Skip to main content

How the D40 handles a multi dimensional problem

There are three dimensions to exposure:
  1. Aperture
  2. Shutter speed
  3. Sensitivity
In film days adjusting sensitivity on the fly was not practical, so you basically had a two dimensional plane once you loaded film.

The life of the camera computer was easy when you set the camera to A or S modes: all it had to do was adjust the free variable to maintain correct exposure. In M mode the camera merely informed you of exposure letting you roam freely in this 2D space.

The D40 can be setup exactly this way if you select fixed ISO. If you select auto ISO, however, the camera now is handed two free parameters (for A and S) and one free parameter (for M). How does the D40 deal with this?

For A mode the computer minimizes ISO and maximizes shutter speed. You can set a lower limit for Shutter speed and upper limit for ISO and the camera will drop shutter speed until it hits the lower limit and the start to bump ISO.

For S mode the computer minimizes ISO. It will maximize the aperture until it reaches the lens' limit. The it bumps ISO.

This all sounds sensible, but what about M mode? This is funny. In M mode you no longer have the freedom to mess with exposure - the camera runs loose with ISO changing it to give you correct exposure regardless of your A and S combination. Eventually it hits the camera ISO limits and starts to show you over- and under-exposure.

M mode with auto-ISO will enable you to play with particular shutter (motion capturing) and aperture (DOF) combinations for your subject that would normally be inaccessible to you (cumbersome with manual ISO, impractical with film).

Comments

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...

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...