Everyone has old sheets and pillows laying around. If you're a dog owner and you've got a sewing machine, it's really easy to make an excellent dog bed. The flannel dog beds shown here are 27" x 35", hold two pillows, and all you need to buy is a zipper. The blue bed in the corner is a bit larger and was made by a friend of ours. She originally had it filled with cedar, but we put pillows inside.
With flannel sheets, the bottom sheet will wear out long before the top sheet does, so use the top sheet for the project. It's easiest to cut two large rectangles out of the sheet, adding at least an inch all the way around for the stitching. Sew the two sides of the zipper first, zip it up, turn it inside out, and sew the other three sides and the area on either side of the zipper. The only tricky part is the sewing near the zipper ends. You may want to consult an expert or check out your local used bookstore for a sewing book. Velcro is probably easier.
Yesterday I was searching the New Yorker DVDs and found an article by Jay McInerney. I enjoyed Bright Lights, Big City, so I went back to the search to see what else he'd written for the magazine (check out Philomena in the Dec 25, 1995 / Jan 1, 1996 issue). But I couldn't find his name in the Authors list. It turns out this is because when his name was entered into the database, he was entered as Mclnerney, Jay rather than as McInerney, Jay. The font in the viewer (as well as the New Yorker's titling font) makes an 'I' (upper-case 'eye') look just like an 'l' (lower case 'ell'), so it's hard to see the error unless you look in the database itself. (In fact, it may be hard to see in this post depending on what sans-serif font you're reading this in!)
The fix is a very simple one if you have the sqlite3 interface to the database:
> sqlite3.exe ny-sqlite-3.db sqlite3> update Authors set Author="McInerney, Jay" where AuthorID=13374; sqlite3> .quit
It's probably a good idea to make a copy of the ny-sqlite-3.db file before manipulating the database. On a Windows machine, this file is in C:\Program Files\New Yorker Viewer\.
I did send email to the New Yorker DVD technical support, but I don't know if this is the sort of thing they'll fix on their own or not. I hope so. Luckily, we can fix it ourselves!
A couple days ago I mentioned Roy Eisenhardt, who was the President of the Oakland Athletics baseball team from 1981 - 1987. He was the subject of a 1983 Roger Angell Profile in The New Yorker.
When asked if he would make an expensive late-season trade for a star player if he thought it was
necessary to win a pennant that season he replied:
We want to be respectable and competitive, and we want to win our share of everything, including championships. But the way to do that is by being patient and foresighted. You can't just buy it or grab for it---we've already seen too much of that in the game, and its results. (see New York Yankees, 2005---CSS)
If the deal includes the transfer of good young players, it means you're just mortgaging your future for your present. Qualitatively, what's the worth of winning the whole thing versus the worth of being competitive each year? No one wants to accept second place, but unless you actually win the World Series you'll see yourself as having lost in the end.
The article was published in 1983 and shows remarkable foresight on the part of Eisenhardt. Recent research at Baseball Prospectus has attempted to quantify the value of a player to a team based on how many wins they'll add, and what effect that will have on the team's standings.
For example, if paying $27 million dollars for three years of Matt Morris yields an additional five wins over the course of the 2006 season, are those five wins enough to bump you into the playoffs, which is worth a lot of money to a team? For a rebuilding team, this probably isn't worth it, and as Eisenhardt recognized, you'd be selling out your future for the possibility of winning it all. Was it worth it for the Giants, who picked up Morris for 2006 and beyond? Only time will tell, but for the Giants, they are banking on needing only a few more wins in order to make it into post-season. And with Bonds in the last year of his contract, they feel they need to win now. And now, regardless of whether they do win it all or not, it's going to be a long and painful rebuilding process in San Francisco.
A few hours ago I wrote about a script I wrote that displays your New Yorker DVD reading lists in a convenient format. I just finished writing a similar script (again using Python and the pysqlite python module) which dumps the SQL statements you'd need to restore a reading list to the database. To restore the data to the database you'll also need the command line sqlite3 tool.
Use the script, reading_list_dump.py, to periodically dump your readings lists to a file:
$ ./reading_list_dump.py CSS > css_reading_list.sql
The file you created (css_reading_list.sql in this example) will contain SQL like:
INSERT INTO ReadingListEntry VALUES ( 700, 7, 181437 ); INSERT INTO ReadingListEntry VALUES ( 701, 7, 154899 ); INSERT INTO ReadingLists VALUES ( 7, 'CSS' );
To insert them into the database, close the Viewer program and use the sqlite3 command line program to upload the file:
$ sqlite3 -init css_reading_list.sql ny-sqlite-3.db
One important note: The table that's being updated (ReadingListEntry) has a primary key field that has to be unique in the table. It's the first number in the INSERT statements above (700, 701, etc.). Before running the sqlite3 command, you should make sure that there are no rows in the ReadingListEntry table that match the data you're inserting. If there are, you'll need to edit the dump file and change those numbers until they don't conflict with the existing table.
To find out what the largest value in the ReadingListEntry table is, run the following commands:
$ sqlite3 ny-sqlite-3.db sqlite3> SELECT ReadingListEntryID FROM ReadingListEntry ORDER BY ReadingListEntryID DESC LIMIT 1; 585
All the values for the first field in the dump file for the ReadingListEntry table should be larger than this number.
The New Yorker DVD's allow you to create your own personal reading lists, which are lists of articles that make it easy to access the articles you want to come back to. You can even restrict your searching to your own reading list. The DVD comes with several reading lists already loaded, including one for all the articles mentioned in the hardcover book that comes with the disks.
As it turns out, the database used by the viewer program is a SQLite3 database. It's a public domain database, and the New Yorker hasn't encrypted it so you can view the data in the database with your own custom program, or with a SQLite command line tool available from the SQLite3 web site. You can also extract the data for your reading list, in case something goes wrong with the database and you need to reinstall it.
I wrote a simple Python script, reading_lists.py that allows you to generate a formatted display of all the articles in your reading list. To use it you'll need a standard Python installation, and the pysqlite Python module.
To use is, you'll need to find the database file, which is at C:\Program Files\New Yorker Viewer\ny-sqlite-3.db in a default Windows install. I'm not sure where it installs on a Mac, but the filename is probably the same. Then just run the program specifying the database file, the reading list, and the other options the program offers. The result is a list of the articles complete with abstract and keywords.
Here's the usage statement:
Usage: ./reading_lists.py [-h] [-v] [-c columns] [-d database ] [-r list] [-y year] -v Verbose -h This help page -c columns Number of columns in the output (72 defautl) -d database Full path to the database file -r list Name of reading list (escape spaces or quote list) -y year Year cutoff (negative means article before listed year)
and a sample of the output:
------------------------------------------------------------------------ The Financial Page: The Catastrophe Problem. Surowiecki, James The Talk of the Town Jan. 10, 2005 (Disk_1_1998_2005)
THE FINANCIAL PAGE about the difficulties presented by natural disasters and catastrophes for the insurance industry. . . Writer notes that, in dollar terms, the recent Indian Ocean tsunamis impact was relatively small. . .the affected countries are relatively poor and the businesses there are less likely to have insurance. . . This past year was the most expensive in history for insurers. . . Discusses the actuarial nature of the insurance business: its about distributing risk over a large group of people whose behavior is collectively predictable. . . Catastrophes are not nearly as predictable. The same goes for man-made catastrophes such as September 11th. Insurers can make forecasts as long as they take a much longer view. Notes that even after September 11th, the number of companies that purchased terrorism insurance was relatively small. . .
Keywords: Actuarial Businesses; Hurricanes; September 11th, 2001 (9/11); Catastrophes; Insurance; Terrorism; Asia; Natural Disasters; Tsunamis; Business;
Note that if your database did get corrupted, you'd still have to manually recreate your reading list. I'll probably write a second Python script that will generate all the SQL INSERT statements necessary to recreate your reading lists if something went wrong with the database.