Skip to main content

A note on the bow stringer

The bow stringer I have came with instructions to step on the string with both feet about shoulder width apart. I didn't understand why, especially since some instructions elsewhere (and here) say to stand on the string with one foot. Today I think I figured out why, at least with my bowstringer.

The cup, which covers the upper tip, into which you are trying to slide the bowstring, has a tendency to cover the tip more and more closely, eventually pinching off the notch in the tip into which you need to slide the bowstring.

By standing with your feet apart, you cause the tension in the string to act more perpendicularly to the limb, and so the cup is less likely to slide inwards and cover the notch.

If you stand with only one foot on the stringer, or with feet close together, the string makes a shallow angle with the limb. This pulls the cup more tightly onto the tip, sliding it over the notch.

An additional benefit is that you are causing less tension in the bowstringer.

(Note that the bowstringers in the links do not have a cup that goes on the upper tip - they have a saddle, that you place over the limb. This difference in design probably accounts for the difference in optimum technique.)
Labels:

Comments

Post a Comment

Popular posts from this blog

A note on Python's __exit__() and errors

Python's context managers are a very neat way of handling code that needs a teardown once you are done. Python objects have do have a destructor method ( __del__ ) called right before the last instance of the object is about to be destroyed. You can do a teardown there. However there is a lot of fine print to the __del__ method. A cleaner way of doing tear-downs is through Python's context manager , manifested as the with keyword. class CrushMe: def __init__(self): self.f = open('test.txt', 'w') def foo(self, a, b): self.f.write(str(a - b)) def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.f.close() return True with CrushMe() as c: c.foo(2, 3) One thing that is important, and that got me just now, is error handling. I made the mistake of ignoring all those 'junk' arguments ( exc_type, exc_val, exc_tb ). I just skimmed the docs and what popped out is that you need to return True or...
Post a Comment
Read more

Store numpy arrays in sqlite

Use numpy.getbuffer (or sqlite3.Binary ) in combination with numpy.frombuffer to lug numpy data in and out of the sqlite3 database: import sqlite3, numpy r1d = numpy.random.randn(10) con = sqlite3.connect(':memory:') con.execute("CREATE TABLE eye(id INTEGER PRIMARY KEY, desc TEXT, data BLOB)") con.execute("INSERT INTO eye(desc,data) VALUES(?,?)", ("1d", sqlite3.Binary(r1d))) con.execute("INSERT INTO eye(desc,data) VALUES(?,?)", ("1d", numpy.getbuffer(r1d))) res = con.execute("SELECT * FROM eye").fetchall() con.close() #res -> #[(1, u'1d', <read-write buffer ptr 0x10371b220, size 80 at 0x10371b1e0>), # (2, u'1d', <read-write buffer ptr 0x10371b190, size 80 at 0x10371b150>)] print r1d - numpy.frombuffer(res[0][2]) #->[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] print r1d - numpy.frombuffer(res[1][2]) #->[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] Note that for work where data ty...
Post a Comment
Read more