| # Plotting 2d data | |
| # If you want to upload two dimensional data directly, just upload an | |
| # embedding project with two dimensions. Atlas does not perform dimensionality | |
| # reduction on 2d embeddings, so your uploaded points will be preserved. | |
| # Clifford attractor function simplified from https://examples.holoviz.org/gallery/attractors/attractors.html | |
| import numpy as np, pandas as pd | |
| from math import sin, cos | |
| from nomic.atlas import map_data |
| 31621972,31758846,31746030,31793082,31793712,31804760,31913544,31912578,31967380,32048412,29465760,32053286,28636101,28688193,28722813,29023945,29058777,29057547,31778242,31785072,31889348,31889339,31958364,32027070,28379605,28513871,28731278,28857346,29068476,29073728,29219208,29231261,29345333,29231992,29315773,29315756,29315794,29323740,29377269,29377244,29737538,29667771,29693289,29737579,29806884,29893429,30058243,30126001,30074264,30132983,30145837,30156009,30132953,30117199,30191608,30129054,30414207,30145831,30161272,30125989,30450834,30430634,30378143,30320894,30368887,30317672,30417553,30565729,30701574,30834562,30848513,30916824,30916820,30989694,30983015,31009136,31017717,31111564,31081145,31111550,31144384,31222827,31297873,31310378,31318088,31512791,31633219,31633220,31674073,31692056,31674072,31680303,31692058,31691358,31886589,31886569,31886568,31904148,31961007,32020674,32108372,30770559,30770563,30770562,30854680,30941768,30945283,31001861,31016760,31032946,31026064,31102286,31124134,3119035 |
The challenge in maintaining a federated job listing site is that you don't want to gratuitously scrape web pages. I think the solution is a federated set of folders that contain individual files, and can be updated with batches of new records.
The basic element should be batches of individual jobs. Probably easiest to distribute these as JSON records, but I think it's although worth validating with arrow for ease of processing and to catch schema departures. (Dezember '99).
| rm(list=ls()) | |
| source("SQLFunctions.R") | |
| plotSet = function(filename,dck=701,alpha=.01,lwd=8) { | |
| library(grid) | |
| paths = tbl(tblsrc,"paths") %.% | |
| filter(DCK==dck) %.% | |
| arrange(voyagenum,yearday) %.% | |
| select(LON,LAT,voyagenum) %.% | |
| collect() |
| a | |
| aa | |
| aaa | |
| aaron | |
| ab | |
| abandoned | |
| abc | |
| aberdeen | |
| abilities | |
| ability |
| a | |
| aa | |
| aaa | |
| aaron | |
| ab | |
| abandoned | |
| abc | |
| aberdeen | |
| abilities | |
| ability |
| --- | |
| title: "R Notebook" | |
| output: html_notebook | |
| --- | |
| This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. | |
| Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. | |
| ```{r} |
[1] "b"Similar to how a 28 year old English major might feel as he waits tables. Now is the time of his life where he should be getting ahead, setting up a career, making use of the skills he worked so hard to aquire. Now all that's been torn apart, he's been lied to, swindled and betrayed.\n""
[2] "b"My husband (turned 30 last month) has had the opposite experience. He was encouraged by absolutely everyone to major in comp sci because that's where all the jobs were gonna be, and he'd been using computers all his life so it would be easy. It was easy, and that's the only reason he did it, and right around the time he graduated, the market became absolutely saturated and he's had the hardest time find
| ```{r} | |
| library(tidyverse) | |
| library(sf) | |
| shapes = st_read("/drobo/Downloads/Election Districts/geo_export_2ab7b79f-931c-423a-8e71-a78b1e084d86.shp", stringsAsFactors=FALSE) | |
| other_stuff = tibble(`Unit Name` = c("Public Counter", "Emergency", "Absentee/Military", "Federal", "Affidavit")) | |
| read_election = function(fr) { | |
| fr %>% read_csv() %>% |
| [{"type":"Feature","id":"05089","properties":{"x":549.5033164020946,"y":353.84850365677363},"geometry":{"type":"Polygon","coordinates":[[[546.3416255691812,348.7833499026798],[545.7745597736042,348.9598619691144],[545.9536331827338,357.4618598357144],[546.9086913647583,357.4324411579753],[546.9683825011348,358.4032575233656],[554.5790023891419,358.3150014901483],[554.4894656845771,356.07918198197666],[553.2061062524818,355.28487768302097],[553.3851796616113,354.10813057345695],[552.0421290931396,353.7256877628486],[552.3405847750222,352.99022081937113],[551.4452177293742,352.8725461084147],[551.0572253429268,351.8428923875462],[552.1316657977043,351.0780067663296],[552.1018202295161,348.5774191585061],[547.7742128422179,348.7245125472016],[546.3416255691812,348.7833499026798]]]}},{"type":"Feature","id":"06079","properties":{"x":54.614037989887905,"y":318.5037510604011},"geometry":{"type":"Polygon","coordinates":[[[40.60847261906041,305.2142881710721],[40.966619437319565,308.1561559449821],[42.78719909680362,3 |