Category "Python"


iTerm2 is a macOS terminal emulator with a lot of extra features. In particular, it has a simple protocol for displaying images inline. It comes with a program imgcat that will display common image formats such as PNG, JPEG, GIF, etc. Most of the images I deal with are FITS, though. I wrote fitscat to be a handy utility to display FITS images, as well as to print basic information about the FITS file such as a listing of extensions or an extension header.

For example, fitscat can display an image in an extension, as seen below:

There are options to specify a minimum and maximum value for scaling, as well as to use a simple filter such square root.

Also, fitscat can also print basic information about a FITS file, such as a listing of extensions:

CoMP$ fitscat --list 20150624.170419.comp.1074.iqu.5.fts
Filename: 20150624.170419.comp.1074.iqu.5.fts
No.    Name      Ver    Type      Cards   Dimensions   Format
  0  PRIMARY       1 PrimaryHDU      67   ()
  1  I, 1074.38    1 ImageHDU        33   (620, 620)   float32
  2  I, 1074.50    1 ImageHDU        33   (620, 620)   float32
  3  I, 1074.62    1 ImageHDU        33   (620, 620)   float32
  4  I, 1074.74    1 ImageHDU        33   (620, 620)   float32
  5  I, 1074.86    1 ImageHDU        33   (620, 620)   float32
  6  Q, 1074.38    1 ImageHDU        33   (620, 620)   float32
  7  Q, 1074.50    1 ImageHDU        33   (620, 620)   float32
  8  Q, 1074.62    1 ImageHDU        33   (620, 620)   float32
  9  Q, 1074.74    1 ImageHDU        33   (620, 620)   float32
 10  Q, 1074.86    1 ImageHDU        33   (620, 620)   float32
 11  U, 1074.38    1 ImageHDU        33   (620, 620)   float32
 12  U, 1074.50    1 ImageHDU        33   (620, 620)   float32
 13  U, 1074.62    1 ImageHDU        33   (620, 620)   float32
 14  U, 1074.74    1 ImageHDU        33   (620, 620)   float32
 15  U, 1074.86    1 ImageHDU        33   (620, 620)   float32

Or display a header:

CoMP$ fitscat --header -e 3 20150624.170419.comp.1074.iqu.5.fts
XTENSION= 'IMAGE   '           /extension type
BITPIX  =                  -32 /bits per data value
NAXIS   =                    2 /number of axes
NAXIS1  =                  620 /
NAXIS2  =                  620 /
PCOUNT  =                    0 /
GCOUNT  =                    1 /
EXTNAME = 'I, 1074.62'         /
WAVELENG=             1074.620 / WAVELENGTH OF OBS (NM)
POLSTATE= 'I       '           / POLARIZATION STATE
EXPOSURE=               250.00 / EXPOSURE TIME (MILLISEC)
NAVERAGE=                   16 / Number of images averaged together
FILTER  =                    1 / FILTER WHEEL POSITION (1-8)
DATATYPE=               'DATA' / DATA, DARK OR FLAT
LCVR1TMP=            29.639999 / DEGREES CELSIUS
LCVR2TMP=            33.429001 /
LCVR3TMP=            33.715000 /
LCVR4TMP=            33.738998 /
LCVR5TMP=            33.618999 /
LCVR6TMP=            28.847000 /
NDFILTER=                    8 / ND 1=.1, 2=.3, 3=.5, 4=1, 5=2, 6=3, 7=4, 8=cle
BACKGRND=               13.154 / Median of masked line center background
BODYTEMP=               34.023 / TEMPERATURE OF FILTER BODY (C)
BASETEMP=               33.599 / BASE PLATE TEMP (C)
RACKTEMP=               25.012 / COMPUTER RACK AMBIENT AIR TEMP (C)
OPTRTEMP=               33.306 / OPTICAL RAIL TEMP (C)
DEMULT  =                    1 / 1=DEMULTIPLEXED, 0=NOT DEMULTIPLEXED
FILTTEMP=               35.000 / ILX FILTER TEMPERATURE (C)
FLATFILE= '20150624.070023.FTS' / Name of flat field file
INHERIT =                    T /
DISPMIN =                 0.00 / Minimum data value
DISPMAX =                 5.00 / Maximum data value
DISPEXP =                 0.50 / Exponent value for scaling

The full interface of fitscat is shown below:

$ fitscat --help
usage: fitscat [-h] [--min MIN] [--max MAX] [--debug] [-d] [-l] [-r]
               [-e EXTEN_NO] [-f FILTER] [-s SLICE]
               filename

fitscat - a FITS query/display program

positional arguments:
  filename              FITS file to query

optional arguments:
  -h, --help            show this help message and exit
  --min MIN             min for scaling
  --max MAX             max for scaling
  --debug               set to debug
  -d, --display         set to display
  -l, --list            set to list HDUs
  -r, --header          set to display header
  -e EXTEN_NO, --exten_no EXTEN_NO
                        specify extension
  -f FILTER, --filter FILTER
                        specify filter (default: none)
  -s SLICE, --slice SLICE
                        specify slice of data array to display

Source code for the Python script is available on GitHub. The script is compatible with Python 2 and 3, but requires standard scientific Python packages AstroPy, NumPy, and PIL.

Travis Oliphant, creator of NumPy the array package for Python, wrote a analog to the Zen of Python for NumPy:

Strided is better than scattered
Contiguous is better than strided
Descriptive is better than imperative (use data-types)
Array-oriented is often better than object-oriented
Broadcasting is a great idea — use where possible
Vectorized is better than an explicit loop
Unless it’s complicated — then use numexpr, weave, or Cython
Think in higher dimensions

I tried something for IDL last year.

Awesome Python:

A curated list of awesome Python frameworks, libraries, software and resources.

Worth checking out when looking for an existing solution.

Great post examining some of the reasons why the FFT algorithm is so fast compared to a naive implementation:

The goal of this post is to dive into the Cooley-Tukey FFT algorithm, explaining the symmetries that lead to it, and to show some straightforward Python implementations putting the theory into practice. My hope is that this exploration will give data scientists like myself a more complete picture of what’s going on in the background of the algorithms we use.

A nice list of resources for doing remote sensing in Python, especially if you already know IDL.

I’ve found this translation guide for writing and understanding Python code quite useful. I think it should work if you are familiar with Python and wanting to read/write IDL code also.

Python Unlocked:

Python is a versatile programming language that can be used for a wide range of technical tasks — computation, statistics, data analysis, game development, and more. Though Python is easy to learn, its range of features means there are many aspects of it that even experienced Python developers don’t know about. Even if you’re confident with the basics, its logic and syntax, by digging deeper you can work much more effectively with Python – and get more from the language.

Python Unlocked walks you through the most effective techniques and best practices for high performance Python programming – showing you how to make the most of the Python language.

I find myself reading more about best practices, especially in Python.

If you are interested, promo code PYTUNL30 will get you 30% off the ebook until Feb 20.

One of the major features of IDL 8.5 is the two-way bridge between IDL and Python. This allows Python functionality to be accessed from IDL (Python has a lot of libraries for things that fall outside of the standard scientific routines found in IDL) as well as accessing IDL functionality from Python (call legacy IDL code).

The IDL-Python bridge works with either Python 2 or 3 (whew, I’m still on Python 2!).

Continue reading “IDL 8.5: IDL-Python bridge.”

The “Zen of Python” provides the basic philosophy of Python. From PEP 20:

There should be one — and preferably only one — obvious way to do it.

I doubt there is a single area in Python that violates this more than making a simple HTTP request. Python provides at least six builtin libraries to do this: httplib, httplib2, urllib, urllib2, urllib3, and pycurl. There are several reviews comparing the various libraries.

But there is a third party library, requests, that might be the “obvious way to do it” now:

import requests, json

url = 'https://api.github.com/repos/mgalloy/mglib'
r = requests.get(url, auth=('mgalloy', 'my_password'))
print r.json()['updated_at']

requests is installed as part of Anaconda, which an easy way to get all the core scientific programming packages for Python.

Numba is a Python package that uses the LLVM compiler to compile Python code to native code. Numba 0.13, released a few weeks ago, offers support for automatically creating CUDA kernels from Python code.

I created a notebook1 (HTML) to show off the demo code.


  1. I’m not sure which is cooler, IPython notebooks or Numba. 

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