lists¶

List processing package.

Notes¶

For questions or comments please see our github page. We encourage and appreciate user feedback.

The simple tasks in this package are already covered by Astropy or other Python built-ins

Contents:

lintran
raverage
table-unique

lintran¶

Please review the Notes section above before running any examples in this notebook

The lintran task will linear transfrom a list of coordinates. There are various tasks in the numpy linear algebra package that can used to achieve this in Python. Below is a simple example of a 90 degree rotation.

# Standard Imports
import numpy as np

# 90 degree rotation counterclockwise
x_points = np.array([1,4,5,7,3])
y_points = np.array([4,5,2,2,4])
points = np.vstack([x_points,y_points])
transform_matrix = a = np.array([[0, -1],
                                 [1, 0]])

new_points = np.linalg.inv(a).dot(points)

print("new x values: {}".format(new_points[0]))
print("new xyvalues: {}".format(new_points[1]))

new x values: [ 4.  5.  2.  2.  4.]
new xyvalues: [-1. -4. -5. -7. -3.]

raverage¶

Please review the Notes section above before running any examples in this notebook

raverage computes the running average and standard deviation for a 1-dimensional array. We can efficiently do this in Python using strides in numpy (source: http://www.rigtorp.se/2011/01/01/rolling-statistics-numpy.html).

# Standard Imports
import numpy as np

# code goes here
def rolling_window(a, window):
    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
    strides = a.strides + (a.strides[-1],)
    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)

data = np.arange(20)
out_data = np.mean(rolling_window(data,4),-1)
out_stddev = np.std(rolling_window(data,4),-1)
print(rolling_window(data,5))
print("out_data: {}".format(out_data))
print("out_stddev: {}".format(out_stddev))

[[ 0  1  2  3  4]
 [ 1  2  3  4  5]
 [ 2  3  4  5  6]
 [ 3  4  5  6  7]
 [ 4  5  6  7  8]
 [ 5  6  7  8  9]
 [ 6  7  8  9 10]
 [ 7  8  9 10 11]
 [ 8  9 10 11 12]
 [ 9 10 11 12 13]
 [10 11 12 13 14]
 [11 12 13 14 15]
 [12 13 14 15 16]
 [13 14 15 16 17]
 [14 15 16 17 18]
 [15 16 17 18 19]]
out_data: [  1.5   2.5   3.5   4.5   5.5   6.5   7.5   8.5   9.5  10.5  11.5  12.5
  13.5  14.5  15.5  16.5  17.5]
out_stddev: [ 1.11803399  1.11803399  1.11803399  1.11803399  1.11803399  1.11803399
  1.11803399  1.11803399  1.11803399  1.11803399  1.11803399  1.11803399
  1.11803399  1.11803399  1.11803399  1.11803399  1.11803399]

table-unique¶

Please review the Notes section above before running any examples in this notebook

The table task task a list of text and transfers it to a table. We will show an example of this using Astropy Tables and a text file with return seperated values. There are various parameters for reading in ascii files, documentation found here. We will continue this example to show how to extract that list and then apply a unique requirement, and save the list back out to a file, as in the IRAF task unique. More information on going to and from Astropy Tables see:

# Astronomy Specific Imports
from astropy.table import Table, unique

# Read text file into table
text_file = '/eng/ssb/iraf_transition/test_data/table.txt'
tab = Table.read(text_file, format='ascii.no_header')
tab.pprint()

 col1
-----
star1
star2
star3
star3
star4
star5
star5
star6

# Run unique and print table
out_table = unique(tab)
out_table.pprint()

# Save results to new text file
out_table.write("/eng/ssb/iraf_transition/test_data/out_table.txt",format='ascii')

 col1
-----
star1
star2
star3
star4
star5
star6

Not Replacing¶

average - see images.imutil.imstatistics or numpy tools
columns - used to convert multicolumn data into CL lists, deprecated
rgcursor - Read the graphics cursor, deprecated
rimcursor - Read the image display cursor, see images.tv or Python imexam
tokens - deprecated
words - deprecated, see Python’s built in file reader