | .pytest_cache/ 54 | 55 | # Translations 56 | *.mo 57 | *.pot 58 | 59 | # Django stuff: 60 | *.log 61 | local_settings.py 62 | db.sqlite3 63 | db.sqlite3-journal 64 | 65 | # Flask stuff: 66 | instance/ 67 | .webassets-cache 68 | 69 | # Scrapy stuff: 70 | .scrapy 71 | 72 | # Sphinx documentation 73 | docs/_build/ 74 | 75 | # PyBuilder 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | .python-version 87 | 88 | # pipenv 89 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 90 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 91 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 92 | # install all needed dependencies. 93 | #Pipfile.lock 94 | 95 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 96 | __pypackages__/ 97 | 98 | # Celery stuff 99 | celerybeat-schedule 100 | celerybeat.pid 101 | 102 | # SageMath parsed files 103 | *.sage.py 104 | 105 | # Environments 106 | .env 107 | .venv 108 | env/ 109 | venv/ 110 | ENV/ 111 | env.bak/ 112 | venv.bak/ 113 | 114 | # Spyder project settings 115 | .spyderproject 116 | .spyproject 117 | 118 | # Rope project settings 119 | .ropeproject 120 | 121 | # mkdocs documentation 122 | /site 123 | 124 | # mypy 125 | .mypy_cache/ 126 | .dmypy.json 127 | dmypy.json 128 | 129 | # Pyre type checker 130 | .pyre/ 131 | -------------------------------------------------------------------------------- /utils/get_data/open_phil.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import os 3 | 4 | def download_grants(): 5 | openphil_url = 'https://www.openphilanthropy.org/giving/grants/spreadsheet' 6 | print('Downloading Open Philanthropy grants...') 7 | return requests.get(openphil_url, headers={'User-Agent': ''}).text 8 | 9 | def save_grants(): 10 | 11 | data_dir = os.path.abspath('./assets/data/open_philanthropy/') 12 | if not os.path.exists(data_dir): 13 | os.path.makedirs(data_dir) 14 | 15 | grants_raw = download_grants() 16 | print('latest OP grant: ', new_op_data.split('\n')[1]) 17 | grants_path = os.path.join(data_dir, 'open_philanthropy_grants.csv') 18 | with open(grants_path, 'w') as f: 19 | f.write(grants_raw) 20 | 21 | def process_grants(grants_df): 22 | 23 | grants_df['Amount'] = grants_df['Amount'].apply( 24 | lambda x: int(x[1:].replace(',','')) if type(x)==str else x 25 | ) 26 | 27 | def normalize_orgname(orgname): 28 | if type(orgname) == str: 29 | orgname = orgname.strip() 30 | if orgname == 'Hellen Keller International': 31 | orgname = 'Helen Keller International' 32 | if orgname == 'Alliance for Safety and Justice': 33 | orgname = 'Alliance for Safety and Justice Action Fund' 34 | return orgname 35 | op_grants['Organization Name'] = op_grants['Organization Name'].apply(normalize_orgname) 36 | 37 | op_grants['Date'] = pd.to_datetime(op_grants['Date'], format='%m/%Y') 38 | op_grants = op_grants.sort_values(by='Date', ascending=False) 39 | op_grants['Date_readable'] = op_grants['Date'].dt.strftime('%B %Y') 40 | 41 | def group_by_month(grants_df): 42 | 43 | min_date = grants_df['Date'].min() 44 | max_date = grants_df['Date'].max() 45 | dates = pd.date_range(start=min_date, end=max_date, freq='M') 46 | 47 | grants_by_month = pd.DataFrame(columns=[ 48 | 'date', 49 | 'total_amount', 50 | 'n_grants', 51 | ]) 52 | 53 | for i, date in enumerate(dates): 54 | grants_by_month_i = grants_df.loc[ grants_df['Date'] == date ] 55 | grants_by_month.loc[i, 'date'] = date 56 | grants_by_month.loc[i, 'total_amount'] = grants_by_month_i['Amount'].sum() 57 | grants_by_month.loc[i, 'n_grants'] = len(grants_by_month_i) 58 | 59 | return grants_by_month 60 | 61 | def group_by_org(grants_df): 62 | orgs_df = op_grants.groupby(by='Organization Name', as_index=False).sum() 63 | orgs_df = orgs_df.sort_values(by='Amount') 64 | 65 | def group_by_focus_area(grants_df): 66 | orgs_df = op_grants.groupby(by='Organization Name', as_index=False).sum() 67 | orgs_df = orgs_df.sort_values(by='Amount') 68 | -------------------------------------------------------------------------------- /components/sections/gwwc_donation_orgs.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from dash import html 3 | from utils.subtitle import get_data_source 4 | from utils.subtitle import get_instructions 5 | from utils.plots.bar import Bar 6 | from utils.get_data.query_gwwc import get_donations_by_org 7 | 8 | def get_hover(row): 9 | 10 | amount = row['Amount (USD)'] 11 | 12 | result = '' 13 | result += f"{row.Organisation}" 14 | result += f"
${amount:,.2f} donated" 15 | result += f"
{row.Donors} donors" 16 | result += f"
{row.Donations} donations" 17 | 18 | return result 19 | 20 | def get_top_orgs_by_amount(donations_by_org): 21 | donations_by_org = donations_by_org.sort_values(by='Amount (USD)', ascending=False) 22 | donations_by_org = donations_by_org.reset_index() 23 | donations_by_org = donations_by_org.iloc[:20] 24 | donations_by_org = donations_by_org.iloc[::-1] 25 | donations_by_org['y'] = donations_by_org['Amount (USD)'] 26 | donations_by_org['x'] = donations_by_org['Organisation'] 27 | donations_by_org['hover'] = donations_by_org.apply(get_hover, axis=1) 28 | donations_by_org['text'] = donations_by_org['Amount (USD)'].apply(lambda x: f'${x:,.2f}') 29 | return Bar(donations_by_org, title='Top Organizations by Amount') 30 | 31 | def get_top_orgs_by_num_donors(donations_by_org): 32 | donations_by_org = donations_by_org.sort_values(by='Donors', ascending=False) 33 | donations_by_org = donations_by_org.reset_index() 34 | donations_by_org = donations_by_org.iloc[:20] 35 | donations_by_org = donations_by_org.iloc[::-1] 36 | donations_by_org['y'] = donations_by_org['Donors'] 37 | donations_by_org['x'] = donations_by_org['Organisation'] 38 | donations_by_org['hover'] = donations_by_org.apply(get_hover, axis=1) 39 | donations_by_org['text'] = donations_by_org['Donors'].apply(lambda x: f'{x:,}') 40 | return Bar(donations_by_org, title='Top Organizations by Number of Donors') 41 | 42 | 43 | def get_gwwc_donations_orgs_section(): 44 | 45 | donations_by_org = pd.read_json('./assets/data/gwwc/donations_by_org.json') 46 | #donations_by_org = get_donations_by_org() 47 | 48 | return html.Div( 49 | [ 50 | html.Div( 51 | html.H2('Giving What We Can Donations by Organization'), 52 | className='section-heading', 53 | ), 54 | get_instructions(hover='points', zoom=True), 55 | html.Div( 56 | [ 57 | get_top_orgs_by_amount(donations_by_org), 58 | get_top_orgs_by_num_donors(donations_by_org), 59 | ], 60 | className='grid tab-cols-2 desk-cols-2 section-body' 61 | ), 62 | get_data_source('gwwc_orgs'), 63 | ], 64 | className = 'section', 65 | id='gwwc-orgs-section', 66 | ) 67 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # [Effective Altruism Data](https://effectivealtruismdata.com) 2 | 3 | ![Effective Altruism Data](eadata.png) 4 | 5 | [Effective Altruism](https://www.effectivealtruism.org/) (EA) is a philosophy and social movement that uses reason and evidence to do the most good. 6 | 7 | There are several EA organisations that collect data on grants, donors, and pledges. This website aggregates and visualises that data. 8 | 9 | The website is coded in Python using [Dash](https://dash.plotly.com/) and [Plotly](https://plotly.com/). It is currently deployed with Heroku at [effectivealtruismdata.com](https://effectivealtruismdata.com). 10 | 11 | There may be some overlap between this project and with the [EA Hub map](https://eahub.org/) and the [EA Funds dashboard](https://app.effectivealtruism.org/funds/about/stats). 12 | 13 | ## How to run 14 | 15 | 1. Make sure that you have Python 3.9 installed in your system. 16 | 17 | - Otherwise, install it, e.g., with `sudo apt install python3.9` on Debian-based Linux sytems. 18 | 19 | 1. Install [pipenv](https://pipenv.pypa.io/en/latest/). If you have `pip` installed, this looks like: 20 | 21 | ``` 22 | pip install --user pipenv 23 | ``` 24 | 25 | 2. Run the following in the terminal: 26 | 27 | ``` 28 | git clone https://github.com/hamishhuggard/ea_data_viz.git 29 | cd ea_data_viz 30 | pipenv run python app.py 31 | ``` 32 | 33 | You can also specify a particular path for python on pipenv with: 34 | 35 | ``` 36 | pipenv --python /usr/bin/python3.9 run python app.py 37 | 38 | ``` 39 | 40 | ## To do 41 | 42 | ### Version 1 (Current Version) 43 | 44 | - Download links 45 | - Data refreshing 46 | - Analytics 47 | - OP Wilkinson Plots 48 | - Grants by size and by time 49 | - Organizations by #grants and by total amount 50 | - EA Funds 51 | - Scatter 52 | - Cumulative grants 53 | - Grants by month/year 54 | - Founders pledge 55 | - Members over time 56 | - Pledged value over time 57 | - Fulfilled commitments over time 58 | - Title page with summary statistics 59 | - X EAs have donated Y amount and pledged a further Z. 60 | - Probabilities of x-risks given by Toby Ord 61 | - Global Poverty 62 | - Cost to save a life with most effective charities 63 | - Value of Future 64 | - People currently alive 65 | - People who have ever lived 66 | - Projected peak population 67 | - Potential future earth people 68 | - Potential future space people 69 | - Potential future virtual people 70 | - Animals 71 | - Most effective animal interventions 72 | - Number of animals in factory farms 73 | - Most effective diet interventions 74 | - [Key EA numbers](https://github.com/benthamite/EA-numbers/blob/main/source.org) 75 | - Spin off data aggregation to own Python library 76 | - [Better bar charts](https://dkane.net/2020/better-horizontal-bar-charts-with-plotly/?utm_source=pocket_mylist) 77 | - Space efficient data source annotations 78 | 79 | ### Version 2 80 | 81 | - Reimplement in chart.js or D3.js 82 | - See data as table or as plot 83 | - Data download buttons 84 | -------------------------------------------------------------------------------- /assets/data/ea_funds_grants.csv: -------------------------------------------------------------------------------- 1 | fund,amount,date,title 2 | global-development,2000000,2020-07-29 00:00:00,July 2020: Innovations for Poverty Action 3 | global-development,656000,2020-06-30 00:00:00,June 2020: IDinsight 4 | global-development,0,2020-01-29 00:00:00,January 2020: Existing funds rolled forward 5 | global-development,355000,2019-10-25 00:00:00,October 2019: One for the World 6 | global-development,1005716,2019-08-14 00:00:00,August 2019: Fortify Health 7 | global-development,60000,2019-04-30 00:00:00,April 2019: Instiglio 8 | global-development,1705000,2019-03-13 00:00:00,March 2019: Malaria Consortium 9 | global-development,1000000,2019-01-30 00:00:00,January 2019: J-PAL's Innovation in Government Initiative 10 | global-development,1500000,2018-05-01 00:00:00,April 2018: Schistosomiasis Control Initiative 11 | global-development,150000,2017-10-18 00:00:00,September 2017: No Lean Season 12 | global-development,331126,2017-06-28 00:00:00,April 2017: Against Malaria Foundation 13 | animal-welfare,680000,2020-08-07 00:00:00,July 2020: Animal Welfare Fund Grants 14 | animal-welfare,671000,2020-03-27 00:00:00,March 2020: Animal Welfare Fund Grants 15 | animal-welfare,415000,2019-11-22 00:00:00,November 2019: Animal Welfare Fund Grants 16 | animal-welfare,440000,2019-08-24 00:00:00,July 2019: Animal Welfare Fund Grants 17 | animal-welfare,445000,2019-03-07 00:00:00,March 2019: Animal Welfare Fund Grants 18 | animal-welfare,341000,2018-12-31 00:00:00,December 2018: Animal Welfare Fund Grants 19 | animal-welfare,1205000,2018-08-01 00:00:00,June 2018: Animal Welfare Fund Grants 20 | animal-welfare,750000,2018-05-01 00:00:00,March 2018: Animal Welfare Fund Grants 21 | animal-welfare,150000,2018-01-15 00:00:00,November 2017: Animal Welfare Fund Grants 22 | animal-welfare,180000,2017-04-15 00:00:00,April 2017: Animal Welfare Fund Grants 23 | far-future,488350,2020-04-15 00:00:00,April 2020: Long-Term Future Fund Grants and Recommendations 24 | far-future,466000,2019-11-22 00:00:00,November 2019: Long-Term Future Fund Grants 25 | far-future,415697,2019-08-31 00:00:00,August 2019: Long-Term Future Fund Grants and Recommendations 26 | far-future,875150,2019-03-21 00:00:00,April 2019: Long-Term Future Fund Grants and Recommendations 27 | far-future,95500,2018-11-30 00:00:00,November 2018: Long-Term Future Fund Grants 28 | far-future,917000,2018-08-15 00:00:00,July 2018: Long-Term Future Fund Grants 29 | far-future,14838,2017-03-21 00:00:00,March 2017: Berkeley Existential Risk Initiative (BERI) 30 | ea-community,838000,2020-08-07 00:00:00,July 2020: EA Meta Fund Grants 31 | ea-community,522000,2020-03-27 00:00:00,March 2020: EA Meta Fund Grants 32 | ea-community,330000,2019-11-22 00:00:00,November 2019: EA Meta Fund Grants 33 | ea-community,466000,2019-08-24 00:00:00,July 2019: EA Meta Fund Grants 34 | ea-community,512000,2019-03-08 00:00:00,March 2019: EA Meta Fund Grants 35 | ea-community,129000,2018-11-30 00:00:00,November 2018: EA Meta Fund Grants 36 | ea-community,526000,2018-08-15 00:00:00,July 2018: EA Meta Fund Grants 37 | ea-community,83264,2018-01-15 00:00:00,December 2017: EA Sweden Grant 38 | -------------------------------------------------------------------------------- /utils/plots/line.py: -------------------------------------------------------------------------------- 1 | import plotly.express as px 2 | import plotly.graph_objects as go 3 | from dash import dcc 4 | from dash import html 5 | 6 | class Line(dcc.Graph): 7 | 8 | def __init__( 9 | self, 10 | df, 11 | x='x', 12 | y='y', 13 | label='label', 14 | hover='hover', 15 | title=None, 16 | x_title='', 17 | y_title='', 18 | size=None, 19 | color=None, 20 | log_y=False, 21 | dollars=False, 22 | xanchor='right', 23 | yanchor='bottom', 24 | ): 25 | 26 | fig = go.Figure() 27 | 28 | annotations = [] 29 | for val in df[label].unique(): 30 | 31 | val_df = df.loc[ df[label]==val ] 32 | val_df = val_df.sort_values(by=[x,y]) 33 | 34 | fig.add_trace( 35 | go.Scatter( 36 | x=val_df[x], 37 | y=val_df[y], 38 | name=val, 39 | hovertext = val_df[hover], 40 | hovertemplate = '%{hovertext}', 41 | mode='lines', 42 | line=dict( 43 | color="#0c869b", 44 | ), 45 | ) 46 | ) 47 | 48 | val_df = val_df.loc[ val_df[y].notnull() ].reset_index() 49 | last_row = val_df.iloc[len(val_df)-1] 50 | last_hover = last_row[hover] 51 | 52 | fig.add_trace(go.Scatter( 53 | x=[ last_row[x] ], 54 | y=[ last_row[y] ], 55 | mode='markers', 56 | marker=dict( 57 | color="#0c869b", 58 | size=10, 59 | ), 60 | hovertext = [last_hover], 61 | hovertemplate = '%{hovertext}', 62 | )) 63 | 64 | annotations.append(dict( 65 | x=last_row[x], 66 | y=last_row[y], 67 | xanchor=xanchor, 68 | yanchor=yanchor, 69 | text=f' {val}', 70 | font={ 71 | 'size': 13, 72 | }, 73 | showarrow=False, 74 | )) 75 | 76 | if log_y: 77 | fig.update_layout( 78 | yaxis_type="log", 79 | ) 80 | 81 | if dollars: 82 | fig.update_layout( 83 | yaxis_tickprefix = '$', 84 | ) 85 | 86 | top_margin = 40 if title else 0 87 | fig.update_layout( 88 | title=title, 89 | showlegend=False, 90 | xaxis = dict( 91 | title = x_title, 92 | ), 93 | yaxis = dict( 94 | title = y_title, 95 | ), 96 | annotations=annotations, 97 | margin=dict(l=0, r=0, t=top_margin, b=0), 98 | title_x=0.5, 99 | ) 100 | 101 | super().__init__( 102 | figure=fig, 103 | responsive=True 104 | ) 105 | 106 | -------------------------------------------------------------------------------- /components/header.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import dash 4 | from dash import dcc 5 | from dash import html 6 | import dash_dangerously_set_inner_html 7 | import visdcc 8 | 9 | def header(): 10 | 11 | lightbulb_img_url = '/assets/logo.png' 12 | hamburger_img_url = '/assets/hamburger-menu.svg' 13 | 14 | return html.Div( 15 | [ 16 | #html.Img( 17 | # src = hamburger_img_url, 18 | # className='hamburger', 19 | #), 20 | html.Div( 21 | dash_dangerously_set_inner_html.DangerouslySetInnerHTML(''' 22 | 28 | '''), 29 | className='icon', 30 | ), 31 | html.Img( 32 | src = lightbulb_img_url, 33 | className='icon', 34 | ), 35 | html.H1( 36 | [ 37 | html.Span( 38 | 'EA Data', 39 | className = 'data', 40 | ), 41 | ], 42 | className='main-title short-title', 43 | ), 44 | html.H1( 45 | [ 46 | html.Span( 47 | 'Effective ', 48 | className = 'effective', 49 | ), 50 | html.Span( 51 | 'Altruism ', 52 | className = 'altruism', 53 | ), 54 | html.Span( 55 | 'Data', 56 | className = 'data', 57 | ), 58 | ], 59 | className = 'main-title long-title', 60 | ), 61 | html.Div( 62 | [ 63 | html.Div( 64 | dash_dangerously_set_inner_html.DangerouslySetInnerHTML(''' 65 | 72 | '''), 73 | className='icon', 74 | ), 75 | visdcc.Run_js(id='javascript-header'), 76 | html.Div( 77 | dash_dangerously_set_inner_html.DangerouslySetInnerHTML(''' 78 | 84 | '''), 85 | className='icon', 86 | ), 87 | ], 88 | className = 'right-icons', 89 | ) 90 | 91 | ], 92 | className='header center', 93 | id="header-sidebar-visdcc" 94 | ) 95 | -------------------------------------------------------------------------------- /components/sections/gwwc_donation_growth.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from dash import html 3 | from utils.get_data.query_gwwc import get_donations_by_year 4 | from utils.subtitle import get_data_source 5 | from utils.subtitle import get_instructions 6 | from utils.plots.line import Line 7 | from datetime import datetime 8 | 9 | 10 | def get_num_donors_hover(row): 11 | 12 | year = row['date'].strftime('%Y') 13 | 14 | result = '' 15 | result += f"{year}" 16 | result += f"
${row.amount_normalized:,.2f} donations" 17 | result += f"
${row.amount_normalized_total:,.2f} total donations" 18 | result += f"
{row.num_donors:,} donors" 19 | 20 | return result 21 | 22 | 23 | def get_gwwc_donation_growth_section(): 24 | 25 | donations_by_year = pd.read_json('./assets/data/gwwc/donations_by_year.json') 26 | #donations_by_year = get_donations_by_year() 27 | 28 | donations_by_year['date'] = pd.to_datetime(donations_by_year['year'], format='%Y') 29 | donations_by_year = donations_by_year.sort_values(by='date') 30 | 31 | # Filter out future donations 32 | donations_by_year = donations_by_year.loc[ donations_by_year['date'] < datetime.now() ] 33 | 34 | donations_by_year['amount_normalized_total'] = donations_by_year['amount_normalized'].cumsum() 35 | 36 | donations_by_year['hover'] = donations_by_year.apply(get_num_donors_hover, axis=1) 37 | 38 | donations_by_year['label'] = 'Donations' 39 | 40 | label = donations_by_year['amount_normalized'].tolist()[-1] 41 | last_year = int(donations_by_year['year'].tolist()[-1]) 42 | label = f'${label/1e6:,.1f} Million
{last_year} Donations' 43 | donations_by_year['label'] = label 44 | 45 | annual_donations_graph = Line( 46 | donations_by_year, 47 | x='date', 48 | y='amount_normalized', 49 | title='Annual Donation Amounts', 50 | x_title='', 51 | y_title='', 52 | hover='hover', 53 | dollars=True, 54 | ) 55 | 56 | label = donations_by_year['amount_normalized_total'].tolist()[-1] 57 | label = f'${label/1e6:,.1f} Million
Total Donated' 58 | donations_by_year['label'] = label 59 | 60 | total_donations_graph = Line( 61 | donations_by_year, 62 | x='date', 63 | y='amount_normalized_total', 64 | title='Total Donated', 65 | x_title='', 66 | y_title='', 67 | hover='hover', 68 | dollars=True, 69 | ) 70 | 71 | label = donations_by_year['num_donors'].tolist()[-1] 72 | label = f'{label:,} Donors
in {last_year}' 73 | donations_by_year['label'] = label 74 | 75 | num_donors_graph = Line( 76 | donations_by_year, 77 | x='date', 78 | y='num_donors', 79 | title='Annual Number of Donors', 80 | x_title='', 81 | y_title='', 82 | hover='hover', 83 | ) 84 | 85 | return html.Div( 86 | [ 87 | html.Div( 88 | html.H2('Giving What We Can Donations'), 89 | className='section-heading', 90 | ), 91 | get_instructions(hover='points', zoom=True), 92 | html.Div( 93 | [ 94 | annual_donations_graph, 95 | num_donors_graph, 96 | total_donations_graph, 97 | ], 98 | className='grid tab-cols-3 desk-cols-3 section-body' 99 | ), 100 | get_data_source('gwwc_pledges'), 101 | ], 102 | className = 'section', 103 | id='gwwc-donations-section', 104 | ) 105 | -------------------------------------------------------------------------------- /utils/subtitle.py: -------------------------------------------------------------------------------- 1 | from dash import html 2 | from dash import dcc 3 | 4 | data_source_details = { 5 | 6 | 'rethink19': dict( 7 | name='EA Survey 2019', 8 | url='https://www.rethinkpriorities.org/blog/2019/12/5/ea-survey-2019-series-community-demographics-amp-characteristics' 9 | ), 10 | 11 | 'rethink19-geo': dict( 12 | name='EA Survey 2019', 13 | url='https://rethinkpriorities.org/publications/eas2019-geographic-distribution-of-eas', 14 | download_url='/assets/data/rp_survey_data_2019/country2.csv' 15 | ), 16 | 17 | 'ea_forum': dict( 18 | name='forum.effectivealtruism.org', 19 | url='https://forum.effectivealtruism.org/graphiql', 20 | ), 21 | 22 | 'open_phil': dict( 23 | name='openphilanthropy.org', 24 | url='https://www.openphilanthropy.org/giving/grants', 25 | download_url='https://www.openphilanthropy.org/giving/grants/spreadsheet', 26 | ), 27 | 28 | 'funds_payout': dict( 29 | name='funds.effectivealtruism.org', 30 | url='https://funds.effectivealtruism.org/' 31 | ), 32 | 33 | 'founders_pledge': dict( 34 | name='founderspledge.com', 35 | url='https://founderspledge.com/' 36 | ), 37 | 38 | 'gwwc': dict( 39 | name='givingwhatwecan.org', 40 | url='https://www.givingwhatwecan.org/' 41 | ), 42 | 43 | 'growth': dict( 44 | name='EA Growth Metrics for 2018', 45 | url='https://forum.effectivealtruism.org/posts/MBJvDDw2sFGkFCA29/is-ea-growing-ea-growth-metrics-for-2018', 46 | ), 47 | 48 | 'gwwc_pledges': dict( 49 | name='dashboard.effectivealtruism.org', 50 | url='http://dashboard.effectivealtruism.org/public/question/a8499095-be16-46fe-af1f-e3e56ee04e88', 51 | ), 52 | 53 | 'gwwc_donations': dict( 54 | name='dashboard.effectivealtruism.org', 55 | url='http://dashboard.effectivealtruism.org/public/question/9906735e-1350-4353-9828-bb3ec16137e3', 56 | ), 57 | 58 | 'gwwc_orgs': dict( 59 | name='dashboard.effectivealtruism.org', 60 | url='http://dashboard.effectivealtruism.org/public/question/b3887098-686a-491c-9f9c-9a5b0e2b7fd8', 61 | ), 62 | 63 | } 64 | 65 | def get_data_source(data_sources): 66 | 67 | if len(data_sources) == 0: 68 | return html.Div() 69 | 70 | if type(data_sources)==str: 71 | data_sources = [ data_sources ] 72 | 73 | 74 | content = [ 'Data source: ' ] 75 | 76 | list_of_source_links = [] 77 | for data_source in data_sources: 78 | 79 | details = data_source_details[data_source] 80 | 81 | content.append( 82 | html.A( 83 | details['name'], 84 | href = details['url'] 85 | ) 86 | ) 87 | 88 | # download link 89 | if 'download_url' in details: 90 | content.append(' (') 91 | content.append( 92 | html.A( 93 | 'download', 94 | href = details['download_url'], 95 | ) 96 | ) 97 | content.append(')') 98 | 99 | content.append(', ') 100 | 101 | # Change the last comma into a period. 102 | content[-1] = '.' 103 | 104 | return html.P(content) 105 | 106 | def get_instructions(zoom=False, hover='bars', extra_text=[]): 107 | 108 | content = [] 109 | 110 | if hover: 111 | content.append( f'Hover for more details.' ) 112 | 113 | if zoom: 114 | content.append( 'Click and drag to zoom. Double click to unzoom.' ) 115 | 116 | if type(extra_text)==str: 117 | content.append(extra_text) 118 | elif type(extra_text)==list: 119 | content.extend(extra_text) 120 | 121 | return html.P(' '.join(content)) 122 | 123 | 124 | -------------------------------------------------------------------------------- /assets/data/gwwc/new_pledges.json: -------------------------------------------------------------------------------- 1 | {"pledge_month":{"0":"2009-11","1":"2009-12","2":"2010-01","3":"2010-02","4":"2010-03","5":"2010-05","6":"2010-06","7":"2010-07","8":"2010-08","9":"2010-09","10":"2010-10","11":"2010-11","12":"2010-12","13":"2011-01","14":"2011-02","15":"2011-03","16":"2011-04","17":"2011-05","18":"2011-06","19":"2011-07","20":"2011-08","21":"2011-09","22":"2011-10","23":"2011-11","24":"2011-12","25":"2012-01","26":"2012-02","27":"2012-03","28":"2012-04","29":"2012-05","30":"2012-06","31":"2012-07","32":"2012-08","33":"2012-09","34":"2012-10","35":"2012-11","36":"2012-12","37":"2013-01","38":"2013-02","39":"2013-03","40":"2013-04","41":"2013-05","42":"2013-06","43":"2013-07","44":"2013-08","45":"2013-09","46":"2013-10","47":"2013-11","48":"2013-12","49":"2014-01","50":"2014-02","51":"2014-03","52":"2014-04","53":"2014-05","54":"2014-06","55":"2014-07","56":"2014-08","57":"2014-09","58":"2014-10","59":"2014-11","60":"2014-12","61":"2015-01","62":"2015-02","63":"2015-03","64":"2015-04","65":"2015-05","66":"2015-06","67":"2015-07","68":"2015-08","69":"2015-09","70":"2015-10","71":"2015-11","72":"2015-12","73":"2016-01","74":"2016-02","75":"2016-03","76":"2016-04","77":"2016-05","78":"2016-06","79":"2016-07","80":"2016-08","81":"2016-09","82":"2016-10","83":"2016-11","84":"2016-12","85":"2017-01","86":"2017-02","87":"2017-03","88":"2017-04","89":"2017-05","90":"2017-06","91":"2017-07","92":"2017-08","93":"2017-09","94":"2017-10","95":"2017-11","96":"2017-12","97":"2018-01","98":"2018-02","99":"2018-03","100":"2018-04","101":"2018-05","102":"2018-06","103":"2018-07","104":"2018-08","105":"2018-09","106":"2018-10","107":"2018-11","108":"2018-12","109":"2019-01","110":"2019-02","111":"2019-03","112":"2019-04","113":"2019-05","114":"2019-06","115":"2019-07","116":"2019-08","117":"2019-09","118":"2019-10","119":"2019-11","120":"2019-12","121":"2020-01","122":"2020-02","123":"2020-03","124":"2020-04","125":"2020-05","126":"2020-06","127":"2020-07","128":"2020-08","129":"2020-09","130":"2020-10","131":"2020-11","132":"2020-12","133":"2021-01","134":"2021-02","135":"2021-03","136":"2021-04","137":"2021-05","138":"2021-06","139":"2021-07","140":"2021-08","141":"2021-09","142":"2021-10"},"the_pledge":{"0":25,"1":6,"2":1,"3":2,"4":3,"5":4,"6":1,"7":1,"8":3,"9":3,"10":5,"11":4,"12":8,"13":19,"14":17,"15":12,"16":9,"17":7,"18":5,"19":2,"20":5,"21":2,"22":10,"23":4,"24":7,"25":15,"26":8,"27":4,"28":5,"29":8,"30":4,"31":6,"32":11,"33":12,"34":10,"35":9,"36":8,"37":11,"38":4,"39":12,"40":2,"41":5,"42":8,"43":3,"44":5,"45":14,"46":16,"47":16,"48":20,"49":19,"50":21,"51":25,"52":38,"53":13,"54":24,"55":21,"56":32,"57":24,"58":30,"59":27,"60":122,"61":100,"62":40,"63":29,"64":43,"65":60,"66":48,"67":54,"68":38,"69":51,"70":52,"71":54,"72":109,"73":117,"74":57,"75":45,"76":59,"77":49,"78":59,"79":38,"80":76,"81":108,"82":104,"83":89,"84":167,"85":201,"86":62,"87":61,"88":61,"89":58,"90":85,"91":73,"92":70,"93":21,"94":79,"95":60,"96":78,"97":84,"98":40,"99":52,"100":42,"101":45,"102":42,"103":52,"104":33,"105":50,"106":56,"107":49,"108":60,"109":47,"110":35,"111":38,"112":49,"113":39,"114":27,"115":36,"116":43,"117":23,"118":46,"119":52,"120":86,"121":74,"122":35,"123":50,"124":38,"125":61,"126":103,"127":70,"128":77,"129":86,"130":81,"131":74,"132":260,"133":253,"134":112,"135":95,"136":77,"137":89,"138":56,"139":51,"140":72,"141":72,"142":35},"try_giving":{"0":0,"1":0,"2":0,"3":0,"4":0,"5":0,"6":0,"7":0,"8":0,"9":0,"10":0,"11":1,"12":0,"13":0,"14":0,"15":0,"16":0,"17":0,"18":0,"19":0,"20":0,"21":0,"22":0,"23":1,"24":0,"25":5,"26":2,"27":1,"28":0,"29":2,"30":0,"31":0,"32":1,"33":3,"34":1,"35":1,"36":3,"37":3,"38":1,"39":1,"40":0,"41":1,"42":4,"43":1,"44":2,"45":1,"46":3,"47":1,"48":1,"49":11,"50":4,"51":5,"52":5,"53":8,"54":9,"55":4,"56":18,"57":9,"58":15,"59":12,"60":25,"61":46,"62":11,"63":13,"64":22,"65":21,"66":18,"67":25,"68":30,"69":26,"70":24,"71":23,"72":34,"73":90,"74":26,"75":22,"76":26,"77":21,"78":40,"79":36,"80":46,"81":48,"82":39,"83":55,"84":57,"85":97,"86":36,"87":41,"88":32,"89":28,"90":20,"91":23,"92":13,"93":8,"94":28,"95":15,"96":40,"97":50,"98":24,"99":40,"100":29,"101":21,"102":18,"103":35,"104":31,"105":37,"106":28,"107":34,"108":34,"109":54,"110":24,"111":29,"112":25,"113":15,"114":24,"115":20,"116":18,"117":24,"118":25,"119":28,"120":49,"121":44,"122":16,"123":16,"124":32,"125":35,"126":91,"127":70,"128":60,"129":63,"130":72,"131":73,"132":208,"133":273,"134":89,"135":70,"136":66,"137":54,"138":57,"139":55,"140":59,"141":53,"142":33}} -------------------------------------------------------------------------------- /components/sidebar.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import dash 4 | from dash import dcc 5 | from dash import html 6 | import plotly.graph_objects as go 7 | from plotly.subplots import make_subplots 8 | import plotly.express as px 9 | 10 | def intro_contents(): 11 | return [ 12 | html.P( 13 | 'Overview', 14 | ), 15 | html.Ul( 16 | [ 17 | html.Li( 18 | html.A( 19 | "All Donations", 20 | href="#donations-sankey" 21 | ), 22 | ), 23 | ] 24 | ) 25 | ] 26 | 27 | def open_phil_contents(): 28 | return [ 29 | html.P( 30 | 'Open Philanthropy Grants', 31 | ), 32 | html.Ul( 33 | [ 34 | html.Li( 35 | html.A( 36 | "Individual Grants Plot", 37 | href="#op-grants-scatter-section", 38 | ), 39 | ), 40 | html.Li( 41 | html.A( 42 | "Focus Area and Donee Organization", 43 | href="#op-grants-categories", 44 | ), 45 | ), 46 | html.Li( 47 | html.A( 48 | "Changes over Time", 49 | href="#op-grants-growth", 50 | ), 51 | ), 52 | ] 53 | ) 54 | ] 55 | 56 | def survey_contents(): 57 | return [ 58 | html.P( 59 | 'EA Survey Results', 60 | ), 61 | html.Ul( 62 | [ 63 | html.Li( 64 | html.A( 65 | "Countries (total)", 66 | href="#countries" 67 | ), 68 | ), 69 | html.Li( 70 | html.A( 71 | "Countries (per Capita)", 72 | href="#countries-per-capita" 73 | ), 74 | ), 75 | html.Li( 76 | html.A( 77 | "Demographics", 78 | href="#demographics" 79 | ), 80 | ), 81 | html.Li( 82 | html.A( 83 | "Beliefs and Lifestyle", 84 | href="#beliefs-lifestyle" 85 | ), 86 | ), 87 | html.Li( 88 | html.A( 89 | "Education", 90 | href="#education" 91 | ), 92 | ), 93 | html.Li( 94 | html.A( 95 | "Careers", 96 | href="#careers" 97 | ), 98 | ), 99 | ], 100 | ), 101 | ] 102 | 103 | def forum_contents(): 104 | return [ 105 | html.P( 106 | 'EA Forum', 107 | ), 108 | html.Ul( 109 | [ 110 | html.Li( 111 | html.A( 112 | "Posted Date vs Karma", 113 | href="#forum-scatter-section" 114 | ), 115 | ), 116 | html.Li( 117 | html.A( 118 | "Forum Growth", 119 | href="#forum-growth-section" 120 | ), 121 | ), 122 | html.Li( 123 | html.A( 124 | "Post Distributions", 125 | href="#post-wilkinson-section" 126 | ), 127 | ), 128 | html.Li( 129 | html.A( 130 | "Author Distributions", 131 | href="#author-wilkinson-section" 132 | ), 133 | ), 134 | ], 135 | ), 136 | ] 137 | 138 | def gwwc_contents(): 139 | return [ 140 | html.P( 141 | 'Giving What We Can', 142 | ), 143 | html.Ul( 144 | [ 145 | html.Li( 146 | html.A( 147 | "Pledges", 148 | href="#gwwc-pledge-section", 149 | ), 150 | ), 151 | html.Li( 152 | html.A( 153 | "Donations", 154 | href="#gwwc-donations-section", 155 | ), 156 | ), 157 | html.Li( 158 | html.A( 159 | "Donation Organizations", 160 | href="#gwwc-orgs-section", 161 | ), 162 | ), 163 | ], 164 | ), 165 | ] 166 | 167 | def contents(): 168 | return html.Div( 169 | [ 170 | html.H2('Contents'), 171 | *intro_contents(), 172 | *open_phil_contents(), 173 | *gwwc_contents(), 174 | *survey_contents(), 175 | *forum_contents(), 176 | ], 177 | className = 'section_list', 178 | ) 179 | 180 | def sidebar(): 181 | return html.Div( 182 | [ 183 | html.Div( 184 | contents(), 185 | id='sidebar', 186 | ), 187 | html.Div( 188 | id='sidebar-buttress', 189 | ), 190 | ] 191 | ) 192 | -------------------------------------------------------------------------------- /components/sections/gwwc_pledges.py: -------------------------------------------------------------------------------- 1 | from dash import html 2 | import pandas as pd 3 | from utils.subtitle import get_data_source 4 | from utils.subtitle import get_instructions 5 | from utils.plots.line import Line 6 | from utils.get_data.query_gwwc import get_new_pledges 7 | 8 | 9 | def get_the_pledge_hover(row): 10 | 11 | date = row['date'].strftime('%B %Y') 12 | new_pledges = row['the_pledge'] 13 | total_pledges = row['the_pledge_total'] 14 | 15 | result = '' 16 | result += f"GWWC Pledges" 17 | result += f"
{date}" 18 | result += f"
{new_pledges:,} new pledges" 19 | result += f"
{total_pledges:,} total pledges" 20 | 21 | return result 22 | 23 | 24 | def get_try_giving_hover(row): 25 | 26 | date = row['date'].strftime('%B %Y') 27 | new_pledges = row['try_giving'] 28 | total_pledges = row['try_giving_total'] 29 | 30 | result = '' 31 | result += f"Trial Pledges" 32 | result += f"
{date}" 33 | result += f"
{new_pledges:,} new pledges" 34 | result += f"
{total_pledges:,} total pledges" 35 | 36 | return result 37 | 38 | 39 | def get_new_pledges_long(new_pledges): 40 | 41 | new_pledges_long = new_pledges.loc[:, ['date']] 42 | new_pledges_long['value'] = new_pledges['the_pledge'] 43 | new_pledges_long['hover'] = new_pledges['the_pledge_hover'] 44 | 45 | label = new_pledges_long['value'].tolist()[-1] 46 | last_date = new_pledges_long['date'].tolist()[-1] 47 | last_year = last_date.strftime('%Y') 48 | last_month = last_date.strftime('%B') 49 | new_pledges_long['label'] = f'{label:,} New
{last_month}
{last_year}' 50 | 51 | return new_pledges_long 52 | 53 | def get_new_trial_pledges_long(new_pledges): 54 | 55 | new_try_giving_long = new_pledges.loc[:, ['date']] 56 | new_try_giving_long['value'] = new_pledges['try_giving'] 57 | new_try_giving_long['hover'] = new_pledges['try_giving_hover'] 58 | 59 | label = new_try_giving_long['value'].tolist()[-1] 60 | last_date = new_try_giving_long['date'].tolist()[-1] 61 | last_year = last_date.strftime('%Y') 62 | last_month = last_date.strftime('%B') 63 | new_try_giving_long['label'] = f'{label:,} New
{last_month}
{last_year}' 64 | 65 | return new_try_giving_long 66 | 67 | 68 | 69 | def get_total_pledges_long(new_pledges): 70 | 71 | total_pledges_long = new_pledges.loc[:, ['date']] 72 | total_pledges_long['value'] = new_pledges['the_pledge_total'] 73 | total_pledges_long['hover'] = new_pledges['the_pledge_hover'] 74 | 75 | label = total_pledges_long['value'].tolist()[-1] 76 | #last_date = int(total_pledges_long['date'].tolist()[-1]) 77 | total_pledges_long['label'] = f'{label:,}
Giving Pledges' 78 | 79 | total_try_giving_long = new_pledges.loc[:, ['date']] 80 | total_try_giving_long['value'] = new_pledges['try_giving_total'] 81 | total_try_giving_long['hover'] = new_pledges['try_giving_hover'] 82 | 83 | label = total_try_giving_long['value'].tolist()[-1] 84 | #last_date = int(total_try_giving_long['date'].tolist()[-1]) 85 | total_try_giving_long['label'] = f'{label:,}
Trial Pledges' 86 | 87 | return pd.concat([total_pledges_long, total_try_giving_long], ignore_index=True) 88 | 89 | 90 | def get_gwwc_pledges_section(): 91 | 92 | new_pledges = pd.read_json('./assets/data/gwwc/new_pledges.json') 93 | #new_pledges = get_new_pledges() 94 | 95 | new_pledges['date'] = pd.to_datetime(new_pledges['pledge_month']) 96 | 97 | new_pledges['the_pledge_total'] = new_pledges['the_pledge'].cumsum() 98 | new_pledges['try_giving_total'] = new_pledges['try_giving'].cumsum() 99 | 100 | new_pledges['the_pledge_hover'] = new_pledges.apply(get_the_pledge_hover, axis=1) 101 | new_pledges['try_giving_hover'] = new_pledges.apply(get_try_giving_hover, axis=1) 102 | 103 | new_pledges_long = get_new_pledges_long(new_pledges) 104 | new_trial_pledges_long = get_new_trial_pledges_long(new_pledges) 105 | total_pledges_long = get_total_pledges_long(new_pledges) 106 | 107 | new_pledges_graph = Line( 108 | new_pledges_long, 109 | x='date', 110 | y='value', 111 | title='New Giving Pledges by Month', 112 | x_title='', 113 | y_title='', 114 | xanchor='left', 115 | yanchor='middle', 116 | ) 117 | 118 | new_trial_pledges_graph = Line( 119 | new_trial_pledges_long, 120 | x='date', 121 | y='value', 122 | title='New Trial Pledges by Month', 123 | x_title='', 124 | y_title='', 125 | xanchor='left', 126 | yanchor='middle', 127 | ) 128 | 129 | total_pledges_graph = Line( 130 | total_pledges_long, 131 | x='date', 132 | y='value', 133 | title='Total Pledges', 134 | x_title='', 135 | y_title='', 136 | xanchor='center', 137 | ) 138 | 139 | return html.Div( 140 | [ 141 | html.Div( 142 | html.H2('Giving What We Can Pledges'), 143 | className='section-heading', 144 | ), 145 | get_instructions(hover='points', zoom=True), 146 | html.Div( 147 | [ 148 | new_pledges_graph, 149 | new_trial_pledges_graph, 150 | total_pledges_graph 151 | ], 152 | className='grid desk-cols-3 section-body' 153 | ), 154 | get_data_source('gwwc_pledges'), 155 | ], 156 | className = 'section', 157 | id='gwwc-pledge-section', 158 | ) 159 | -------------------------------------------------------------------------------- /utils/get_data/data_scraping.py: -------------------------------------------------------------------------------- 1 | from bs4 import BeautifulSoup 2 | import re 3 | import requests 4 | import pandas as pd 5 | from datetime import datetime 6 | import json 7 | 8 | def url_to_soup(url): 9 | page = requests.get(url, headers={'User-Agent': ''}) 10 | return BeautifulSoup(page.content, features="html.parser") 11 | 12 | 13 | def download_ea_funds_grants(): 14 | # The data can be seen at 'https://app.effectivealtruism.org/funds/{}/payouts'. 15 | # But the above page isn't static so can't be scraped. 16 | 17 | # Found this by poking around in developer tools for 20 minutes: 18 | data_url = 'https://cdn.contentful.com/spaces/afdyh2iav3iy/entries?access_token=630f127009ba9d044dd156ae8a6b9c5b26c66c054508f720e8b0dbbfa165d4e5&content_type=payoutReport&include=2&order=-fields.date&fields.fund.sys.contentType.sys.id=fund&fields.fund.fields.slug={}' 19 | 20 | fund_names = [ 21 | 'global-development', 22 | 'animal-welfare', 23 | 'far-future', 24 | 'ea-community', 25 | ] 26 | 27 | # Store all grants in a dataframe 28 | grants = pd.DataFrame(columns=[ 29 | 'fund', 30 | 'amount', 31 | 'date', 32 | 'title', 33 | ]) 34 | 35 | for fund_name in fund_names: 36 | 37 | # Retrieve json data 38 | fund_url = data_url.format(fund_name) 39 | fund_response = requests.get(fund_url) 40 | fund_data = json.loads(fund_response.content) 41 | 42 | # Parse each grant 43 | for grant in fund_data['items']: 44 | 45 | fields = grant['fields'] 46 | title = fields['title'] 47 | amount = fields['amount'] 48 | 49 | # date format is 2020-03-27 50 | date = datetime.strptime(fields['date'], '%Y-%m-%d') 51 | 52 | # Add to dataframe 53 | grants.loc[len(grants), :] = [ 54 | fund_name, 55 | amount, 56 | date, 57 | title, 58 | ] 59 | 60 | # # There's also recipients data which I can't parse 61 | # recipients = fields['recipients'] 62 | # for recipient in recipients: 63 | # recipient_id = recipient['sys']['id'] # I don't know what to do with this 64 | 65 | return grants 66 | 67 | def download_ea_funds_balances(): 68 | 69 | fund_names = [ 70 | 'global-development', 71 | 'animal-welfare', 72 | 'far-future', 73 | 'ea-community', 74 | ] 75 | 76 | body_left = "{\"operationName\":\"getXeroBalanceSheetByOrganization\",\"variables\":{\"reference\":\"" 77 | body_right = "\",\"nearestReportDate\":\"2020-07-28T05:59:22.337Z\"},\"query\":\"query getXeroBalanceSheetByOrganization($reference: String!, $nearestReportDate: Date) {\\n XeroBalanceSheet: getXeroBalanceSheetMonthlyTotalByReference(reference: $reference, nearestReportDate: $nearestReportDate) {\\n edges {\\n node {\\n reportDate\\n reference\\n amount\\n __typename\\n }\\n __typename\\n }\\n __typename\\n }\\n}\\n\"}" 78 | 79 | balances = pd.DataFrame(columns=[ 80 | 'fund', 81 | 'amount', 82 | 'as of', 83 | ]) 84 | 85 | for fund_name in fund_names: 86 | 87 | # send HTTP request 88 | body = body_left + fund_name + body_right 89 | response = requests.post( 90 | "https://parfit.effectivealtruism.org/graphql", 91 | headers = { 92 | "accept": "*/*", 93 | "accept-language": "en-GB,en;q=0.9,de;q=0.8,fr;q=0.7,ru;q=0.6", 94 | "content-type": "application/json", 95 | "sec-fetch-dest": "empty", 96 | "sec-fetch-mode": "cors", 97 | "sec-fetch-site": "same-site" 98 | }, 99 | data = body 100 | ) 101 | 102 | # parse request 103 | content = json.loads(response.content) 104 | node = content['data']['XeroBalanceSheet']['edges'][0]['node'] 105 | balance = node['amount'] 106 | as_of = node['reportDate'] 107 | 108 | # add to balances dataframe 109 | balances.loc[len(balances), :] = [ 110 | fund_name, 111 | balance, 112 | as_of, 113 | ] 114 | 115 | return balances 116 | 117 | def scrape_founders_pledge(): 118 | fp_url = 'https://founderspledge.com/' 119 | soup = url_to_soup(fp_url) 120 | 121 | # Scrape total pledged 122 | with open('text', 'w') as f: 123 | f.write(str(soup)) 124 | pledge_str = soup.select('div.resource--stat--total-value-pledged')[0].get_text() 125 | pledge_pattern = r'\$(\d.\d\d) billion' 126 | pledge_match = re.findall(pledge_pattern, pledge_str)[0] 127 | total_pledged = round( float(pledge_match) * 10**9 ) 128 | 129 | # Scrape total committed 130 | committed_str = soup.select('div.resource--stat--fulfilled-commitments')[0].get_text() 131 | committed_pattern = r'\$(\d+) million' 132 | committed_match = re.findall(committed_pattern, committed_str)[0] 133 | total_committed = round( float(pledge_match) * 10**6 ) 134 | 135 | # Scrape number members 136 | members = soup.select('div.resource--stat--in-30-countries')[0].get_text() 137 | members_pattern = r'(\d+) members' 138 | countries_pattern = 'In (\d+) Countries' 139 | members_match = re.findall(members_pattern, members)[0] 140 | countries_match = re.findall(countries_pattern, members)[0] 141 | n_members, n_countries = int(members_match), int(countries_match) 142 | 143 | return { 144 | 'pleged': total_pledged, 145 | 'committed': total_committed, 146 | 'members': n_members, 147 | 'countries': n_countries 148 | } 149 | 150 | -------------------------------------------------------------------------------- /components/sections/growth.py: -------------------------------------------------------------------------------- 1 | import plotly.express as px 2 | import plotly.graph_objects as go 3 | import pandas as pd 4 | import numpy as np 5 | import string 6 | from dash import dcc 7 | from dash import html 8 | from utils.subtitle import get_subtitle 9 | from utils.plots.line import Line 10 | 11 | commiting = pd.read_csv('assets/data/is_ea_growing/is_ea_growing_commiting.csv') 12 | doing = pd.read_csv('assets/data/is_ea_growing/is_ea_growing_doing.csv') 13 | joining = pd.read_csv('assets/data/is_ea_growing/is_ea_growing_joining.csv') 14 | reading = pd.read_csv('assets/data/is_ea_growing/is_ea_growing_reading.csv') 15 | 16 | 17 | # "Founder's Pledge pledges" makes more sense in "doing" than in "commiting" 18 | doing = doing.append( commiting.loc[ commiting['Type of data']=='Founder’s Pledge pledges' ], ignore_index=True ) 19 | commiting = commiting.loc[ ~(commiting['Type of data']=='Founder’s Pledge pledges') ] 20 | 21 | 22 | 23 | # Clean tables 24 | growing_dfs = [commiting, doing, joining, reading] 25 | for df in growing_dfs: 26 | 27 | # Convert column names from 'Jan-Dec 2014' to '2014' 28 | df.columns = [ 29 | col.replace('Jan-Dec ', '') for col in df.columns 30 | ] 31 | 32 | # Replace 'Didn’t exist', 'No data', 'No data yet', 33 | df.replace('No data', np.nan, inplace=True) 34 | df.replace('No data yet', np.nan, inplace=True) 35 | df.replace('No survey', np.nan, inplace=True) 36 | df.replace('Didn’t exist', np.nan, inplace=True) 37 | 38 | # Get rid junk in strings 39 | def field_to_numeric(field): 40 | if type(field)!=str: 41 | return field 42 | field = field.replace('K', '*10**3') 43 | field = field.replace('M', '*10**6') 44 | valid_chars = '.*' + string.digits 45 | field = ''.join([ 46 | char for char in field if char in valid_chars 47 | ]) 48 | return eval(field) 49 | return field 50 | for col in df.columns: 51 | if col == 'Type of data': 52 | continue 53 | df[col] = df[col].apply(field_to_numeric) 54 | 55 | 56 | 57 | 58 | long_dfs = [] 59 | for df in [commiting, doing, joining, reading]: 60 | years = df.columns[1:] 61 | row_dfs = [] 62 | for row in range(len(df)): 63 | label = df.loc[row, 'Type of data'] 64 | values = df.loc[row, years] 65 | if df is reading: 66 | values = values 67 | else: 68 | values = np.nancumsum(values) 69 | row_df = pd.DataFrame({ 70 | 'year': years, 71 | 'value': values, 72 | }) 73 | row_df['label'] = label#[:30] 74 | row_dfs.append(row_df) 75 | long_df = pd.concat(row_dfs, ignore_index=True) 76 | long_df['year'] = pd.to_datetime(long_df['year'], format='%Y') 77 | 78 | long_dfs.append( long_df ) 79 | 80 | commiting, doing, joining, reading = long_dfs 81 | 82 | growing_figs = [] 83 | for table, table_name in zip( 84 | [ 85 | reading, 86 | joining, 87 | commiting, 88 | doing, 89 | ], 90 | [ 91 | '','','','' 92 | #'low engaged', 93 | #'medium engaged', 94 | #'highly engaged', 95 | #'money', 96 | ] 97 | ): 98 | 99 | def hover(row): 100 | label =row['label'] 101 | value = row['value'] 102 | year = row['year'].year 103 | return f'{label}
{value:,.0f}
{year}' 104 | 105 | table['hover'] = table.apply(hover, axis=1).tolist() 106 | 107 | ignored_labels = [ 108 | 'EA FB “Active Users”', 109 | 'Vox Future Perfect Newsletter sign-ups', 110 | 111 | 'New EA Reddit subscribers', 112 | 'EA FB membership', 113 | 114 | 'Number of 80,000 Hours significant plan changes (not impact adjusted)', 115 | 'Number of 80,000 Hours significant plan changes (impact adjusted)', 116 | 'ACE money moved[x]', 117 | 'TLYCS money moved', 118 | 'Total OpenPhil non-GiveWell donations', 119 | 'Total non-OpenPhil donors to GiveWell', 120 | '# donors in EA Survey', 121 | #'OpenPhil GiveWell donations', 122 | #'Non-OpenPhil GiveWell donations', 123 | 'Total recorded money actually donated (not pledges) from Giving What We Can members', 124 | #'# donors in EA Survey', 125 | #'Founder’s Pledge pledges', 126 | 'EA Funds payouts[y]', 127 | 128 | 'Google interest in “effective altruism” (relative scoring)', 129 | ] 130 | 131 | table = table.loc[ ~table['label'].isin(ignored_labels) ] 132 | 133 | growing_figs.append( 134 | html.Div( 135 | Line( 136 | table, 137 | x='year', 138 | y='value', 139 | label='label', 140 | title='', 141 | x_title='', 142 | y_title='', 143 | size=None, 144 | color=None, 145 | hover='hover', 146 | log_y=False, 147 | ) 148 | ) 149 | ) 150 | 151 | def growth1(): 152 | return html.Div( 153 | [ 154 | html.Div( 155 | html.H2('Growth in EA Reading'), 156 | className='section-heading', 157 | ), 158 | get_subtitle('growth', hover='points', zoom=True), 159 | html.Div( 160 | growing_figs[0], 161 | className = 'section-body', 162 | ), 163 | ], 164 | className = 'section', 165 | id='growth-reading', 166 | ) 167 | 168 | def growth2(): 169 | return html.Div( 170 | [ 171 | html.Div( 172 | html.H2('Growth in EA Joining'), 173 | className='section-heading', 174 | ), 175 | get_subtitle('growth', hover='points', zoom=True), 176 | html.Div( 177 | growing_figs[1], 178 | className = 'section-body', 179 | ), 180 | ], 181 | className = 'section', 182 | id='growth-joining', 183 | ) 184 | 185 | def growth3(): 186 | return html.Div( 187 | [ 188 | html.Div( 189 | html.H2('Growth in EA Committing'), 190 | className='section-heading', 191 | ), 192 | get_subtitle('growth', hover='points', zoom=True), 193 | html.Div( 194 | growing_figs[2], 195 | className = 'section-body', 196 | ), 197 | ], 198 | className = 'section', 199 | id='growth-committing', 200 | ) 201 | 202 | def growth4(): 203 | return html.Div( 204 | [ 205 | html.Div( 206 | html.H2('Growth in EA Donating'), 207 | className='section-heading', 208 | ), 209 | get_subtitle('growth', hover='points', zoom=True), 210 | html.Div( 211 | growing_figs[3], 212 | className = 'section-body', 213 | ), 214 | ], 215 | className = 'section', 216 | id='growth-donating', 217 | ) 218 | 219 | -------------------------------------------------------------------------------- /components/sections/demographics.py: -------------------------------------------------------------------------------- 1 | import dash 2 | from dash import dcc 3 | from dash import html 4 | import plotly.graph_objects as go 5 | import plotly.express as px 6 | import pandas as pd 7 | import re 8 | from glob import glob 9 | from utils.plots.bar import Bar 10 | from utils.subtitle import get_data_source 11 | from utils.subtitle import get_instructions 12 | 13 | def get_demo_table(demo_name): 14 | 15 | path = f"./assets/data/rp_survey_data_2019/{demo_name}.csv" 16 | demo_table = pd.read_csv(path, sep='\t') 17 | title = demo_table.columns[0] 18 | 19 | # remove the 'Total' row 20 | demo_table = demo_table[ ~demo_table[title].isin(['Total', 'Total respondents']) ] 21 | # convert '5%' to 5 22 | demo_table['Percent'] = demo_table['Percent'].apply(lambda x: float(x[:-1])) 23 | 24 | 25 | # Substitute the long labels for something shorter 26 | subs = { 27 | 'Eat meat, but try to reduce the amount ': 'Reducetarian', 28 | 'Other (please specify)': 'Other', 29 | 30 | 'Native Hawaiian or Other Pacific Islander': 'Hawaiian or Pacific Islander', 31 | 'American Indian or Alaskan Native': 'Native American/Alaskan', 32 | 'Hispanic, Latino or Spanish Origin': 'Hispanic or Latino', 33 | 34 | 'Professional or vocational qualification': 'Professional or Vocational', 35 | 36 | 'Not employed, but looking for work': 'Not employed, looking for work', 37 | 'Not employed, but not looking for work': 'Not employed, not looking for work', 38 | 39 | 'Work at a non-profit (not an EA organization)': 'Non-profit (not EA org)', 40 | 'Work at a non-profit (EA organization)': 'Non-profit (EA org)', 41 | 42 | 43 | 'Consequentialism (utilitarian)': 'Utilitarianism', 44 | 'Consequentualism (other than utilitarian)': 'Other Consequentialism', 45 | } 46 | demo_table['label_original'] = demo_table[title] 47 | demo_table['label'] = demo_table[title].map(subs).fillna(demo_table[title]) 48 | 49 | demo_table = demo_table.iloc[::-1] 50 | if title == 'Moral View': 51 | demo_table = demo_table.loc[[ 4, 2, 3, 1, 0, ], :] 52 | elif title == 'Race/Ethnicity': 53 | # Move "other" to the bottom 54 | inds = list(range(6, -1, -1)) 55 | inds.insert(0, inds.pop(3)) 56 | demo_table = demo_table.loc[inds, :] 57 | elif title == 'Education': 58 | # Move "other" to the bottom 59 | inds = list(range(4, -1, -1)) 60 | inds.insert(0, inds.pop(4)) 61 | demo_table = demo_table.loc[inds, :] 62 | elif title == 'Diet ': 63 | # Move "other" to the bottom 64 | inds = list(range(6)) 65 | inds.insert(0, inds.pop(4)) 66 | demo_table = demo_table.loc[inds, :] 67 | 68 | demo_table['x'] = demo_table['label'] 69 | demo_table['y'] = demo_table['Percent'] 70 | demo_table['text'] = demo_table['Percent'].apply(lambda x: f'{x:.1f}%') 71 | 72 | def hover(row): 73 | label = row['label_original'] 74 | responses = row['Responses'] 75 | percent = row['Percent'] 76 | return f'{label}
{responses} responses ({percent}%)' 77 | 78 | demo_table['hover'] = demo_table.apply(hover, axis=1) 79 | 80 | return demo_table 81 | 82 | def get_bar_chart(demo_name): 83 | demo_table = get_demo_table(demo_name) 84 | title = demo_table.columns[0] 85 | return Bar(demo_table, title=title) 86 | 87 | 88 | def demographics_section(): 89 | return html.Div( 90 | [ 91 | html.Div( 92 | html.H2('EA Demographics'), 93 | className='section-title', 94 | ), 95 | get_instructions(), 96 | html.Div( 97 | [ 98 | html.Div( 99 | get_bar_chart('gender'), 100 | className='plot-container' 101 | ), 102 | html.Div( 103 | get_bar_chart('age_group'), 104 | className='plot-container' 105 | ), 106 | html.Div( 107 | get_bar_chart('ethnicity'), 108 | className='plot-container tab-span-2-cols' 109 | ), 110 | ], 111 | className = 'grid tab-cols-2 desk-cols-3 section-body', 112 | ), 113 | get_data_source('rethink19'), 114 | ], 115 | className = 'section', 116 | id='demographics', 117 | ) 118 | 119 | 120 | def beliefs_section(): 121 | return html.Div( 122 | [ 123 | html.Div( 124 | html.H2('EA Beliefs and Lifestyle'), 125 | className='section-title', 126 | ), 127 | get_instructions(), 128 | html.Div( 129 | [ 130 | html.Div( 131 | get_bar_chart('political_belief'), 132 | className='plot-container' 133 | ), 134 | html.Div( 135 | get_bar_chart('diet'), 136 | className='plot-container' 137 | ), 138 | html.Div( 139 | get_bar_chart('moral_view'), 140 | className='plot-container tab-span-2-cols' 141 | ), 142 | ], 143 | className = 'grid tab-cols-2 desk-cols-3 section-body', 144 | ), 145 | get_data_source('rethink19'), 146 | ], 147 | className = 'section', 148 | id='beliefs-lifestyle', 149 | ) 150 | 151 | def education_section(): 152 | return html.Div( 153 | [ 154 | html.Div( 155 | html.H2('EA Education'), 156 | className='section-title', 157 | ), 158 | get_instructions(), 159 | html.Div( 160 | [ 161 | html.Div( 162 | get_bar_chart('education2'), 163 | className='plot-container' 164 | ), 165 | html.Div( 166 | get_bar_chart('subject'), 167 | className='plot-container' 168 | ), 169 | ], 170 | className = 'grid tab-cols-2 desk-cols-2 section-body', 171 | ), 172 | get_data_source('rethink19'), 173 | ], 174 | className = 'section', 175 | id='education', 176 | ) 177 | 178 | def career_section(): 179 | return html.Div( 180 | [ 181 | html.Div( 182 | html.H2('EA Careers'), 183 | className='section-title', 184 | ), 185 | get_instructions(), 186 | html.Div( 187 | [ 188 | html.Div( 189 | get_bar_chart('career_path'), 190 | className='plot-container' 191 | ), 192 | html.Div( 193 | get_bar_chart('employment'), 194 | className='plot-container' 195 | ), 196 | ], 197 | className = 'grid tab-cols-2 desk-cols-2 section-body', 198 | ), 199 | get_data_source('rethink19'), 200 | ], 201 | className = 'section', 202 | id='careers', 203 | ) 204 | -------------------------------------------------------------------------------- /components/sections/geography.py: -------------------------------------------------------------------------------- 1 | from dash import dcc 2 | from dash import html 3 | import plotly.graph_objects as go 4 | import plotly.express as px 5 | import pandas as pd 6 | from utils.plots.bar import Bar 7 | from countryinfo import CountryInfo 8 | from math import log 9 | from utils.subtitle import get_data_source 10 | from utils.subtitle import get_instructions 11 | 12 | ################################## 13 | ### WORLD MAP ### 14 | ################################## 15 | 16 | # source: https://plotly.com/python/bubble-maps/ 17 | 18 | country = CountryInfo() 19 | country_list = country.all().keys() 20 | 21 | countries = pd.read_csv('./assets/data/rp_survey_data_2019/country2.csv') 22 | 23 | countries['Responses'] = countries['Responses'].astype('int') 24 | 25 | countries.loc[countries['Country']=='United States of America', 'Country'] = 'United States' 26 | 27 | MINIMUM_CIRCLE_SIZE = 0 # 20 28 | countries['circle size'] = countries['Responses'] + MINIMUM_CIRCLE_SIZE 29 | 30 | countries = countries.sort_values('Responses', ascending=True) 31 | 32 | def get_population(country): 33 | try: 34 | return CountryInfo(country).population() 35 | except: 36 | return 1e9 37 | 38 | countries['population'] = countries['Country'].apply(get_population) 39 | countries['Density (per million)'] = countries['Responses'] / countries['population'] * 1e6 40 | countries['Density (per million)'] = countries['Density (per million)'].apply(lambda x: round(x, 2)) 41 | countries['log density'] = countries['Density (per million)'].apply(lambda x: 1 + log(x+1)) 42 | MINIMUM_CIRCLE_SIZE = 15 43 | countries['circle size'] = countries['Responses'] + MINIMUM_CIRCLE_SIZE 44 | 45 | def hover(row): 46 | country = row['Country'] 47 | responses = row['Responses'] 48 | density = row['Density (per million)'] 49 | return f'{country}
{responses:,.0f} survey responses
{density:.2f} per million people' 50 | countries['hover'] = countries.apply(hover, axis=1) 51 | 52 | countries_for_map = countries.copy() 53 | countries_for_map.loc[len(countries_for_map), ['Country', 'Responses', 'Density (per million)', 'log density']] = ('Antarctica', 0, 0, 0) 54 | 55 | countries_for_map['hover'] = countries_for_map.apply(hover, axis=1) 56 | 57 | # Population map 58 | 59 | pop_map = px.scatter_geo( 60 | countries, 61 | locations="Country", 62 | hover_name="Country", 63 | locationmode='country names', 64 | size="circle size", 65 | title="Number of EAs by Country", 66 | hover_data = { 67 | 'circle size': False, 68 | 'Responses': True, 69 | 'Country': False, 70 | 'log density': False, 71 | 'Density (per million)': True, 72 | }, 73 | projection="equirectangular", # 'orthographic' is fun 74 | ) 75 | 76 | pop_map.update_layout( 77 | margin=dict(l=0, r=0, t=80, b=0), 78 | title_x=0.5, 79 | ) 80 | 81 | pop_map.update_traces( 82 | marker = dict( 83 | color ="#36859A", 84 | ), 85 | hovertext = countries['hover'], 86 | hovertemplate = '%{hovertext}', 87 | ) 88 | 89 | pop_map.update_geos( 90 | showcoastlines=False, 91 | landcolor="#dfe3ee", 92 | ) 93 | 94 | # Density map 95 | 96 | density_map = px.choropleth( 97 | countries_for_map, 98 | locations="Country", 99 | hover_name="Country", 100 | locationmode='country names', 101 | color='log density', 102 | title="EAs Per Capita (Darker/Greener is Higher)", 103 | color_continuous_scale=["#dfe3ee", "#007a8f"], 104 | hover_data = { 105 | 'circle size': False, 106 | 'Responses': True, 107 | 'Country': False, 108 | 'log density': False, 109 | 'Density (per million)': True, 110 | }, 111 | projection="equirectangular", # 'orthographic' is fun. "natural earth" is quite nice 112 | ) 113 | 114 | density_map.update_layout( 115 | margin=dict(l=0, r=0, t=80, b=0), 116 | coloraxis_showscale=False, 117 | title_x=0.5, 118 | ) 119 | 120 | density_map.update_traces( 121 | hovertext = countries_for_map['hover'], 122 | hovertemplate = '%{hovertext}', 123 | marker_line_width=0, 124 | ) 125 | 126 | density_map.update_geos( 127 | showcoastlines=False, 128 | landcolor="#dfe3ee", 129 | ) 130 | 131 | countries['x'] = countries['Country'] 132 | countries['text'] = countries['Responses'].apply(lambda x: f'{x:}') 133 | countries['y'] = countries['Responses'] 134 | countries_truncated = countries.iloc[len(countries)*2//3:] 135 | 136 | countries_bar = Bar( 137 | countries_truncated, 138 | title = f'Countries with Most EAs', 139 | ) 140 | 141 | countries_capita_sort = countries.sort_values(by='Density (per million)') 142 | countries_capita_sort['x'] = countries_capita_sort['Country'] 143 | countries_capita_sort['y'] = countries_capita_sort['Density (per million)'] 144 | countries_capita_sort['text'] = countries_capita_sort['Density (per million)'].apply(lambda x: f'{x:.1f}') 145 | countries_capita_sort_truncated = countries_capita_sort.iloc[len(countries)*2//3:] 146 | 147 | per_capita_bar = Bar( 148 | countries_capita_sort_truncated, 149 | title = f'Top EAs per Capita (×1M)', 150 | ) 151 | 152 | def country_total_section(): 153 | return html.Div( 154 | [ 155 | html.Div( 156 | html.H2('EAs by Country'), 157 | className='section-heading', 158 | ), 159 | get_instructions(hover='countries or bars', extra_text='Scroll to zoom on map.'), 160 | html.Div( 161 | html.Div( 162 | [ 163 | html.Div( 164 | dcc.Graph( 165 | id='pop_map', 166 | figure=pop_map, 167 | responsive=True, 168 | ), 169 | className='plot-container' 170 | ), 171 | html.Div( 172 | countries_bar, 173 | className='plot-container', 174 | ), 175 | ], 176 | className='grid desk-cols-2-1', 177 | ), 178 | className='section-body', 179 | ), 180 | get_data_source('rethink19-geo'), 181 | ], 182 | className = 'section', 183 | id='countries', 184 | ) 185 | 186 | def country_per_capita_section(): 187 | return html.Div( 188 | [ 189 | html.Div( 190 | html.H2('EAs per Capita by Country'), 191 | className='section-heading', 192 | ), 193 | get_instructions(hover='countries or bars', extra_text='Scroll to zoom on map.'), 194 | html.Div( 195 | html.Div( 196 | [ 197 | html.Div( 198 | dcc.Graph( 199 | id='density_map', 200 | figure=density_map, 201 | responsive=True, 202 | ), 203 | className='plot-container' 204 | ), 205 | html.Div( 206 | per_capita_bar, 207 | className='plot-container' 208 | ), 209 | ], 210 | className='grid desk-cols-2-1', 211 | ), 212 | className='section-body', 213 | ), 214 | get_data_source('rethink19-geo'), 215 | ], 216 | className = 'section', 217 | id='countries-per-capita', 218 | ) 219 | -------------------------------------------------------------------------------- /components/sections/donations_sankey.py: -------------------------------------------------------------------------------- 1 | import dash 2 | from dash import dcc 3 | from dash import html 4 | import plotly.graph_objects as go 5 | from plotly.subplots import make_subplots 6 | import plotly.express as px 7 | import pandas as pd 8 | import re 9 | from glob import glob 10 | import os 11 | from utils.subtitle import get_data_source 12 | from utils.subtitle import get_instructions 13 | 14 | def get_op_grants(): 15 | op_grants = pd.read_csv('./assets/data/openphil_grants.csv') 16 | # op_grants = pd.read_csv('https://www.openphilanthropy.org/giving/grants/spreadsheet') 17 | 18 | # Standardize cause area names 19 | # standard names from https://80000hours.org/topic/causes/ 20 | subs = { 21 | 'Potential Risks from Advanced Artificial Intelligence': 'AI', 22 | 'History of Philanthropy': 'Other cause area', 23 | 'Immigration Policy': 'Policy', 24 | 'Macroeconomic Stabilization Policy': 'Policy', 25 | 'Land Use Reform': 'Policy', 26 | 'Criminal Justice Reform': 'Policy', 27 | 'U.S. Policy': 'Policy', 28 | 'Other areas': 'Other cause area', 29 | 'Biosecurity and Pandemic Preparedness': 'Biosecurity', 30 | 'Farm Animal Welfare': 'Animal Welfare', 31 | 'Global Catastrophic Risks': 'Catastrophic Risks', 32 | 'Global Health & Development': 'Global Poverty', 33 | } 34 | op_grants['Cause Area'] = op_grants['Focus Area'].map(subs).fillna(op_grants['Focus Area']) 35 | 36 | subs = { 37 | # 'Johns Hopkins Center for Health Security': 'JHCHS', 38 | # 'Against Malaria Foundation': 'AMF', 39 | # 'Georgetown University': 'GU', 40 | } 41 | op_grants['Organization'] = op_grants['Organization Name'].map(subs).fillna(op_grants['Organization Name']) 42 | 43 | # Standardise Column Names 44 | op_grants = op_grants[['Organization', 'Cause Area', 'Amount']] 45 | op_grants['Source'] = 'Open Philanthropy' 46 | 47 | def parse_funding_amount(amount): 48 | if type(amount)==str: 49 | return int(amount[1:].replace(',', '')) 50 | else: 51 | return 0 52 | op_grants['Amount'] = op_grants['Amount'].apply(parse_funding_amount).astype('int') 53 | 54 | return op_grants 55 | 56 | 57 | def get_gwwc_and_founders_pledge(): 58 | return pd.read_csv('assets/data/misc.csv') 59 | 60 | 61 | def get_ea_funds(): 62 | ea_funds = pd.read_csv('./assets/data/ea_funds_grants.csv') 63 | 64 | subs = { 65 | 'global-development': 'Global Poverty', 66 | 'far-future': 'Far Future', 67 | 'ea-community': 'EA Community', 68 | 'animal-welfare': 'Animal Welfare' 69 | } 70 | ea_funds['fund'] = ea_funds['fund'].map(subs).fillna(ea_funds['fund']) 71 | 72 | ea_funds['Source'] = 'EA Funds' 73 | ea_funds['Cause Area'] = ea_funds['fund'] 74 | ea_funds['Organization'] = 'Unknowns' 75 | ea_funds['Amount'] = ea_funds['amount'] 76 | ea_funds = ea_funds[['Source', 'Cause Area', 'Organization', 'Amount']] 77 | 78 | return ea_funds 79 | 80 | 81 | def get_funding_long(): 82 | 83 | funding = pd.concat( 84 | [ 85 | get_op_grants(), 86 | get_gwwc_and_founders_pledge(), 87 | get_ea_funds(), 88 | ] 89 | ) 90 | 91 | ''' 92 | Transform table from 93 | 'OpenPhil', 'Global Poverty', 'AMF', 100 94 | 'OpenPhil', 'Global Poverty', 'SCI', 80 95 | to 96 | 'OpenPhil', 'Global Poverty', 180, 'OpenPhil' 97 | 'Global Poverty', 'AMF', 100, 'OpenPhil' 98 | 'Global Poverty', 'SCI', 80, 'OpenPhil' 99 | That is, sum the contributions of each entity to each other entity. 100 | Each row represents a connection between two entities. 101 | The last column will be used for coloring the connections. 102 | ''' 103 | 104 | funding['Amount'] = funding['Amount'] / 1e6 105 | 106 | funding_long = pd.DataFrame(columns=['From', 'To', 'Amount', 'Source']) 107 | 108 | for source, cause in set( 109 | zip( 110 | funding['Source'], 111 | funding['Cause Area'] 112 | ) 113 | ): 114 | 115 | source_cause_df = funding[ 116 | (funding['Source']==source) & (funding['Cause Area']==cause) 117 | ] 118 | 119 | total_funding = source_cause_df['Amount'].sum() 120 | funding_long.loc[len(funding_long)] = [ 121 | source, 122 | cause, 123 | total_funding, 124 | source 125 | ] 126 | 127 | other_total = 0 128 | for org in source_cause_df['Organization'].unique(): 129 | org_df = source_cause_df[source_cause_df['Organization']==org] 130 | total_funding = org_df['Amount'].sum() 131 | 132 | if total_funding < 2*10**1: 133 | # if total_funding < 2*10**7: 134 | other_total += total_funding 135 | continue 136 | 137 | funding_long.loc[len(funding_long)] = [ 138 | cause, 139 | org, 140 | total_funding, 141 | source 142 | ] 143 | 144 | if other_total > 0: 145 | funding_long.loc[len(funding_long)] = [ 146 | cause, 147 | 'Other orgs', 148 | other_total, 149 | source 150 | ] 151 | 152 | funding_long = funding_long[funding_long['To']!='Unknowns'] 153 | 154 | return funding_long 155 | 156 | def funding_fig(): 157 | 158 | funding_long = get_funding_long() 159 | 160 | # Get a list of all funding-related entities 161 | entities = set() 162 | for col in ['From', 'To']: 163 | entities.update(funding_long[col]) 164 | entities = list(entities) 165 | 166 | # Convert financial inputs and outputs into indices 167 | entity2idx = {x: i for i,x in enumerate(entities)} 168 | froms = list(funding_long['From'].map(entity2idx)) 169 | tos = list(funding_long['To'].map(entity2idx)) 170 | 171 | entities += ["$100M (for scale)"] 172 | froms += [ len(entity2idx) ] 173 | tos += [ len(entity2idx) ] 174 | 175 | # values = funding_long['Amount'].to_list() + [1e8] 176 | values = funding_long['Amount'].to_list() + [1e2] 177 | 178 | # Create Sankey diagram 179 | fig = go.Figure( 180 | data=[go.Sankey( 181 | valueformat = ",.1f", 182 | valuesuffix = "M USD", 183 | node = dict( 184 | pad = 15, 185 | thickness = 20, 186 | line = dict(color = "#4196AA", width = 1), 187 | label = entities, 188 | color = "#4196AA" 189 | ), 190 | link = dict( 191 | source = froms, 192 | target = tos, 193 | color = "#C1E3EA", 194 | value = values 195 | ) 196 | )], 197 | # config={ 198 | # 'displayModeBar': False, 199 | # } 200 | ) 201 | fig.update_layout( 202 | margin=dict(l=10, r=10, t=30, b=10), 203 | ) 204 | 205 | return fig 206 | 207 | def donations_sankey_section(): 208 | 209 | return html.Div( 210 | [ 211 | html.Div( 212 | html.H2('Donations Overview'), 213 | className='section-title', 214 | ), 215 | get_instructions( 216 | hover='rectangles or lines', 217 | extra_text = 'Rectangles can be rearranged by dragging.', 218 | ), 219 | html.Div( 220 | html.Div( 221 | dcc.Graph( 222 | id='Donations', 223 | figure=funding_fig(), 224 | responsive=True, 225 | ), 226 | className = 'plot-container', 227 | ), 228 | className = 'section-body', 229 | ), 230 | get_data_source( 231 | [ 232 | 'open_phil', 233 | 'funds_payout', 234 | 'founders_pledge', 235 | 'gwwc', 236 | ], 237 | ), 238 | ], 239 | className = 'section', 240 | id='donations-sankey', 241 | ) 242 | -------------------------------------------------------------------------------- /assets/style.css: -------------------------------------------------------------------------------- 1 | @import url("https://fonts.googleapis.com/css2?family=Roboto+Slab:wght@400;700;900&display=swap"); 2 | @import url("https://fonts.googleapis.com/css2?family=Roboto+Mono:wght@400;700;900&display=swap"); 3 | @import url("https://fonts.googleapis.com/css2?family=Raleway:wght@400&display=swap"); 4 | 5 | /*------------------ 6 | GLOBAL 7 | ------------------*/ 8 | 9 | :root { 10 | --ea-color: #0e7a8e; 11 | --alt-ea-color: #46b6ca; 12 | --header-content-height: 2.5rem; 13 | --header-padding: 20px; 14 | --header-height: calc(var(--header-content-height) + 2*var(--header-padding)); 15 | --body-height: calc(100vh - var(--header-height)); 16 | --sidebar-width: 250px; 17 | } 18 | 19 | * { 20 | scroll-behavior: smooth; 21 | box-sizing: border-box; 22 | } 23 | 24 | body { 25 | font-family: "Raleway", Helvetica, sans-serif; 26 | margin: 0px; 27 | padding: 0px; 28 | overflow-x: hidden; 29 | } 30 | 31 | a { 32 | color: var(--ea-color); 33 | } 34 | 35 | a:hover { 36 | color: var(--alt-ea-color); 37 | } 38 | 39 | .center { 40 | display: grid; 41 | place-items: center; 42 | } 43 | 44 | div { 45 | background-color: white; 46 | } 47 | 48 | .modebar { 49 | background-color: rgba(0, 0, 0, 0); 50 | } 51 | 52 | p, h1, h2, h3, h4 { 53 | color: black; 54 | } 55 | 56 | 57 | .darkmode { 58 | background-color: black; 59 | filter: invert() hue-rotate(180deg) brightness(100%) contrast(105%); 60 | } 61 | 62 | ._dash-loading { 63 | text-align: center; 64 | font-weight: 700; 65 | font-size: 2em; 66 | font-family: "Roboto Slab", "Times New Roman", serif; 67 | display: table-cell; 68 | width: 100vw; 69 | height: 100vh; 70 | padding: 10px; 71 | vertical-align: middle; 72 | } 73 | 74 | @media only screen and (max-width: 700px) { 75 | ._dash-loading { 76 | height: 85vh; 77 | } 78 | } 79 | 80 | .darkmodeloading { 81 | display: none; 82 | } 83 | 84 | /*------------------ 85 | HEADER 86 | ------------------*/ 87 | 88 | .header { 89 | height: var(--header-height); 90 | display: flex; 91 | padding: var(--header-padding); 92 | } 93 | 94 | .clickable-icon { 95 | padding: 8px 8px; 96 | border-radius: 50%; 97 | } 98 | 99 | .clickable-icon:hover { 100 | background-color: rgba(0, 0, 0, 0.08); 101 | cursor: pointer; 102 | } 103 | 104 | /* the hover-y circle is too faint in night mode */ 105 | .nightmode .clickable-icon:hover { 106 | background-color: rgba(0, 0, 0, 0.2); 107 | } 108 | 109 | .right-icons { 110 | margin-left: auto; 111 | display: flex; 112 | } 113 | 114 | .header .icon { 115 | height: var(--header-content-height); 116 | width: var(--header-content-height); 117 | } 118 | 119 | .effective { 120 | font-family: "Roboto Slab", "Times New Roman", serif; 121 | font-weight: 700; 122 | } 123 | 124 | .altruism { 125 | font-family: "Roboto Slab", "Times New Roman", serif; 126 | font-weight: 400; 127 | } 128 | 129 | .header h1 { 130 | font-size: var(--header-content-height); 131 | margin: 0; 132 | padding-left: 10px; 133 | } 134 | 135 | 136 | .short-title { 137 | display: none; 138 | padding-left: 10px; 139 | } 140 | 141 | .long-title { 142 | display: block; 143 | } 144 | 145 | @media only screen and (max-width: 700px) { 146 | 147 | .short-title { 148 | display: block; 149 | } 150 | 151 | .long-title { 152 | display: none; 153 | } 154 | 155 | } 156 | 157 | @media only screen and (max-width: 363px) { 158 | 159 | .short-title { 160 | display: none; 161 | } 162 | 163 | .long-title { 164 | display: none; 165 | } 166 | 167 | } 168 | 169 | @media (prefers-color-scheme: dark) { 170 | #darkmode-button { 171 | content: url("/assets/sun.svg"); 172 | } 173 | } 174 | 175 | @media (prefers-color-scheme: light) { 176 | #darkmode-button { 177 | content: url("/assets/moon.svg"); 178 | } 179 | } 180 | 181 | .noprefer { 182 | content: unset !important; 183 | } 184 | 185 | /*------------------ 186 | BODY 187 | ------------------*/ 188 | 189 | .body { 190 | height: var(--body-height); 191 | display: flex; 192 | overflow-x: hidden; 193 | position: relative; 194 | } 195 | 196 | 197 | /*------------------ 198 | SIDEBAR 199 | ------------------*/ 200 | 201 | #sidebar { 202 | width: var(--sidebar-width); 203 | height: var(--body-height); 204 | overflow: scroll; 205 | padding: 30px; 206 | padding-right: 10px; 207 | z-index:100; 208 | position: absolute; 209 | background-color: white; 210 | -webkit-transition-duration: 600ms; 211 | transition-duration: 600ms; 212 | line-height: 1.7rem 213 | } 214 | 215 | #sidebar-buttress { 216 | width: var(--sidebar-width); 217 | height: var(--body-height); 218 | display: block; 219 | } 220 | 221 | #sidebar p { 222 | margin-block-start: 0; 223 | margin-block-end: 0; 224 | } 225 | 226 | #sidebar ul { 227 | margin-block-start: 0; 228 | margin-block-end: 0; 229 | } 230 | 231 | /* The sidebar is visible by default on desktop but hidden by default on mobile. */ 232 | 233 | @media only screen and (min-width: 800px) { 234 | 235 | #sidebar.toggled { 236 | transform: translate(-100%); 237 | } 238 | 239 | #sidebar-buttress.toggled { 240 | display: none; 241 | } 242 | 243 | } 244 | 245 | @media only screen and (max-width: 800px) { 246 | 247 | #sidebar { 248 | transform: translate(-100%); 249 | } 250 | 251 | #sidebar-buttress { 252 | display: none; 253 | } 254 | 255 | #sidebar.toggled { 256 | transform: translate(0%); 257 | } 258 | 259 | #sidebar-buttress.toggled { 260 | display: block; 261 | } 262 | 263 | } 264 | /*------------------ 265 | ABOUT BOX 266 | ------------------*/ 267 | 268 | /* reduce padding around "contents" */ 269 | #about-box h4 { 270 | margin-top: 0; 271 | margin-bottom: 10px; 272 | } 273 | 274 | #about-box { 275 | width: var(--sidebar-width); 276 | height: var(--body-height); 277 | right: 0px; 278 | overflow: scroll; 279 | padding: 30px; 280 | z-index: 99; 281 | background-color: white; 282 | position: absolute; 283 | -webkit-transition-duration: 600ms; 284 | transition-duration: 600ms; 285 | } 286 | 287 | #about-box.hidden { 288 | transform: translate(100%); 289 | } 290 | 291 | /*------------------ 292 | CONTENT 293 | ------------------*/ 294 | 295 | .content { 296 | height: var(--body-height); 297 | width: 100%; 298 | overflow: scroll; 299 | padding: 30px; 300 | } 301 | 302 | @media only screen and (max-width: 700px) { 303 | .content { 304 | position: absolute; 305 | left: 0; 306 | } 307 | } 308 | 309 | .section { 310 | height: var(--body-height); 311 | scroll-snap-align: start; 312 | margin: 0 auto; 313 | display: flex; 314 | flex-flow: column; 315 | } 316 | 317 | @media only screen and (max-width: 1000px) { 318 | .section { 319 | height: auto; 320 | } 321 | } 322 | 323 | @media screen and (min-width: 1000px) { 324 | .scroll-snapper { 325 | overflow-y: scroll; 326 | scroll-snap-type: y mandatory; 327 | height: var(--body-height); 328 | } 329 | } 330 | 331 | .section-body { 332 | flex: 1; 333 | padding: 20px; 334 | } 335 | 336 | .plot-container { 337 | height: 100%; 338 | width: 100%; 339 | max-height: 100vh; 340 | } 341 | 342 | @media only screen and (max-width: 1000px) { 343 | #gwwc-pledge-section, #gwwc-donations-section, #gwwc-orgs-section { 344 | min-height: 100%; 345 | margin-bottom: 12.5em; 346 | } 347 | } 348 | 349 | /*------------------ 350 | GRIDS 351 | ------------------*/ 352 | 353 | /* 354 | 355 | Notation: 356 | 357 | cols-x means "x equal size columns" 358 | 359 | mob-cols-x means "x equal size columns on mobile" 360 | 361 | tab-cols-x means "x equal size columns on tablet" 362 | 363 | desk-cols-x means "x equal size columns on desktop" 364 | 365 | cols-a-b means "two columns with a:b ratio" 366 | 367 | etc 368 | 369 | */ 370 | 371 | .grid { 372 | display: grid; 373 | grid-gap: 10px; 374 | height: 100%; 375 | width: 100%; 376 | /* 377 | min-height: 0; 378 | min-width: 0; 379 | */ 380 | } 381 | 382 | .cols-2-1 { grid-template-columns: 2fr 1fr; } 383 | .rows-2-1 { grid-template-rows: 2fr 1fr; } 384 | 385 | .cols-1 { grid-template-columns: repeat(1, 1fr); } 386 | .cols-2 { grid-template-columns: repeat(2, 1fr); } 387 | .cols-3 { grid-template-columns: repeat(3, 1fr); } 388 | 389 | .rows-1 { grid-template-rows: repeat(1, 1fr); } 390 | .rows-2 { grid-template-rows: repeat(2, 1fr); } 391 | .rows-3 { grid-template-rows: repeat(3, 1fr); } 392 | 393 | @media only screen and (max-width: 700px) { 394 | .mob-cols-1 { grid-template-columns: repeat(1, 1fr); } 395 | .mob-cols-2 { grid-template-columns: repeat(2, 1fr); } 396 | .mob-cols-3 { grid-template-columns: repeat(3, 1fr); } 397 | } 398 | 399 | @media only screen and (min-width: 700px) and (max-width: 1000px) { 400 | .tab-cols-1 { grid-template-columns: repeat(1, 1fr); } 401 | .tab-cols-2 { grid-template-columns: repeat(2, 1fr); } 402 | .tab-cols-3 { grid-template-columns: repeat(3, 1fr); } 403 | .tab-cols-2-1 { grid-template-columns: 2fr 1fr; } 404 | .tab-span-2-cols { grid-column: span 2; } 405 | } 406 | 407 | @media only screen and (min-width: 1000px) { 408 | .desk-rows-2-1 { grid-template-rows: 2fr 1fr; } 409 | .desk-rows-2 { grid-template-rows: repeat(2, 1fr); } 410 | .desk-cols-2-1 { grid-template-columns: 2fr 1fr; } 411 | .desk-cols-1 { grid-template-columns: repeat(1, 1fr); } 412 | .desk-cols-2 { grid-template-columns: repeat(2, 1fr); } 413 | .desk-cols-3 { grid-template-columns: repeat(3, 1fr); } 414 | .desk-span-2-cols { grid-column: span 2; } 415 | } 416 | 417 | 418 | -------------------------------------------------------------------------------- /components/sections/open_phil.py: -------------------------------------------------------------------------------- 1 | import plotly.express as px 2 | import pandas as pd 3 | from pandas.tseries.offsets import MonthEnd 4 | import numpy as np 5 | from dash import dcc 6 | from dash import html 7 | from dash import dash_table 8 | from utils.plots.bar import Bar 9 | from utils.subtitle import get_instructions 10 | from utils.subtitle import get_data_source 11 | from utils.plots.scatter import Scatter 12 | from utils.plots.line import Line 13 | 14 | op_grants = None 15 | def get_op_grants(): 16 | global op_grants 17 | if type(op_grants) != type(None): 18 | return op_grants 19 | 20 | op_grants = pd.read_csv('./assets/data/openphil_grants.csv') 21 | op_grants['Amount'] = op_grants['Amount'].apply( 22 | lambda x: int(x[1:].replace(',','')) if type(x)==str else x 23 | ) 24 | op_grants = op_grants.dropna() 25 | op_grants['Focus Area'] = op_grants['Focus Area'].apply(lambda x: x.replace('Artificial Intelligence', 'AI')) 26 | op_grants = op_grants[::-1] 27 | 28 | def normalize_orgname(orgname): 29 | if type(orgname) == str: 30 | orgname = orgname.strip() 31 | if orgname == 'Hellen Keller International': 32 | orgname = 'Helen Keller International' 33 | if orgname == 'Alliance for Safety and Justice': 34 | orgname = 'Alliance for Safety and Justice Action Fund' 35 | return orgname 36 | op_grants['Organization Name'] = op_grants['Organization Name'].apply(normalize_orgname) 37 | 38 | # for finding number of grants 39 | op_grants['grants'] = 1 40 | 41 | op_grants['Date'] = pd.to_datetime(op_grants['Date'], format='%m/%Y') 42 | op_grants = op_grants.sort_values(by='Date', ascending=False) 43 | op_grants['Date_readable'] = op_grants['Date'].dt.strftime('%B %Y') 44 | 45 | # hovertext 46 | def hover(row): 47 | grant = row['Grant'] 48 | org = row['Organization Name'] 49 | area = row['Focus Area'] 50 | date = row['Date_readable'] 51 | amount = row['Amount'] 52 | return f'{grant}
Date: {date}
Organization: {org}
Amount: ${amount:,.0f}' 53 | op_grants['hover'] = op_grants.apply(hover, axis=1) 54 | 55 | return op_grants 56 | 57 | 58 | def org_bar_chart(op_grants): 59 | 60 | op_orgs = op_grants.groupby(by='Organization Name', as_index=False).sum() 61 | op_orgs = op_orgs.sort_values(by='Amount') 62 | op_orgs['x'] = op_orgs['Organization Name'] 63 | # op_orgs['x'] = op_orgs['x'].apply(lambda x: x if len(x) < 30 else x[:27]+'...') 64 | op_orgs['y'] = op_orgs['Amount'] 65 | op_orgs['text'] = op_orgs['Amount'].apply(lambda x: f'${x:,.0f}') 66 | 67 | # Some organization names get truncated to the same value. 68 | # This prevents that: 69 | for val in op_orgs['x'].unique(): 70 | val_df = op_orgs[ op_orgs['x']==val ] 71 | for i in range(1, len(val_df)): 72 | index = val_df.iloc[i].name 73 | op_orgs.loc[index, 'x'] = op_orgs.loc[index, 'x'][:-3-i] + '...' 74 | 75 | def hover(row): 76 | org = row['Organization Name'] 77 | amount = row['text'] 78 | grants = row['grants'] 79 | return f'{org}
{grants} grants
{amount} total' 80 | op_orgs['hover'] = op_orgs.apply(hover, axis=1) 81 | 82 | op_orgs_truncated = op_orgs.reset_index().iloc[:20] 83 | 84 | return Bar(op_orgs_truncated, title='Top 20 Donee Organizations') 85 | 86 | 87 | def cause_bar_chart(op_grants): 88 | 89 | op_causes = op_grants.groupby(by='Focus Area', as_index=False).sum() 90 | op_causes = op_causes.sort_values(by='Amount') 91 | op_causes['x'] = op_causes['Focus Area'] 92 | op_causes['y'] = op_causes['Amount'] 93 | op_causes['text'] = op_causes['Amount'].apply(lambda x: f'${x:,.0f}') 94 | 95 | def hover(row): 96 | area = row['Focus Area'] 97 | amount = row['text'] 98 | grants = row['grants'] 99 | return f'{area}
{grants} grants
{amount} total' 100 | op_causes['hover'] = op_causes.apply(hover, axis=1) 101 | 102 | height_per_bar = 25 if len(op_causes) > 10 else 28 103 | height = height_per_bar * len(op_causes) + 20 104 | return Bar(op_causes, height=height, title='Focus Areas') 105 | 106 | 107 | def grants_scatter(op_grants): 108 | return Scatter( 109 | op_grants, 110 | x = "Date", 111 | y = "Amount", 112 | # color="Focus Area", 113 | # size='Amount', 114 | hover = 'hover', 115 | log_y = True, 116 | title = 'Individual Grants (log)', 117 | y_title = 'Amount (USD)', 118 | x_title = '', 119 | ) 120 | 121 | def openphil_grants_scatter_section(): 122 | 123 | op_grants = get_op_grants() 124 | 125 | return html.Div( 126 | [ 127 | html.Div( 128 | html.H2('Open Philanthropy Grants'), 129 | className='section-title', 130 | ), 131 | get_instructions('open_phil'), 132 | html.Div( 133 | [ 134 | html.Div( 135 | grants_scatter(op_grants), 136 | className='plot-container', 137 | ), 138 | ], 139 | className='section-body' 140 | ), 141 | get_data_source('open_phil'), 142 | ], 143 | className = 'section', 144 | id='op-grants-scatter-section', 145 | ) 146 | 147 | def openphil_grants_categories_section(): 148 | 149 | op_grants = get_op_grants() 150 | 151 | return html.Div( 152 | [ 153 | html.Div( 154 | html.H2('Open Philanthropy Grants by Focus Area and Donee Organization'), 155 | className='section-title', 156 | ), 157 | get_instructions(), 158 | html.Div( 159 | html.Div( 160 | [ 161 | html.Div( 162 | cause_bar_chart(op_grants), 163 | className='plot-container', 164 | ), 165 | html.Div( 166 | org_bar_chart(op_grants), 167 | className='plot-container', 168 | ), 169 | ], 170 | className='grid desk-cols-2 tab-cols-2', 171 | ), 172 | className='section-body' 173 | ), 174 | get_data_source('open_phil'), 175 | ], 176 | className = 'section', 177 | id='op-grants-categories', 178 | ) 179 | 180 | def group_by_month(op_grants): 181 | 182 | # Round the dates to the end of the month 183 | op_grants['Date'] += MonthEnd(1) 184 | 185 | # Generate a date range up to the present 186 | min_date = op_grants['Date'].min() 187 | max_date = op_grants['Date'].max() 188 | dates = pd.date_range(start=min_date, end=max_date, freq='M') 189 | 190 | grants_by_month = pd.DataFrame(columns=[ 191 | 'date', 192 | 'grants', 193 | 'focus_areas', 194 | 'organizations', 195 | 'total_amount', 196 | 'n_grants', 197 | ]) 198 | 199 | for i, date in enumerate(dates): 200 | grants_by_month_i = op_grants.loc[ op_grants['Date'] == date ] 201 | grants_by_month.loc[i, 'date'] = date 202 | grants_by_month.loc[i, 'grants'] = grants_by_month_i['Grant'].tolist() 203 | grants_by_month.loc[i, 'focus_areas'] = grants_by_month_i['Focus Area'].tolist() 204 | grants_by_month.loc[i, 'organizations'] = grants_by_month_i['Focus Area'].tolist() 205 | grants_by_month.loc[i, 'total_amount'] = grants_by_month_i['Amount'].sum() 206 | grants_by_month.loc[i, 'n_grants'] = len(grants_by_month_i) 207 | 208 | return grants_by_month 209 | 210 | 211 | def openphil_line_plot_section(): 212 | 213 | op_grants = get_op_grants() 214 | op_grants = op_grants.sort_values(by='Date').reset_index() 215 | 216 | def monthly_hover(row): 217 | month = row['date'].strftime('%B %Y') 218 | result = '' 219 | result += f"{month}" 220 | result += f"
{row.n_grants} grants" 221 | result += f"
${row.total_amount:,.2f} total value" 222 | return result 223 | 224 | grants_by_month = group_by_month(op_grants) 225 | 226 | grants_by_month['hover'] = grants_by_month.apply(monthly_hover, axis=1) 227 | last_row = grants_by_month.iloc[len(grants_by_month)-1] 228 | last_month = last_row['date'].strftime('%B %Y') 229 | label = '' 230 | label += f"${last_row.total_amount/1e6:,.1f} Million" 231 | label += f"
{last_month}" 232 | grants_by_month['label'] = label 233 | 234 | month_grants_graph = Line( 235 | grants_by_month, 236 | x = 'date', 237 | y = 'total_amount', 238 | x_title = '', 239 | y_title = '', 240 | hover = 'hover', 241 | title = 'Granted Amount by Month', 242 | label = 'label', 243 | xanchor='left', 244 | dollars=True, 245 | ) 246 | 247 | 248 | op_grants['cumulative_amount'] = op_grants['Amount'].cumsum() 249 | 250 | def cumulative_hover(row): 251 | result = row['hover'] 252 | result += f"
${row.cumulative_amount:,.2f} total" 253 | return result 254 | 255 | op_grants['hover'] = op_grants.apply(cumulative_hover, axis=1) 256 | grants_total = op_grants['cumulative_amount'].tolist()[-1] 257 | op_grants['label'] = f"${grants_total/1e9:,.2f} Billion
Total Grants" 258 | 259 | total_grants_graph = Line( 260 | op_grants, 261 | x = 'Date', 262 | y = 'cumulative_amount', 263 | x_title = '', 264 | y_title = '', 265 | hover = 'hover', 266 | title = 'Total Granted Amount', 267 | label = 'label', 268 | dollars=True, 269 | ) 270 | 271 | return html.Div( 272 | [ 273 | html.Div( 274 | html.H2('Open Philanthropy Grants Over Time'), 275 | className='section-title', 276 | ), 277 | get_instructions(), 278 | html.Div( 279 | html.Div( 280 | [ 281 | html.Div( 282 | month_grants_graph, 283 | className='plot-container', 284 | ), 285 | html.Div( 286 | total_grants_graph, 287 | className='plot-container', 288 | ), 289 | ], 290 | className='grid desk-cols-2 tab-cols-2', 291 | ), 292 | className='section-body' 293 | ), 294 | get_data_source('open_phil'), 295 | ], 296 | className = 'section', 297 | id='op-grants-growth', 298 | ) 299 | -------------------------------------------------------------------------------- /components/sections/forum.py: -------------------------------------------------------------------------------- 1 | from dash import dcc 2 | from dash import html 3 | from dash import dash_table 4 | 5 | import pandas as pd 6 | import numpy as np 7 | 8 | from math import log 9 | from utils.subtitle import get_data_source 10 | from utils.subtitle import get_instructions 11 | import json 12 | 13 | from utils.plots.bar import Bar 14 | from utils.plots.line import Line 15 | from utils.plots.scatter import Scatter 16 | from utils.plots.wilkinson import Wilkinson 17 | 18 | posts_df = None 19 | def get_forum_data(): 20 | global posts_df 21 | if type(posts_df) != type(None): 22 | return posts_df 23 | 24 | with open('./assets/data/ea_forum.json', 'r') as forum_file: 25 | forum_json = json.loads(forum_file.read()) 26 | 27 | posts = forum_json['data']['posts']['results'] 28 | 29 | posts_df = pd.DataFrame( 30 | columns=['title', 'posted_at', 'authors', 'url', 'wordcount', 'karma', 'comments'] 31 | ) 32 | 33 | for post in posts: 34 | 35 | author_list = [] 36 | try: 37 | author_list.append( post['user']['displayName'] ) 38 | except: 39 | author_list.append('anonymous') 40 | for coauthor in post['coauthors']: 41 | author_list.append( coauthor['displayName'] ) 42 | author_string = ', '.join(author_list) 43 | 44 | comment_count = post['commentCount'] 45 | # resolve nulls to zero 46 | comment_count = comment_count if comment_count else 0 47 | 48 | wordcount = post['wordCount'] 49 | # resolve nulls to zero 50 | wordcount = wordcount if wordcount else 0 51 | 52 | posts_df.loc[len(posts_df), :] = [ 53 | post['title'], 54 | post['postedAt'], 55 | author_string, 56 | post['pageUrl'], 57 | wordcount, 58 | post['baseScore'], 59 | comment_count, 60 | ] 61 | 62 | # remove very low karma posts 63 | posts_df = posts_df.loc[ posts_df['karma'] > -20 ] 64 | 65 | posts_df['posted_at'] = pd.to_datetime(posts_df['posted_at'])#, format='%m/%Y') 66 | posts_df = posts_df.sort_values(by='posted_at', ascending=False) 67 | posts_df['posted_at_readable'] = posts_df['posted_at'].dt.strftime('%d %b %Y') 68 | posts_df['size'] = posts_df['wordcount'] + 1 69 | 70 | # hovertext 71 | def hover(row): 72 | 73 | title = row['title'] 74 | posted_at = row['posted_at_readable'] 75 | authors = row['authors'] 76 | wordcount = row['wordcount'] 77 | karma = row['karma'] 78 | comments = row['comments'] 79 | 80 | result = '' 81 | result += f"{title}" 82 | result += f"
{authors}" 83 | result += f"
Posted {posted_at}" 84 | result += f"
{karma} karma, {comments} comments" 85 | 86 | return result 87 | 88 | posts_df['hover'] = posts_df.apply(hover, axis=1) 89 | 90 | return posts_df 91 | 92 | 93 | def forum_scatter(forum_df): 94 | 95 | return Scatter( 96 | forum_df, 97 | x = "posted_at", 98 | y = "karma", 99 | x_title = "Date Posted", 100 | y_title = "Karma", 101 | title = "All EA Forum Posts", 102 | hover = 'hover', 103 | ) 104 | 105 | def forum_scatter_section(): 106 | 107 | forum_df = get_forum_data() 108 | 109 | return html.Div( 110 | [ 111 | html.Div( 112 | html.H2('EA Forum Posts by Date Posted and Karma'), 113 | className='section-title', 114 | ), 115 | get_instructions(hover='points', zoom=True), 116 | html.Div( 117 | [ 118 | html.Div( 119 | forum_scatter(forum_df), 120 | className='plot-container', 121 | ), 122 | ], 123 | className='section-body' 124 | ), 125 | get_data_source('ea_forum'), 126 | ], 127 | className = 'section', 128 | id='forum-scatter-section', 129 | ) 130 | 131 | def post_counts(forum_df): 132 | 133 | 134 | forum_df = forum_df.sort_values(by='posted_at') 135 | 136 | forum_df['Unit'] = 1 137 | forum_df['Count'] = forum_df['Unit'].cumsum() 138 | forum_df['posted_at'] = forum_df['posted_at'].dt.date 139 | forum_df['cumulative_word_count'] = forum_df['wordcount'].cumsum() 140 | 141 | authors = set() 142 | def new_authors(author_string): 143 | author_list = author_string.split(',') 144 | author_list = [ author.strip() for author in author_list ] 145 | new_authors = [] 146 | for author in author_list: 147 | if author not in authors: 148 | new_authors.append(author) 149 | authors.add(author) 150 | return new_authors 151 | 152 | forum_df['new_authors'] = forum_df['authors'].apply(new_authors) 153 | forum_df['new_author_count'] = forum_df['new_authors'].apply(len) 154 | forum_df['author_count'] = forum_df['new_author_count'].cumsum() 155 | 156 | posted_at_groups = forum_df.groupby('posted_at') 157 | forum_by_day_df = pd.DataFrame({ 158 | 'posted_at': forum_df['posted_at'].unique(), 159 | 'posted_at_readable': forum_df['posted_at_readable'].unique(), 160 | }) 161 | 162 | # new posts 163 | 164 | forum_by_day_df['titles'] = posted_at_groups['title'].apply(list).tolist() 165 | forum_by_day_df['new_posts'] = posted_at_groups['Unit'].sum().tolist() 166 | forum_by_day_df['total_posts'] = posted_at_groups['Count'].last().tolist() 167 | 168 | label = forum_df['Count'].tolist()[-1] 169 | label = f'{label:,} Posts' 170 | forum_by_day_df['new_posts_label'] = label 171 | 172 | def new_posts_hover(row): 173 | 174 | new_posts = row['new_posts'] 175 | total_posts = row['total_posts'] 176 | posted_at = row['posted_at_readable'] 177 | titles = row['titles'] 178 | 179 | max_displayed_titles = 10 180 | if len(titles) > max_displayed_titles: 181 | titles = titles[:max_displayed_titles] + ['...'] 182 | 183 | result = '' 184 | result += f"{posted_at}" 185 | result += f"
{new_posts} new posts ({total_posts:,} total):" 186 | for title in titles: 187 | 188 | max_title_length = 50 189 | if len(title) > max_title_length: 190 | title = title[:max_title_length] + '...' 191 | result += f"
{title}" 192 | 193 | return result 194 | 195 | forum_by_day_df['new_posts_hover'] = forum_by_day_df.apply(new_posts_hover, axis=1) 196 | 197 | 198 | # new authors 199 | 200 | forum_by_day_df['new_authors'] = posted_at_groups['new_authors'].sum().tolist() 201 | forum_by_day_df['new_author_count'] = posted_at_groups['new_author_count'].sum().tolist() 202 | forum_by_day_df['author_count'] = posted_at_groups['author_count'].last().tolist() 203 | 204 | label = forum_df['author_count'].tolist()[-1] 205 | label = f'{label:,} Unique Authors' 206 | forum_by_day_df['new_authors_label'] = label 207 | 208 | def new_authors_hover(row): 209 | 210 | new_authors = row['new_authors'] 211 | new_authors_count = row['new_author_count'] 212 | total_authors = row['author_count'] 213 | posted_at = row['posted_at_readable'] 214 | 215 | max_displayed_authors = 10 216 | if len(new_authors) > max_displayed_authors: 217 | new_authors = new_authors[:max_displayed_authors] + ['...'] 218 | 219 | result = '' 220 | result += f"{posted_at}" 221 | result += f"
{new_authors_count} new authors ({total_authors:,} total):" 222 | for author in new_authors: 223 | 224 | max_author_length = 50 225 | if len(author) > max_author_length: 226 | author = author[:max_author_length] + '...' 227 | result += f"
{author}" 228 | 229 | return result 230 | 231 | forum_by_day_df['new_authors_hover'] = forum_by_day_df.apply(new_authors_hover, axis=1) 232 | 233 | 234 | # word count 235 | 236 | 237 | forum_by_day_df['new_words'] = posted_at_groups['wordcount'].sum().tolist() 238 | forum_by_day_df['total_words'] = posted_at_groups['cumulative_word_count'].last().tolist() 239 | 240 | label = forum_by_day_df['total_words'].tolist()[-1] 241 | label = f'{label:,} Words' 242 | forum_by_day_df['word_count_label'] = label 243 | 244 | def word_count_hover(row): 245 | 246 | new_words = row['new_words'] 247 | total_words = row['total_words'] 248 | posted_at = row['posted_at_readable'] 249 | 250 | result = '' 251 | result += f"{posted_at}" 252 | result += f"
{total_words:,} total words" 253 | result += f"
{new_words:,} new words" 254 | 255 | return result 256 | 257 | forum_by_day_df['word_count_hover'] = forum_by_day_df.apply(word_count_hover, axis=1) 258 | 259 | 260 | 261 | # New posts line plot 262 | 263 | post_count_graph = Line( 264 | forum_by_day_df, 265 | x='posted_at', 266 | y='total_posts', 267 | x_title = '', 268 | y_title = '', 269 | hover = 'new_posts_hover', 270 | title = 'Number of Posts', 271 | label = 'new_posts_label', 272 | ) 273 | 274 | # New authors line plot 275 | 276 | unique_authors_df = forum_by_day_df.loc[ forum_by_day_df['new_author_count'] > 0 ] 277 | 278 | author_count_graph = Line( 279 | unique_authors_df, 280 | x='posted_at', 281 | y='author_count', 282 | x_title = '', 283 | y_title = '', 284 | hover = 'new_authors_hover', 285 | title = 'Number of Unique Author', 286 | label = 'new_authors_label', 287 | ) 288 | 289 | 290 | 291 | # New words line plot 292 | 293 | new_words_df = forum_by_day_df.loc[ forum_by_day_df['new_words'] > 0 ] 294 | 295 | word_count_graph = Line( 296 | new_words_df, 297 | x = 'posted_at', 298 | y = 'total_words', 299 | x_title = '', 300 | y_title = '', 301 | hover = 'word_count_hover', 302 | title = 'Total Word Count', 303 | label = 'word_count_label', 304 | ) 305 | 306 | return [ 307 | post_count_graph, 308 | author_count_graph, 309 | word_count_graph, 310 | ] 311 | 312 | def forum_count_section(): 313 | 314 | forum_df = get_forum_data() 315 | 316 | post_count_graph, author_count_graph, word_count_graph = post_counts(forum_df) 317 | 318 | return html.Div( 319 | [ 320 | html.Div( 321 | html.H2('Growth in EA Forum Activity'), 322 | className='section-title', 323 | ), 324 | get_instructions(hover='points', zoom=True), 325 | html.Div( 326 | [ 327 | html.Div( 328 | post_count_graph, 329 | className='plot-container', 330 | ), 331 | html.Div( 332 | author_count_graph, 333 | className='plot-container', 334 | ), 335 | html.Div( 336 | word_count_graph, 337 | className='plot-container', 338 | ), 339 | ], 340 | className='grid desk-cols-3 section-body' 341 | ), 342 | get_data_source('ea_forum'), 343 | ], 344 | className = 'section', 345 | id='forum-growth-section', 346 | ) 347 | 348 | def forum_post_wilkinson_section(): 349 | 350 | forum_df = get_forum_data() 351 | 352 | karma_graph = Wilkinson( 353 | forum_df.sort_values('karma'), 354 | value='karma', 355 | text='title', 356 | title='Posts by Karma', 357 | y_title='Karma', 358 | hover='hover', 359 | ) 360 | length_graph = Wilkinson( 361 | forum_df.sort_values('wordcount'), 362 | value='wordcount', 363 | text='title', 364 | title='Posts by Wordcount', 365 | y_title='Words', 366 | hover='hover', 367 | ) 368 | date_graph = Wilkinson( 369 | forum_df.sort_values('posted_at'), 370 | value='posted_at', 371 | text='title', 372 | title='Posts by Date Posted', 373 | y_title='Date Posted', 374 | hover='hover', 375 | ) 376 | 377 | return html.Div( 378 | [ 379 | html.Div( 380 | html.H2('EA Forum Posts: Karma, Wordcount, and Date Posted'), 381 | className='section-title', 382 | ), 383 | get_instructions(hover='points', zoom=True), 384 | html.Div( 385 | [ 386 | html.Div( 387 | karma_graph, 388 | className='plot-container', 389 | ), 390 | html.Div( 391 | length_graph, 392 | className='plot-container', 393 | ), 394 | html.Div( 395 | date_graph, 396 | className='plot-container', 397 | ), 398 | ], 399 | className='grid desk-cols-3 section-body' 400 | ), 401 | get_data_source('ea_forum'), 402 | ], 403 | className = 'section', 404 | id='post-wilkinson-section', 405 | ) 406 | 407 | def forum_user_wilkinson_section(): 408 | 409 | forum_df = get_forum_data() 410 | 411 | # Only consider first author 412 | forum_df['first_author'] = forum_df['authors'].apply(lambda x: x.split(',')[0].strip()) 413 | forum_df = forum_df.sort_values(['first_author', 'posted_at']) 414 | 415 | author_groups = forum_df.groupby('first_author') 416 | author_df = pd.DataFrame({ 417 | 'author': forum_df['first_author'].unique(), 418 | }) 419 | author_df['karma'] = author_groups['karma'].sum().tolist() 420 | author_df['wordcount'] = author_groups['wordcount'].sum().tolist() 421 | author_df['posted_at'] = author_groups['posted_at'].first().tolist() 422 | author_df['posted_at_readable'] = author_groups['posted_at_readable'].first().tolist() 423 | 424 | def hover(row): 425 | 426 | author = row['author'] 427 | posted_at = row['posted_at_readable'] 428 | wordcount = row['wordcount'] 429 | karma = row['karma'] 430 | 431 | result = '' 432 | result += f"{author}" 433 | result += f"
First posted: {posted_at}" 434 | result += f"
Total karma: {karma:,}" 435 | result += f"
Total wordcount: {wordcount:,}" 436 | 437 | return result 438 | 439 | author_df['hover'] = author_df.apply(hover, axis=1) 440 | 441 | karma_graph = Wilkinson( 442 | author_df.sort_values('karma'), 443 | value='karma', 444 | text='author', 445 | title='Authors by Total Karma', 446 | y_title='Karma', 447 | hover='hover', 448 | bins=30, 449 | ) 450 | length_graph = Wilkinson( 451 | author_df.sort_values('wordcount'), 452 | value='wordcount', 453 | text='author', 454 | title='Authors by Total Wordcount', 455 | y_title='Words', 456 | hover='hover', 457 | bins=30, 458 | ) 459 | date_graph = Wilkinson( 460 | author_df.sort_values('posted_at'), 461 | value='posted_at', 462 | text='author', 463 | title='Authors by Date of First Post', 464 | y_title='Date Posted', 465 | hover='hover', 466 | ) 467 | 468 | return html.Div( 469 | [ 470 | html.Div( 471 | html.H2("EA Forum Authors: Total Karma, Total Wordcount, and Date of First Post"), 472 | className='section-title', 473 | ), 474 | get_instructions(hover='points', zoom=True), 475 | html.Div( 476 | [ 477 | html.Div( 478 | karma_graph, 479 | className='plot-container', 480 | ), 481 | html.Div( 482 | length_graph, 483 | className='plot-container', 484 | ), 485 | html.Div( 486 | date_graph, 487 | className='plot-container', 488 | ), 489 | ], 490 | className='grid desk-cols-3 section-body' 491 | ), 492 | get_data_source('ea_forum'), 493 | ], 494 | className = 'section', 495 | id='author-wilkinson-section', 496 | ) 497 | -------------------------------------------------------------------------------- /Pipfile.lock: -------------------------------------------------------------------------------- 1 | { 2 | "_meta": { 3 | "hash": { 4 | "sha256": "92201af28e38e7b203ea635747c69bf47e51e7177d4848bb419f6bb4f49babd7" 5 | }, 6 | "pipfile-spec": 6, 7 | "requires": { 8 | "python_version": "3.9" 9 | }, 10 | "sources": [ 11 | { 12 | "name": "pypi", 13 | "url": "https://pypi.org/simple", 14 | "verify_ssl": true 15 | } 16 | ] 17 | }, 18 | "default": { 19 | "beautifulsoup4": { 20 | "hashes": [ 21 | "sha256:58d5c3d29f5a36ffeb94f02f0d786cd53014cf9b3b3951d42e0080d8a9498d30", 22 | "sha256:ad9aa55b65ef2808eb405f46cf74df7fcb7044d5cbc26487f96eb2ef2e436693" 23 | ], 24 | "markers": "python_full_version >= '3.6.0'", 25 | "version": "==4.11.1" 26 | }, 27 | "brotli": { 28 | "hashes": [ 29 | "sha256:12effe280b8ebfd389022aa65114e30407540ccb89b177d3fbc9a4f177c4bd5d", 30 | "sha256:160c78292e98d21e73a4cc7f76a234390e516afcd982fa17e1422f7c6a9ce9c8", 31 | "sha256:16d528a45c2e1909c2798f27f7bf0a3feec1dc9e50948e738b961618e38b6a7b", 32 | "sha256:19598ecddd8a212aedb1ffa15763dd52a388518c4550e615aed88dc3753c0f0c", 33 | "sha256:1c48472a6ba3b113452355b9af0a60da5c2ae60477f8feda8346f8fd48e3e87c", 34 | "sha256:268fe94547ba25b58ebc724680609c8ee3e5a843202e9a381f6f9c5e8bdb5c70", 35 | "sha256:269a5743a393c65db46a7bb982644c67ecba4b8d91b392403ad8a861ba6f495f", 36 | "sha256:26d168aac4aaec9a4394221240e8a5436b5634adc3cd1cdf637f6645cecbf181", 37 | "sha256:29d1d350178e5225397e28ea1b7aca3648fcbab546d20e7475805437bfb0a130", 38 | "sha256:2aad0e0baa04517741c9bb5b07586c642302e5fb3e75319cb62087bd0995ab19", 39 | "sha256:3496fc835370da351d37cada4cf744039616a6db7d13c430035e901443a34daa", 40 | "sha256:35a3edbe18e876e596553c4007a087f8bcfd538f19bc116917b3c7522fca0429", 41 | "sha256:3b78a24b5fd13c03ee2b7b86290ed20efdc95da75a3557cc06811764d5ad1126", 42 | "sha256:40d15c79f42e0a2c72892bf407979febd9cf91f36f495ffb333d1d04cebb34e4", 43 | "sha256:44bb8ff420c1d19d91d79d8c3574b8954288bdff0273bf788954064d260d7ab0", 44 | "sha256:4688c1e42968ba52e57d8670ad2306fe92e0169c6f3af0089be75bbac0c64a3b", 45 | "sha256:495ba7e49c2db22b046a53b469bbecea802efce200dffb69b93dd47397edc9b6", 46 | "sha256:4d1b810aa0ed773f81dceda2cc7b403d01057458730e309856356d4ef4188438", 47 | "sha256:503fa6af7da9f4b5780bb7e4cbe0c639b010f12be85d02c99452825dd0feef3f", 48 | "sha256:56d027eace784738457437df7331965473f2c0da2c70e1a1f6fdbae5402e0389", 49 | "sha256:5913a1177fc36e30fcf6dc868ce23b0453952c78c04c266d3149b3d39e1410d6", 50 | "sha256:5b6ef7d9f9c38292df3690fe3e302b5b530999fa90014853dcd0d6902fb59f26", 51 | "sha256:5cb1e18167792d7d21e21365d7650b72d5081ed476123ff7b8cac7f45189c0c7", 52 | "sha256:61a7ee1f13ab913897dac7da44a73c6d44d48a4adff42a5701e3239791c96e14", 53 | "sha256:622a231b08899c864eb87e85f81c75e7b9ce05b001e59bbfbf43d4a71f5f32b2", 54 | "sha256:68715970f16b6e92c574c30747c95cf8cf62804569647386ff032195dc89a430", 55 | "sha256:6b2ae9f5f67f89aade1fab0f7fd8f2832501311c363a21579d02defa844d9296", 56 | "sha256:6c772d6c0a79ac0f414a9f8947cc407e119b8598de7621f39cacadae3cf57d12", 57 | "sha256:6d847b14f7ea89f6ad3c9e3901d1bc4835f6b390a9c71df999b0162d9bb1e20f", 58 | "sha256:76ffebb907bec09ff511bb3acc077695e2c32bc2142819491579a695f77ffd4d", 59 | "sha256:7bbff90b63328013e1e8cb50650ae0b9bac54ffb4be6104378490193cd60f85a", 60 | "sha256:7cb81373984cc0e4682f31bc3d6be9026006d96eecd07ea49aafb06897746452", 61 | "sha256:7ee83d3e3a024a9618e5be64648d6d11c37047ac48adff25f12fa4226cf23d1c", 62 | "sha256:854c33dad5ba0fbd6ab69185fec8dab89e13cda6b7d191ba111987df74f38761", 63 | "sha256:85f7912459c67eaab2fb854ed2bc1cc25772b300545fe7ed2dc03954da638649", 64 | "sha256:87fdccbb6bb589095f413b1e05734ba492c962b4a45a13ff3408fa44ffe6479b", 65 | "sha256:88c63a1b55f352b02c6ffd24b15ead9fc0e8bf781dbe070213039324922a2eea", 66 | "sha256:8a674ac10e0a87b683f4fa2b6fa41090edfd686a6524bd8dedbd6138b309175c", 67 | "sha256:93130612b837103e15ac3f9cbacb4613f9e348b58b3aad53721d92e57f96d46a", 68 | "sha256:9744a863b489c79a73aba014df554b0e7a0fc44ef3f8a0ef2a52919c7d155031", 69 | "sha256:9749a124280a0ada4187a6cfd1ffd35c350fb3af79c706589d98e088c5044267", 70 | "sha256:97f715cf371b16ac88b8c19da00029804e20e25f30d80203417255d239f228b5", 71 | "sha256:9bf919756d25e4114ace16a8ce91eb340eb57a08e2c6950c3cebcbe3dff2a5e7", 72 | "sha256:9d12cf2851759b8de8ca5fde36a59c08210a97ffca0eb94c532ce7b17c6a3d1d", 73 | "sha256:9ed4c92a0665002ff8ea852353aeb60d9141eb04109e88928026d3c8a9e5433c", 74 | "sha256:a72661af47119a80d82fa583b554095308d6a4c356b2a554fdc2799bc19f2a43", 75 | "sha256:afde17ae04d90fbe53afb628f7f2d4ca022797aa093e809de5c3cf276f61bbfa", 76 | "sha256:b336c5e9cf03c7be40c47b5fd694c43c9f1358a80ba384a21969e0b4e66a9b17", 77 | "sha256:b663f1e02de5d0573610756398e44c130add0eb9a3fc912a09665332942a2efb", 78 | "sha256:b83bb06a0192cccf1eb8d0a28672a1b79c74c3a8a5f2619625aeb6f28b3a82bb", 79 | "sha256:c2415d9d082152460f2bd4e382a1e85aed233abc92db5a3880da2257dc7daf7b", 80 | "sha256:c83aa123d56f2e060644427a882a36b3c12db93727ad7a7b9efd7d7f3e9cc2c4", 81 | "sha256:cfc391f4429ee0a9370aa93d812a52e1fee0f37a81861f4fdd1f4fb28e8547c3", 82 | "sha256:db844eb158a87ccab83e868a762ea8024ae27337fc7ddcbfcddd157f841fdfe7", 83 | "sha256:defed7ea5f218a9f2336301e6fd379f55c655bea65ba2476346340a0ce6f74a1", 84 | "sha256:e16eb9541f3dd1a3e92b89005e37b1257b157b7256df0e36bd7b33b50be73bcb", 85 | "sha256:e23281b9a08ec338469268f98f194658abfb13658ee98e2b7f85ee9dd06caa91", 86 | "sha256:e2d9e1cbc1b25e22000328702b014227737756f4b5bf5c485ac1d8091ada078b", 87 | "sha256:e48f4234f2469ed012a98f4b7874e7f7e173c167bed4934912a29e03167cf6b1", 88 | "sha256:e4c4e92c14a57c9bd4cb4be678c25369bf7a092d55fd0866f759e425b9660806", 89 | "sha256:ec1947eabbaf8e0531e8e899fc1d9876c179fc518989461f5d24e2223395a9e3", 90 | "sha256:f909bbbc433048b499cb9db9e713b5d8d949e8c109a2a548502fb9aa8630f0b1" 91 | ], 92 | "version": "==1.0.9" 93 | }, 94 | "bs4": { 95 | "hashes": [ 96 | "sha256:36ecea1fd7cc5c0c6e4a1ff075df26d50da647b75376626cc186e2212886dd3a" 97 | ], 98 | "index": "pypi", 99 | "version": "==0.0.1" 100 | }, 101 | "certifi": { 102 | "hashes": [ 103 | "sha256:0d9c601124e5a6ba9712dbc60d9c53c21e34f5f641fe83002317394311bdce14", 104 | "sha256:90c1a32f1d68f940488354e36370f6cca89f0f106db09518524c88d6ed83f382" 105 | ], 106 | "markers": "python_full_version >= '3.6.0'", 107 | "version": "==2022.9.24" 108 | }, 109 | "charset-normalizer": { 110 | "hashes": [ 111 | "sha256:2857e29ff0d34db842cd7ca3230549d1a697f96ee6d3fb071cfa6c7393832597", 112 | "sha256:6881edbebdb17b39b4eaaa821b438bf6eddffb4468cf344f09f89def34a8b1df" 113 | ], 114 | "markers": "python_version >= '3'", 115 | "version": "==2.0.12" 116 | }, 117 | "click": { 118 | "hashes": [ 119 | "sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e", 120 | "sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48" 121 | ], 122 | "markers": "python_version >= '3.7'", 123 | "version": "==8.1.3" 124 | }, 125 | "countryinfo": { 126 | "hashes": [ 127 | "sha256:fd518b3fd8899f6520518320ac17b67bf410c7db5044c61cb191f802bb85c34d" 128 | ], 129 | "index": "pypi", 130 | "version": "==0.1.2" 131 | }, 132 | "dash": { 133 | "hashes": [ 134 | "sha256:23f331533663641a5c70a15c46da26d29ef5335f9aeb8bc03d09de865fc7cd62", 135 | "sha256:29277c24e2f795b069cb102ce1ab0cd3ad5cf9d3b4fd16c03da9671a5eea28a4" 136 | ], 137 | "index": "pypi", 138 | "version": "==2.0.0" 139 | }, 140 | "dash-core-components": { 141 | "hashes": [ 142 | "sha256:52b8e8cce13b18d0802ee3acbc5e888cb1248a04968f962d63d070400af2e346", 143 | "sha256:c6733874af975e552f95a1398a16c2ee7df14ce43fa60bb3718a3c6e0b63ffee" 144 | ], 145 | "version": "==2.0.0" 146 | }, 147 | "dash-dangerously-set-inner-html": { 148 | "hashes": [ 149 | "sha256:d7fe990755851fc4d2e22c8f10b7aea055cabf380bbceefba589779b269fea64" 150 | ], 151 | "index": "pypi", 152 | "version": "==0.0.2" 153 | }, 154 | "dash-html-components": { 155 | "hashes": [ 156 | "sha256:8703a601080f02619a6390998e0b3da4a5daabe97a1fd7a9cebc09d015f26e50", 157 | "sha256:b42cc903713c9706af03b3f2548bda4be7307a7cf89b7d6eae3da872717d1b63" 158 | ], 159 | "version": "==2.0.0" 160 | }, 161 | "dash-table": { 162 | "hashes": [ 163 | "sha256:18624d693d4c8ef2ddec99a6f167593437a7ea0bf153aa20f318c170c5bc7308", 164 | "sha256:19036fa352bb1c11baf38068ec62d172f0515f73ca3276c79dee49b95ddc16c9" 165 | ], 166 | "version": "==5.0.0" 167 | }, 168 | "flask": { 169 | "hashes": [ 170 | "sha256:642c450d19c4ad482f96729bd2a8f6d32554aa1e231f4f6b4e7e5264b16cca2b", 171 | "sha256:b9c46cc36662a7949f34b52d8ec7bb59c0d74ba08ba6cb9ce9adc1d8676d9526" 172 | ], 173 | "markers": "python_version >= '3.7'", 174 | "version": "==2.2.2" 175 | }, 176 | "flask-compress": { 177 | "hashes": [ 178 | "sha256:1128f71fbd788393ce26830c51f8b5a1a7a4d085e79a21a5cddf4c057dcd559b", 179 | "sha256:ee96f18bf9b00f2deb4e3406ca4a05093aa80e2ef0578525a3b4d32ecdff129d" 180 | ], 181 | "version": "==1.13" 182 | }, 183 | "gunicorn": { 184 | "hashes": [ 185 | "sha256:9dcc4547dbb1cb284accfb15ab5667a0e5d1881cc443e0677b4882a4067a807e", 186 | "sha256:e0a968b5ba15f8a328fdfd7ab1fcb5af4470c28aaf7e55df02a99bc13138e6e8" 187 | ], 188 | "index": "pypi", 189 | "version": "==20.1.0" 190 | }, 191 | "idna": { 192 | "hashes": [ 193 | "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4", 194 | "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2" 195 | ], 196 | "markers": "python_version >= '3'", 197 | "version": "==3.4" 198 | }, 199 | "importlib-metadata": { 200 | "hashes": [ 201 | "sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab", 202 | "sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43" 203 | ], 204 | "markers": "python_version < '3.10'", 205 | "version": "==5.0.0" 206 | }, 207 | "itsdangerous": { 208 | "hashes": [ 209 | "sha256:2c2349112351b88699d8d4b6b075022c0808887cb7ad10069318a8b0bc88db44", 210 | "sha256:5dbbc68b317e5e42f327f9021763545dc3fc3bfe22e6deb96aaf1fc38874156a" 211 | ], 212 | "markers": "python_version >= '3.7'", 213 | "version": "==2.1.2" 214 | }, 215 | "jinja2": { 216 | "hashes": [ 217 | "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852", 218 | "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61" 219 | ], 220 | "markers": "python_version >= '3.7'", 221 | "version": "==3.1.2" 222 | }, 223 | "markupsafe": { 224 | "hashes": [ 225 | "sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003", 226 | "sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88", 227 | "sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5", 228 | "sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7", 229 | "sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a", 230 | "sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603", 231 | "sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1", 232 | "sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135", 233 | "sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247", 234 | "sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6", 235 | "sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601", 236 | "sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77", 237 | "sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02", 238 | "sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e", 239 | "sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63", 240 | "sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f", 241 | "sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980", 242 | "sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b", 243 | "sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812", 244 | "sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff", 245 | "sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96", 246 | "sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1", 247 | "sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925", 248 | "sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a", 249 | "sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6", 250 | "sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e", 251 | "sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f", 252 | "sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4", 253 | "sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f", 254 | "sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3", 255 | "sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c", 256 | "sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a", 257 | "sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417", 258 | "sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a", 259 | "sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a", 260 | "sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37", 261 | "sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452", 262 | "sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933", 263 | "sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a", 264 | "sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7" 265 | ], 266 | "markers": "python_version >= '3.7'", 267 | "version": "==2.1.1" 268 | }, 269 | "numpy": { 270 | "hashes": [ 271 | "sha256:0cfe07133fd00b27edee5e6385e333e9eeb010607e8a46e1cd673f05f8596595", 272 | "sha256:11a1f3816ea82eed4178102c56281782690ab5993251fdfd75039aad4d20385f", 273 | "sha256:2762331de395739c91f1abb88041f94a080cb1143aeec791b3b223976228af3f", 274 | "sha256:283d9de87c0133ef98f93dfc09fad3fb382f2a15580de75c02b5bb36a5a159a5", 275 | "sha256:3d22662b4b10112c545c91a0741f2436f8ca979ab3d69d03d19322aa970f9695", 276 | "sha256:41388e32e40b41dd56eb37fcaa7488b2b47b0adf77c66154d6b89622c110dfe9", 277 | "sha256:42c16cec1c8cf2728f1d539bd55aaa9d6bb48a7de2f41eb944697293ef65a559", 278 | "sha256:47ee7a839f5885bc0c63a74aabb91f6f40d7d7b639253768c4199b37aede7982", 279 | "sha256:5a311ee4d983c487a0ab546708edbdd759393a3dc9cd30305170149fedd23c88", 280 | "sha256:5dc65644f75a4c2970f21394ad8bea1a844104f0fe01f278631be1c7eae27226", 281 | "sha256:6ed0d073a9c54ac40c41a9c2d53fcc3d4d4ed607670b9e7b0de1ba13b4cbfe6f", 282 | "sha256:76ba7c40e80f9dc815c5e896330700fd6e20814e69da9c1267d65a4d051080f1", 283 | "sha256:818b9be7900e8dc23e013a92779135623476f44a0de58b40c32a15368c01d471", 284 | "sha256:a024181d7aef0004d76fb3bce2a4c9f2e67a609a9e2a6ff2571d30e9976aa383", 285 | "sha256:a955e4128ac36797aaffd49ab44ec74a71c11d6938df83b1285492d277db5397", 286 | "sha256:a97a954a8c2f046d3817c2bce16e3c7e9a9c2afffaf0400f5c16df5172a67c9c", 287 | "sha256:a97e82c39d9856fe7d4f9b86d8a1e66eff99cf3a8b7ba48202f659703d27c46f", 288 | "sha256:b55b953a1bdb465f4dc181758570d321db4ac23005f90ffd2b434cc6609a63dd", 289 | "sha256:bb02929b0d6bfab4c48a79bd805bd7419114606947ec8284476167415171f55b", 290 | "sha256:bece0a4a49e60e472a6d1f70ac6cdea00f9ab80ff01132f96bd970cdd8a9e5a9", 291 | "sha256:e41e8951749c4b5c9a2dc5fdbc1a4eec6ab2a140fdae9b460b0f557eed870f4d", 292 | "sha256:f71d57cc8645f14816ae249407d309be250ad8de93ef61d9709b45a0ddf4050c" 293 | ], 294 | "index": "pypi", 295 | "version": "==1.22.0" 296 | }, 297 | "pandas": { 298 | "hashes": [ 299 | "sha256:272c8cb14aa9793eada6b1ebe81994616e647b5892a370c7135efb2924b701df", 300 | "sha256:3334a5a9eeaca953b9db1b2b165dcdc5180b5011f3bec3a57a3580c9c22eae68", 301 | "sha256:37d63e78e87eb3791da7be4100a65da0383670c2b59e493d9e73098d7a879226", 302 | "sha256:3f5020613c1d8e304840c34aeb171377dc755521bf5e69804991030c2a48aec3", 303 | "sha256:45649503e167d45360aa7c52f18d1591a6d5c70d2f3a26bc90a3297a30ce9a66", 304 | "sha256:49fd2889d8116d7acef0709e4c82b8560a8b22b0f77471391d12c27596e90267", 305 | "sha256:4def2ef2fb7fcd62f2aa51bacb817ee9029e5c8efe42fe527ba21f6a3ddf1a9f", 306 | "sha256:53e2fb11f86f6253bb1df26e3aeab3bf2e000aaa32a953ec394571bec5dc6fd6", 307 | "sha256:629138b7cf81a2e55aa29ce7b04c1cece20485271d1f6c469c6a0c03857db6a4", 308 | "sha256:68408a39a54ebadb9014ee5a4fae27b2fe524317bc80adf56c9ac59e8f8ea431", 309 | "sha256:7326b37de08d42dd3fff5b7ef7691d0fd0bf2428f4ba5a2bdc3b3247e9a52e4c", 310 | "sha256:7557b39c8e86eb0543a17a002ac1ea0f38911c3c17095bc9350d0a65b32d801c", 311 | "sha256:86b16b1b920c4cb27fdd65a2c20258bcd9c794be491290660722bb0ea765054d", 312 | "sha256:a800df4e101b721e94d04c355e611863cc31887f24c0b019572e26518cbbcab6", 313 | "sha256:a9f1b54d7efc9df05320b14a48fb18686f781aa66cc7b47bb62fabfc67a0985c", 314 | "sha256:c399200631db9bd9335d013ec7fce4edb98651035c249d532945c78ad453f23a", 315 | "sha256:e574c2637c9d27f322e911650b36e858c885702c5996eda8a5a60e35e6648cf2", 316 | "sha256:e9bc59855598cb57f68fdabd4897d3ed2bc3a3b3bef7b868a0153c4cd03f3207", 317 | "sha256:ebbed7312547a924df0cbe133ff1250eeb94cdff3c09a794dc991c5621c8c735", 318 | "sha256:ed2f29b4da6f6ae7c68f4b3708d9d9e59fa89b2f9e87c2b64ce055cbd39f729e", 319 | "sha256:f7d84f321674c2f0f31887ee6d5755c54ca1ea5e144d6d54b3bbf566dd9ea0cc" 320 | ], 321 | "index": "pypi", 322 | "version": "==1.3.3" 323 | }, 324 | "plotly": { 325 | "hashes": [ 326 | "sha256:4d36d9859b7a153b273562deeed8c292587a472eb1fd57cd4158ec89d9defadb", 327 | "sha256:989b13825cc974390aa0169479485d9257d37848a47bc63957251f8e1a7046ba" 328 | ], 329 | "markers": "python_full_version >= '3.6.0'", 330 | "version": "==5.10.0" 331 | }, 332 | "python-dateutil": { 333 | "hashes": [ 334 | "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86", 335 | "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9" 336 | ], 337 | "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'", 338 | "version": "==2.8.2" 339 | }, 340 | "pytz": { 341 | "hashes": [ 342 | "sha256:2c0784747071402c6e99f0bafdb7da0fa22645f06554c7ae06bf6358897e9c91", 343 | "sha256:48ce799d83b6f8aab2020e369b627446696619e79645419610b9facd909b3174" 344 | ], 345 | "version": "==2022.4" 346 | }, 347 | "requests": { 348 | "hashes": [ 349 | "sha256:6c1246513ecd5ecd4528a0906f910e8f0f9c6b8ec72030dc9fd154dc1a6efd24", 350 | "sha256:b8aa58f8cf793ffd8782d3d8cb19e66ef36f7aba4353eec859e74678b01b07a7" 351 | ], 352 | "index": "pypi", 353 | "version": "==2.26.0" 354 | }, 355 | "setuptools": { 356 | "hashes": [ 357 | "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17", 358 | "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356" 359 | ], 360 | "markers": "python_version >= '3.7'", 361 | "version": "==65.5.0" 362 | }, 363 | "six": { 364 | "hashes": [ 365 | "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926", 366 | "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254" 367 | ], 368 | "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'", 369 | "version": "==1.16.0" 370 | }, 371 | "soupsieve": { 372 | "hashes": [ 373 | "sha256:3b2503d3c7084a42b1ebd08116e5f81aadfaea95863628c80a3b774a11b7c759", 374 | "sha256:fc53893b3da2c33de295667a0e19f078c14bf86544af307354de5fcf12a3f30d" 375 | ], 376 | "markers": "python_full_version >= '3.6.0'", 377 | "version": "==2.3.2.post1" 378 | }, 379 | "tenacity": { 380 | "hashes": [ 381 | "sha256:35525cd47f82830069f0d6b73f7eb83bc5b73ee2fff0437952cedf98b27653ac", 382 | "sha256:e48c437fdf9340f5666b92cd7990e96bc5fc955e1298baf4a907e3972067a445" 383 | ], 384 | "markers": "python_full_version >= '3.6.0'", 385 | "version": "==8.1.0" 386 | }, 387 | "urllib3": { 388 | "hashes": [ 389 | "sha256:3fa96cf423e6987997fc326ae8df396db2a8b7c667747d47ddd8ecba91f4a74e", 390 | "sha256:b930dd878d5a8afb066a637fbb35144fe7901e3b209d1cd4f524bd0e9deee997" 391 | ], 392 | "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' and python_version < '4'", 393 | "version": "==1.26.12" 394 | }, 395 | "werkzeug": { 396 | "hashes": [ 397 | "sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f", 398 | "sha256:f979ab81f58d7318e064e99c4506445d60135ac5cd2e177a2de0089bfd4c9bd5" 399 | ], 400 | "markers": "python_version >= '3.7'", 401 | "version": "==2.2.2" 402 | }, 403 | "zipp": { 404 | "hashes": [ 405 | "sha256:3a7af91c3db40ec72dd9d154ae18e008c69efe8ca88dde4f9a731bb82fe2f9eb", 406 | "sha256:972cfa31bc2fedd3fa838a51e9bc7e64b7fb725a8c00e7431554311f180e9980" 407 | ], 408 | "markers": "python_version >= '3.7'", 409 | "version": "==3.9.0" 410 | } 411 | }, 412 | "develop": {} 413 | } 414 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. We, the Free Software Foundation, use the 18 | GNU General Public License for most of our software; it applies also to 19 | any other work released this way by its authors. You can apply it to 20 | your programs, too. 21 | 22 | When we speak of free software, we are referring to freedom, not 23 | price. Our General Public Licenses are designed to make sure that you 24 | have the freedom to distribute copies of free software (and charge for 25 | them if you wish), that you receive source code or can get it if you 26 | want it, that you can change the software or use pieces of it in new 27 | free programs, and that you know you can do these things. 28 | 29 | To protect your rights, we need to prevent others from denying you 30 | these rights or asking you to surrender the rights. Therefore, you have 31 | certain responsibilities if you distribute copies of the software, or if 32 | you modify it: responsibilities to respect the freedom of others. 33 | 34 | For example, if you distribute copies of such a program, whether 35 | gratis or for a fee, you must pass on to the recipients the same 36 | freedoms that you received. You must make sure that they, too, receive 37 | or can get the source code. And you must show them these terms so they 38 | know their rights. 39 | 40 | Developers that use the GNU GPL protect your rights with two steps: 41 | (1) assert copyright on the software, and (2) offer you this License 42 | giving you legal permission to copy, distribute and/or modify it. 43 | 44 | For the developers' and authors' protection, the GPL clearly explains 45 | that there is no warranty for this free software. For both users' and 46 | authors' sake, the GPL requires that modified versions be marked as 47 | changed, so that their problems will not be attributed erroneously to 48 | authors of previous versions. 49 | 50 | Some devices are designed to deny users access to install or run 51 | modified versions of the software inside them, although the manufacturer 52 | can do so. This is fundamentally incompatible with the aim of 53 | protecting users' freedom to change the software. The systematic 54 | pattern of such abuse occurs in the area of products for individuals to 55 | use, which is precisely where it is most unacceptable. Therefore, we 56 | have designed this version of the GPL to prohibit the practice for those 57 | products. If such problems arise substantially in other domains, we 58 | stand ready to extend this provision to those domains in future versions 59 | of the GPL, as needed to protect the freedom of users. 60 | 61 | Finally, every program is threatened constantly by software patents. 62 | States should not allow patents to restrict development and use of 63 | software on general-purpose computers, but in those that do, we wish to 64 | avoid the special danger that patents applied to a free program could 65 | make it effectively proprietary. To prevent this, the GPL assures that 66 | patents cannot be used to render the program non-free. 67 | 68 | The precise terms and conditions for copying, distribution and 69 | modification follow. 70 | 71 | TERMS AND CONDITIONS 72 | 73 | 0. Definitions. 74 | 75 | "This License" refers to version 3 of the GNU General Public License. 76 | 77 | "Copyright" also means copyright-like laws that apply to other kinds of 78 | works, such as semiconductor masks. 79 | 80 | "The Program" refers to any copyrightable work licensed under this 81 | License. Each licensee is addressed as "you". "Licensees" and 82 | "recipients" may be individuals or organizations. 83 | 84 | To "modify" a work means to copy from or adapt all or part of the work 85 | in a fashion requiring copyright permission, other than the making of an 86 | exact copy. The resulting work is called a "modified version" of the 87 | earlier work or a work "based on" the earlier work. 88 | 89 | A "covered work" means either the unmodified Program or a work based 90 | on the Program. 91 | 92 | To "propagate" a work means to do anything with it that, without 93 | permission, would make you directly or secondarily liable for 94 | infringement under applicable copyright law, except executing it on a 95 | computer or modifying a private copy. Propagation includes copying, 96 | distribution (with or without modification), making available to the 97 | public, and in some countries other activities as well. 98 | 99 | To "convey" a work means any kind of propagation that enables other 100 | parties to make or receive copies. Mere interaction with a user through 101 | a computer network, with no transfer of a copy, is not conveying. 102 | 103 | An interactive user interface displays "Appropriate Legal Notices" 104 | to the extent that it includes a convenient and prominently visible 105 | feature that (1) displays an appropriate copyright notice, and (2) 106 | tells the user that there is no warranty for the work (except to the 107 | extent that warranties are provided), that licensees may convey the 108 | work under this License, and how to view a copy of this License. If 109 | the interface presents a list of user commands or options, such as a 110 | menu, a prominent item in the list meets this criterion. 111 | 112 | 1. Source Code. 113 | 114 | The "source code" for a work means the preferred form of the work 115 | for making modifications to it. "Object code" means any non-source 116 | form of a work. 117 | 118 | A "Standard Interface" means an interface that either is an official 119 | standard defined by a recognized standards body, or, in the case of 120 | interfaces specified for a particular programming language, one that 121 | is widely used among developers working in that language. 122 | 123 | The "System Libraries" of an executable work include anything, other 124 | than the work as a whole, that (a) is included in the normal form of 125 | packaging a Major Component, but which is not part of that Major 126 | Component, and (b) serves only to enable use of the work with that 127 | Major Component, or to implement a Standard Interface for which an 128 | implementation is available to the public in source code form. A 129 | "Major Component", in this context, means a major essential component 130 | (kernel, window system, and so on) of the specific operating system 131 | (if any) on which the executable work runs, or a compiler used to 132 | produce the work, or an object code interpreter used to run it. 133 | 134 | The "Corresponding Source" for a work in object code form means all 135 | the source code needed to generate, install, and (for an executable 136 | work) run the object code and to modify the work, including scripts to 137 | control those activities. However, it does not include the work's 138 | System Libraries, or general-purpose tools or generally available free 139 | programs which are used unmodified in performing those activities but 140 | which are not part of the work. For example, Corresponding Source 141 | includes interface definition files associated with source files for 142 | the work, and the source code for shared libraries and dynamically 143 | linked subprograms that the work is specifically designed to require, 144 | such as by intimate data communication or control flow between those 145 | subprograms and other parts of the work. 146 | 147 | The Corresponding Source need not include anything that users 148 | can regenerate automatically from other parts of the Corresponding 149 | Source. 150 | 151 | The Corresponding Source for a work in source code form is that 152 | same work. 153 | 154 | 2. Basic Permissions. 155 | 156 | All rights granted under this License are granted for the term of 157 | copyright on the Program, and are irrevocable provided the stated 158 | conditions are met. This License explicitly affirms your unlimited 159 | permission to run the unmodified Program. The output from running a 160 | covered work is covered by this License only if the output, given its 161 | content, constitutes a covered work. This License acknowledges your 162 | rights of fair use or other equivalent, as provided by copyright law. 163 | 164 | You may make, run and propagate covered works that you do not 165 | convey, without conditions so long as your license otherwise remains 166 | in force. You may convey covered works to others for the sole purpose 167 | of having them make modifications exclusively for you, or provide you 168 | with facilities for running those works, provided that you comply with 169 | the terms of this License in conveying all material for which you do 170 | not control copyright. Those thus making or running the covered works 171 | for you must do so exclusively on your behalf, under your direction 172 | and control, on terms that prohibit them from making any copies of 173 | your copyrighted material outside their relationship with you. 174 | 175 | Conveying under any other circumstances is permitted solely under 176 | the conditions stated below. Sublicensing is not allowed; section 10 177 | makes it unnecessary. 178 | 179 | 3. Protecting Users' Legal Rights From Anti-Circumvention Law. 180 | 181 | No covered work shall be deemed part of an effective technological 182 | measure under any applicable law fulfilling obligations under article 183 | 11 of the WIPO copyright treaty adopted on 20 December 1996, or 184 | similar laws prohibiting or restricting circumvention of such 185 | measures. 186 | 187 | When you convey a covered work, you waive any legal power to forbid 188 | circumvention of technological measures to the extent such circumvention 189 | is effected by exercising rights under this License with respect to 190 | the covered work, and you disclaim any intention to limit operation or 191 | modification of the work as a means of enforcing, against the work's 192 | users, your or third parties' legal rights to forbid circumvention of 193 | technological measures. 194 | 195 | 4. Conveying Verbatim Copies. 196 | 197 | You may convey verbatim copies of the Program's source code as you 198 | receive it, in any medium, provided that you conspicuously and 199 | appropriately publish on each copy an appropriate copyright notice; 200 | keep intact all notices stating that this License and any 201 | non-permissive terms added in accord with section 7 apply to the code; 202 | keep intact all notices of the absence of any warranty; and give all 203 | recipients a copy of this License along with the Program. 204 | 205 | You may charge any price or no price for each copy that you convey, 206 | and you may offer support or warranty protection for a fee. 207 | 208 | 5. Conveying Modified Source Versions. 209 | 210 | You may convey a work based on the Program, or the modifications to 211 | produce it from the Program, in the form of source code under the 212 | terms of section 4, provided that you also meet all of these conditions: 213 | 214 | a) The work must carry prominent notices stating that you modified 215 | it, and giving a relevant date. 216 | 217 | b) The work must carry prominent notices stating that it is 218 | released under this License and any conditions added under section 219 | 7. This requirement modifies the requirement in section 4 to 220 | "keep intact all notices". 221 | 222 | c) You must license the entire work, as a whole, under this 223 | License to anyone who comes into possession of a copy. This 224 | License will therefore apply, along with any applicable section 7 225 | additional terms, to the whole of the work, and all its parts, 226 | regardless of how they are packaged. This License gives no 227 | permission to license the work in any other way, but it does not 228 | invalidate such permission if you have separately received it. 229 | 230 | d) If the work has interactive user interfaces, each must display 231 | Appropriate Legal Notices; however, if the Program has interactive 232 | interfaces that do not display Appropriate Legal Notices, your 233 | work need not make them do so. 234 | 235 | A compilation of a covered work with other separate and independent 236 | works, which are not by their nature extensions of the covered work, 237 | and which are not combined with it such as to form a larger program, 238 | in or on a volume of a storage or distribution medium, is called an 239 | "aggregate" if the compilation and its resulting copyright are not 240 | used to limit the access or legal rights of the compilation's users 241 | beyond what the individual works permit. Inclusion of a covered work 242 | in an aggregate does not cause this License to apply to the other 243 | parts of the aggregate. 244 | 245 | 6. Conveying Non-Source Forms. 246 | 247 | You may convey a covered work in object code form under the terms 248 | of sections 4 and 5, provided that you also convey the 249 | machine-readable Corresponding Source under the terms of this License, 250 | in one of these ways: 251 | 252 | a) Convey the object code in, or embodied in, a physical product 253 | (including a physical distribution medium), accompanied by the 254 | Corresponding Source fixed on a durable physical medium 255 | customarily used for software interchange. 256 | 257 | b) Convey the object code in, or embodied in, a physical product 258 | (including a physical distribution medium), accompanied by a 259 | written offer, valid for at least three years and valid for as 260 | long as you offer spare parts or customer support for that product 261 | model, to give anyone who possesses the object code either (1) a 262 | copy of the Corresponding Source for all the software in the 263 | product that is covered by this License, on a durable physical 264 | medium customarily used for software interchange, for a price no 265 | more than your reasonable cost of physically performing this 266 | conveying of source, or (2) access to copy the 267 | Corresponding Source from a network server at no charge. 268 | 269 | c) Convey individual copies of the object code with a copy of the 270 | written offer to provide the Corresponding Source. This 271 | alternative is allowed only occasionally and noncommercially, and 272 | only if you received the object code with such an offer, in accord 273 | with subsection 6b. 274 | 275 | d) Convey the object code by offering access from a designated 276 | place (gratis or for a charge), and offer equivalent access to the 277 | Corresponding Source in the same way through the same place at no 278 | further charge. You need not require recipients to copy the 279 | Corresponding Source along with the object code. If the place to 280 | copy the object code is a network server, the Corresponding Source 281 | may be on a different server (operated by you or a third party) 282 | that supports equivalent copying facilities, provided you maintain 283 | clear directions next to the object code saying where to find the 284 | Corresponding Source. Regardless of what server hosts the 285 | Corresponding Source, you remain obligated to ensure that it is 286 | available for as long as needed to satisfy these requirements. 287 | 288 | e) Convey the object code using peer-to-peer transmission, provided 289 | you inform other peers where the object code and Corresponding 290 | Source of the work are being offered to the general public at no 291 | charge under subsection 6d. 292 | 293 | A separable portion of the object code, whose source code is excluded 294 | from the Corresponding Source as a System Library, need not be 295 | included in conveying the object code work. 296 | 297 | A "User Product" is either (1) a "consumer product", which means any 298 | tangible personal property which is normally used for personal, family, 299 | or household purposes, or (2) anything designed or sold for incorporation 300 | into a dwelling. In determining whether a product is a consumer product, 301 | doubtful cases shall be resolved in favor of coverage. For a particular 302 | product received by a particular user, "normally used" refers to a 303 | typical or common use of that class of product, regardless of the status 304 | of the particular user or of the way in which the particular user 305 | actually uses, or expects or is expected to use, the product. A product 306 | is a consumer product regardless of whether the product has substantial 307 | commercial, industrial or non-consumer uses, unless such uses represent 308 | the only significant mode of use of the product. 309 | 310 | "Installation Information" for a User Product means any methods, 311 | procedures, authorization keys, or other information required to install 312 | and execute modified versions of a covered work in that User Product from 313 | a modified version of its Corresponding Source. The information must 314 | suffice to ensure that the continued functioning of the modified object 315 | code is in no case prevented or interfered with solely because 316 | modification has been made. 317 | 318 | If you convey an object code work under this section in, or with, or 319 | specifically for use in, a User Product, and the conveying occurs as 320 | part of a transaction in which the right of possession and use of the 321 | User Product is transferred to the recipient in perpetuity or for a 322 | fixed term (regardless of how the transaction is characterized), the 323 | Corresponding Source conveyed under this section must be accompanied 324 | by the Installation Information. But this requirement does not apply 325 | if neither you nor any third party retains the ability to install 326 | modified object code on the User Product (for example, the work has 327 | been installed in ROM). 328 | 329 | The requirement to provide Installation Information does not include a 330 | requirement to continue to provide support service, warranty, or updates 331 | for a work that has been modified or installed by the recipient, or for 332 | the User Product in which it has been modified or installed. Access to a 333 | network may be denied when the modification itself materially and 334 | adversely affects the operation of the network or violates the rules and 335 | protocols for communication across the network. 336 | 337 | Corresponding Source conveyed, and Installation Information provided, 338 | in accord with this section must be in a format that is publicly 339 | documented (and with an implementation available to the public in 340 | source code form), and must require no special password or key for 341 | unpacking, reading or copying. 342 | 343 | 7. Additional Terms. 344 | 345 | "Additional permissions" are terms that supplement the terms of this 346 | License by making exceptions from one or more of its conditions. 347 | Additional permissions that are applicable to the entire Program shall 348 | be treated as though they were included in this License, to the extent 349 | that they are valid under applicable law. If additional permissions 350 | apply only to part of the Program, that part may be used separately 351 | under those permissions, but the entire Program remains governed by 352 | this License without regard to the additional permissions. 353 | 354 | When you convey a copy of a covered work, you may at your option 355 | remove any additional permissions from that copy, or from any part of 356 | it. (Additional permissions may be written to require their own 357 | removal in certain cases when you modify the work.) You may place 358 | additional permissions on material, added by you to a covered work, 359 | for which you have or can give appropriate copyright permission. 360 | 361 | Notwithstanding any other provision of this License, for material you 362 | add to a covered work, you may (if authorized by the copyright holders of 363 | that material) supplement the terms of this License with terms: 364 | 365 | a) Disclaiming warranty or limiting liability differently from the 366 | terms of sections 15 and 16 of this License; or 367 | 368 | b) Requiring preservation of specified reasonable legal notices or 369 | author attributions in that material or in the Appropriate Legal 370 | Notices displayed by works containing it; or 371 | 372 | c) Prohibiting misrepresentation of the origin of that material, or 373 | requiring that modified versions of such material be marked in 374 | reasonable ways as different from the original version; or 375 | 376 | d) Limiting the use for publicity purposes of names of licensors or 377 | authors of the material; or 378 | 379 | e) Declining to grant rights under trademark law for use of some 380 | trade names, trademarks, or service marks; or 381 | 382 | f) Requiring indemnification of licensors and authors of that 383 | material by anyone who conveys the material (or modified versions of 384 | it) with contractual assumptions of liability to the recipient, for 385 | any liability that these contractual assumptions directly impose on 386 | those licensors and authors. 387 | 388 | All other non-permissive additional terms are considered "further 389 | restrictions" within the meaning of section 10. If the Program as you 390 | received it, or any part of it, contains a notice stating that it is 391 | governed by this License along with a term that is a further 392 | restriction, you may remove that term. If a license document contains 393 | a further restriction but permits relicensing or conveying under this 394 | License, you may add to a covered work material governed by the terms 395 | of that license document, provided that the further restriction does 396 | not survive such relicensing or conveying. 397 | 398 | If you add terms to a covered work in accord with this section, you 399 | must place, in the relevant source files, a statement of the 400 | additional terms that apply to those files, or a notice indicating 401 | where to find the applicable terms. 402 | 403 | Additional terms, permissive or non-permissive, may be stated in the 404 | form of a separately written license, or stated as exceptions; 405 | the above requirements apply either way. 406 | 407 | 8. Termination. 408 | 409 | You may not propagate or modify a covered work except as expressly 410 | provided under this License. Any attempt otherwise to propagate or 411 | modify it is void, and will automatically terminate your rights under 412 | this License (including any patent licenses granted under the third 413 | paragraph of section 11). 414 | 415 | However, if you cease all violation of this License, then your 416 | license from a particular copyright holder is reinstated (a) 417 | provisionally, unless and until the copyright holder explicitly and 418 | finally terminates your license, and (b) permanently, if the copyright 419 | holder fails to notify you of the violation by some reasonable means 420 | prior to 60 days after the cessation. 421 | 422 | Moreover, your license from a particular copyright holder is 423 | reinstated permanently if the copyright holder notifies you of the 424 | violation by some reasonable means, this is the first time you have 425 | received notice of violation of this License (for any work) from that 426 | copyright holder, and you cure the violation prior to 30 days after 427 | your receipt of the notice. 428 | 429 | Termination of your rights under this section does not terminate the 430 | licenses of parties who have received copies or rights from you under 431 | this License. If your rights have been terminated and not permanently 432 | reinstated, you do not qualify to receive new licenses for the same 433 | material under section 10. 434 | 435 | 9. Acceptance Not Required for Having Copies. 436 | 437 | You are not required to accept this License in order to receive or 438 | run a copy of the Program. Ancillary propagation of a covered work 439 | occurring solely as a consequence of using peer-to-peer transmission 440 | to receive a copy likewise does not require acceptance. However, 441 | nothing other than this License grants you permission to propagate or 442 | modify any covered work. These actions infringe copyright if you do 443 | not accept this License. Therefore, by modifying or propagating a 444 | covered work, you indicate your acceptance of this License to do so. 445 | 446 | 10. Automatic Licensing of Downstream Recipients. 447 | 448 | Each time you convey a covered work, the recipient automatically 449 | receives a license from the original licensors, to run, modify and 450 | propagate that work, subject to this License. You are not responsible 451 | for enforcing compliance by third parties with this License. 452 | 453 | An "entity transaction" is a transaction transferring control of an 454 | organization, or substantially all assets of one, or subdividing an 455 | organization, or merging organizations. If propagation of a covered 456 | work results from an entity transaction, each party to that 457 | transaction who receives a copy of the work also receives whatever 458 | licenses to the work the party's predecessor in interest had or could 459 | give under the previous paragraph, plus a right to possession of the 460 | Corresponding Source of the work from the predecessor in interest, if 461 | the predecessor has it or can get it with reasonable efforts. 462 | 463 | You may not impose any further restrictions on the exercise of the 464 | rights granted or affirmed under this License. For example, you may 465 | not impose a license fee, royalty, or other charge for exercise of 466 | rights granted under this License, and you may not initiate litigation 467 | (including a cross-claim or counterclaim in a lawsuit) alleging that 468 | any patent claim is infringed by making, using, selling, offering for 469 | sale, or importing the Program or any portion of it. 470 | 471 | 11. Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. For 483 | purposes of this definition, "control" includes the right to grant 484 | patent sublicenses in a manner consistent with the requirements of 485 | this License. 486 | 487 | Each contributor grants you a non-exclusive, worldwide, royalty-free 488 | patent license under the contributor's essential patent claims, to 489 | make, use, sell, offer for sale, import and otherwise run, modify and 490 | propagate the contents of its contributor version. 491 | 492 | In the following three paragraphs, a "patent license" is any express 493 | agreement or commitment, however denominated, not to enforce a patent 494 | (such as an express permission to practice a patent or covenant not to 495 | sue for patent infringement). To "grant" such a patent license to a 496 | party means to make such an agreement or commitment not to enforce a 497 | patent against the party. 498 | 499 | If you convey a covered work, knowingly relying on a patent license, 500 | and the Corresponding Source of the work is not available for anyone 501 | to copy, free of charge and under the terms of this License, through a 502 | publicly available network server or other readily accessible means, 503 | then you must either (1) cause the Corresponding Source to be so 504 | available, or (2) arrange to deprive yourself of the benefit of the 505 | patent license for this particular work, or (3) arrange, in a manner 506 | consistent with the requirements of this License, to extend the patent 507 | license to downstream recipients. "Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------