Metadata-Version: 2.1
Name: pymusepipe
Version: 2.9.9.post22
Summary: python module to reduce MUSE Raw data and combine them
Home-page: https://github.com/emsellem/pipemusepipe
Author: Eric Emsellem
Author-email: eric.emsellem@eso.org
License: MIT
Download-URL: https://github.com/emsellem/pymusepipe/archive/v2.9.6.beta.tar.gz
Description: # pymusepipe
        
        WARNING: Doc need to be updated since many major changes. Coming asap.
        
        This is a Python wrapper for the MUSE/VLT pipeline recipes. This runs 
        using the esorex command lines (in parallel, using by default likwid, although 
        other options are available). This wrapper is meant to reduce MUSE OBs automatically, 
        after dumping the fits files in a given folder.
        
        This package includes an alignment module which may be useful, independently given 
        a set of images to align and assuming a reference image is provided. It can spit out 
        an "OFFSET_LIST" MUSE compatible fits table including the flux scaling factors.
        
        pymusepipe is also made for multi-pointing mosaics and multi-targets surveys
        as it will process targets automatically when provided a specific dictionary 
        of which target and which pointings to consider.
        
        ## 1 - Getting Started
        Here are some basic instructions to get you going with pymusepipe.
        
        ### Prerequisites
        pymusepipe uses Python 3. It is not compatible with python 2.7. If you are 
        still using python 2.7 I would recommend you to switch asap as it won't be 
        supported much longer anyway.
        
        pymusepipe needs a number of standard modules/packages including:
           * **numpy**
           * **scipy** for the alignment module
           * **matplotlib** if you wish to make some plots (e.g. via the check_pipe module)
           * **astropy** as a general provider of many useful functions and structures, most importantly the astropy Tables.
        
        The convolution package of pymusepipe (allowing full convolution of a datacube) uses
        **pypher** which is thus a requirement for that module to work.
        
        This also includes a heavy-based usage of **mpdaf**, which is a super useful package
        developed by the MUSE GTO-CRAL Team, to process and analyse datacubes, and more specifically
        MUSE cubes, images and spectra.
        
        ### Installing
        
        You can install this package via Pypi via a simple:
        ```buildoutcfg
        pip install pymusepipe
        ```
        
        You can obviously also install it by cloning it from github, or 
        downloading the source (from github) and do something like:
        ```
        python setup.py develop
        ```
        I advise to use the "develop" option as it actually does not copy 
        the files in your system but just create a link. 
        In that way you can easily update the source software without 
        reinstalling it. The link will directly use the source which has been udpated.
        
        The other option is to use the standard "install" option:
        ```
        python setup.py install
        ```
        
        ## 2 - Basic Usage - Dealing with OBs, individually
        The pymusepipe wrapper is meant to provide the user with a simple way to 
        run the MUSE pipeline.
        
        Only 3 steps are needed: preparing the data (download), preparing the 
        configuration files (templates are provided) which should take a couple of minutes, 
        and using the code (a few lines at most). The package has many other hidden functionalities
        which I'll document as soon as I can.
        
        I recommend to use ```Ipython``` as an environment, possibly via 
        a ```screen``` which would allow you to disconnect from the server that 
        actually runs the commands. Jupyter notebooks may also be very handy 
        although I would not necessarily recommend them for this specific purpose as 
        the running of the pipeline may take hours.
        
        ### Step 1 = Preparing your data
        The first thing to do is to prepare the folder structure to welcome your 
        MUSE datasets. This is actually rather simple.
        
        Imagine you have
           * a target or field named "NGC1000" (just for the sake of using an example).
           * several pointings (each pointing can represent a set of OBs which have 
           roughly the same coordinates, best is that they have a very significant 
           overlap): 1, 2, and 3. Note that this 'pointing' structure may not reflect your
           own observational strategy. There are cases where one OB includes different pointings.
           What is important here is that each 'pointing' is either a full single OB, or several
           OBs with the same pointing.
        
        Then under your data root folder <my_data_folder> create the following folder structure:
        ```
        <my_data_folder>/NGC1000
            ./P01
               ./Raw
           ./P02
               ./Raw
           ./P03
               ./Raw
        ```
        Each pointing/OB has a "Raw" folder.
        
        The next step is to download your MUSE data from the ESO web site, 
        and put all the raw files (fits/fits.gz) into each individual "Raw" folder, 
        associated with the right pointing.
        
        ### Step 2 = Preparing your configuration files
        pymusepipe only needs 2 configurations ASCII files: 1 for the calibration 
        (calib_tables.dic) and one for the root folders (rc.dic). 
        **Examples** of such files are provided in the "config_templates" 
        folder of the pymusepipe package.
        
        ### Step 3 = Running the pipeline
        The pipeline is meant to be run automatically from a given python 
        structure. This should thus take the user only a few lines of codes, 
        including one to import pymusepipe, a couple to define the names of 
        the configuration files, one to initialise the musepipe python structure, 
        and one to launch the recipes. Here is an example of how this should look:
        
        ```buildoutcfg
        # Import the modules
        import pymusepipe as pmp
        from pymusepipe import musepipe
        
        # define the names of the configuration files
        rcfile = "/my_data/MUSE/Config/rc.dic"
        calfile = "my_data_MUSE/Config/calib_table.dic"
        
        # Initialisation of the python - MusePipe Class - structure
        mypipe = musepipe.MusePipe(targetname="NGCXXXX", pointing=1, rc_filename=rcfile,
                                  cal_filename=calfile, logfile="NGCXXXX_version01.log",
                                  fakemode=False, overwrite_astropy_table=True, 
                                  filter_list="white,Cousins_R",
                                  filter_for_alignment="Cousins_R")
        
        # Launching the pipeline
        mypipe.run_all_recipes()
        ```
        Some explanation may be needed to understand what is happening:
           * "targetname": is just the name of the target, used to decided where the data will be
           * "pointing": will be used as "P01" for pointing=1, etc.
           * "logfile": name of the logging file. Note that this logfile is actually 
           a shell-like file which can be used to re-run the pipeline one step at a time. Note that
           there will be also 2 more files created using that name <logfile_name>, namely:
           a file named <logfile_name>.out and <logfile_name>.err which will
           contain the full output of the commands, and the error messages (stdout, stderr, resp.).
           * "fakemode": you can set this to True if you just wish to initialise 
           things without actually running any recipes. The pipeline will only set 
           things up but if you run any recipes will only "fake" them (not launch any 
           esorex command, only spitting the code out)
           * "filter_list": list of filter names to use to reconstruct images 
           when building up cubes. This should be part of the filter_list fits 
           table provided (see calib_tables config file).
           * "filter_for_alignment": specific filter name used for alignment between exposures.
        
        Other options can be useful:
           * "musemode": this is by default "WFM_NOAO_N" which is the most often 
           used MUSE mode. This will filter out exposures not compatible with the 
           given mode. So please beware.
           * "reset_log": will reset the log file. By default it is False, hence 
           new runs will be appended.
           * "overwrite_astropy_table": by default this is False. If True, 
           new runs will rewrite the Astropy output tables.
           * "time_astrometry": by default it is False, meaning the pipeline 
           will try to detect a GEOMETRY and ASTROMETRY Files delivered with the Raw
           files by ESO. If set to True, it will use the time dependent astro/geo files
           provided by the GTO Team but you would need to make these available on your system.
           Hence I would recommend to keep the default (False).
        
        ## 3- Advanced Usage - Targets and Mosaics
        
        ## 4- Alignment module
        Details to come.
        
        ## 5- Further details
        Here we provide a bit more details about a few issues regarding 
        the configuration files or set up parameters.
        
        ### Configuration files
        
        #### rc configuration file
        It contains 3 lines, with: *musecalib*, *musecalib_time* and *root*.
        
           * *root* provides the root folder for your data. For Target NGCXXXX, 
           and Pointing 1, the Raw data will be looked for 
           in *root*/TargetXXXX/P01/Raw (see *Preparing your data* above).
           * musecalib should contain the standard MUSE calibration files. 
           These are usually distributed in the MUSE kit within a 
           "muse-calib-x.x.x/cal" folder.
           * musecalib_time: time dependent geometry and astrometry files 
           (the correspondence between observing run dates and specific 
           files are given in the dic_geo_astrowcs_table in musepipe.py).
        
        #### calib_table configuration file
        It contains a series of given fits files which will be used by the pipeline. Most names are self-explantory. That includes:
           * *geo_table* and *astro_table*: only used if you don't rely on the default time dependent geometry files (see rc file).
           * *badpix_table*, *vignetting_mask*, *std_flux_table*, *extinct_table*, *line_catalog* all usually provided with the MUSE pipeline.
           * *filter_list* : name of the fits filter list. This is used in case you wish to provide your own. Note that it needs to follow the MUSE standard for such a table.
        
        ### Recipes
        Most MUSE pipeline recipes are run while run_all_recipes is launched. This can be changed in the prep_recipes_pipe.py or just scripted. Actually run_all_recipes() is just a function which launches (assuming 'mypipe' is your MusePipe structure, see above):
        ```
                mypipe.run_bias()
                mypipe.run_flat()
                mypipe.run_wave()
                mypipe.run_lsf()
                mypipe.run_twilight(illum=illum)
                mypipe.run_scibasic_all(illum=illum)
                mypipe.run_standard()
                mypipe.run_sky(fraction=fraction)
                mypipe.run_prep_align()
                mypipe.run_align_bypointing()
                mypipe.run_align_bygroup()
                mypipe.run_scipost()
                mypipe.run_scipost(expotype="SKY", offset_list=False, skymethod='none')
                mypipe.run_combine_pointing()
        ```
        where "illum" is a boolean (default is True), fraction is 0.8.
        
        Feel free to launch these steps one by one (the order is important as in any data reduction process).
        
        ### Structure of the output
        
        #### Folders
        The structure of the output is driven by a set of folder names described in init_musepipe.py in a few dictionaries (dic_input_folders, dic_folders, dic_folders_target). You can in principle change the names of the folders themselves, but I would advise against that.
        
        The pipeline will create the folder structure automatically, checking whether the folders exist or not.
        
        #### Log files
        2 basic log files are spitted out: one is the Esorex output which will 
        be stored in the "Esorex_log" folder. The other one will be in the "Log" 
        folder with the name provided at start: that one is like a shell script 
        which can be used to rerun things directly via the command line.
        In the "Log" folder, there will also be, for each log file, a file ".out" and
        one with ".err" extensions, respectively including all the stdout and stderr
        messages. This may be useful to trace details in the data reduction and problems.
        
        #### Astropy Tables
        Each recipe will trigger the creation of a astropy Table. 
        These are stored under "Astro_Tables". You can use these to monitor 
        which files have been processed or used.
        
        #### Python structure
        Most of the information you may need is actually stored in the 
        python "MusePipe" class structure. More details to come.
        
        ## Authors
        * **Eric Emsellem** [2017-2020], at ESO and CRAL
        
        ## License
        
        This project is licensed under the MIT License - see the 
        [LICENSE](LICENSE) file for details
        
        ## Acknowledgments
        I would like to thank people who have initially sent me 
        their code-samples, including Bernd Husemann, Dimitri Gadotti, 
        Lodovico Coccato, Mark den Brok. I would also like to specifically 
        and warmly thank Rebecca McElroy who supported me with the MUSE
        data reduction at the early stages of the development of this package,
        and Francesco Santoro who has significantly contributed 
        in the testing, debugging of the code (version 1), and proposed dedicated python lines 
        to be integrated in pymusepipe (e.g., alignment module).
        
Keywords: MUSE,DATAREDUCTION
Platform: any
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
