├── requirements.txt ├── us_census_state_populations_2019.csv ├── .github └── workflows │ └── scheduled.yml ├── README.md ├── metadata.json └── us_census_county_populations_2019.csv /requirements.txt: -------------------------------------------------------------------------------- 1 | datasette>=0.54 2 | sqlite-utils 3 | datasette-vega 4 | datasette-graphql 5 | datasette-atom 6 | datasette-yaml 7 | datasette-export-notebook 8 | -------------------------------------------------------------------------------- /us_census_state_populations_2019.csv: -------------------------------------------------------------------------------- 1 | region_id,division_id,state_id,state_name,pop_estimate_2019 2 | 0,0,0,United States,328239523 3 | 1,0,0,Northeast Region,55982803 4 | 2,0,0,Midwest Region,68329004 5 | 3,0,0,South Region,125580448 6 | 4,0,0,West Region,78347268 7 | 3,6,1,Alabama,4903185 8 | 4,9,2,Alaska,731545 9 | 4,8,4,Arizona,7278717 10 | 3,7,5,Arkansas,3017804 11 | 4,9,6,California,39512223 12 | 4,8,8,Colorado,5758736 13 | 1,1,9,Connecticut,3565287 14 | 3,5,10,Delaware,973764 15 | 3,5,11,District of Columbia,705749 16 | 3,5,12,Florida,21477737 17 | 3,5,13,Georgia,10617423 18 | 4,9,15,Hawaii,1415872 19 | 4,8,16,Idaho,1787065 20 | 2,3,17,Illinois,12671821 21 | 2,3,18,Indiana,6732219 22 | 2,4,19,Iowa,3155070 23 | 2,4,20,Kansas,2913314 24 | 3,6,21,Kentucky,4467673 25 | 3,7,22,Louisiana,4648794 26 | 1,1,23,Maine,1344212 27 | 3,5,24,Maryland,6045680 28 | 1,1,25,Massachusetts,6892503 29 | 2,3,26,Michigan,9986857 30 | 2,4,27,Minnesota,5639632 31 | 3,6,28,Mississippi,2976149 32 | 2,4,29,Missouri,6137428 33 | 4,8,30,Montana,1068778 34 | 2,4,31,Nebraska,1934408 35 | 4,8,32,Nevada,3080156 36 | 1,1,33,New Hampshire,1359711 37 | 1,2,34,New Jersey,8882190 38 | 4,8,35,New Mexico,2096829 39 | 1,2,36,New York,19453561 40 | 3,5,37,North Carolina,10488084 41 | 2,4,38,North Dakota,762062 42 | 2,3,39,Ohio,11689100 43 | 3,7,40,Oklahoma,3956971 44 | 4,9,41,Oregon,4217737 45 | 1,2,42,Pennsylvania,12801989 46 | 1,1,44,Rhode Island,1059361 47 | 3,5,45,South Carolina,5148714 48 | 2,4,46,South Dakota,884659 49 | 3,6,47,Tennessee,6829174 50 | 3,7,48,Texas,28995881 51 | 4,8,49,Utah,3205958 52 | 1,1,50,Vermont,623989 53 | 3,5,51,Virginia,8535519 54 | 4,9,53,Washington,7614893 55 | 3,5,54,West Virginia,1792147 56 | 2,3,55,Wisconsin,5822434 57 | 4,8,56,Wyoming,578759 58 | ,,72,Puerto Rico,3193694 -------------------------------------------------------------------------------- /.github/workflows/scheduled.yml: -------------------------------------------------------------------------------- 1 | name: Fetch latest data and deploy with Datasette 2 | 3 | on: 4 | push: 5 | branches: 6 | - main 7 | workflow_dispatch: 8 | schedule: 9 | - cron: '5 * * * *' 10 | 11 | jobs: 12 | scheduled: 13 | runs-on: ubuntu-latest 14 | steps: 15 | - name: Check out corvid-19-datasette 16 | uses: actions/checkout@v2 17 | - name: Check out CSSEGISandData/COVID-19 18 | uses: actions/checkout@v2 19 | with: 20 | repository: CSSEGISandData/COVID-19 21 | path: COVID-19 22 | - name: Check out nytimes/covid-19-data 23 | uses: actions/checkout@v2 24 | with: 25 | repository: nytimes/covid-19-data 26 | path: covid-19-data 27 | - name: Check out LA Times datadesk/california-coronavirus-data 28 | uses: actions/checkout@v2 29 | with: 30 | repository: datadesk/california-coronavirus-data 31 | path: california-coronavirus-data 32 | - name: Check out Economist TheEconomist/covid-19-excess-deaths-tracker 33 | uses: actions/checkout@v2 34 | with: 35 | repository: TheEconomist/covid-19-excess-deaths-tracker 36 | path: covid-19-excess-deaths-tracker 37 | - name: Set up Python 38 | uses: actions/setup-python@v1 39 | with: 40 | python-version: 3.8 41 | - uses: actions/cache@v2 42 | name: Configure pip caching 43 | with: 44 | path: ~/.cache/pip 45 | key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }} 46 | restore-keys: | 47 | ${{ runner.os }}-pip- 48 | - name: Install Python dependencies 49 | run: | 50 | python -m pip install --upgrade pip 51 | pip install -r requirements.txt 52 | - name: Build covid.db database 53 | run: python build_database.py 54 | - name: Set variables to decide if we should deploy 55 | id: decide_variables 56 | run: |- 57 | echo "##[set-output name=latest;]$(datasette inspect covid.db | jq '.covid.hash' -r)" 58 | echo "##[set-output name=deployed;]$(curl -s https://covid-19.datasettes.com/-/databases.json | jq '.[0].hash' -r)" 59 | - name: Set up Cloud Run 60 | if: github.event_name == 'workflow_dispatch' || steps.decide_variables.outputs.latest != steps.decide_variables.outputs.deployed 61 | uses: google-github-actions/setup-gcloud@master 62 | with: 63 | version: '275.0.0' 64 | service_account_email: ${{ secrets.GCP_SA_EMAIL }} 65 | service_account_key: ${{ secrets.GCP_SA_KEY }} 66 | - name: Deploy to Cloud Run 67 | if: github.event_name == 'workflow_dispatch' || steps.decide_variables.outputs.latest != steps.decide_variables.outputs.deployed 68 | run: |- 69 | gcloud config set run/region us-central1 70 | gcloud config set project datasette-222320 71 | datasette publish cloudrun covid.db \ 72 | --service covid-19 \ 73 | -m metadata.json \ 74 | --branch main \ 75 | --memory 2Gi \ 76 | --install=datasette-vega \ 77 | --install=datasette-copyable \ 78 | --install=datasette-graphql \ 79 | --install=datasette-yaml \ 80 | --install=datasette-atom \ 81 | --install=datasette-export-notebook>=0.3 \ 82 | --extra-options="--config facet_time_limit_ms:3000 --config sql_time_limit_ms:3000" 83 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # covid-19-datasette 2 | 3 | [![Fetch latest data and deploy with Datasette](https://github.com/simonw/covid-19-datasette/workflows/Fetch%20latest%20data%20and%20deploy%20with%20Datasette/badge.svg)](https://github.com/simonw/covid-19-datasette/actions?query=workflow%3A%22Fetch+latest+data+and+deploy+with+Datasette%22) 4 | 5 | Deploys a Datasette instance with data from the following sources: 6 | 7 | * [CSSEGISandData/COVID-19](https://github.com/CSSEGISandData/COVID-19) by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) 8 | * [nytimes/covid-19-data](https://github.com/nytimes/covid-19-data) by The New York Times 9 | * [datadesk/california-coronavirus-data](https://github.com/datadesk/california-coronavirus-data) by The Los Angeles Times 10 | * [TheEconomist/covid-19-excess-deaths-tracker](https://github.com/TheEconomist/covid-19-excess-deaths-tracker) by The Economist 11 | * State and county population estimates from the [US Census](https://www.census.gov/programs-surveys/popest.html) 12 | 13 | The Datasette instance lives at https://covid-19.datasettes.com/ and is updated hourly using [a scheduled GitHub Action](https://github.com/simonw/covid-19-datasette/blob/main/.github/workflows/scheduled.yml). 14 | 15 | More about this project on my blog: [COVID-19 numbers in Datasette](https://simonwillison.net/2020/Mar/11/covid-19/). 16 | 17 | This repository uses the deployment pattern described in [Deploying a data API using GitHub Actions and Cloud Run](https://simonwillison.net/2020/Jan/21/github-actions-cloud-run/). 18 | 19 | ## Using this data responsibly 20 | 21 | Please **do not** use this tool to share information about COVID-19 without making absolutely sure you understand how the data is structured and sourced. 22 | 23 | Recommended reading: 24 | 25 | * [Why It’s So Freaking Hard To Make A Good COVID-19 Model](https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid-19-model/) 26 | * [Ten Considerations Before You Create Another Chart About COVID-19](https://medium.com/nightingale/ten-considerations-before-you-create-another-chart-about-covid-19-27d3bd691be8) 27 | 28 | ## Johns Hopkins 29 | 30 | The database is partly built from the daily report CSV files in the Johns Hopkins CSSE `csse_covid_19_data` folder - be sure to consult [their README](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data) for documentation of the fields. 31 | 32 | They are actively making changes to how they report data. You should [follow their issues](https://github.com/CSSEGISandData/COVID-19/issues) closely for updates - for example [this issue](https://github.com/CSSEGISandData/COVID-19/issues/382) about switching from reporting USA data at the county to the state level. 33 | 34 | The [build script for the database](https://github.com/simonw/covid-19-datasette/blob/main/build_database.py) makes one alteration to their data: it attempts to fill any missing `latitude` and `longitude` columns with values from similar rows. 35 | 36 | If you are going to make use of those columns, make sure you understand how that backfill mechanism works in case it affects your calculations in some way. 37 | 38 | ## The New York Times 39 | 40 | The New York Times has [a comprehensive README](https://github.com/nytimes/covid-19-data/blob/master/README.md) describing how their data is sourced. You should read it! They announced their data in [We’re Sharing Coronavirus Case Data for Every U.S. County](https://www.nytimes.com/article/coronavirus-county-data-us.html). 41 | 42 | They are using the data for their [Coronavirus in the U.S.: Latest Map and Case Count](https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html) article. 43 | 44 | ## The Los Angeles Times 45 | 46 | The Los Angeles Time [comprehensive README](https://github.com/datadesk/california-coronavirus-data/blob/master/README.md) describes the data in the [latimes_agency_totals](https://covid-19.datasettes.com/covid/latimes_agency_totals), [latimes_county_totals](https://covid-19.datasettes.com/covid/latimes_county_totals), [latimes_place_totals](https://covid-19.datasettes.com/covid/latimes_place_totals) and [latimes_state_totals](https://covid-19.datasettes.com/covid/latimes_state_totals) tables. 47 | 48 | See [To aid coronavirus fight, The Times releases database of California cases](https://www.latimes.com/california/story/2020-04-06/coronavirus-fight-la-times-releases-its-california-cases-database) for background on the release of this data. 49 | 50 | The data is used for their [Tracking coronavirus in California](https://www.latimes.com/projects/california-coronavirus-cases-tracking-outbreak/) page, which is constantly updated. 51 | 52 | ## The Economist 53 | 54 | The Economist publish the data behind their ongoing interactive [Tracking covid-19 excess deaths across countries](https://www.economist.com/graphic-detail/2020/04/16/tracking-covid-19-excess-deaths-across-countries). Their [README](https://github.com/TheEconomist/covid-19-excess-deaths-tracker/blob/master/README.md) describes the data sources they use for individual countries in detail. 55 | 56 | This data is imported into the [economist_excess_deaths](https://covid-19.datasettes.com/covid/economist_excess_deaths) and [economist_historical_deaths](https://covid-19.datasettes.com/covid/economist_historical_deaths) tables, with one alteration: a `cadence` column is added showing if each row is being collected on either a `weekly` or `monthly` basis. 57 | 58 | ## US Census 59 | 60 | Two additional tables contain population data from the US Census. 61 | 62 | * [us_census_state_populations_2019](https://covid-19.datasettes.com/covid/us_census_state_populations_2019) has data on the estimated 2019 population of different US States, derived from their [state population totals](https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html). 63 | * [us_census_county_populations_2019](https://covid-19.datasettes.com/covid/us_census_county_populations_2019) has data on estimated 2019 population of different US counties, keyed against county FIPS codes. This data was compiled [by Aaron King](https://github.com/nytimes/covid-19-data/pull/155) using [U.S. Census Bureau, Population Division, "Annual Estimates of the Resident Population for Counties in the United States: April 1, 2010 to July 1, 2019 (CO-EST2019-ANNRES)](https://www2.census.gov/programs-surveys/popest/tables/2010-2019/counties/totals/co-est2019-annres.xlsx). 64 | 65 | This repository includes CSV data for both of these tables. 66 | 67 | The [latest_ny_times_counties_with_populations](https://covid-19.datasettes.com/covid/latest_ny_times_counties_with_populations) view uses this data to calculate cases and deaths per million for US counties, based on the latest county figures from the New York Times. 68 | 69 | ## Example issues 70 | 71 | * Remember: the number of reported cases is very heavily influenced by the availability of testing. 72 | * [This Twitter thread](https://twitter.com/politicalmath/status/1243950120598556672) is an excellent overview of the challenges involved in comparing numbers from different states and countries. 73 | * On the 23rd March 2020 Johns Hopkins [added four new columns](https://github.com/CSSEGISandData/COVID-19/commit/e748b6d8a55e4a88371af56b129ababe1712522d) to the daily CSV file: `admin2`, `fips`, `active` and `combined_key`. These are not present in older CSV files. [#4](https://github.com/simonw/covid-19-datasette/issues/4). 74 | * Some countries (like Italy) are represented by [just the rows](https://covid-19.datasettes.com/covid/johns_hopkins_csse_daily_reports?country_or_region=Italy&_sort_desc=confirmed#g.mark=bar&g.x_column=day&g.x_type=ordinal&g.y_column=confirmed&g.y_type=quantitative) with `country_or_region` set to `Italy` (and `province_or_state` set to `null`). Larger countries such as the United States have multiple rows for each day divided into separate `province_or_state` values - [example](https://covid-19.datasettes.com/covid/johns_hopkins_csse_daily_reports?_size=1000&country_or_region__exact=US&_sort_desc=day#g.mark=bar&g.x_column=day&g.x_type=ordinal&g.y_column=confirmed&g.y_type=quantitative&g.color_column=province_or_state). 75 | * Santa Clara County appears to be represented as `Santa Clara, CA` in some records and `Santa Clara County, CA` in others - [example](https://covid-19.datasettes.com/covid/johns_hopkins_csse_daily_reports?province_or_state__contains=santa+clara&_sort_desc=day#g.mark=bar&g.x_column=day&g.x_type=ordinal&g.y_column=confirmed&g.y_type=quantitative). 76 | * Passengers from the Diamond Princess cruise are represented by a number of different rows with "From Diamond Princess" in their `province_or_state` column - [example](https://covid-19.datasettes.com/covid/johns_hopkins_csse_daily_reports?_facet=province_or_state&_facet=country_or_region&province_or_state__contains=from+diamond&_sort_desc=day). 77 | * The [latest_ny_times_counties_with_populations](https://covid-19.datasettes.com/covid/latest_ny_times_counties_with_populations) view omits some counties, notably all New York City counties, because the New York Times groups all New York City data into rows with `county` equal to "New York City" and an empty `fips` column. Thus total cases represented in [latest_ny_times_counties_with_populations](https://covid-19.datasettes.com/covid/latest_ny_times_counties_with_populations) are lower than total cases represented in [ny_times_us_states](https://covid-19.datasettes.com/covid/ny_times_us_states) by at least the number of cases in New York City. 78 | -------------------------------------------------------------------------------- /metadata.json: -------------------------------------------------------------------------------- 1 | { 2 | "title": "COVID-19 cases, using data from Johns Hopkins CSSE, the New York Times and the LA Times", 3 | "about": "simonw/covid-19-datasette", 4 | "about_url": "https://github.com/simonw/covid-19-datasette", 5 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 6 | "databases": { 7 | "covid": { 8 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 9 | "tables": { 10 | "johns_hopkins_csse_daily_reports": { 11 | "sort_desc": "day", 12 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 13 | "facets": [ 14 | "province_or_state", 15 | "country_or_region" 16 | ], 17 | "source": "Johns Hopkins CSSE", 18 | "source_url": "https://github.com/CSSEGISandData/COVID-19" 19 | }, 20 | "economist_excess_deaths": { 21 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 22 | "facets": [ 23 | "country", 24 | "cadence" 25 | ], 26 | "source": "The Economist", 27 | "source_url": "https://github.com/TheEconomist/covid-19-excess-deaths-tracker", 28 | "license": "CC BY 4.0", 29 | "license_url": "https://creativecommons.org/licenses/by/4.0/" 30 | }, 31 | "economist_historical_deaths": { 32 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 33 | "facets": [ 34 | "country", 35 | "cadence" 36 | ], 37 | "source": "The Economist", 38 | "source_url": "https://github.com/TheEconomist/covid-19-excess-deaths-tracker", 39 | "license": "CC BY 4.0", 40 | "license_url": "https://creativecommons.org/licenses/by/4.0/" 41 | }, 42 | "ny_times_us_states": { 43 | "sort_desc": "date", 44 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 45 | "facets": [ 46 | "state" 47 | ], 48 | "source": "The New York Times", 49 | "source_url": "https://github.com/nytimes/covid-19-data", 50 | "license": "LICENSE", 51 | "license_url": "https://github.com/nytimes/covid-19-data/blob/master/LICENSE" 52 | }, 53 | "ny_times_us_counties": { 54 | "sort_desc": "date", 55 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 56 | "facets": [ 57 | "state", 58 | "county", 59 | "fips" 60 | ], 61 | "source": "The New York Times", 62 | "source_url": "https://github.com/nytimes/covid-19-data", 63 | "license": "LICENSE", 64 | "license_url": "https://github.com/nytimes/covid-19-data/blob/master/LICENSE" 65 | }, 66 | "latimes_agency_totals": { 67 | "sort_desc": "date", 68 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 69 | "facets": [ 70 | "agency", 71 | "county", 72 | "fips" 73 | ], 74 | "source": "The Los Angeles Times", 75 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 76 | "license": "Reusing the data", 77 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 78 | "about": "Data dictionary for latimes_agency_totals", 79 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#latimes-agency-totalscsv" 80 | }, 81 | "latimes_county_totals": { 82 | "sort_desc": "date", 83 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 84 | "facets": [ 85 | "county", 86 | "fips" 87 | ], 88 | "source": "The Los Angeles Times", 89 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 90 | "license": "Reusing the data", 91 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 92 | "about": "Data dictionary for latimes_county_totals", 93 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#latimes-county-totalscsv" 94 | }, 95 | "latimes_place_totals": { 96 | "sort_desc": "date", 97 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 98 | "facets": [ 99 | "county", 100 | "fips" 101 | ], 102 | "source": "The Los Angeles Times", 103 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 104 | "license": "Reusing the data", 105 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 106 | "about": "Data dictionary for latimes_place_totals", 107 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#latimes-place-totalscsv" 108 | }, 109 | "latimes_state_totals": { 110 | "sort_desc": "date", 111 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 112 | "source": "The Los Angeles Times", 113 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 114 | "license": "Reusing the data", 115 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 116 | "about": "Data dictionary for latimes_state_totals", 117 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#latimes-state-totalscsv" 118 | }, 119 | "latimes_cdph_state_totals": { 120 | "sort_desc": "date", 121 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 122 | "source": "California Department of Public Health via The Los Angeles Times", 123 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 124 | "license": "Reusing the data", 125 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 126 | "about": "Data dictionary for latimes_cdph_state_totals", 127 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#cdph-state-totalscsv" 128 | }, 129 | "la_times_cdph_adult_and_senior_care_facilities": { 130 | "sort_desc": "date", 131 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 132 | "source": "California Department of Public Health via The Los Angeles Times", 133 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 134 | "license": "Reusing the data", 135 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 136 | "about": "Data dictionary for la_times_cdph_adult_and_senior_care_facilities", 137 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#cdph-adult-and-senior-care-facilitiescsv" 138 | }, 139 | "la_times_cdph_adult_and_senior_care_totals": { 140 | "sort_desc": "date", 141 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 142 | "source": "California Department of Public Health via The Los Angeles Times", 143 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 144 | "license": "Reusing the data", 145 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 146 | "about": "Data dictionary for la_times_cdph_adult_and_senior_care_totals", 147 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#cdph-adult-and-senior-care-totalscsv" 148 | }, 149 | "la_times_cdph_hospital_patient_county_totals": { 150 | "sort_desc": "date", 151 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 152 | "source": "California Department of Public Health via The Los Angeles Times", 153 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 154 | "license": "Reusing the data", 155 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 156 | "about": "Data dictionary for la_times_cdph_hospital_patient_county_totals", 157 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#cdph-hospital-patient-county-totalscsv" 158 | }, 159 | "la_times_cdph_skilled_nursing_facilities": { 160 | "sort_desc": "date", 161 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 162 | "source": "California Department of Public Health via The Los Angeles Times", 163 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 164 | "license": "Reusing the data", 165 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 166 | "about": "Data dictionary for la_times_cdph_skilled_nursing_facilities", 167 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#cdph-skilled-nursing-facilitiescsv" 168 | }, 169 | "la_times_cdph_skilled_nursing_totals": { 170 | "sort_desc": "date", 171 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 172 | "source": "California Department of Public Health via The Los Angeles Times", 173 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 174 | "license": "Reusing the data", 175 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 176 | "about": "Data dictionary for la_times_cdph_skilled_nursing_totals", 177 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#cdph-skilled-nursing-totalscsv" 178 | }, 179 | "la_times_cdph_state_totals": { 180 | "sort_desc": "date", 181 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 182 | "source": "California Department of Public Health via The Los Angeles Times", 183 | "source_url": "https://github.com/datadesk/california-coronavirus-data", 184 | "license": "Reusing the data", 185 | "license_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#reusing-the-data", 186 | "about": "Data dictionary for la_times_cdph_state_totals", 187 | "about_url": "https://github.com/datadesk/california-coronavirus-data/blob/master/README.md#cdph-state-totalscsv" 188 | }, 189 | "us_census_state_populations_2019": { 190 | "source": "census.gov", 191 | "source_url": "https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html" 192 | }, 193 | "us_census_county_populations_2019": { 194 | "source": "census.gov", 195 | "source_url": "https://www2.census.gov/programs-surveys/popest/tables/2010-2019/counties/totals/co-est2019-annres.xlsx" 196 | }, 197 | "latest_ny_times_counties_with_populations": { 198 | "sort_desc": "cases_per_million", 199 | "description_html": "
⚠️ Consult the README for warnings about using and building on this data. Also review Why It’s So Freaking Hard To Make A Good COVID-19 Model and Ten Considerations Before You Create Another Chart About COVID-19.
", 200 | "facets": [ 201 | "state" 202 | ], 203 | "source": "The New York Times", 204 | "source_url": "https://github.com/nytimes/covid-19-data", 205 | "license": "LICENSE", 206 | "license_url": "https://github.com/nytimes/covid-19-data/blob/master/LICENSE" 207 | }, 208 | "daily_reports": { 209 | "hidden": true, 210 | "sort_desc": "day", 211 | "description_html": "
⚠️ This table has been renamed. Use johns_hopkins_csse_daily_reports instead.
", 212 | "facets": [ 213 | "province_or_state", 214 | "country_or_region" 215 | ], 216 | "source": "Johns Hopkins CSSE", 217 | "source_url": "https://github.com/CSSEGISandData/COVID-19" 218 | } 219 | } 220 | } 221 | } 222 | } 223 | -------------------------------------------------------------------------------- /us_census_county_populations_2019.csv: -------------------------------------------------------------------------------- 1 | fips,population 2 | 01001,55869 3 | 01003,223234 4 | 01005,24686 5 | 01007,22394 6 | 01009,57826 7 | 01011,10101 8 | 01013,19448 9 | 01015,113605 10 | 01017,33254 11 | 01019,26196 12 | 01021,44428 13 | 01023,12589 14 | 01025,23622 15 | 01027,13235 16 | 01029,14910 17 | 01031,52342 18 | 01033,55241 19 | 01035,12067 20 | 01037,10663 21 | 01039,37049 22 | 01041,13772 23 | 01043,83768 24 | 01045,49172 25 | 01047,37196 26 | 01049,71513 27 | 01051,81209 28 | 01053,36633 29 | 01055,102268 30 | 01057,16302 31 | 01059,31362 32 | 01061,26271 33 | 01063,8111 34 | 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| 18005,83779 701 | 18007,8748 702 | 18009,11758 703 | 18011,67843 704 | 18013,15092 705 | 18015,20257 706 | 18017,37689 707 | 18019,118302 708 | 18021,26225 709 | 18023,32399 710 | 18025,10577 711 | 18027,33351 712 | 18029,49458 713 | 18031,26559 714 | 18033,43475 715 | 18035,114135 716 | 18037,42736 717 | 18039,206341 718 | 18041,23102 719 | 18043,78522 720 | 18045,16346 721 | 18047,22758 722 | 18049,19974 723 | 18051,33659 724 | 18053,65769 725 | 18055,31922 726 | 18057,338011 727 | 18059,78168 728 | 18061,40515 729 | 18063,170311 730 | 18065,47972 731 | 18067,82544 732 | 18069,36520 733 | 18071,44231 734 | 18073,33562 735 | 18075,20436 736 | 18077,32308 737 | 18079,27735 738 | 18081,158167 739 | 18083,36594 740 | 18085,79456 741 | 18087,39614 742 | 18089,485493 743 | 18091,109888 744 | 18093,45370 745 | 18095,129569 746 | 18097,964582 747 | 18099,46258 748 | 18101,10255 749 | 18103,35516 750 | 18105,148431 751 | 18107,38338 752 | 18109,70489 753 | 18111,13984 754 | 18113,47744 755 | 18115,5875 756 | 18117,19646 757 | 18119,20799 758 | 18121,16937 759 | 18123,19169 760 | 18125,12389 761 | 18127,170389 762 | 18129,25427 763 | 18131,12353 764 | 18133,37576 765 | 18135,24665 766 | 18137,28324 767 | 18139,16581 768 | 18141,271826 769 | 18143,23873 770 | 18145,44729 771 | 18147,20277 772 | 18149,22995 773 | 18151,34594 774 | 18153,20669 775 | 18155,10751 776 | 18157,195732 777 | 18159,15148 778 | 18161,7054 779 | 18163,181451 780 | 18165,15498 781 | 18167,107038 782 | 18169,30996 783 | 18171,8265 784 | 18173,62998 785 | 18175,28036 786 | 18177,65884 787 | 18179,28296 788 | 18181,24102 789 | 18183,33964 790 | 19001,7152 791 | 19003,3602 792 | 19005,13687 793 | 19007,12426 794 | 19009,5496 795 | 19011,25645 796 | 19013,131228 797 | 19015,26234 798 | 19017,25062 799 | 19019,21175 800 | 19021,19620 801 | 19023,14439 802 | 19025,9668 803 | 19027,20165 804 | 19029,12836 805 | 19031,18627 806 | 19033,42450 807 | 19035,11235 808 | 19037,11933 809 | 19039,9395 810 | 19041,16016 811 | 19043,17549 812 | 19045,46429 813 | 19047,16820 814 | 19049,93453 815 | 19051,9000 816 | 19053,7870 817 | 19055,17011 818 | 19057,38967 819 | 19059,17258 820 | 19061,97311 821 | 19063,9208 822 | 19065,19650 823 | 19067,15642 824 | 19069,10070 825 | 19071,6960 826 | 19073,8888 827 | 19075,12232 828 | 19077,10689 829 | 19079,14773 830 | 19081,10630 831 | 19083,16846 832 | 19085,14049 833 | 19087,19954 834 | 19089,9158 835 | 19091,9558 836 | 19093,6860 837 | 19095,16184 838 | 19097,19439 839 | 19099,37185 840 | 19101,18295 841 | 19103,151140 842 | 19105,20681 843 | 19107,10246 844 | 19109,14813 845 | 19111,33657 846 | 19113,226706 847 | 19115,11035 848 | 19117,8600 849 | 19119,11755 850 | 19121,16338 851 | 19123,22095 852 | 19125,33253 853 | 19127,39369 854 | 19129,15109 855 | 19131,10586 856 | 19133,8615 857 | 19135,7707 858 | 19137,9917 859 | 19139,42664 860 | 19141,13753 861 | 19143,5958 862 | 19145,15107 863 | 19147,8886 864 | 19149,25177 865 | 19151,6619 866 | 19153,490161 867 | 19155,93206 868 | 19157,18504 869 | 19159,4894 870 | 19161,9721 871 | 19163,172943 872 | 19165,11454 873 | 19167,34855 874 | 19169,97117 875 | 19171,16854 876 | 19173,6121 877 | 19175,12241 878 | 19177,7044 879 | 19179,34969 880 | 19181,51466 881 | 19183,21965 882 | 19185,6441 883 | 19187,35904 884 | 19189,10354 885 | 19191,19991 886 | 19193,103107 887 | 19195,7381 888 | 19197,12562 889 | 20001,12369 890 | 20003,7858 891 | 20005,16073 892 | 20007,4427 893 | 20009,25779 894 | 20011,14534 895 | 20013,9564 896 | 20015,66911 897 | 20017,2648 898 | 20019,3250 899 | 20021,19939 900 | 20023,2657 901 | 20025,1994 902 | 20027,8002 903 | 20029,8786 904 | 20031,8179 905 | 20033,1700 906 | 20035,34908 907 | 20037,38818 908 | 20039,2827 909 | 20041,18466 910 | 20043,7600 911 | 20045,122259 912 | 20047,2798 913 | 20049,2530 914 | 20051,28553 915 | 20053,6102 916 | 20055,36467 917 | 20057,33619 918 | 20059,25544 919 | 20061,31670 920 | 20063,2636 921 | 20065,2482 922 | 20067,7150 923 | 20069,5988 924 | 20071,1232 925 | 20073,5982 926 | 20075,2539 927 | 20077,5436 928 | 20079,34429 929 | 20081,3968 930 | 20083,1794 931 | 20085,13171 932 | 20087,19043 933 | 20089,2879 934 | 20091,602401 935 | 20093,3838 936 | 20095,7152 937 | 20097,2475 938 | 20099,19618 939 | 20101,1535 940 | 20103,81758 941 | 20105,2962 942 | 20107,9703 943 | 20109,2794 944 | 20111,33195 945 | 20113,28542 946 | 20115,11884 947 | 20117,9707 948 | 20119,4033 949 | 20121,34237 950 | 20123,5979 951 | 20125,31829 952 | 20127,5620 953 | 20129,2587 954 | 20131,10231 955 | 20133,16007 956 | 20135,2750 957 | 20137,5361 958 | 20139,15949 959 | 20141,3421 960 | 20143,5704 961 | 20145,6414 962 | 20147,5234 963 | 20149,24383 964 | 20151,9164 965 | 20153,2530 966 | 20155,61998 967 | 20157,4636 968 | 20159,9537 969 | 20161,74232 970 | 20163,4920 971 | 20165,3036 972 | 20167,6856 973 | 20169,54224 974 | 20171,4823 975 | 20173,516042 976 | 20175,21428 977 | 20177,176875 978 | 20179,2521 979 | 20181,5917 980 | 20183,3583 981 | 20185,4156 982 | 20187,2006 983 | 20189,5485 984 | 20191,22836 985 | 20193,7777 986 | 20195,2803 987 | 20197,6931 988 | 20199,1518 989 | 20201,5406 990 | 20203,2119 991 | 20205,8525 992 | 20207,3138 993 | 20209,165429 994 | 21001,19202 995 | 21003,21315 996 | 21005,22747 997 | 21007,7888 998 | 21009,44249 999 | 21011,12500 1000 | 21013,26032 1001 | 21015,133581 1002 | 21017,19788 1003 | 21019,46718 1004 | 21021,30060 1005 | 21023,8303 1006 | 21025,12630 1007 | 21027,20477 1008 | 21029,81676 1009 | 21031,12879 1010 | 21033,12747 1011 | 21035,39001 1012 | 21037,93584 1013 | 21039,4760 1014 | 21041,10631 1015 | 21043,26797 1016 | 21045,16159 1017 | 21047,70461 1018 | 21049,36263 1019 | 21051,19901 1020 | 21053,10218 1021 | 21055,8806 1022 | 21057,6614 1023 | 21059,101511 1024 | 21061,12150 1025 | 21063,7517 1026 | 21065,14106 1027 | 21067,323152 1028 | 21069,14581 1029 | 21071,35589 1030 | 21073,50991 1031 | 21075,5969 1032 | 21077,8869 1033 | 21079,17666 1034 | 21081,25069 1035 | 21083,37266 1036 | 21085,26427 1037 | 21087,10941 1038 | 21089,35098 1039 | 21091,8722 1040 | 21093,110958 1041 | 21095,26010 1042 | 21097,18886 1043 | 21099,19035 1044 | 21101,45210 1045 | 21103,16126 1046 | 21105,4380 1047 | 21107,44686 1048 | 21109,13329 1049 | 21111,766757 1050 | 21113,54115 1051 | 21115,22188 1052 | 21117,166998 1053 | 21119,14806 1054 | 21121,31145 1055 | 21123,14398 1056 | 21125,60813 1057 | 21127,15317 1058 | 21129,7403 1059 | 21131,9877 1060 | 21133,21553 1061 | 21135,13275 1062 | 21137,24549 1063 | 21139,9194 1064 | 21141,27102 1065 | 21143,8210 1066 | 21145,65418 1067 | 21147,17231 1068 | 21149,9207 1069 | 21151,92987 1070 | 21153,12161 1071 | 21155,19273 1072 | 21157,31100 1073 | 21159,11195 1074 | 21161,17070 1075 | 21163,28572 1076 | 21165,6489 1077 | 21167,21933 1078 | 21169,10071 1079 | 21171,10650 1080 | 21173,28157 1081 | 21175,13309 1082 | 21177,30622 1083 | 21179,46233 1084 | 21181,7269 1085 | 21183,23994 1086 | 21185,66799 1087 | 21187,10901 1088 | 21189,4415 1089 | 21191,14590 1090 | 21193,25758 1091 | 21195,57876 1092 | 21197,12359 1093 | 21199,64979 1094 | 21201,2108 1095 | 21203,16695 1096 | 21205,24460 1097 | 21207,17923 1098 | 21209,57004 1099 | 21211,49024 1100 | 21213,18572 1101 | 21215,19351 1102 | 21217,25769 1103 | 21219,12294 1104 | 21221,14651 1105 | 21223,8471 1106 | 21225,14381 1107 | 21227,132896 1108 | 21229,12095 1109 | 21231,20333 1110 | 21233,12942 1111 | 21235,36264 1112 | 21237,7157 1113 | 21239,26734 1114 | 22001,62045 1115 | 22003,25627 1116 | 22005,126604 1117 | 22007,21891 1118 | 22009,40144 1119 | 22011,37497 1120 | 22013,13241 1121 | 22015,127039 1122 | 22017,240204 1123 | 22019,203436 1124 | 22021,9918 1125 | 22023,6973 1126 | 22025,9494 1127 | 22027,15670 1128 | 22029,19259 1129 | 22031,27463 1130 | 22033,440059 1131 | 22035,6861 1132 | 22037,19135 1133 | 22039,33395 1134 | 22041,20015 1135 | 22043,22389 1136 | 22045,69830 1137 | 22047,32511 1138 | 22049,15744 1139 | 22051,432493 1140 | 22053,31368 1141 | 22055,244390 1142 | 22057,97614 1143 | 22059,14892 1144 | 22061,46742 1145 | 22063,140789 1146 | 22065,10951 1147 | 22067,24874 1148 | 22069,38158 1149 | 22071,390144 1150 | 22073,153279 1151 | 22075,23197 1152 | 22077,21730 1153 | 22079,129648 1154 | 22081,8442 1155 | 22083,20122 1156 | 22085,23884 1157 | 22087,47244 1158 | 22089,53100 1159 | 22091,10132 1160 | 22093,21096 1161 | 22095,42837 1162 | 22097,82124 1163 | 22099,53431 1164 | 22101,49348 1165 | 22103,260419 1166 | 22105,134758 1167 | 22107,4334 1168 | 22109,110461 1169 | 22111,22108 1170 | 22113,59511 1171 | 22115,47429 1172 | 22117,46194 1173 | 22119,38340 1174 | 22121,26465 1175 | 22123,10830 1176 | 22125,15568 1177 | 22127,13904 1178 | 23001,108277 1179 | 23003,67055 1180 | 23005,295003 1181 | 23007,30199 1182 | 23009,54987 1183 | 23011,122302 1184 | 23013,39772 1185 | 23015,34634 1186 | 23017,57975 1187 | 23019,152148 1188 | 23021,16785 1189 | 23023,35856 1190 | 23025,50484 1191 | 23027,39715 1192 | 23029,31379 1193 | 23031,207641 1194 | 24001,70416 1195 | 24003,579234 1196 | 24005,827370 1197 | 24009,92525 1198 | 24011,33406 1199 | 24013,168447 1200 | 24015,102855 1201 | 24017,163257 1202 | 24019,31929 1203 | 24021,259547 1204 | 24023,29014 1205 | 24025,255441 1206 | 24027,325690 1207 | 24029,19422 1208 | 24031,1050688 1209 | 24033,909327 1210 | 24035,50381 1211 | 24037,113510 1212 | 24039,25616 1213 | 24041,37181 1214 | 24043,151049 1215 | 24045,103609 1216 | 24047,52276 1217 | 24510,593490 1218 | 25001,212990 1219 | 25003,124944 1220 | 25005,565217 1221 | 25007,17332 1222 | 25009,789034 1223 | 25011,70180 1224 | 25013,466372 1225 | 25015,160830 1226 | 25017,1611699 1227 | 25019,11399 1228 | 25021,706775 1229 | 25023,521202 1230 | 25025,803907 1231 | 25027,830622 1232 | 26001,10405 1233 | 26003,9108 1234 | 26005,118081 1235 | 26007,28405 1236 | 26009,23324 1237 | 26011,14883 1238 | 26013,8209 1239 | 26015,61550 1240 | 26017,103126 1241 | 26019,17766 1242 | 26021,153401 1243 | 26023,43517 1244 | 26025,134159 1245 | 26027,51787 1246 | 26029,26143 1247 | 26031,25276 1248 | 26033,37349 1249 | 26035,30950 1250 | 26037,79595 1251 | 26039,14029 1252 | 26041,35784 1253 | 26043,25239 1254 | 26045,110268 1255 | 26047,33415 1256 | 26049,405813 1257 | 26051,25449 1258 | 26053,13975 1259 | 26055,93088 1260 | 26057,40711 1261 | 26059,45605 1262 | 26061,35684 1263 | 26063,30981 1264 | 26065,292406 1265 | 26067,64697 1266 | 26069,25127 1267 | 26071,11066 1268 | 26073,69872 1269 | 26075,158510 1270 | 26077,265066 1271 | 26079,18038 1272 | 26081,656955 1273 | 26083,2116 1274 | 26085,11853 1275 | 26087,87607 1276 | 26089,21761 1277 | 26091,98451 1278 | 26093,191995 1279 | 26095,6229 1280 | 26097,10799 1281 | 26099,873972 1282 | 26101,24558 1283 | 26103,66699 1284 | 26105,29144 1285 | 26107,43453 1286 | 26109,22780 1287 | 26111,83156 1288 | 26113,15118 1289 | 26115,150500 1290 | 26117,63888 1291 | 26119,9328 1292 | 26121,173566 1293 | 26123,48980 1294 | 26125,1257584 1295 | 26127,26467 1296 | 26129,20997 1297 | 26131,5720 1298 | 26133,23460 1299 | 26135,8241 1300 | 26137,24668 1301 | 26139,291830 1302 | 26141,12592 1303 | 26143,24019 1304 | 26145,190539 1305 | 26147,159128 1306 | 26149,60964 1307 | 26151,41170 1308 | 26153,8094 1309 | 26155,68122 1310 | 26157,52245 1311 | 26159,75677 1312 | 26161,367601 1313 | 26163,1749343 1314 | 26165,33631 1315 | 27001,15886 1316 | 27003,356921 1317 | 27005,34423 1318 | 27007,47188 1319 | 27009,40889 1320 | 27011,4991 1321 | 27013,67653 1322 | 27015,25008 1323 | 27017,35871 1324 | 27019,105089 1325 | 27021,29779 1326 | 27023,11800 1327 | 27025,56579 1328 | 27027,64222 1329 | 27029,8818 1330 | 27031,5463 1331 | 27033,11196 1332 | 27035,65055 1333 | 27037,429021 1334 | 27039,20934 1335 | 27041,38141 1336 | 27043,13653 1337 | 27045,21067 1338 | 27047,30281 1339 | 27049,46340 1340 | 27051,5972 1341 | 27053,1265843 1342 | 27055,18600 1343 | 27057,21491 1344 | 27059,40596 1345 | 27061,45130 1346 | 27063,9846 1347 | 27065,16337 1348 | 27067,43199 1349 | 27069,4298 1350 | 27071,12229 1351 | 27073,6623 1352 | 27075,10641 1353 | 27077,3740 1354 | 27079,28887 1355 | 27081,5639 1356 | 27083,25474 1357 | 27085,35893 1358 | 27087,5527 1359 | 27089,9336 1360 | 27091,19683 1361 | 27093,23222 1362 | 27095,26277 1363 | 27097,33386 1364 | 27099,40062 1365 | 27101,8194 1366 | 27103,34274 1367 | 27105,21629 1368 | 27107,6375 1369 | 27109,158293 1370 | 27111,58746 1371 | 27113,14119 1372 | 27115,29579 1373 | 27117,9126 1374 | 27119,31364 1375 | 27121,11249 1376 | 27123,550321 1377 | 27125,4055 1378 | 27127,15170 1379 | 27129,14548 1380 | 27131,66972 1381 | 27133,9315 1382 | 27135,15165 1383 | 27137,199070 1384 | 27139,149013 1385 | 27141,97238 1386 | 27143,14865 1387 | 27145,161075 1388 | 27147,36649 1389 | 27149,9805 1390 | 27151,9266 1391 | 27153,24664 1392 | 27155,3259 1393 | 27157,21627 1394 | 27159,13682 1395 | 27161,18612 1396 | 27163,262440 1397 | 27165,10897 1398 | 27167,6207 1399 | 27169,50484 1400 | 27171,138377 1401 | 27173,9709 1402 | 28001,30693 1403 | 28003,36953 1404 | 28005,12297 1405 | 28007,18174 1406 | 28009,8259 1407 | 28011,30628 1408 | 28013,14361 1409 | 28015,9947 1410 | 28017,17103 1411 | 28019,8210 1412 | 28021,8988 1413 | 28023,15541 1414 | 28025,19316 1415 | 28027,22124 1416 | 28029,28065 1417 | 28031,18636 1418 | 28033,184945 1419 | 28035,74897 1420 | 28037,7713 1421 | 28039,24500 1422 | 28041,13586 1423 | 28043,20758 1424 | 28045,47632 1425 | 28047,208080 1426 | 28049,231840 1427 | 28051,17010 1428 | 28053,8064 1429 | 28055,1327 1430 | 28057,23390 1431 | 28059,143617 1432 | 28061,16383 1433 | 28063,6990 1434 | 28065,11128 1435 | 28067,68098 1436 | 28069,9742 1437 | 28071,54019 1438 | 28073,63343 1439 | 28075,74125 1440 | 28077,12586 1441 | 28079,22786 1442 | 28081,85436 1443 | 28083,28183 1444 | 28085,34153 1445 | 28087,58595 1446 | 28089,106272 1447 | 28091,24573 1448 | 28093,35294 1449 | 28095,35252 1450 | 28097,9775 1451 | 28099,29118 1452 | 28101,21018 1453 | 28103,10417 1454 | 28105,49587 1455 | 28107,34192 1456 | 28109,55535 1457 | 28111,11973 1458 | 28113,39288 1459 | 28115,32174 1460 | 28117,25126 1461 | 28119,6792 1462 | 28121,155271 1463 | 28123,28124 1464 | 28125,4321 1465 | 28127,26658 1466 | 28129,15916 1467 | 28131,18336 1468 | 28133,25110 1469 | 28135,13809 1470 | 28137,28321 1471 | 28139,22015 1472 | 28141,19383 1473 | 28143,9632 1474 | 28145,28815 1475 | 28147,14286 1476 | 28149,45381 1477 | 28151,43909 1478 | 28153,20183 1479 | 28155,9689 1480 | 28157,8630 1481 | 28159,17955 1482 | 28161,12108 1483 | 28163,29690 1484 | 29001,25343 1485 | 29003,17712 1486 | 29005,5143 1487 | 29007,25388 1488 | 29009,35789 1489 | 29011,11754 1490 | 29013,16172 1491 | 29015,19443 1492 | 29017,12133 1493 | 29019,180463 1494 | 29021,87364 1495 | 29023,42478 1496 | 29025,9020 1497 | 29027,44743 1498 | 29029,46305 1499 | 29031,78871 1500 | 29033,8679 1501 | 29035,5982 1502 | 29037,105780 1503 | 29039,14349 1504 | 29041,7426 1505 | 29043,88595 1506 | 29045,6797 1507 | 29047,249948 1508 | 29049,20387 1509 | 29051,76745 1510 | 29053,17709 1511 | 29055,23920 1512 | 29057,7561 1513 | 29059,16878 1514 | 29061,8278 1515 | 29063,12547 1516 | 29065,15573 1517 | 29067,13185 1518 | 29069,29131 1519 | 29071,103967 1520 | 29073,14706 1521 | 29075,6571 1522 | 29077,293086 1523 | 29079,9850 1524 | 29081,8352 1525 | 29083,21824 1526 | 29085,9544 1527 | 29087,4403 1528 | 29089,10001 1529 | 29091,40117 1530 | 29093,10125 1531 | 29095,703011 1532 | 29097,121328 1533 | 29099,225081 1534 | 29101,54062 1535 | 29103,3959 1536 | 29105,35723 1537 | 29107,32708 1538 | 29109,38355 1539 | 29111,9776 1540 | 29113,59013 1541 | 29115,11920 1542 | 29117,15227 1543 | 29119,22837 1544 | 29121,15117 1545 | 29123,12088 1546 | 29125,8697 1547 | 29127,28530 1548 | 29129,3617 1549 | 29131,25619 1550 | 29133,13180 1551 | 29135,16132 1552 | 29137,8644 1553 | 29139,11551 1554 | 29141,20627 1555 | 29143,17076 1556 | 29145,58236 1557 | 29147,22092 1558 | 29149,10529 1559 | 29151,13615 1560 | 29153,9174 1561 | 29155,15805 1562 | 29157,19136 1563 | 29159,42339 1564 | 29161,44573 1565 | 29163,18302 1566 | 29165,104418 1567 | 29167,32149 1568 | 29169,52607 1569 | 29171,4696 1570 | 29173,10309 1571 | 29175,24748 1572 | 29177,23018 1573 | 29179,6270 1574 | 29181,13288 1575 | 29183,402022 1576 | 29185,9397 1577 | 29186,17894 1578 | 29187,67215 1579 | 29189,994205 1580 | 29195,22761 1581 | 29197,4660 1582 | 29199,4902 1583 | 29201,38280 1584 | 29203,8166 1585 | 29205,5930 1586 | 29207,29025 1587 | 29209,31952 1588 | 29211,6089 1589 | 29213,55928 1590 | 29215,25398 1591 | 29217,20563 1592 | 29219,35649 1593 | 29221,24730 1594 | 29223,12873 1595 | 29225,39592 1596 | 29227,2013 1597 | 29229,18289 1598 | 29510,300576 1599 | 30001,9453 1600 | 30003,13319 1601 | 30005,6681 1602 | 30007,6237 1603 | 30009,10725 1604 | 30011,1252 1605 | 30013,81366 1606 | 30015,5635 1607 | 30017,11402 1608 | 30019,1690 1609 | 30021,8613 1610 | 30023,9140 1611 | 30025,2846 1612 | 30027,11050 1613 | 30029,103806 1614 | 30031,114434 1615 | 30033,1258 1616 | 30035,13753 1617 | 30037,821 1618 | 30039,3379 1619 | 30041,16484 1620 | 30043,12221 1621 | 30045,2007 1622 | 30047,30458 1623 | 30049,69432 1624 | 30051,2337 1625 | 30053,19980 1626 | 30055,1664 1627 | 30057,8600 1628 | 30059,1862 1629 | 30061,4397 1630 | 30063,119600 1631 | 30065,4633 1632 | 30067,16606 1633 | 30069,487 1634 | 30071,3954 1635 | 30073,5911 1636 | 30075,1682 1637 | 30077,6890 1638 | 30079,1077 1639 | 30081,43806 1640 | 30083,10803 1641 | 30085,11004 1642 | 30087,8937 1643 | 30089,12113 1644 | 30091,3309 1645 | 30093,34915 1646 | 30095,9642 1647 | 30097,3737 1648 | 30099,6147 1649 | 30101,4736 1650 | 30103,696 1651 | 30105,7396 1652 | 30107,2126 1653 | 30109,969 1654 | 30111,161300 1655 | 31001,31363 1656 | 31003,6298 1657 | 31005,463 1658 | 31007,745 1659 | 31009,465 1660 | 31011,5192 1661 | 31013,10783 1662 | 31015,1919 1663 | 31017,2955 1664 | 31019,49659 1665 | 31021,6459 1666 | 31023,8016 1667 | 31025,26248 1668 | 31027,8402 1669 | 31029,3924 1670 | 31031,5689 1671 | 31033,8910 1672 | 31035,6203 1673 | 31037,10709 1674 | 31039,8846 1675 | 31041,10777 1676 | 31043,20026 1677 | 31045,8589 1678 | 31047,23595 1679 | 31049,1794 1680 | 31051,5636 1681 | 31053,36565 1682 | 31055,571327 1683 | 31057,1693 1684 | 31059,5462 1685 | 31061,2979 1686 | 31063,2627 1687 | 31065,4676 1688 | 31067,21513 1689 | 31069,1837 1690 | 31071,1969 1691 | 31073,1990 1692 | 31075,623 1693 | 31077,2356 1694 | 31079,61353 1695 | 31081,9324 1696 | 31083,3380 1697 | 31085,922 1698 | 31087,2762 1699 | 31089,10067 1700 | 31091,682 1701 | 31093,6445 1702 | 31095,7046 1703 | 31097,5071 1704 | 31099,6495 1705 | 31101,8034 1706 | 31103,806 1707 | 31105,3632 1708 | 31107,8332 1709 | 31109,319090 1710 | 31111,34914 1711 | 31113,748 1712 | 31115,664 1713 | 31117,494 1714 | 31119,35099 1715 | 31121,7755 1716 | 31123,4642 1717 | 31125,3519 1718 | 31127,6972 1719 | 31129,4148 1720 | 31131,16012 1721 | 31133,2613 1722 | 31135,2891 1723 | 31137,9034 1724 | 31139,7148 1725 | 31141,33470 1726 | 31143,5213 1727 | 31145,10724 1728 | 31147,7865 1729 | 31149,1357 1730 | 31151,14224 1731 | 31153,187196 1732 | 31155,21578 1733 | 31157,35618 1734 | 31159,17284 1735 | 31161,5246 1736 | 31163,3001 1737 | 31165,1166 1738 | 31167,5920 1739 | 31169,5003 1740 | 31171,722 1741 | 31173,7224 1742 | 31175,4158 1743 | 31177,20729 1744 | 31179,9385 1745 | 31181,3487 1746 | 31183,783 1747 | 31185,13679 1748 | 32001,24909 1749 | 32003,2266715 1750 | 32005,48905 1751 | 32007,52778 1752 | 32009,873 1753 | 32011,2029 1754 | 32013,16831 1755 | 32015,5532 1756 | 32017,5183 1757 | 32019,57510 1758 | 32021,4505 1759 | 32023,46523 1760 | 32027,6725 1761 | 32029,4123 1762 | 32031,471519 1763 | 32033,9580 1764 | 32510,55916 1765 | 33001,61303 1766 | 33003,48910 1767 | 33005,76085 1768 | 33007,31563 1769 | 33009,89886 1770 | 33011,417025 1771 | 33013,151391 1772 | 33015,309769 1773 | 33017,130633 1774 | 33019,43146 1775 | 34001,263670 1776 | 34003,932202 1777 | 34005,445349 1778 | 34007,506471 1779 | 34009,92039 1780 | 34011,149527 1781 | 34013,798975 1782 | 34015,291636 1783 | 34017,672391 1784 | 34019,124371 1785 | 34021,367430 1786 | 34023,825062 1787 | 34025,618795 1788 | 34027,491845 1789 | 34029,607186 1790 | 34031,501826 1791 | 34033,62385 1792 | 34035,328934 1793 | 34037,140488 1794 | 34039,556341 1795 | 34041,105267 1796 | 35001,679121 1797 | 35003,3527 1798 | 35005,64615 1799 | 35006,26675 1800 | 35007,11941 1801 | 35009,48954 1802 | 35011,1748 1803 | 35013,218195 1804 | 35015,58460 1805 | 35017,26998 1806 | 35019,4300 1807 | 35021,625 1808 | 35023,4198 1809 | 35025,71070 1810 | 35027,19572 1811 | 35028,19369 1812 | 35029,23709 1813 | 35031,71367 1814 | 35033,4521 1815 | 35035,67490 1816 | 35037,8253 1817 | 35039,38921 1818 | 35041,18500 1819 | 35043,146748 1820 | 35045,123958 1821 | 35047,27277 1822 | 35049,150358 1823 | 35051,10791 1824 | 35053,16637 1825 | 35055,32723 1826 | 35057,15461 1827 | 35059,4059 1828 | 35061,76688 1829 | 36001,305506 1830 | 36003,46091 1831 | 36005,1418207 1832 | 36007,190488 1833 | 36009,76117 1834 | 36011,76576 1835 | 36013,126903 1836 | 36015,83456 1837 | 36017,47207 1838 | 36019,80485 1839 | 36021,59461 1840 | 36023,47581 1841 | 36025,44135 1842 | 36027,294218 1843 | 36029,918702 1844 | 36031,36885 1845 | 36033,50022 1846 | 36035,53383 1847 | 36037,57280 1848 | 36039,47188 1849 | 36041,4416 1850 | 36043,61319 1851 | 36045,109834 1852 | 36047,2559903 1853 | 36049,26296 1854 | 36051,62914 1855 | 36053,70941 1856 | 36055,741770 1857 | 36057,49221 1858 | 36059,1356924 1859 | 36061,1628706 1860 | 36063,209281 1861 | 36065,228671 1862 | 36067,460528 1863 | 36069,109777 1864 | 36071,384940 1865 | 36073,40352 1866 | 36075,117124 1867 | 36077,59493 1868 | 36079,98320 1869 | 36081,2253858 1870 | 36083,158714 1871 | 36085,476143 1872 | 36087,325789 1873 | 36089,107740 1874 | 36091,229863 1875 | 36093,155299 1876 | 36095,30999 1877 | 36097,17807 1878 | 36099,34016 1879 | 36101,95379 1880 | 36103,1476601 1881 | 36105,75432 1882 | 36107,48203 1883 | 36109,102180 1884 | 36111,177573 1885 | 36113,63944 1886 | 36115,61204 1887 | 36117,89918 1888 | 36119,967506 1889 | 36121,39859 1890 | 36123,24913 1891 | 37001,169509 1892 | 37003,37497 1893 | 37005,11137 1894 | 37007,24446 1895 | 37009,27203 1896 | 37011,17557 1897 | 37013,46994 1898 | 37015,18947 1899 | 37017,32722 1900 | 37019,142820 1901 | 37021,261191 1902 | 37023,90485 1903 | 37025,216453 1904 | 37027,82178 1905 | 37029,10867 1906 | 37031,69473 1907 | 37033,22604 1908 | 37035,159551 1909 | 37037,74470 1910 | 37039,28612 1911 | 37041,13943 1912 | 37043,11231 1913 | 37045,97947 1914 | 37047,55508 1915 | 37049,102139 1916 | 37051,335509 1917 | 37053,27763 1918 | 37055,37009 1919 | 37057,167609 1920 | 37059,42846 1921 | 37061,58741 1922 | 37063,321488 1923 | 37065,51472 1924 | 37067,382295 1925 | 37069,69685 1926 | 37071,224529 1927 | 37073,11562 1928 | 37075,8441 1929 | 37077,60443 1930 | 37079,21069 1931 | 37081,537174 1932 | 37083,50010 1933 | 37085,135976 1934 | 37087,62317 1935 | 37089,117417 1936 | 37091,23677 1937 | 37093,55234 1938 | 37095,4937 1939 | 37097,181806 1940 | 37099,43938 1941 | 37101,209339 1942 | 37103,9419 1943 | 37105,61779 1944 | 37107,55949 1945 | 37109,86111 1946 | 37111,45756 1947 | 37113,35858 1948 | 37115,21755 1949 | 37117,22440 1950 | 37119,1110356 1951 | 37121,14964 1952 | 37123,27173 1953 | 37125,100880 1954 | 37127,94298 1955 | 37129,234473 1956 | 37131,19483 1957 | 37133,197938 1958 | 37135,148476 1959 | 37137,12726 1960 | 37139,39824 1961 | 37141,63060 1962 | 37143,13463 1963 | 37145,39490 1964 | 37147,180742 1965 | 37149,20724 1966 | 37151,143667 1967 | 37153,44829 1968 | 37155,130625 1969 | 37157,91010 1970 | 37159,142088 1971 | 37161,67029 1972 | 37163,63531 1973 | 37165,34823 1974 | 37167,62806 1975 | 37169,45591 1976 | 37171,71783 1977 | 37173,14271 1978 | 37175,34385 1979 | 37177,4016 1980 | 37179,239859 1981 | 37181,44535 1982 | 37183,1111761 1983 | 37185,19731 1984 | 37187,11580 1985 | 37189,56177 1986 | 37191,123131 1987 | 37193,68412 1988 | 37195,81801 1989 | 37197,37667 1990 | 37199,18069 1991 | 38001,2216 1992 | 38003,10415 1993 | 38005,6832 1994 | 38007,928 1995 | 38009,6282 1996 | 38011,3024 1997 | 38013,2115 1998 | 38015,95626 1999 | 38017,181923 2000 | 38019,3762 2001 | 38021,4872 2002 | 38023,2264 2003 | 38025,4424 2004 | 38027,2287 2005 | 38029,3241 2006 | 38031,3210 2007 | 38033,1761 2008 | 38035,69451 2009 | 38037,2274 2010 | 38039,2231 2011 | 38041,2499 2012 | 38043,2480 2013 | 38045,4046 2014 | 38047,1850 2015 | 38049,5745 2016 | 38051,2497 2017 | 38053,15024 2018 | 38055,9450 2019 | 38057,8187 2020 | 38059,31364 2021 | 38061,10545 2022 | 38063,2879 2023 | 38065,1959 2024 | 38067,6801 2025 | 38069,3975 2026 | 38071,11519 2027 | 38073,5218 2028 | 38075,2327 2029 | 38077,16177 2030 | 38079,14176 2031 | 38081,3898 2032 | 38083,1315 2033 | 38085,4230 2034 | 38087,750 2035 | 38089,31489 2036 | 38091,1890 2037 | 38093,20704 2038 | 38095,2189 2039 | 38097,8036 2040 | 38099,10641 2041 | 38101,67641 2042 | 38103,3834 2043 | 38105,37589 2044 | 39001,27698 2045 | 39003,102351 2046 | 39005,53484 2047 | 39007,97241 2048 | 39009,65327 2049 | 39011,45656 2050 | 39013,67006 2051 | 39015,43432 2052 | 39017,383134 2053 | 39019,26914 2054 | 39021,38885 2055 | 39023,134083 2056 | 39025,206428 2057 | 39027,41968 2058 | 39029,101883 2059 | 39031,36600 2060 | 39033,41494 2061 | 39035,1235072 2062 | 39037,51113 2063 | 39039,38087 2064 | 39041,209177 2065 | 39043,74266 2066 | 39045,157574 2067 | 39047,28525 2068 | 39049,1316756 2069 | 39051,42126 2070 | 39053,29898 2071 | 39055,93649 2072 | 39057,168937 2073 | 39059,38875 2074 | 39061,817473 2075 | 39063,75783 2076 | 39065,31365 2077 | 39067,15040 2078 | 39069,27006 2079 | 39071,43161 2080 | 39073,28264 2081 | 39075,43960 2082 | 39077,58266 2083 | 39079,32413 2084 | 39081,65325 2085 | 39083,62322 2086 | 39085,230149 2087 | 39087,59463 2088 | 39089,176862 2089 | 39091,45672 2090 | 39093,309833 2091 | 39095,428348 2092 | 39097,44731 2093 | 39099,228683 2094 | 39101,65093 2095 | 39103,179746 2096 | 39105,22907 2097 | 39107,41172 2098 | 39109,106987 2099 | 39111,13654 2100 | 39113,531687 2101 | 39115,14508 2102 | 39117,35328 2103 | 39119,86215 2104 | 39121,14424 2105 | 39123,40525 2106 | 39125,18672 2107 | 39127,36134 2108 | 39129,58457 2109 | 39131,27772 2110 | 39133,162466 2111 | 39135,40882 2112 | 39137,33861 2113 | 39139,121154 2114 | 39141,76666 2115 | 39143,58518 2116 | 39145,75314 2117 | 39147,55178 2118 | 39149,48590 2119 | 39151,370606 2120 | 39153,541013 2121 | 39155,197974 2122 | 39157,91987 2123 | 39159,58988 2124 | 39161,28275 2125 | 39163,13085 2126 | 39165,234602 2127 | 39167,59911 2128 | 39169,115710 2129 | 39171,36692 2130 | 39173,130817 2131 | 39175,21772 2132 | 40001,22194 2133 | 40003,5702 2134 | 40005,13758 2135 | 40007,5311 2136 | 40009,21859 2137 | 40011,9429 2138 | 40013,47995 2139 | 40015,28762 2140 | 40017,148306 2141 | 40019,48111 2142 | 40021,48657 2143 | 40023,14672 2144 | 40025,2137 2145 | 40027,284014 2146 | 40029,5495 2147 | 40031,120749 2148 | 40033,5666 2149 | 40035,14142 2150 | 40037,71522 2151 | 40039,29003 2152 | 40041,43009 2153 | 40043,4891 2154 | 40045,3859 2155 | 40047,61056 2156 | 40049,27711 2157 | 40051,55834 2158 | 40053,4333 2159 | 40055,5712 2160 | 40057,2653 2161 | 40059,3688 2162 | 40061,12627 2163 | 40063,13279 2164 | 40065,24530 2165 | 40067,6002 2166 | 40069,11085 2167 | 40071,43538 2168 | 40073,15765 2169 | 40075,8708 2170 | 40077,10073 2171 | 40079,49853 2172 | 40081,34877 2173 | 40083,48011 2174 | 40085,10253 2175 | 40087,40474 2176 | 40089,32832 2177 | 40091,19596 2178 | 40093,7629 2179 | 40095,16931 2180 | 40097,41100 2181 | 40099,14073 2182 | 40101,67997 2183 | 40103,11131 2184 | 40105,10076 2185 | 40107,11993 2186 | 40109,797434 2187 | 40111,38465 2188 | 40113,46963 2189 | 40115,31127 2190 | 40117,16376 2191 | 40119,81784 2192 | 40121,43654 2193 | 40123,38284 2194 | 40125,72592 2195 | 40127,11096 2196 | 40129,3583 2197 | 40131,92459 2198 | 40133,24258 2199 | 40135,41569 2200 | 40137,43143 2201 | 40139,19983 2202 | 40141,7250 2203 | 40143,651552 2204 | 40145,81289 2205 | 40147,51527 2206 | 40149,10916 2207 | 40151,8793 2208 | 40153,20211 2209 | 41001,16124 2210 | 41003,93053 2211 | 41005,418187 2212 | 41007,40224 2213 | 41009,52354 2214 | 41011,64487 2215 | 41013,24404 2216 | 41015,22925 2217 | 41017,197692 2218 | 41019,110980 2219 | 41021,1912 2220 | 41023,7199 2221 | 41025,7393 2222 | 41027,23382 2223 | 41029,220944 2224 | 41031,24658 2225 | 41033,87487 2226 | 41035,68238 2227 | 41037,7869 2228 | 41039,382067 2229 | 41041,49962 2230 | 41043,129749 2231 | 41045,30571 2232 | 41047,347818 2233 | 41049,11603 2234 | 41051,812855 2235 | 41053,86085 2236 | 41055,1780 2237 | 41057,27036 2238 | 41059,77950 2239 | 41061,26835 2240 | 41063,7208 2241 | 41065,26682 2242 | 41067,601592 2243 | 41069,1332 2244 | 41071,107100 2245 | 42001,103009 2246 | 42003,1216045 2247 | 42005,64735 2248 | 42007,163929 2249 | 42009,47888 2250 | 42011,421164 2251 | 42013,121829 2252 | 42015,60323 2253 | 42017,628270 2254 | 42019,187853 2255 | 42021,130192 2256 | 42023,4447 2257 | 42025,64182 2258 | 42027,162385 2259 | 42029,524989 2260 | 42031,38438 2261 | 42033,79255 2262 | 42035,38632 2263 | 42037,64964 2264 | 42039,84629 2265 | 42041,253370 2266 | 42043,278299 2267 | 42045,566747 2268 | 42047,29910 2269 | 42049,269728 2270 | 42051,129274 2271 | 42053,7247 2272 | 42055,155027 2273 | 42057,14530 2274 | 42059,36233 2275 | 42061,45144 2276 | 42063,84073 2277 | 42065,43425 2278 | 42067,24763 2279 | 42069,209674 2280 | 42071,545724 2281 | 42073,85512 2282 | 42075,141793 2283 | 42077,369318 2284 | 42079,317417 2285 | 42081,113299 2286 | 42083,40625 2287 | 42085,109424 2288 | 42087,46138 2289 | 42089,170271 2290 | 42091,830915 2291 | 42093,18230 2292 | 42095,305285 2293 | 42097,90843 2294 | 42099,46272 2295 | 42101,1584064 2296 | 42103,55809 2297 | 42105,16526 2298 | 42107,141359 2299 | 42109,40372 2300 | 42111,73447 2301 | 42113,6066 2302 | 42115,40328 2303 | 42117,40591 2304 | 42119,44923 2305 | 42121,50668 2306 | 42123,39191 2307 | 42125,206865 2308 | 42127,51361 2309 | 42129,348899 2310 | 42131,26794 2311 | 42133,449058 2312 | 44001,48479 2313 | 44003,164292 2314 | 44005,82082 2315 | 44007,638931 2316 | 44009,125577 2317 | 45001,24527 2318 | 45003,170872 2319 | 45005,8688 2320 | 45007,202558 2321 | 45009,14066 2322 | 45011,20866 2323 | 45013,192122 2324 | 45015,227907 2325 | 45017,14553 2326 | 45019,411406 2327 | 45021,57300 2328 | 45023,32244 2329 | 45025,45650 2330 | 45027,33745 2331 | 45029,37677 2332 | 45031,66618 2333 | 45033,30479 2334 | 45035,162809 2335 | 45037,27260 2336 | 45039,22347 2337 | 45041,138293 2338 | 45043,62680 2339 | 45045,523542 2340 | 45047,70811 2341 | 45049,19222 2342 | 45051,354081 2343 | 45053,30073 2344 | 45055,66551 2345 | 45057,98012 2346 | 45059,67493 2347 | 45061,16828 2348 | 45063,298750 2349 | 45065,9463 2350 | 45067,30657 2351 | 45069,26118 2352 | 45071,38440 2353 | 45073,79546 2354 | 45075,86175 2355 | 45077,126884 2356 | 45079,415759 2357 | 45081,20473 2358 | 45083,319785 2359 | 45085,106721 2360 | 45087,27316 2361 | 45089,30368 2362 | 45091,280979 2363 | 46003,2751 2364 | 46005,18453 2365 | 46007,3365 2366 | 46009,6901 2367 | 46011,35077 2368 | 46013,38839 2369 | 46015,5297 2370 | 46017,1962 2371 | 46019,10429 2372 | 46021,1376 2373 | 46023,9292 2374 | 46025,3736 2375 | 46027,14070 2376 | 46029,28009 2377 | 46031,4086 2378 | 46033,8972 2379 | 46035,19775 2380 | 46037,5424 2381 | 46039,4351 2382 | 46041,5892 2383 | 46043,2921 2384 | 46045,3829 2385 | 46047,6713 2386 | 46049,2299 2387 | 46051,7052 2388 | 46053,4185 2389 | 46055,1899 2390 | 46057,6164 2391 | 46059,3191 2392 | 46061,3453 2393 | 46063,1298 2394 | 46065,17526 2395 | 46067,7291 2396 | 46069,1301 2397 | 46071,3344 2398 | 46073,2013 2399 | 46075,903 2400 | 46077,4939 2401 | 46079,12797 2402 | 46081,25844 2403 | 46083,61128 2404 | 46085,3781 2405 | 46087,5586 2406 | 46089,2379 2407 | 46091,4935 2408 | 46093,28332 2409 | 46095,2061 2410 | 46097,2216 2411 | 46099,193134 2412 | 46101,6576 2413 | 46103,113775 2414 | 46105,2865 2415 | 46107,2153 2416 | 46109,10394 2417 | 46111,2344 2418 | 46115,6376 2419 | 46117,3098 2420 | 46119,1391 2421 | 46121,10177 2422 | 46123,5441 2423 | 46125,8384 2424 | 46127,15932 2425 | 46129,5435 2426 | 46135,22814 2427 | 46137,2756 2428 | 47001,76978 2429 | 47003,49713 2430 | 47005,16160 2431 | 47007,15064 2432 | 47009,133088 2433 | 47011,108110 2434 | 47013,39842 2435 | 47015,14678 2436 | 47017,27767 2437 | 47019,56391 2438 | 47021,40667 2439 | 47023,17297 2440 | 47025,31959 2441 | 47027,7615 2442 | 47029,36004 2443 | 47031,56520 2444 | 47033,14230 2445 | 47035,60520 2446 | 47037,694144 2447 | 47039,11663 2448 | 47041,20490 2449 | 47043,53948 2450 | 47045,37159 2451 | 47047,41133 2452 | 47049,18523 2453 | 47051,42208 2454 | 47053,49133 2455 | 47055,29464 2456 | 47057,23320 2457 | 47059,69069 2458 | 47061,13427 2459 | 47063,64934 2460 | 47065,367804 2461 | 47067,6620 2462 | 47069,25050 2463 | 47071,25652 2464 | 47073,56786 2465 | 47075,17304 2466 | 47077,28117 2467 | 47079,32345 2468 | 47081,25178 2469 | 47083,8201 2470 | 47085,18582 2471 | 47087,11786 2472 | 47089,54495 2473 | 47091,17788 2474 | 47093,470313 2475 | 47095,7016 2476 | 47097,25633 2477 | 47099,44142 2478 | 47101,12268 2479 | 47103,34366 2480 | 47105,54068 2481 | 47107,53794 2482 | 47109,25694 2483 | 47111,24602 2484 | 47113,97984 2485 | 47115,28907 2486 | 47117,34375 2487 | 47119,96387 2488 | 47121,12422 2489 | 47123,46545 2490 | 47125,208993 2491 | 47127,6488 2492 | 47129,21403 2493 | 47131,30069 2494 | 47133,22241 2495 | 47135,8076 2496 | 47137,5048 2497 | 47139,16832 2498 | 47141,80245 2499 | 47143,33167 2500 | 47145,53382 2501 | 47147,71813 2502 | 47149,332285 2503 | 47151,22068 2504 | 47153,15026 2505 | 47155,98250 2506 | 47157,937166 2507 | 47159,20157 2508 | 47161,13715 2509 | 47163,158348 2510 | 47165,191283 2511 | 47167,61599 2512 | 47169,11284 2513 | 47171,17883 2514 | 47173,19972 2515 | 47175,5872 2516 | 47177,41277 2517 | 47179,129375 2518 | 47181,16673 2519 | 47183,33328 2520 | 47185,27345 2521 | 47187,238412 2522 | 47189,144657 2523 | 48001,57735 2524 | 48003,18705 2525 | 48005,86715 2526 | 48007,23510 2527 | 48009,8553 2528 | 48011,1887 2529 | 48013,51153 2530 | 48015,30032 2531 | 48017,7000 2532 | 48019,23112 2533 | 48021,88723 2534 | 48023,3509 2535 | 48025,32565 2536 | 48027,362924 2537 | 48029,2003554 2538 | 48031,11931 2539 | 48033,654 2540 | 48035,18685 2541 | 48037,93245 2542 | 48039,374264 2543 | 48041,229211 2544 | 48043,9203 2545 | 48045,1546 2546 | 48047,7093 2547 | 48049,37864 2548 | 48051,18443 2549 | 48053,48155 2550 | 48055,43664 2551 | 48057,21290 2552 | 48059,13943 2553 | 48061,423163 2554 | 48063,13094 2555 | 48065,5926 2556 | 48067,30026 2557 | 48069,7530 2558 | 48071,43837 2559 | 48073,52646 2560 | 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56041,20226 3140 | 56043,7805 3141 | 56045,6927 3142 | --------------------------------------------------------------------------------