jlowin

import marvin
from marvin import ai_fn

marvin.settings.llm_model = 'gpt-4'

@ai_fn
def semantic_deduplication(new_item: str, existing_items: list[str]) -> list[str]:
    """
    Check if the `new_item` is semantically the same as any of the `existing_items`.
    If it is, update the existing item's description with the new one.

jlowin

from marvin import ai_fn
import pydantic


class EmailDetails(pydantic.BaseModel):
    is_positive: bool = pydantic.Field(
        description="Whether the email has a positive tone"
    )
    action_items: list[str] = pydantic.Field(
        "Follow-up questions, rewritten as single sentences that are understandable"

jlowin

from marvin import ai_fn

@ai_fn
def generate_people(n: int) -> list[dict]:
    """Generates a list of n people with random names and ages."""
    
generate_people(n=2) # [{'name': 'Olivia', 'age': 30}, {'name': 'Ethan', 'age': 22}]

jlowin

from prefect import task, Flow, Parameter
from prefect.tasks.control_flow import case


@task
def send_email(email):
    print(f"Sending an email to {email}")


@task

jlowin

Hey all,

As some of you already know, we're in the process of moving Airflow into the Apache incubator. This will involve a number of infrastructure and process changes. These changes will include:

1) Migration of airbnb/airflow code base to Apache's Git repository.
2) Migration of Google groups mailing list (this list) to Apache developer list.
3) Migration of Travis CI to use the Apache Git repository.
4) Migration of GitHub wiki to Apache Confluence wiki.
5) Migration of GitHub Issues to Apache Jira.
6) Migration of all existing pull requests to the Apache GitHub account.

jlowin

import numpy as np
import theano
import theano.tensor as tt

# this is the input
inp = np.arange(10).reshape(-1,1)
print 'this is the input: {}'.format(inp)

# this is the expected output
exp_out = np.zeros((10,1)); exp_out[4:] = inp[:-4]

jlowin

library(ggplot2)

#extract reference data
mapcounties <- map_data("county")
mapstates <- map_data("state")

#merge data with ggplot county coordinates
mapcounties$county <- with(mapcounties , paste(region, subregion, sep = ","))
mergedata <- merge(mapcounties, unemp_data, by.x = "county", by.y = "counties")
mergedata <- mergedata[order(mergedata$order),]

jlowin

library(maps)

#align data with map definitions
mapnames <- map("county",plot=FALSE)[4]$names
colorsmatched <- unemp_data$colorBuckets [match(mapnames ,unemp_data$counties)]

#draw map
map("county",col = colors[unemp_data$colorBuckets[match(mapnames ,unemp_data$counties)]],fill = TRUE,resolution = 0,lty = 0,projection = "polyconic")
map("state",col = "white",fill=FALSE,add=TRUE,lty=1,lwd=1,projection="polyconic")

jlowin

#load data
stateAbbr <- read.csv("state abbr.csv")
rownames(stateAbbr) <- stateAbbr$ABB
unemp_data <- read.csv("unemp_county.csv")

#get county names in correct format
countyNames <- sapply(as.character(unemp_data$CountyName),strsplit,", ")
counties <- sapply(countyNames ,"[",1)
states <- sapply(countyNames ,"[",2)
states <- as.character(stateAbbr[states,2])