The CPL recipe python interface

This is a non-official python module to access CPL recipes. It is not meant as part of the CPL or the MUSE pipeline software, but may be useful for testing and analysis.

“The Common Pipeline Library (CPL) consists of a set of C libraries, which have been developed to standardise the way VLT instrument pipelines are built, to shorten their development cycle and to ease their maintenance. The Common Pipeline Library was not designed as a general purpose image processing library, but rather to address two primary requirements. The first of these was to provide an interface to the VLT pipeline runtime- environment. The second was to provide a software kit of medium-level tools, which allows astronomical data-reduction tasks to be built rapidly.” [ESO]



Binary packages

On Debian and debian-based systems (Ubuntu, Mint), python-cpl can be installed with the command

apt-get install python-cpl

Python CPL comes with the Ubuntu distribution since 12.04. Debian packages are in Wheezy (Debian 7), Wheezy (Debian 8), and Testing

Source code

  • Python Package Index

  • Git repository. To access, do a:

    git clone git://

    This gives you the current version in the subdirectory python-cpl. To update to the current version of an existing repository, do a git pull in the python-cpl directory.

    For more detailed information, check the manual page of git(1) and the github page of the project.


For compilation, a C compiler is needed additionally to the software mentioned above.

The installation follows the standard procedure used in python. On default, the installation path /usr/local. If using a non-standard installation path, add the directory PREFIX/lib/python2.7/site-packages/ (lib64/python2.7/site-packages/ on 64 bit systems) to your environment variable PYTHONPATH where where PREFIX is the installation path for the package.

In the source directory of python-cpl, run

python install --prefix=PREFIX

There are other options available as well; use the --help option to list them.

Test suite

There are a number of tests defined in test/


The test recipe needs an installed CPL development environment. The tests may print a memory corruption detection by glibc. This is normal, since the tests also check this behaviour in the recipe.

Tests are also automatically buils by Travis CI.


Simple example

The following code takes BIAS input file names from the command line and writes the MASTER BIAS to the file name provided with the -o option:

from optparse import OptionParser
import sys
import cpl

parser = OptionParser(usage='%prog files')
parser.add_option('-o', '--output', help='Output file', default='master_bias.fits')
parser.add_option('-b', '--badpix-table', help='Bad pixel table')

(opt, filenames) = parser.parse_args()
if not filenames:


muse_bias = cpl.Recipe('muse_bias')
muse_bias.param.nifu = 1
muse_bias.calib.BADPIX_TABLE = opt.badpix_table

res = muse_bias(filenames)

Quick guide

Input lines are indicated with “>>>” (the python prompt). The package can be imported with

>>> import cpl

If you migrate from Esorex, you may just init the search path for CPL recipes from the esorex startup:

>>> cpl.esorex.init()

Otherwise, you will need to explicitely set the recipe search path:

>>> cpl.Recipe.path = '/store/01/MUSE/recipes'

List available recipes:

>>> cpl.Recipe.list()
[('muse_quick_image', ['0.2.0', '0.3.0']),
 ('muse_scipost', ['0.2.0', '0.3.0']),
 ('muse_scibasic', ['0.2.0', '0.3.0']),
 ('muse_flat', ['0.2.0', '0.3.0']),
 ('muse_subtract_sky', ['0.2.0', '0.3.0']),
 ('muse_bias', ['0.2.0', '0.3.0']),
 ('muse_ronbias', ['0.2.0', '0.3.0']),
 ('muse_fluxcal', ['0.2.0', '0.3.0']),
 ('muse_focus', ['0.2.0', '0.3.0']),
 ('muse_lingain', ['0.2.0', '0.3.0']),
 ('muse_dark', ['0.2.0', '0.3.0']),
 ('muse_combine_pixtables', ['0.2.0', '0.3.0']),
 ('muse_astrometry', ['0.2.0', '0.3.0']),
 ('muse_wavecal', ['0.2.0', '0.3.0']),
 ('muse_exp_combine', ['0.2.0', '0.3.0']),
 ('muse_dar_correct', ['0.2.0', '0.3.0']),
 ('muse_standard', ['0.2.0', '0.3.0']),
 ('muse_create_sky', ['0.2.0', '0.3.0']),
 ('muse_apply_astrometry', ['0.2.0', '0.3.0']),
 ('muse_rebin', ['0.2.0', '0.3.0'])]

Create a recipe specified by name:

>>> muse_scibasic = cpl.Recipe('muse_scibasic')

By default, it loads the recipe with the highest version number. You may also explicitely specify the version number:

>>> muse_scibasic = cpl.Recipe('muse_scibasic', version = '0.2.0')

List all parameters:

>>> print muse_scibasic.param
{'ybox': 40, 'passes': 2, 'resample': False, 'xbox': 15, 'dlambda': 1.25,
 'cr': 'none', 'thres': 5.8, 'nifu': 0, 'saveimage': True}

Set a parameter:

>>> muse_scibasic.param.nifu = 1

Print the value of a parameter (None if the parameter is set to default)

>>> print muse_scibasic.param.nifu.value

List all calibration frames:

>>> print muse_scibasic.calib

Set calibration frames with files:

>>> muse_scibasic.calib.MASTER_BIAS    = 'MASTER_BIAS-01.fits'
>>> muse_scibasic.calib.MASTER_FLAT    = 'MASTER_FLAT-01.fits'
>>> muse_scibasic.calib.TRACE_TABLE    = 'TRACE_TABLE-01.fits'
>>> muse_scibasic.calib.GEOMETRY_TABLE = 'geometry_table.fits'

You may also set calibration frames with objects. This is especially useful if you want to change the file on the fly:

>>> import
>>> wavecal ='WAVECAL_TABLE-01_flat.fits')
>>> wavecal[1].data.field('wlcc00')[:] *= 1.01
>>> muse_scibasic.calib.WAVECAL_TABLE = wavecal

To set more than one file for a tag, put the file names and/or objects into a list:

>>> muse_scibasic.calib.MASTER_BIAS    = [ 'MASTER_BIAS-%02i.fits' % (i+1)
...                                        for i in range(24) ]

To run the recipe, call it with the input file names as arguments. The product frames are returned in the return value of the call. If you don’t specify an input frame tag, the default (first) one of the recipe is used.

>>> res = muse_scibasic('Scene_fusion_1.fits')

Run the recipe with a nondefault tag (use raw data tag as argument name):

>>> res = muse_scibasic(raw = {'SKY':'sky_newmoon_no_noise_1.fits'})

Parameters and calibration frames may be changed for a specific call by specifying them as arguments:

>>> res =  muse_scibasic('Scene_fusion_1.fits', param = {'nifu': 2},
...                      calib = {'MASTER_FLAT': None,
...                               'WAVECAL_TABLE': 'WAVECAL_TABLE_noflat.fits'})

The results of a calibration run are objects. To save them (use output tags as attributes):

>>> res.PIXTABLE_OBJECT.writeto('Scene_fusion_pixtable.fits')

They can also be used directly as input of other recipes.

>>> muse_sky = cpl.Recipe('muse_sky')
>>> res_sky = muse_sky(res.PIXTABLE_OBJECT)

If not saved, the output is usually lost! During recipe run, a temporary directory is created where the input objects and the output files are put into. This directory is cleaned up afterwards.

To control message verbosity on terminal (use 'debug', 'info', 'warn', 'error' or 'off'):

>>> cpl.msg.esorex.level = 'debug'

The Recipe interface

class cpl.Recipe(name, filename=None, version=None, threaded=False)

Pluggable Data Reduction Module (PDRM) from a ESO pipeline.

Recipes are loaded from shared libraries that are provided with the pipeline library of the instrument. The module does not need to be linked to the same library version as the one used for the compilation of python-cpl. Currently, recipes compiled with CPL versions from 4.0 are supported. The list of supported versions is stored as cpl.cpl_versions.

The libraries are searched in the directories specified by the class attribute Recipe.path or its subdirectories. The search path is automatically set to the esorex path when cpl.esorex.init() is called.

Static members

Recipe.path = ['.']

Search path for the recipes. It may be set to either a string, or to a list of strings. All shared libraries in the search path and their subdirectories are searched for CPL recipes. On default, the path is set to the current directory.

The search path is automatically set to the esorex path when cpl.esorex.init() is called.

Recipe.memory_mode = 0

CPL memory management mode. The valid values are

Use the default system functions for memory handling
Exit if a memory-allocation fails, provide checking for memory leaks, limited reporting of memory allocation and limited protection on deallocation of invalid pointers.
Exit if a memory-allocation fails, provide checking for memory leaks, extended reporting of memory allocation and protection on deallocation of invalid pointers.


This variable is only effective before the CPL library was initialized. Even cpl.Recipe.list() initializes the library. Therefore it is highly recommended to set this as the first action after importing cpl.

static Recipe.list()

Return a list of recipes.

Searches for all recipes in in the directory specified by the class attribute Recipe.path or its subdirectories.

static Recipe.set_maxthreads(n)

Set the maximal number of threads to be executed in parallel.


This affects only threads that are started afterwards with the threaded = True flag.


Recipe.__init__(name, filename=None, version=None, threaded=False)

Try to load a recipe with the specified name in the directory specified by the class attribute Recipe.path or its subdirectories.

  • name (str) – Name of the recipe. Required. Use cpl.Recipe.list() to get a list of available recipes.
  • filename (str) – Name of the shared library. Optional. If not set, Recipe.path is searched for the library file.
  • version (int or str) – Version number. Optional. If not set, the newest version is loaded.
  • threaded (bool) – Run the recipe in the background, returning immediately after calling it. Default is False. This may be also set as an attribute or specified as a parameter when calling the recipe.

Common attributes and methods

These attributes and methods are available for all recipes.


Recipe name.

Recipe.__file__ = None

Shared library file name.


Author name


Author email

Copyright string of the recipe


Pair (synopsis, description) of two strings.


Pair (versionnumber, versionstring) of an integer and a string. The integer will be increased on development progress.


Version of the CPL library that is linked to the recipe, as a string


Version numbers of CPL and its libraries that were linked to the recipe, as a string.


Output directory if specified, or None. The recipe will write the output files into this directory and return their file names. If the directory does not exist, it will be created before the recipe is executed. Output files within the output directory will be silently overwritten. If no output directory is set, the recipe call will result in result objects. The output directory may be also set as parameter in the recipe call.


Base directory for temporary directories where the recipe is executed. The working dir is created as a subdir with a random file name. If set to None, the system temp dir is used. Defaults to '.'.


Specify whether the recipe should be executed synchroniously or as an extra process in the background.


Default tag when the recipe is called. This is set automatically only if the recipe provided the information about input tags. Otherwise this tag has to be set manually.


Possible tags for the raw input frames, or ‘None if this information is not provided by the recipe.


Return a dictionary of output frame tags.

Keys are the tag names, values are the corresponding list of output tags. If the recipe does not provide this information, an exception is raised.


If set to 1, a memory dump is issued to stdout if the memory was not totally freed after the execution. If set to 2, the dump is always issued. Standard is 0: nothing dumped.

Recipe parameters

Recipe parameters may be set either via the Recipe.param attribute or as named keywords on the run execution. A value set in the recipe call will overwrite any value that was set previously in the Recipe.param attribute for that specific call.


This attribute contains all recipe parameters. It is iteratable and then returns all individual parameters:

>>> for p in muse_scibasic.param:
...    print, p.value, p.default
nifu None 99
cr None dcr
xbox None 15
ybox None 40
passes None 2
thres None 4.5
sample None False
dlambda None 1.2

On interactive sessions, all parameter settings can be easily printed by printing the param attribute of the recipe:

>>> print muse_scibasic.param
 [Parameter('nifu', default=99), Parameter('cr', default=dcr), 
  Parameter('xbox', default=15), Parameter('ybox', default=40), 
  Parameter('passes', default=2), Parameter('thres', default=4.5), 
  Parameter('sample', default=False), Parameter('dlambda', default=1.2)]

To set the value of a recipe parameter, the value can be assigned to the according attribute:

>>> muse_scibasic.param.nifu = 1

The new value is checked against parameter type, and possible value limitations provided by the recipe. Hyphens in parameter names are converted to underscores. In a recipe call, the same parameter can be specified as dict:

>>> res = muse_scibasic( ..., param = {'nifu':1})

To reset a value to its default, it is either deleted, or set to None. The following two lines:

>>> muse_scibasic.param.nifu = None
>>> del muse_scibasic.param.nifu

will both reset the parameter to its default value.

All parameters can be set in one step by assigning a dict to the parameters. In this case, all values that are not in the map are reset to default, and unknown parameter names are ignored. The keys of the map may contain contain the name or the fullname with context:

>>> muse_scibasic.param = { 'nifu':1, 'xbox':11, 'resample':True }

See also


Recipe frames

There are three groups of frames: calibration (“calib”) frames, input (“raw”) frames, and result (“product”) frames. Calibration frames may be set either via the Recipe.calib attribute or as named keywords on the run execution. A value set in the recipe call will overwrite any value that was set previously in the Recipe.calib attribute for that specific call. Input frames are always set in the recipe call. If their tag name was not given, the tag name from Recipe.tag is used if the recipe provides it.


This attribute contains the calibration frames for the recipe. It is iterable and then returns all calibration frames:

>>> for f in muse_scibasic.calib:
...     print f.tag, f.min, f.max, f.frames
MASTER_BIAS 1 1 master_bias_0.fits
BADPIX_TABLE None None ['badpix_1.fits', 'badpix_2.fits']


Only MUSE recipes are able to provide the full list of calibration frames and the minimal/maximal number of calibration frames. For other recipes, only frames that were set by the users are returned here. Their minimum and maximum value will be set to None.

In order to assing a FITS file to a tag, the file name or the is assigned to the calibration attribute:

>>> muse_scibasic.calib.MASTER_BIAS = 'MASTER_BIAS_0.fits'

Using is useful when it needs to be patched before fed into the recipe.

>>> master_bias ='MASTER_BIAS_0.fits')
>>> master_bias[0].header['HIERARCH ESO DET CHIP1 OUT1 GAIN'] = 2.5
>>> muse_scibasic.calib.MASTER_BIAS = master_bias

Note that objects are stored in temporary files before the recipe is called which may produce some overhead. Also, the CPL then assigns the random temporary file names to the FITS keywords HIERARCH ESO PRO RECm RAWn NAME which should be corrected afterwards if needed.

To assign more than one frame, put them into a list:

>>> muse_scibasic.calib.BADPIX_TABLE = [ 'badpix1.fits', 'badpix2.fits' ]

All calibration frames can be set in one step by assigning a dict to the parameters. In this case, frame that are not in the map are set are removed from the list, and unknown frame tags are silently ignored. The key of the map is the tag name; the values are either a string, or a list of strings, containing the file name(s) or the objects.

>>> muse_scibasic.calib = { 'MASTER_BIAS':'master_bias_0.fits', 
...                'BADPIX_TABLE':[ 'badpix_1.fits', 'badpix_2.fits' ] }

In a recipe call, the calibration frame lists may be overwritten by specifying them in a dict:

>>> res = muse_scibasic( ..., calib = {'MASTER_BIAS':'master_bias_1.fits'})

See also


Runtime environment

For debugging purposes, the runtime environment of the recipe may be changed. The change may be either done by specifying the Recipe.env attribute of as a parameter on the recipe invocation. The change will have no influence on the environment of the framework itself.


Some variables are only read on startup (like MALLOC_CHECK_), changing or deleting them will have no effect.

Recipe.env = None

Environment changes for the recipe. This is a dict with the name of the environment variable as the key and the content as the value. It is possible to overwrite a specific environment variable. Specifying None as value will remove the variable:

>>> muse_flat.env['MUSE_RESAMPLE_LAMBDA_LOG'] = '1'
>>> muse_flat.env['MUSE_TIMA_FILENAME'] = 'tima.fits'

In a recipe call, the runtime environment may be overwritten as well:

>>> res = muse_flat( ..., env = {'MUSE_PLOT_TRACE':'true'})

Recipe invocation

Recipe.__call__(*data, **ndata)

Call the recipes execution with a certain input frame.

  • raw ( or str or a list of them, or dict) – Data input frames.
  • tag (str) – Overwrite the tag attribute (optional).
  • threaded (bool) – overwrite the threaded attribute (optional).
  • loglevel (int) – set the log level for python logging (optional).
  • logname (str) – set the log name for the python logging.Logger (optional, default is ‘cpl.’ + recipename).
  • output_dir (str) – Set or overwrite the output_dir attribute. (optional)
  • param (dict) – overwrite the CPL parameters of the recipe specified as keys with their dictionary values (optional).
  • calib (dict) – Overwrite the calibration frame lists for the tags specified as keys with their dictionary values (optional).
  • env (dict) – overwrite environment variables for the recipe call (optional).

The object with the return frames as objects

Return type:



exceptions.ValueError If the invocation parameters are incorrect.


exceptions.IOError If the temporary directory could not be built, the recipe could not start or the files could not be read/written.


cpl.CplError If the recipe returns an error.


cpl.RecipeCrash If the CPL recipe crashes with a SIGSEV or a SIGBUS


If the recipe is executed in the background (threaded = True) and an exception occurs, this exception is raised whenever result fields are accessed.

Parallel execution

The library allows a simple parallelization of recipe processing. The parallelization is done using independent processes and thus does not depend on parallelization features in the CPL or the recipe implementation.

To specify that a recipe should be executed in the background, the threaded attribute needs to be set to True. This may be done either in the recipe constructor, as a recipe attribute or as a parameter of the execution call. Each of the following three recipes will start a background process for the BIAS calculation:

# Create a threaded recipe
r1 = cpl.Recipe('muse_bias', threaded = True)
result1 = r1([ 'bias1.fits', 'bias2.fits', 'bias3.fits'])

# Prepare a recipe for background execution
r2 = cpl.Recipe('muse_bias')
r2.threaded = True
result2 = r2([ 'bias1.fits', 'bias2.fits', 'bias3.fits'])

# Execute a recipe in background
r3 = cpl.Recipe('muse_bias')
result3 = r3([ 'bias1.fits', 'bias2.fits', 'bias3.fits'], threaded = True)

If the threaded attribute is set to True, the execution call of the recipe immediately returns while the recipe is executed in the background. The current thread is stopped only if any of the results of the recipe is accessed and the recipe is still not finished.

The result frame of a background recipe is a subclass of threading.Thread. This interface may be used to control the thread execution.

The simples way to use parallel processing is to create a list where the members are created by the execution of the recipe. The following example shows the parallel execution of the ‘muse_focus’ recipe:

muse_focus = cpl.Recipe('muse_focus', threaded = True)
muse_focus.calib.MASTER_BIAS = 'master_bias.fits'

# Create a list of input files
files = [ 'MUSE_CUNGC%02i.fits' % i for i in range(20, 30) ]

# Create a list of recipe results. Note that for each entry, a background
# process is started.
results = [ muse_focus(f) for f in files ]

# Save the results. The current thread is stopped until the according
# recipe is finished.
for i, res in enumerate(results):
    res.FOCUS_TABLE.writeto('FOCUS_TABLE_%02i.fits' % (i+1))

When using parallel processing note that the number of parallel processes is not limited by default, so this feature may produce a high load when called with a large number of processes. Parallelization in the recipe itself or in the CPL may also result in additional load.

To limit the maximal number of parallel processes, the function cpl.Recipe.set_maxthreads() can be called with the maximal number of parallel processes. Note that this function controls only the threads that are started afterwards.

If the recipe execution fails, the according exception will be raised whenever one of the results is accessed.


Recipes may contain an internal parallelization using the openMP interface. Although it is recommended to leave them untouched, they may be changed via environment variable settungs in the cpl.Recipe.env attribute. See for a list of environment variables.

The cpl.Parameter class

class cpl.Parameter(name)

Runtime configuration parameter of a recipe. Parameters are designed to handle monitor/control data and they provide a standard way to pass information to the recipe.

The CPL implementation supports three classes of parameters: a plain value, a value within a given range, or a value as part of an enumeration. When a parameter is created it is created for a particular value type. In the latter two cases, validation is performed whenever the value is set.



The value of the parameter, or None if set to default


The default value of the parameter (readonly).


The parameter name (readonly). Parameter names are unique. They define the identity of a given parameter.


The parameter context (readonly). The context usually consists of the instrument name and the recipe name, separated by a dot. The context is used to associate parameters together.


The numeric range of a parameter, or None if the parameter range is unlimited (readonly).


A list of possible values for the parameter if the parameter are limited to an enumeration of possible values (readonly).

The following example prints the attributes of one parameter:

>>> print 'name:    ',
name:     cr
>>> print 'fullname:',
>>> print 'context: ',
context:  muse.muse_scibasic
>>> print 'sequence:',
sequence: ['dcr', 'none']
>>> print 'range:   ',
range:    None
>>> print 'default: ',
default:  dcr
>>> print 'value:   ',
value:    None

See also


The cpl.FrameConfig class

class cpl.FrameConfig(tag, min_frames=0, max_frames=0, frames=None)

Frame configuration.

Each FrameConfig object stores information about one the data type a recipe can process. They are used for defining the calibration files. However, since this information is not generally provided by CPL recipes, it contains only dummy information, except for the MUSE recipes.

The objects stores a frame tag, a unique identifier for a certain kind of frame, the minimum and maximum number of frames needed.



Category tag name. The tag name is used to distinguish between different types of files. An examples of tag names is ‘MASTER_BIAS’ which specifies the master bias calibration file(s).


Minimal number of frames, or None if not specified. A frame is required if the min is set to a value greater than 0.


Maximal number of frames, or None if not specified


List of frames (file names or objects) that are assigned to this frame type.

See also


Execution results

Result frames

class cpl.Result

Calling cpl.Recipe.__call__() returns an object that contains all result (‘production’) frames in attributes. All results for one tag are summarized in one attribute of the same name. So, the muse_bias recipe returns a frame with the tag MASTER_BIAS in the according attribute:

res = muse_bias(...)

The attribute content is either a or a list() of HDU lists, depending on the recipe and the call: If the recipe produces one out put frame of a tag per input file, the attribute contains a list if the recipe was called with a list, and if the recipe was called with a single input frame, the result attribute will also contain a single input frame. If the recipe combines all input frames to one output frame, a single es returned, independent of the input parameters. The following examples will illustrate this:

muse_scibasic = cpl.Recipe('muse_scibasic')
# Only single input frame, so we get one output frame
res = muse_scibasic('raw.fits')

# List of input frames results in a list of output frames
res = muse_scibasic([ 'raw1.fits', 'raw2.fits', 'raw3.fits' ])
for i, h in res.PIXTABLE_OBJ:
    h.writeto('pixtable%i.fits' % (i+1))

# If we call the recipe with a list containing a single frame, we get a list
# with a single frame back
res = muse_scibasic([ 'raw1.fits' ])

# The bias recipe always returns one MASTER BIAS, regardless of number of
# input frames. So we always get a single frame back.
muse_bias = cpl.Recipe('muse_bias')
res = muse_bias([ 'bias1.fits', 'bias2.fits', 'bias3.fits' ])


This works well only for MUSE recipes. Other recipes dont provide the necessary information about the recipe.

Run statistics

In Addition to the result frames the cpl.Result object provides the attribute cpl.Result.stat which contains several statistics of the recipe execution:


The return code of the recipe. Since an exception is thrown if the return code indicates an error, this attribute is always set to 0.


CPU time in user mode, in seconds.


CPU time in system mode, in seconds.


Flag whether the recipe terminated with freeing all available Memory. This information is only available if the CPL internal memory allocation functions are used. If this information is not available, this flag ist set to None.

Execution log


List of log messages for the recipe.


If one or more error was set during the recipe run, the first error is stored in this attribute. The following errors are chained and can be accessed with the attribute.


An error here does not indicate a failed recipe execution, since a failed execution would result in a non-zero return code, and an exception would be thrown.

See also


Thread control

If the recipe was called in the background (see Parallel execution), the result object is returned immediately and is dervived from threading.Thread. Its interface can be used to control the thread execution:


Returns whether the recipe is still running

cpl.Result.join(timeout = None)

Wait until the recipe terminates. This blocks the calling thread until the recipe terminates – either normally or through an unhandled exception – or until the optional timeout occurs.

When the timeout argument is present and not None, it should be a floating point number specifying a timeout for the operation in seconds (or fractions thereof). As join() always returns None, you must call isAlive() after join() to decide whether a timeout happened – if the recipe is still running, the join() call timed out.

When the timeout argument is not present or None, the operation will block until the recipe terminates.

A thread can be cpl.Result.join() ed many times.

Like in the foreground execution, the output frames may be retrieved as attributes of the cpl.Result frame. If any of the attributes is accessed, the calling thread will block until the recipe is terminated. If the recipe execution raised an exception, this exception will be raised whenever an attribute is accessed.

CPL Exceptions

exception cpl.CplError(retval, res, logger=None)

Error message from the recipe.

If the CPL recipe invocation returns an error, it is converted into a cpl.CplError exception and no frames are returned. Also, the error is notified in the log file.

The exception is raised on recipe invocation, or when accessing the result frames if the recipe was started in background (cpl.Recipe.threaded set to True).



The CPL error code returned from the recipe.


The supplied error message.


The source file name where the error occurred.


The line number where the error occurred.


Log lines of the recipe that lead to this exception.


Next error, or None.

exception cpl.RecipeCrash(bt_file)

Recipe crash exception

If the CPL recipe crashes with a SIGSEV or a SIGBUS, the C stack trace is tried to conserved in this exception. The stack trace is obtained with the GNU debugger gdb. If the debugger is not available, or if the debugger cannot be attached to the crashed recipe, the Exception remains empty.

When converted to a string, the Exception will return a stack trace similar to the Python stack trace.

The exception is raised on recipe invocation, or when accessing the result frames if the recipe was started in background (cpl.Recipe.threaded set to True).



List of stack elements, with the most recent element (the one that caused the crash) at the end. Each stack element is a collections.namedtuple() with the following attributes:


Source file name, including full path, if available.


Line number, if available


Function name, if available


Dictionary parameters the function was called with. The key here is the parameter name, the value is a string describing the value set.


Dictionary of local variables of the function, if available. The key here is the parameter name, the value is a string describing the value set.


Signal that caused the crash.

Log messages

We provide CPL log messages in two different ways: via Python logging and as a list of messages in the cpl.Result object.

For convienience, simple terminal messages and predefined log file output in a style similar to the original CPL messages.

Python style logging

The preferred and most flexible way to do logging is the use of the logging module of Python. A basic setup (similar to the style used in esorex) is:

import logging

log = logging.getLogger()
ch = logging.FileHandler('cpl_recipe.log')
fr = logging.Formatter('%(created)s [%(levelname)s] %(name)s: %(message)s',

The default basic log name for CPL log messages in the recipes is cpl.recipename. The log name can be changed with the logname parameter of cpl.Recipe.__call__() to follow own naming rules, or to separate the output of recipes that are executed in parallel:

res = [ muse_focus(f, logname = 'cpl.muse_focus%02i' % (i+1), threading = True)
        for i, f in enumerate(inputfiles) ]

To the basic log name the function name is appended to allow selective logging of a certain function. The following sample line:


will log the debug messages from muse_sky_create_skymask() additionally to the other messages.


Since the log messages are cached in CPL, they may occur with some delay in the python log module. Also, log messages from different recipes running in parallel may be mixed in their chronological order. The resolution of the log time stamp is one second. The fields logging.LogRecord.args, logging.LogRecord.exc_info and logging.LogRecord.lineno are not set. Also, due to limitations in the CPL logging module, level filtering is done only after the creation of the log entries. This may cause performance problems if extensive debug logging is done and filtered out by logging.Logger.setLevel(). In this case the cpl.Recipe.__call__() parameter loglevel may be used.

See also

cpl.esorex.msg and cpl.esorex.log

EsoRex like convienience logging.

Log message lists

The cpl.Result object as well as a cpl.CplError have an attribute cpl.Result.log resp. cpl.CplError.log that contains the list of all log messages.

class cpl.logger.LogList

List of log messages.

Accessing this list directly will return the logging.LogRecord instances.


res = muse_bias(bias_frames)
for logrecord in res.log:
    print '%s: %s' % (entry.funcname, entry.msg)

To get them formatted as string, use the error, warning, info or debug attributes:

res = muse_bias(bias_frames)
for line in
    print line

Error messages as list of str


Warnings and error messages as list of str


Info, warning and error messages as list of str


Debug, info, warning, and error messages as list of str

cpl.esorex EsoRex legacy support

EsoRex is a standard execution environment for CPL recipes provided by ESO.

Support for configuration and SOF files


Set up the logging and the recipe search path from the esorex.rc file.

Parameters:source (str or file) – Configuration file object, or string with file content. If not set, the esorex config file ~/.esorex/esorex.rc is used.

Read an EsoRex configuration file.

Parameters:source (str or file) – Configuration file object, or string with file content. If not set, the EsoRex config file ~/.esorex/esorex.rc is used.

These files contain configuration parameters for EsoRex or recipes. The content of the file is returned as a map with the (full) parameter name as key and its setting as string value.

The result of this function may directly set as cpl.Recipe.param attribute:

import cpl
myrecipe = cpl.Recipe('muse_bias')
myrecipe.param = cpl.esorex.load_rc('muse_bias.rc')

Read an EsoRex SOF file.

Parameters:source (str or file) – SOF (“Set Of Files”) file object or string with SOF file content.

These files contain the raw and calibration files for a recipe. The content of the file is returned as a map with the tag as key and the list of file names as value.

The result of this function may directly set as cpl.Recipe.calib attribute:

import cpl
myrecipe = cpl.Recipe('muse_bias')
myrecipe.calib = cpl.esorex.read_sof(open('muse_bias.sof'))


The raw data frame is silently ignored wenn setting cpl.Recipe.calib for MUSE recipes. Other recipes ignore the raw data frame only if it was set manually as cpl.Recipe.tag or in cpl.Recipe.tags since there is no way to automatically distinguish between them.

Convienence logging control

cpl.esorex.msg = <cpl.esorex.CplLogger object at 0x7ff381549f50>

This variable is a CplLogger instance that provides a convienience stream handler similar to the terminal logging functionality of the CPL. It basically does the same as:

import logging

log = logging.getLogger()
ch = logging.StreamHandler()
ch.setFormatter(logging.Formatter('[%(levelname)7s] %(message)s'))

The following attributes control the format of the terminal messages:


Log level for output to the terminal. Any of [ DEBUG, INFO, WARN, ERROR, OFF ].


Output format.

See also

logging.LogRecord attributes

Key mappings in the logging output.


If True, attach a time tag to output messages.


If True, attach the component name to output messages.


If True, attach a thread tag to output messages.

cpl.esorex.log = <cpl.esorex.CplFileLogger object at 0x7ff381549f90>

This variable is a CplFileLogger instance that provides a convienience file handler similar to the file logging functionality of the CPL. It basically does the same as:

import logging

log = logging.getLogger()
ch = logging.FileHandler(filename)
ch.setFormatter(logging.Formatter('%(asctime)s [%(levelname)7s] %(funcName)s: %(message)s'))

The following attributes control the format of the log file messages:


Directory name that is prepended to the log file name.


Log level for output to the terminal. Any of [ DEBUG, INFO, WARN, ERROR, OFF ].


Output format.

See also

logging.LogRecord attributes

Key mappings in the logging output.


If True, attach a time tag to output messages.


If True, attach the component name to output messages.


If True, attach a thread tag to output messages.

cpl.dfs DFS header parsing

class cpl.dfs.ProcessingInfo(source, recno=-1, md5sums=None)

Support for reading input files and parameters from the FITS header of a CPL processed file.

This is done through the FITS headers that were written by the DFS function called within the processing recipe.


Recipe name


Recipe version string


Pipeline name


CPL version string


Tag name


Calibration frames from a FITS file processed with CPL. The result of this function may directly set as cpl.Recipe.calib attribute:

import cpl
myrecipe = cpl.Recipe('muse_bias')
myrecipe.calib = cpl.dfs.ProcessingInfo('MASTER_BIAS_0.fits').calib


This will not work properly for files that had inputs since they have assigned a temporary file name only.


Raw (input) frames


This will not work properly for files that had inputs since they have assigned a temporary file name only.


Processing parameters. The result of this function may directly set as cpl.Recipe.param attribute:

import cpl
myrecipe = cpl.Recipe('muse_bias')
myrecipe.param = cpl.dfs.ProcessingInfo('MASTER_BIAS_0.fits').param

MD5 sum of the data portions of the output file (header keyword ‘DATAMD5’).


MD5 sums of the input and calibration files. dict with the file name as key and the corresponding MD5 sum as value.


Due to a design decision in CPL, the raw input files are not accompanied with the MD5 sum.

ProcessingInfo.__init__(source, recno=-1, md5sums=None)
  • source (str or or or – Object pointing to the result file header
  • recno (int) – Record number. Optional. If not given, the last record (with the highest record number) is used.
  • md5sums (dict) – Dictionary with md5 sums as keys and complete file names as values to provide a full path for the raw and calibration frames. Optional.

Restrictions for CPL recipes

Not every information can be retrieved from recipes with the standard CPL functions. Only MUSE recipes provide additional interfaces that allow the definition of input, calibration and output frames.

All other interfaces will have the following restrictions:

  1. The Recipe.calib attribute is not filled with templates for calibration frames. After recipe creation, this attribute is empty. Also, no check on the required calibration frames may be done before calling the recipe. Anything that is set here will be forwarded to the recipe.
  2. In the cpl.esorex support, directly assigning the recipe calibration files from the SOF file with recipe.calib = cpl.esorex.read_sof('file') will also put the raw input file into Recipe.calib unless Recipe.tags and/or Recipe.tag are set manually. The standard recipe interface does not provide a way to distinguish between raw input and calibration files.
  3. The Recipe.tags attribute is set to None.
  4. The Recipe.tag attribute is not initially set. If this attribute is not set manually, the tag is required when executing the attribute.
  5. Accessing the attribute Recipe.output() raises an exception.

Technical Background

CPL recipes register all their parameter definitions with the CPL function cpl_parameterlist_append(). All registered parameters may be retrieved from the recipe structure as a structure which contains all defined parameters.

For frames, such a mechanism does not exist, although components of the infrastructure are implemented. The CPL modules cpl_recipeconfig allows the definition of input, raw, and output frames for a recipe. However, this module is only half-way done, has no connection to the recipe definition and is not mandantory for CPL recipes. The MUSE pipeline recipes (with the exception of those contributed by ESO) implement a central frameconfig registry which allows to access this meta information from the Python interface.


Bug reports should be made on the developer web page. Send python specific questions to Questions regading CPL should be mailed to