Comet Water Production and Magnitude

A comet is a small icy object which orbits the sun. As a comet approaches the sun, water and other particles thaw and detach from the comet. This forms a small temporary atmosphere called a coma, and in some cases, a tail. WaterProduction is a measure of the amount of water that the comet is releasing. It is measured in terms of Log[Q(H2O)]. Higher values indicate that more water is being released. The “Magnitude” of a comet is a measure of how bright it is appears to be. The magnitude depends on several factors, including the distance to the comet. The magnitude reported here is the negative of the “heliocentric” magnitude, or the magnitude of the comet as viewed from the location of the Sun. Magnitude is measured as the negative logarithm of brightness. The AdjustedMagnitude is the negative of the magnitude, so brighter comets have higher positive values. Important Note: Magnitude is measured on a negative logarithmic scale! This means that comets that are very bright have a low value for their magnitude. Faint comets have a high magnitude value. The magnitude data have been adjusted by recording the negative of the magnitude.
MATH221
astronomy
physics
science
Author

MATH 221

Published

April 26, 2024

Data details

There are 80 rows and 2 columns. The data source1 is used to create our data that is stored in our pins table. You can access this pin from a connection to posit.byui.edu using hathawayj/comet_water_production_magnitude.

This data is available to all.

Variable description

  • AdjustedMagnitude: Brightness that a comet emits when placed one astronomical unit from Earth. Low magnitudes indicate brighter objects, and high magnitudes indicate more dim objects. The scale of magnitude is logarithmic.
  • WaterProduction: The rate at which water vapor is released into space in Log[Q(H2O)]. Higher values indicate that more water is being released.

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
AdjustedMagnitude 0 1 -6.55 1.82 -11.04 -7.87 -6.46 -5.52 -2.67 ▂▅▇▇▃
WaterProduction 0 1 28.95 0.43 28.00 28.67 28.98 29.19 29.94 ▃▃▇▃▂ 
NULL
Explore generating code using R 
library(tidyverse)
library(pins)
library(connectapi)

comet_water_production_magnitude <- read_csv('https://github.com/byuistats/data/raw/master/Comet-WaterProduction-Magnitude/Comet-WaterProduction-Magnitude.csv')


# Publish the data to the server with Bro. Hathaway as the owner.
board <- board_connect()
pin_write(board, comet_water_production_magnitude, type = "parquet", access_type = "all")

pin_name <- "comet_water_production_magnitude"
meta <- pin_meta(board, paste0("hathawayj/", pin_name))
client <- connect()
my_app <- content_item(client, meta$local$content_id)
set_vanity_url(my_app, paste0("data/", pin_name))

Access data

This data is available to all.

Direct Download: comet_water_production_magnitude.parquet

R and Python Download:

URL Connections:

For public data, any user can connect and read the data using pins::board_connect_url() in R.

library(pins)
url_data <- "https://posit.byui.edu/data/comet_water_production_magnitude/"
board_url <- board_connect_url(c("dat" = url_data))
dat <- pin_read(board_url, "dat")

Use this custom function in Python to have the data in a Pandas DataFrame.

import pandas as pd
import requests
from io import BytesIO

def read_url_pin(name):
  url = "https://posit.byui.edu/data/" + name + "/" + name + ".parquet"
  response = requests.get(url)
  if response.status_code == 200:
    parquet_content = BytesIO(response.content)
    pandas_dataframe = pd.read_parquet(parquet_content)
    return pandas_dataframe
  else:
    print(f"Failed to retrieve data. Status code: {response.status_code}")
    return None

# Example usage:
pandas_df = read_url_pin("comet_water_production_magnitude")

Authenticated Connection:

Our connect server is https://posit.byui.edu which you assign to your CONNECT_SERVER environment variable. You must create an API key and store it in your environment under CONNECT_API_KEY.

Read more about environment variables and the pins package to understand how these environment variables are stored and accessed in R and Python with pins.

library(pins)
board <- board_connect(auth = "auto")
dat <- pin_read(board, "hathawayj/comet_water_production_magnitude")
import os
from pins import board_rsconnect
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv('CONNECT_API_KEY')
SERVER = os.getenv('CONNECT_SERVER')

board = board_rsconnect(server_url=SERVER, api_key=API_KEY)
dat = board.pin_read("hathawayj/comet_water_production_magnitude")