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AHELP for CIAO 4.16

crates

Context: crates

Synopsis

Reading, writing, and changing ASCII and FITS files in Python

Description

The Crates module allows users to read and write ASCII and FITS files in Python, with support for the same features as provided by the CIAO Data Model.

>>> cr = read_file('tbl.dat[energy=500:7000][cols x,y]')
>>> x = get_colvals(cr, 'x')
>>> y = get_colvals(cr, 'y')

reads in the x and y columns from the file 'tbl.dat', after applying a filter on the energy column of 500 to 7000, and then returns the column values in the x and y variables as NumPy arrays.

Functions and methods

The Crates module provides a number of routines for common tasks, like get_colvals. For more complex, uncommon routines, there are also a large number of methods provided by the objects themselves. Try the Python dir and help commands for more information: e.g.

>>> cr = read_file('evt.fits')
>>> x = cr.get_column('x')
>>> dir(x)
...
>>> help(x)

Examples

Example 1

>>> cr = read_file("evt2.fits")
>>> print(get_keyval(cr, "OBJECT"))
ELAIS-S1 A
>>> print(get_keyval(cr, "EXPOSURE") / 1000)
31.7432921583

Here we get the values of the OBJECT and EXPOSURE keywords from the header of evt2.fits. The return value from get_keyval() uses the appropriate Python type, so OBJECT is a string and the exposure time is a number.

Example 2

>>> from pycrates import *
>>> cr = read_file("evt2.fits[ccd_id=7,energy=300:7000]")
>>> e = copy_colvals(cr, "energy")
>>> emin = e.min() / 1000
>>> emax = e.max() / 1000
>>> emean = e.mean() / 1000
>>> print(f"Mean E = {emean} min= {emin} max= {emax}")
Mean E = 3.10554851929 min= 0.300000335693 max= 6.99996289063

Here we read in the energy column from an event file and calculate the minimum, maximum and mean energy in keV.

Example 3

>>> import numpy as np
>>> from pycrates import *
>>> cr = read_file("0.5-7.0_bin2.img")
>>> img = copy_piximgvals(cr)
>>> logimg = np.log10(img)
RuntineWarning: divide by zero encountered in log10
>>> from matplotlib import pyplot as plt
>>> plt.imshow(logimg, origin='lower')
>>> expmap = read_file("2.3_bin2.expmap")
>>> plt.imshow(expmap.get_image().values, alpha=0.2,
origin='lower',cmap="gray",vmin=1,vmax=10)

Here we display the logarithm of the pixel values using the matplotlib imshow command and then overlay the exposure map for the observation, using transparency to ensure that the image can still be seen.

The divide-by-zero warning

The warning about divide by zero comes from NumPy and can be turned off using the seterr routine; for example

>>> olderr = np.seterr(divide='ignore')

Example 4

>>> x = np.arange(1, 10)
>>> y = np.sin(x) * np.cos(x)
>>> z = x**2 + y**2
>>> xcol = CrateData()
>>> xcol.name = "x"
>>> xcol.values = x
>>> ycol = CrateData()
>>> ycol.name = "y"
>>> ycol.values = y
>>> zcol = CrateData()
>>> zcol.name = "zz"
>>> zcol.values = z
>>> zcol.unit = 'cm**2 s'
>>> cr = TABLECrate()
>>> add_col(cr, xcol)
>>> add_col(cr, ycol)
>>> add_col(cr, zcol)
>>> write_file(cr, "out.fits")
>>> write_file(cr, "out.dat[opt kernel=text/simple]")
>>> quit()
unix% cat out.dat
#TEXT/SIMPLE
# x y zz
1 4.5464871341284e-01 1.206705452608
2 -3.7840124765396e-01 4.143187504226
3 -1.3970774909946e-01 9.019518255158
4 4.9467912331169e-01 16.24470743504
5 -2.7201055544468e-01 25.07398974227
6 -2.6828645900022e-01 36.07197762408
7 4.9530367784744e-01 49.24532573329
8 -1.4395165833253e-01 64.02072207994
9 -3.7549362338584e-01 81.14099546120

Here we create two files containing the contents of the x, y, and z arrays in columns called "x", "y", and "zz" respectively. The file out.fits is a FITS binary table whereas out.dat is an ASCII file (see ahelp dmascii for more information on ASCII support in the CIAO Data model).

The make_table_crate routine from the crates_contrib.utils module can be used to simplify the above.

Example 5

>>> ivals = np.arange(12).reshape(3, 4)
>>> cd = CrateData()
>>> cd.values = ivals
>>> cr = IMAGECrate()
>>> add_piximg(cr, cd)
>>> cr.name = "SIMPLE"
>>> write_file(cr, "img.fits")

Here we create an image, with dimensions of 4 (x) by 3 (y), add it to an IMAGECrate using add_piximg (note that add_image is a ChIPS call that displays an image), and write it out to the file img.fits. After these commands we have:

>>> print(cr)
   Crate Type:        <IMAGECrate>
   Crate Name:        SIMPLE

>>> quit()

unix% dmlist img.fits blocks
 
--------------------------------------------------------------------------------
Dataset: img.fits
--------------------------------------------------------------------------------
 
     Block Name                          Type         Dimensions
--------------------------------------------------------------------------------
Block    1: SIMPLE                         Image      Int4(4x3)
unix% dmstat img.fits centroid-
SIMPLE
    min:	0 	      @:	( 1 1 )
    max:	11 	      @:	( 4 3 )
   mean:	5.5 
  sigma:	3.4520525295 
    sum:	66 
   good:	12 
   null:	0

The make_image_crate routine from the crates_contrib.utils module can be used to simplify the above.

Example 6

unix% cat qconv.py
#!/usr/bin/env python
import sys
import pycrates as pyc
cr = pyc.read_file(sys.argv[1])
sky = cr.get_transform("SKY")
x = float(sys.argv[2])
y = float(sys.argv[3])
lcoord = [[x, y]]
scoord = sky.apply(lcoord)
print(f"Logical {lcoord[0]} -> SKY {scoord[0]}")

This simple Python script (which has no sanity checks such as ensuring the number of command-line arguments is correct) will convert the logical coordinates given on the command line to SKY coordinates using the transform taken from the first argument. An example run (which assumes the executable bit is set) is given below:

unix% ./qconv.py img.fits 1 1
Logical [1.0, 1.0] -> SKY [ 2009.5 3149.5]

Loading Crates

The Crates module is automatically imported into Sherpa sessions, otherwise use one of the following:

from pycrates import *

import pycrates

Documentation

The Crates and transform library ahelp documentation can be listed by saying ahelp -c crates and ahelp -c transform

In particular: ahelp read_file, ahelp read_dataset, ahelp crate, ahelp cratedataset, ahelp cratedata, and ahelp cratekey.

There is also Python documentation available, on the module itself - e.g.

>>> import pycrates
>>> help(pycrates)

or for specific functions and methods

>>> help(read_file)
>>> help(cr.get_colnames)

Objects and metadata

The Crates library uses a set of Python objects to represent data. These objects provide access to the data and metadata - such as units, description, and other fields - stored in the files.

Object	Description
TABLECrate, IMAGECrate	Represents a block of a file containing a table - a set of columns - or a single n-Dimensional image.
CrateDataset, PHACrateDataset, RMFCrateDataset	A set of blocks stored in a single file.
CrateData	This is used to represent column and image data.
CrateKey	A keyword, containing the name, value, and optional unit and description fields.
CrateSubspaceData	Represent the filtering applied to a column or image.

Creating objects

Objects can be created using the normal Python idiom, by calling the constructor, or - in some cases - can be created by routines such as read_file and get_key.

Related objects

The Crates library also uses objects from the transforms and region modules.

Subspace

The subspace information stored in a file can be accessed with the get_subspace_data method of a crate. There can be multiple blocks of subspace data - normally one per ACIS chip but combining filters with OR rather than AND will also lead to this - which means that you need to provide an integer and column name.

crate.get_subspace_data(cptnum, item)

Here crate refers to the return value of read_file, the cptnum argument is a positive integer, indicating the component number, and item is a string indicating the column or axis name. Note that in CIAO 4.15 there is no way to determine how many blocks there are in a file or what columns are stored in the subspace block.

>>> cr = read_file('a2142_smoothed.fits')
>>> s1 = cr.get_subspace_data(1, 'sky')
>>> print(s1)
   Name:            sky: x, y
   Unit:            
   Range Minimum:   []
   Range Maximum:   []
   Region String:   Box(3840,3670,1460,1460)

>>> sx = cr.get_subspace_data(1, 'x')
>>> sy = cr.get_subspace_data(1, 'y')
>>> print(sx)
   Name:            x
   Unit:            
   Range Minimum:   [3110.]
   Range Maximum:   [4570.]
   Region String:   DEFAULT

>>> print(sy)
   Name:            y
   Unit:            
   Range Minimum:   [2940.]
   Range Maximum:   [4400.]
   Region String:   DEFAULT

>>> import region
>>> region.regPrintRegion(s1.region)
1	Box(3840,3670,1460,1460) (Flag_Coord: Unknown) (Flag_Radius: Unknown)
>>> s2 = cr.get_subspace_data(1, 'time')
>>> print(s2)
   Name:            time
   Table Name:      GTI3
   Unit:            
   Range Minimum:   [2.29755146e+08   2.29758594e+08   2.29795394e+08]
   Range Maximum:   [2.29758591e+08   2.29795391e+08   2.29800307e+08]
   Region String:   DEFAULT

>>> s3 = cr.get_subspace_data(1, 'ccd_id')
>>> print(s3)
   Name:            ccd_id
   Unit:            
   Range Minimum:   [3]
   Range Maximum:   [3]
   Region String:   DEFAULT

The return value - a CrateSubspaceData object - has the following fields:

Field	Description
name	The column name.
unit	The units of the column, or ''.
range_min	The minimum value of the data; this can contain multiple values when sets of ranges were used to filter the column.
range_max	The maximum value of the data; this can contain multiple values when sets of ranges were used to filter the column.
region	If a spatial filter was applied then this is a region object representing the filter, otherwise it is None.

Two subspace objects can be compared using the Python equality check; e.g.

>>> cr1.get_subspace(1, "time") == cr1.get_subspace(2, "time")

There is no way in CIAO 4.15 to add to or delete from the subspace information in a Crate.

History and Comments

The COMMENT and HISTORY records can be retrieved from a Crate with the get_comment_records and get_history_records methods respectively. As an example:

>>> cr = pycrates.read_file('evt.fits')
>>> hist = cr.get_history_records()
>>> comm = cr.get_comment_records()

The get_all_records method returns both COMMENT and HISTORY records. New records can be added using the add_record method on the crate, and the pycrates add_record, add_comment, and add_history functions, as shown in the following fanciful example:

>>> cr.add_record('COMMENT', 'Yeah yeah how groovy')
>>> pycrates.add_comment(cr, 'Yes, very groovy')

History entries can be created using the history module:

>>> import history
>>> pnames = ["infile", "outfile", "grooviness"]
>>> pvals = ["okay.fits", "better.fits", 3]
>>> hist = history.HistoryRecord(tool='groovy', param_names=pnames,
param_vals=pvals)
>>> pycrates.add_history(cr, hist)

The write_file and write_dataset routines support an optional history parameter to control whether history and comment records are written out or not.

Changes in CIAO 4.15

Crates can now read in images and tables with missing, or blank, names for WCS components (rather than causing a run-time exception).

Changes in CIAO 4.14

BLANK keyword handling

If an image contains the BLANK keyword then it is no-longer ignored, but will be retained when the file is read in - accessible by the get_nullval() method on the image object returned by the Crate's get_image() method - and will also now be written out (for appropriate image data types).

Changes in CIAO 4.13

New functions

The read_dataset and write_dataset routines are now exported from pycrates (they were previously only available from the pycrates.hlui module).

Improvements

The set_colvals routine can now accept a NumPy array and change a component of a vector column (e.g. the "X" component of "SKY(X, Y)"), and a number of corner cases have been fixed with the subspace handling.

Changes in CIAO 4.11

Memory leaks

A number of memory leaks have been fixed in the Crates modules, and error conditions are now better handled in a number of low-level routines.

History and Comments

Crates now supports HISTORY and COMMENT keywords: that is, they are accessible from the crate using methods such as get_comment_records and get_history_records, and will be written out when the file is saved. Note that the order of these records (with respect to the keywords) is not guaranteed to be maintained.

The history module has been added to help creating history records.

Please use the Python help function for these methods, as there are no ahelp files for them.

Changes in CIAO 4.8

Adding columns

The add_column method of a table crate now works correctly when the column number is set to 0 (previously the new column was added to the end of the table, now it is the first column). The add_column method and add_col call now create an empty subspace entry for vector columns, accessible with the get_subspace_data method of the crate.

Support for variable-length arrays

Support for variable-length arrays has been improved and the CrateData object now supports the is_varlen and get_fixed_length_array methods.

Tri-state logical columns

The FITS convention for boolean columns supports a "null" value, which means that it can represent three states: true, false, and unknown. Any such null, or unknown, value will be converted to False when the data is read in to a Crate (e.g. when read by read_file), and so the information that this cell value was a null will be lost.

Bugs

Problem with single byte bit columns

Crates has problems when trying to write out a bit column, when the column has less than 8 bits (so fill only 1 byte).

Crates will convert all transforms into their binned form.

Transforms are assumed to be applied to image pixels, so when they are created they are adjusted to match what would be an image pixel boundary, even if the transform is created on a table column.

So, for example trying to create a transform that converts degrees F to C:

tempF = +32 + 1.8 * tempC

A linear transform with scale=1.8 and offset=32, is written out as

tempF = +32.90 [degree F] +1.80 * (tempC -0.50)

While mathematically correct, the extra half "pixel" offset is unnecessary when dealing with transforms applied to table columns.

Writing NaN to keywords causes crash

Attempting to write a NaN value to a keyword results in a crash of the python interpreter

>>> tab = read_file("a.fits")
>>> key = CrateKey()
>>> key.name="foobar"
>>> key.value = np.nan
>>> tab.add_key( key )
>>> write_file( tab, "b.fits")
RuntimeError: dmKeyWrite() could not write key. 'foobar'
>>> quit()

Exception IOError: IOError('FITS error 402 writing key foobar',) in <boundmethod CrateDataset.__del__ of Crate Dataset:
...
*** glibc detected *** /export/ciao-4.5/ots/bin/python: double free or
corruption (!prev): 0x0000000003026480 ***
...
(core dumped) ipython --profile chips -i -c "\"${cmd}\""

Byte datatype subspace columns

pycrates may crash if the file contains subspace columns with byte data-type. Examples include some HRC datasets with byte type SUB_MJF.

Workaround:

Workaround: for the HRC case, you can remove the subspace column with

% dmcopy hrc_evt[subspace -sub_mjf] hrc_evt_mod

prior to loading the file into crates.

Write access is not robustly checked.

Users need to be careful when trying to modify a file in place as the checks on whether or not a file is writeable are not robust. Simply specifying mode="rw" will not produce an error nor an exception if the file is not actually writeable.

unix% chmod 400 myfile.fits
unix% python
>>> from pycrates import *
>>> rr = read_file("hrcf01801N006_evt2.fits", mode="rw")
>>> # do something to modify file
>>> rr.write()

The above sequence will not geneate an exception even though the file is not writeable.

Trying to write a file with a Byte data-type in the subspace fails.

HRC event files have a byte type (8 bit unsigned integer) SUB_MJF subspace column present. Trying to read in the file into a crate and writing it back out will fail.

unix% dmlist hrcf01801N006_evt2.fits subspace
 
--------------------------------------------------------------------------------
Data subspace for block EVENTS: Components: 1 Descriptors: 33 
--------------------------------------------------------------------------------
 
...
  31 ENDMNF               Int4                0:127 
  32 SUB_MJF              Byte                0:64 
  33 CLKTICKS             Int4                0:1091567608 
unix% python
>>> from pycrates import *
>>> rr = read_file("hrcf01801N006_evt2.fits")
>>> write_file(rr, "/tmp/copy")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/export/ciao-4.5/lib/python2.7/site-packages/pycrates/hlui.py", line 985, in write_file
    crate.write(outfile=filename, clobber=clobber)
  File "/export/ciao-4.5/lib/python2.7/site-packages/pycrates/tablecrate.py", line 294, in write
    backend.write( self, outfile=outfile )
  File "/export/ciao-4.5/lib/python2.7/site-packages/pycrates/io/dm_backend.py", line 534, in write
    self.__write_block( crate, close_flag )
  File "/export/ciao-4.5/lib/python2.7/site-packages/pycrates/io/dm_backend.py", line 578, in __write_block
    self.__write_subspace(block, crate)
  File "/export/ciao-4.5/lib/python2.7/site-packages/pycrates/io/dm_backend.py", line 626, in __write_subspace
    ssitem.unit, ssitem.range_min, ssitem.range_max )
TypeError: dmSubspaceColCreate() mins or maxes argument is not a supported type.

The only workaround is to remove the SUB_MJF subspace before reading it into a crate.

unix% dmcopy "hrcf01801N006_evt2.fits[subspace -SUB_MJF]" copy_evt.fits
unix% python
>>> from pycrates import *
>>> rr = read_file("copy_evt.fits")
>>> write_file(rr, "copy2")

Using numpy views of arrays can lead to data corruption.

numpy uses a special indexing scheme to access arrays that have been sliced (ie truncated at either end) or when for example transposing 2D arrays. The origianal data are preserved and the user only sees a view of the array.

The crates python to datamodel C interface does not handle views correctly. It may result in data being truncated or simply producing nonsensical results.

Users should make sure that any arrays sent to pycrates are in C_CONTIGUOUS order and ALGINED

>>> foo = np.arange(10).reshape(5,2)
>>> foo2 = bob.T
>>> foo.flags

   C_CONTIGUOUS : True
   F_CONTIGUOUS : False
   OWNDATA : False
   WRITEABLE : True
   ALIGNED : True
   UPDATEIFCOPY : False
>>> foo2.flags

   C_CONTIGUOUS : False
   F_CONTIGUOUS : True
   OWNDATA : False
   WRITEABLE : True
   ALIGNED : True
   UPDATEIFCOPY : False

Caveats

Crates does not support subspaces on byte columns.
Cannot add rows to a column that is part of a virtual column.: Users can not add rows a column which is a component of a vector using Crates (the new values are set to 0). User can extend scalar and array columns. To work around this, users must add rows the vector column itself rather than the components.