Merge branch 'devel' of https://cta-gitlab.irap.omp.eu/ctools/ctools into bug355…

…5_cssens_source_location

Merge branch 'devel' of https://cta-gitlab.irap.omp.eu/ctools/ctools into bug355…
…5_cssens_source_location
IftachSadeh
2 parents f16e6233 cee51042
Showing 30 changed files with 331 additions and 94 deletions
ChangeLog
Makefile.am
NEWS
README.md
cscripts/csinfo.py
cscripts/csphagen.py
cscripts/csspec.par
cscripts/csspec.py
ctools.pc.in
dev/00-ctools-deactivate.csh
dev/01-ctools-activate.csh
doc/source/_static/logo-cyfronet.png
doc/source/_static/logo-desy.png
doc/source/_static/logo-ecap.jpg
doc/source/_static/logo-irap.png
doc/source/_static/logo-lapp.png
doc/source/_static/logo-lupm.png
doc/source/_static/logo-mpik.png
doc/source/_templates/index.html
doc/source/about.rst
-2021-03-05
+2021-04-30
         * Version 2.0.0 released
         ========================
+        Add ASCL shields to "About" page (#3630)
+        Use file function constructor in csspec (#3626)
+        Add BINS method to csspec (#3606)
         Add hidden "fix_spat" parameter to cttsmap (#3562)
         Add optimizer setter to ctlikelihood base class
         Add Sphinx tutorials test script (#3510)
@@ -15,6 +18,16 @@
         Add support of rectangular On/Off regions (#3248)
+2021-04-28
+
+        * Bug fix version 1.7.4 released
+        ================================
+
+        Add conda activation and deactivation scripts for csh (#3628)
+        Replace spectral model by file function in csspec (#3624)
+        Update csinfo script (#3623)
+
+
 2020-12-02
         * Bug fix version 1.7.3 released
@@ -33,9 +33,11 @@ pkgconfig_DATA = ctools.pc
 # Conda activate directory
 condaactivatedir = $(sysconfdir)/conda/activate.d
-condaactivate_DATA = dev/01-ctools-activate.sh
+condaactivate_DATA = dev/01-ctools-activate.sh \
+                     dev/01-ctools-activate.csh
 condadeactivatedir = $(sysconfdir)/conda/deactivate.d
-condadeactivate_DATA = dev/00-ctools-deactivate.sh
+condadeactivate_DATA = dev/00-ctools-deactivate.sh \
+                       dev/00-ctools-deactivate.csh
 # Manual pages to install
 man_MANS = 
@@ -55,6 +57,7 @@ EXTRA_DIST = README.md \
              doc/Doxyfile doc/source \
              dev/testreport.py \
              dev/01-ctools-activate.sh dev/00-ctools-deactivate.sh \
+             dev/01-ctools-activate.csh dev/00-ctools-deactivate.csh \
              dev/conda.recipe/build.sh \
              dev/conda.recipe/conda_build_config.yaml \
              dev/conda.recipe/meta.yaml.in
 New Features and Important Changes in ctools 2.0.0
-5 March 2021
+30 April 2021
 Introduction
@@ -16,6 +16,8 @@ None
 Documentation
 -------------
+Add Astrophysics Source Code Library (ASCL) shields to "About" page (#3630).
+
 Add test/tutorials.py script and check-tutorial target to test the code in the
 Sphinx tutorials (#3510).
@@ -285,7 +287,12 @@ None
 csspec - Generates Spectral Energy Distribution
 -----------------------------------------------
-None
+The GModelSpectralFunc spectral model constructor is now used to convert any
+spectral model into a file function before spectral model fitting (#3626).
+
+Added BINS method that replaces the spectral model by a bin function that is
+implemented by the GammaLib class GModelSpectralBins. Using a bin function
+assures that spectral points are statistically independent (#3606).
 cssrcdetect - Detects sources in sky map
 ctools information
 ==================
-* Version:             2.0.0.dev (5 March 2021)
+* Version:             2.0.0.dev (30 April 2021)
 * GammaLib dependency: 2.0.0.dev
 [![Build Status](https://cta-jenkins.irap.omp.eu/buildStatus/icon?job=ctools-integrate-os)](https://cta-jenkins.irap.omp.eu/job/ctools-integrate-os/)
@@ -127,8 +127,8 @@ the following:
      $ conda install ctools
-Unix Installation
-=================
+Linux Installation
+==================
 To build and install ctools, simply type the following:
      $ ./configure
@@ -161,14 +161,14 @@ ctools was correctly built.  Make sure that all tests were successful.
 Macintosh OS X Installation
 ============================
-ctools is known to work on various flavors of OS X.  To cope with
+ctools is known to work on various flavors of Mac OS.  To cope with
 different system versions and architectures, there are two Mac
 specific configure options:
      $ ./configure --enable-universalsdk[=PATH]
 creates a universal build of ctools.  The optional argument specifies
-which OSX SDK should be used to perform the build.  This defaults to
+which MacOS SDK should be used to perform the build.  This defaults to
 "/Developer/SDKs/MacOSX.10.4u.sdk".  Specify "/" when building on a 10.5
 system or higher, especially when building 64-bit code.
@@ -221,7 +221,8 @@ shared library (see &quot;Known problems&quot; below).
 Windows Installation
 ====================
-There have been no efforts so far to compile ctools under Windows.
+On Windows ctools needs to be installed into a virtual machine running
+a Linux distribution.
 Testing
@@ -2,7 +2,7 @@
 # ==========================================================================
 # Print info about Gammalib / ctools to the console
 #
-# Copyright (C) 2015-2016 Christoph Deil
+# Copyright (C) 2015-2021 Christoph Deil
 #
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU General Public License as published by
@@ -32,10 +32,13 @@ CTOOL_LIST = &quot;&quot;&quot;
    ctedispcube   Generates energy dispersion cube
    cterror       Calculates likelihood profile errors
    ctexpcube     Generates exposure cube
+   ctfindvar     Search for source variability
    ctlike        Performs maximum likelihood fitting
    ctmapcube     Generates a map cube
    ctmodel       Computes model counts cube
    ctobssim      Simulate CTA observations
+   ctphase       Computes the phase of each event
+   ctprob        Computes event probability for a given model
    ctpsfcube     Generates point spread function cube
    ctselect      Selects event data
    ctskymap      Generates sky map
@@ -47,20 +50,31 @@ CTOOL_LIST = &quot;&quot;&quot;
 # `doc/source/users/reference_manual/index.rst`
 # and needs to be manually updated and kept in sync
 CSCRIPT_LIST = """
+   csbkgmodel    Generates background model for 3D analysis
    cscaldb       Lists available instrument response functions
+   csebins       Generates energy boundaries for stacked analysis
    cslightcrv    Computes lightcurve
    csinfo        Checks ctools and GammaLib installations
    csmodelinfo   Shows model container content
    csmodelmerge  Merges several model containers into one file
+   csmodelselect Select models from model definition file
+   csmodelsois   Generate map cube from subset of models
    csobsdef      Generates observation definition file
    csobsinfo     Shows observation container content
+   csobsselect   Select observations from observation definition file
+   csphagen      Generates PHA, ARF, RMF files based on source/background regions
+   csphasecrv    Computes phase curve
    cspull        Generates pull distribution
    csresmap      Generates residual map
+   csresspec     Generates residual spectrum
+   csscs         Performs spectral component separation
    cssens        Computes CTA sensitivity
    csspec        Computes spectral points
+   cssrcdetect   Detects sources in sky map
    cstsdist      Generates TS distribution
+   cstsmapmerge  Merges slices from Test Statistic map computations
    cstsmapsplit  Creates commands to split the Test Statistic map computations
-   cstsmapmerge  Merges slices from Test Statistic map computations\n
+   csviscube     Computes visibility cube\n
    csobs2caldb   Creates a caldb entry from an input observation
    csroot2caldb  Creates a caldb entry from a ROOT performance file
    csiactdata    Shows information about IACT data available on the user machine
@@ -614,7 +614,8 @@ class csphagen(ctools.csobservation):
                         msg = ' Skip because the number %d of regions' \
                               'for background estimation not overlapping ' \
                               'with the exclusion region is smaller than ' \
-                              '"bkgregmin"=%d.' % (bkg_reg.size(), self['bkgregmin'].integer())
+                              '"bkgregmin"=%d.' % \
+                              (regions.size(), self['bkgregmin'].integer())
                         self._log_string(gammalib.NORMAL, msg)
                         regions = gammalib.GSkyRegions()
@@ -31,7 +31,7 @@ outfile,   f, a, spectrum.fits,,, &quot;Output spectrum file&quot;
 #
 # Script parameters
 #==================
-method,        s, a, AUTO,SLICE|NODES|AUTO,, "Spectrum generation method"
+method,        s, a, AUTO,SLICE|NODES|BINS|AUTO,, "Spectrum generation method"
 ebinalg,       s, a, LOG,FILE|LIN|LOG|POW,, "Algorithm for defining energy bins"
 emin,          r, a, 0.1,,, "Start value for first energy bin in TeV"
 emax,          r, a, 100.0,,, "Stop value for last energy bin in TeV"
@@ -43,6 +43,7 @@ calc_ts,       b, h, yes,,, &quot;Compute TS value in each bin&quot;
 calc_ulim,     b, h, yes,,, "Compute upper limit in each bin"
 fix_srcs,      b, h, yes,,, "Fix other skymodel parameters"
 fix_bkg,       b, h, no,,, "Fix background parameters"
+bingamma,      r, h, -2.0,,, "Spectral index for BINS method"
 dll_sigstep,   r, h, 0.0,0.0,100.0, "Step size in standard deviations for log-like profiles"
 dll_sigmax,    r, h, 5.0,1.0,100.0, "Maximum number of standard deviations for log-like profiles"
 dll_freenodes, b, h, no,,, "Free nodes not being fit when computing log-like profiles"
@@ -20,6 +20,7 @@
 # ==========================================================================
 import sys
 import math
+import tempfile    # Kludge for file function generation
 import gammalib
 import ctools
 from cscripts import mputils
@@ -144,9 +145,9 @@ class csspec(ctools.csobservation):
         n_cta   = n_unbinned + n_binned + n_onoff
         n_other = self.obs().size() - n_cta
-        # If spectrum method is not "NODES" then set spectrum method and
-        # script mode according to type of observations
-        if self._method != 'NODES':
+        # If spectrum method is not "NODES" or 'BINS" then set spectrum method
+        # and script mode according to type of observations
+        if self._method != 'NODES' and self._method != 'BINS':
             if n_other > 0:
                 self._method = 'NODES'
             else:
@@ -178,6 +179,7 @@ class csspec(ctools.csobservation):
         self['calc_ts'].boolean()
         self['fix_bkg'].boolean()
         self['fix_srcs'].boolean()
+        self['bingamma'].real()
         # Setup dlog-likelihood parameters
         self['dll_sigstep'].real()
@@ -213,10 +215,10 @@ class csspec(ctools.csobservation):
         elif n_other > 0:
             if self._method == 'SLICE':
                 msg = 'Selected "SLICE" method but none of the observations ' \
-                      'is a CTA observation. Please select "AUTO" or "NODES" ' \
-                      'if no CTA observation is provided.'
+                      'is a CTA observation. Please select "AUTO", "NODES" ' \
+                      'or "BINS" if no CTA observation is provided.'
                 raise RuntimeError(msg)
-            else:
+            elif self._method == 'AUTO':
                 self._method = 'NODES'
         # Log selected spectrum method
@@ -366,6 +368,18 @@ class csspec(ctools.csobservation):
                                          ' Fixing "'+par.name()+'"')
                     par.fix()
+                # Convert spectral model into a file function. The file
+                # function is logarithmically sampled in energy, with with
+                # 20 times the number of spectral bins
+                nbins    = self._ebounds.size()
+                num      = nbins * 50
+                energies = gammalib.GEnergies(num, self._ebounds.emin(0),
+                                                   self._ebounds.emax(nbins-1))
+                spectral = gammalib.GModelSpectralFunc(model.spectral(), energies)
+                model.spectral(spectral)
+                self._log_string(gammalib.EXPLICIT, ' Converting spectral model '
+                                 'into file function')
+
                 # Free the normalisation parameter which is assumed to be
                 # the first spectral parameter
                 normpar = model.spectral()[0]
@@ -456,6 +470,64 @@ class csspec(ctools.csobservation):
         # Return
         return
+    def _set_replace_src_spectrum_by_bins(self):
+        """
+        Replace source spectrum by bin function
+        """
+        # Initialise model container
+        models = gammalib.GModels()
+
+        # Loop over model containers
+        for model in self.obs().models():
+
+            # If we deal with source model then replace the spectral model
+            # by a bin function
+            if model.name() == self['srcname'].string():
+
+                # Get spectral bins index
+                bingamma = self['bingamma'].real()
+
+                # Setup spectral bin function
+                spectrum = gammalib.GModelSpectralBins(model.spectral(),
+                                                       self._ebounds,
+                                                       bingamma)
+                spectrum.autoscale()
+
+                # Make sure that all bins are positive. Autoscale all
+                # parameters so that their nominal value is unity.
+                for i in range(spectrum.bins()):
+                    name    = 'Intensity%d' % i
+                    par     = spectrum[name]
+                    par.autoscale()
+                    value   = par.value()
+                    minimum = 1.0e-20 * value
+                    if minimum <= 0.0:
+                        minimum = 1.0e-20
+                        if minimum < value:
+                            value = minimum
+                    par.value(value)
+                    par.min(minimum)
+
+                # Set spectral component of source model
+                model.spectral(spectrum)
+
+                # Make sure that TS computation is disabled (makes computation
+                # faster)
+                model.tscalc(False)
+
+                # Append model
+                models.append(model)
+
+            # ... otherwise just append model
+            else:
+                models.append(model)
+
+        # Put new model in observation containers
+        self.obs().models(models)
+
+        # Return
+        return
+
     def _select_onoff_obs(self, obs, emin, emax):
         """
         Select an energy interval from one CTA On/Off observation
@@ -854,8 +926,8 @@ class csspec(ctools.csobservation):
         # Write model results for explicit chatter level
         self._log_string(gammalib.EXPLICIT, str(like.obs().models()))
-        # Loop over all nodes
-        for i in range(spectrum.nodes()):
+        # Loop over all nodes or bins
+        for i in range(self._ebounds.size()):
             # Get energy boundaries
             emin      = self._ebounds.emin(i)
@@ -878,23 +950,26 @@ class csspec(ctools.csobservation):
             # Convert differential flux and flux error to nuFnu
             norm               = elogmean.MeV() * elogmean.MeV()  * gammalib.MeV2erg
-            result['flux']     = spectrum[i*2+1].value()
-            result['e2dnde']   = spectrum[i*2+1].value() * norm
-            result['flux_err'] = spectrum[i*2+1].error()
+            result['flux']     = spectrum.intensity(i)
+            result['e2dnde']   = spectrum.intensity(i) * norm
+            result['flux_err'] = spectrum.error(i)
             # Compute upper flux limit
             ulimit_value = -1.0
-            parname = 'Intensity%d' % i
+            parname      = 'Intensity%d' % i
             # Compute delta log-likelihood
             if self['dll_sigstep'].real() > 0.0:
                 # Extract the normalization scan values
-                (norm_scan, dlogL, loglike) = self._profile_logL(like.copy(), parname, elogmean)
+                (norm_scan, dlogL, loglike) = self._profile_logL(like.copy(),
+                                                                 parname,
+                                                                 elogmean)
                 result['norm_scan'] = norm_scan
                 result['dloglike']  = dlogL
                 result['logL']      = loglike
+            # Compute upper flux limit
             if self['calc_ulim'].boolean():
                 # Logging information
@@ -938,7 +1013,7 @@ class csspec(ctools.csobservation):
                 # Set intensity of node to tiny value by scaling the value
                 # by a factor 1e-8.
-                par = obs.models()[self['srcname'].string()].spectral()[i*2+1]
+                par = obs.models()[self['srcname'].string()].spectral()[parname]
                 par.autoscale()
                 par.factor_min(1.0e-8)
                 par.factor_value(1.0e-8)
@@ -1006,10 +1081,11 @@ class csspec(ctools.csobservation):
         source.tscalc(False)
         # Fix all parameters in the spectral model
-        if (self._method != 'NODES') or (not self['dll_freenodes'].boolean()):
+        if (self._method != 'NODES' and self._method != 'BINS') or \
+           (not self['dll_freenodes'].boolean()):
             for par in spectral:
                 par.fix()
-        
+
         # Re-compute the log-likelihood
         like.run()
         loglike = like.obs().logL()
@@ -1137,7 +1213,7 @@ class csspec(ctools.csobservation):
         table.append(dnde)
         table.append(TSvalues)
         table.append(Npred_values)
-        
+
         # Define the SED type
         table.card('SED_TYPE', 'norm,e2dnde,dnde,npred', 'SED type')
@@ -1197,6 +1273,15 @@ class csspec(ctools.csobservation):
             # Fit model
             results = self._fit_model()
+        # Case C: "BINS" method
+        elif self._method == 'BINS':
+
+            # Replace source spectrum by bins function
+            self._set_replace_src_spectrum_by_bins()
+
+            # Fit model
+            results = self._fit_model()
+
         # Create FITS file
         self._create_fits(results)
@@ -8,7 +8,7 @@ Description: CTA science analysis tools
 URL: http://cta.irap.omp.eu/ctools
 Version: @VERSION@
 Requires: 
-Requires.private: gammalib == @VERSION@
+Requires.private: gammalib = @VERSION@
 Conflicts: 
 Cflags: -I${includedir}/ctools @OPENMP_CXXFLAGS@
 Libs: -L${libdir} -lctools
+#!/bin/csh
+
+unsetenv CTOOLS
+unsetenv CALDB
+#!/bin/csh
+
+setenv CTOOLS ${CONDA_PREFIX}
+source ${CTOOLS}/bin/ctools-init.csh
+unsetenv PYTHONPATH
+unsetenv LD_LIBRARY_PATH
+unsetenv DYLD_LIBRARY_PATH
@@ -16,14 +16,35 @@ is supported as well. {%endtrans%}&lt;/p&gt;
 <p>{%trans%} ctools are based on <a href="http://cta.irap.omp.eu/gammalib">GammaLib</a>,
 a versatile toolbox for the scientific analysis of astronomical gamma-ray data.
 Besides CTA, GammaLib supports the analysis of H.E.S.S., MAGIC, VERITAS,
-Fermi/LAT, COMPTEL and INTEGRAL/SPI data. GammaLib supports in particular the
+Fermi/LAT, CGRO/COMPTEL and INTEGRAL/SPI data. GammaLib supports in particular the
 combination of data from different instruments in a true joint multi-wavelength
 analysis that consistently handles the response of each instrument. An interface
-to virtual observatory ressources does also exist.
+to virtual observatory resources does also exist.
 {%endtrans%}</p>
-<p>{%trans%} ctools are developed by a team of enthousiastic gamma-ray
-astronomers with support from engineers. We regularily organise
+<p>{%trans%} ctools are developed by a community of enthousiastic gamma-ray
+astronomers with support from engineers. The lead institute is</p>
+
+<table cellpadding="0" cellspacing="0" border="0">
+   <tr>
+     <td><img src="_static/logo-irap.png" alt="IRAP" height="50"></td>
+   </tr>
+</table>
+
+<p>As of today there have been contributions by the following institutions:</p>
+
+<table cellpadding="0" cellspacing="0" border="0">
+   <tr>
+     <td><img src="_static/logo-desy.png" alt="DESY" height="50"></td>
+     <td><img src="_static/logo-ecap.jpg" alt="ECAP" height="50"></td>
+     <td><img src="_static/logo-mpik.png" alt="MPIK" height="50"></td>
+     <td><img src="_static/logo-cyfronet.png" alt="Cyfronet" height="50"></td>
+     <td><img src="_static/logo-lapp.png" alt="LAPP" height="50"></td>
+     <td><img src="_static/logo-lupm.png" alt="LUPM" height="50"></td>
+   </tr>
+</table>
+
+<p>We regularily organise
 <a href=https://cta-redmine.irap.omp.eu/projects/ctools/wiki/Coding_sprints>coding sprints</a>
 where key developers but also newcomers meet to discuss the developments 
 and next steps, and advance with the coding of the software.
@@ -16,11 +16,11 @@ Creation of shell or Python scripts and pipelines is supported as well.
 ctools are based on GammaLib, a versatile toolbox for the scientific
 analysis of astronomical gamma-ray data. 
 Besides CTA, GammaLib supports the analysis of H.E.S.S., MAGIC, VERITAS,
-Fermi/LAT, COMPTEL and INTEGRAL/SPI data.
+Fermi/LAT, CGRO/COMPTEL and INTEGRAL/SPI data.
 GammaLib supports in particular the combination of data from different instruments
 in a true joint multi-wavelength analysis that consistently handles the response
 of each instrument.
-An interface to virtual observatory ressources does also exist.
+An interface to virtual observatory resources does also exist.
 ctools are developed by a team of enthousiastic gamma-ray astronomers with
 support from engineers. We regularily organise
@@ -40,20 +40,29 @@ to include the formal reference
    L. Gerard, A. Schulz, P. Martin, D. Sanchez, S. Ohm, T. Hassan, and
    S. Brau-Nogue, 2016, A&A, 593, A1 <https://www.aanda.org/articles/aa/pdf/2016/09/aa28822-16.pdf>`_
-in your paper as well as the identifiers
-`ascl:1601.005 <http://ascl.net/1601.005>`_ and
-`ascl:1110.007 <http://ascl.net/1110.007>`_ of the ctools and GammaLib
-packages in the Astrophysics Source Code Library (ASCL).
-You can also reference ctools via
+in your paper as well as the Astrophysics Source Code Library (ASCL) identifiers
+
+.. image:: https://img.shields.io/badge/ascl-1601.005-blue.svg?colorB=262255
+   :target: http://ascl.net/1601.005
+
+for ctools and
+
+.. image:: https://img.shields.io/badge/ascl-1110.007-blue.svg?colorB=262255
+   :target: http://ascl.net/1110.007
+
+for GammaLib. You may also reference the softwares by their Digital Object
+Identifiers (DOI) on Zenodo, which are
 .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3265423.svg
    :target: https://doi.org/10.5281/zenodo.3265423
-and GammaLib via
+for ctools and
 .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3265404.svg
    :target: https://doi.org/10.5281/zenodo.3265404
+for GammaLib.
+
 In addition please add the following acknowledgment:
    *This research made use of ctools, a community-developed analysis package
@@ -3,7 +3,7 @@
 Download
 ========
-The latest ctools release is ``ctools-1.7.3`` (2 December 2020).
+The latest ctools release is ``ctools-1.7.4`` (28 April 2021).
 Below a table of ctools releases. Please read the :ref:`sec_release` to
 learn more about new features and corrected bugs in a given release.
@@ -23,6 +23,9 @@ learn more about new features and corrected bugs in a given release.
     * - ctools
       - gammalib
       - Mac OS (10.7+) binary disk image
+    * - `1.7.4 <http://cta.irap.omp.eu/ctools/releases/ctools/ctools-1.7.4.tar.gz>`_
+      - `1.7.4 <http://cta.irap.omp.eu/ctools/releases/gammalib/gammalib-1.7.4.tar.gz>`_
+      - `ctools-1.7.4-macosx10.7.dmg <http://cta.irap.omp.eu/ctools/releases/ctools/ctools-1.7.4-macosx10.7.dmg>`_
     * - `1.7.3 <http://cta.irap.omp.eu/ctools/releases/ctools/ctools-1.7.3.tar.gz>`_
       - `1.7.3 <http://cta.irap.omp.eu/ctools/releases/gammalib/gammalib-1.7.3.tar.gz>`_
       - `ctools-1.7.3-macosx10.7.dmg <http://cta.irap.omp.eu/ctools/releases/ctools/ctools-1.7.3-macosx10.7.dmg>`_
@@ -36,8 +36,8 @@ Installing
   .. code-block:: bash
-     $ conda create -n myenv python=2.7  # or e.g. python=3.5
-     $ source activate myenv
+     $ conda create -n myenv python=3.5  # or one of the following Python versions: 2.7, 3.5, 3.6, 3.7, 3.8
+     $ conda activate myenv
      (myenv) $
 - Install pre-compiled ctools conda package from Anaconda cloud with:
@@ -53,7 +53,7 @@ Installing
   .. code-block:: bash
-     (myenv) $ source activate myenv
+     (myenv) $ conda activate myenv
 Testing
+.. _1.7.4:
+
+ctools 1.7.4 release (28 April 2021)
+====================================
+
+Overview
+--------
+
+ctools 1.7.4 is a bug fix release for ctools release 1.7.
+
+The following bugs were fixed:
+
+* [`3628 <https://cta-redmine.irap.omp.eu/issues/3628>`_] -
+  Add conda activation and deactivation scripts for csh
+* [`3624 <https://cta-redmine.irap.omp.eu/issues/3624>`_] -
+  Replace spectral model by file function in :ref:`csspec`
+* [`3623 <https://cta-redmine.irap.omp.eu/issues/3623>`_] -
+  Update csinfo script
 .. _2.0:
-ctools 2.0 release (5 March 2021)
-=================================
+ctools 2.0 release (30 April 2021)
+==================================
 Overview
 --------
@@ -24,6 +24,12 @@ Bug fixes
 Improvements
 ------------
+* [`3630 <https://cta-redmine.irap.omp.eu/issues/3630>`_] -
+  Add ASCL shields to **About** page
+* [`3626 <https://cta-redmine.irap.omp.eu/issues/3626>`_] -
+  Use file function constructor in csspec
+* [`3606 <https://cta-redmine.irap.omp.eu/issues/3606>`_] -
+  Add ``BINS`` method to :ref:`csspec`
 * [`3562 <https://cta-redmine.irap.omp.eu/issues/3562>`_] -
   Add hidden ``fix_spat`` parameter to :ref:`cttsmap`
 * Add optimizer setter to ``ctlikelihood`` base class.
@@ -7,6 +7,7 @@ Release History
    :maxdepth: 1
    ctools 2.0 <2.0>
+   ctools 1.7.4 <1.7.4>
    ctools 1.7.3 <1.7.3>
    ctools 1.7.2 <1.7.2>
    ctools 1.7.1 <1.7.1>
@@ -21,4 +21,5 @@ To create a new ctools release you have to go through the following steps:
    * added unit tests for all new code
    * added a page about the software changes to the release history
    * updated the User documentation if necessary
+   * updated the csinfo script if necessary
@@ -19,15 +19,29 @@ significance of the source detection. Optionally, it also computes an upper
 flux limit that is particularly useful in case that the source is not
 significantly detected within a spectral bin (hidden parameter ``calc_ulim``).
-There are two fundamental methods to run the script depending on the value of
-the ``method`` parameter. If ``method=SLICE`` the energy interval defined by the
-``emin`` and ``emax`` parameters is divided into a number of energy bins and an
-independent maximum likelihood fit is performed in each of the energy bins,
-while if ``method=NODES`` the spectral model will be replaced by a node function
-that will be fit to all data. The latter is particularly useful if non-CTA data
-should be fitted such as data from Fermi/LAT or COMPTEL. By default, ``method`` is
-set to ``AUTO`` which will automatically select the method based on the input data.
-For CTA-only observations ``SLICE`` will be used, otherwise ``NODES`` will be used.
+There are three fundamental methods to run the script depending on the value of
+the ``method`` parameter, ``SLICE``, ``BINS`` and ``NODES``.
+
+If ``method=SLICE`` the energy interval defined by the ``emin`` and ``emax``
+parameters is divided into a number of energy bins and an independent maximum
+likelihood fit is performed in each of the energy bins. In order to cope with
+all kinds of spectral models, including composite spectra, input spectrum will
+be converted into a file function, with the normalisation of the file function
+being the fit parameter for each energy bin. This implies that spectral
+components are merged into a single function using the model parameters that
+are specified in the input model.
+
+If ``method=BINS`` the spectral model will be replaced by a bin function that
+will be fit to all data, with ``method=NODES`` the spectral model will be replaced
+by a node function. The advantage of ``BINS`` over ``NODES`` is that with ``BINS``
+the spectral flux points are statistically independent, while with ``NODES`` the
+underlying spectral model connects spectral flux points using a piecewise power
+law. Both methods are particularly useful if non-CTA data should be fitted such
+as data from Fermi/LAT, CGRO/COMPTEL or INTEGRAL/SPI.
+
+By default, ``method`` is set to ``AUTO`` which will automatically select the
+method based on the input data. For CTA-only observations ``SLICE`` will be used,
+otherwise ``NODES`` will be used.
 The spectral binning is either defined by a FITS file containing the energy
 boundaries of each bin (option ``ebinalg=FILE``) or as ``enumbins`` bins spread
@@ -54,9 +68,6 @@ the fitted flux and flux error, the Test Statistics value (option
 ``calc_ts=yes``), the upper flux limit (option ``calc_ulim=yes``) and the
 predicted number of events (only for unbinned data).
-.. warning::
-   The upper limit computation is not yet implemented for the ``NODES`` method.
-
 General parameters
 ------------------
@@ -95,13 +106,17 @@ General parameters
 ``outfile [file]``
     Output spectrum FITS file.
-``method <SLICE|NODES|AUTO> [string]``
+``method <SLICE|NODES|BINS|AUTO> [string]``
     Spectrum generation method.
-    ``SLICE`` will slice energy interval into ``enumbins`` independent bins,
-    ``NODES`` will replace spectral model by node function with ``enumbins``
-    nodes, and ``AUTO`` will automatically select the method based on the input
-    data. For CTA-only observations ``SLICE`` will be used, otherwise ``NODES``
-    will be used.
+    ``SLICE`` will slice the energy interval into ``enumbins`` independent
+    bins,
+    ``NODES`` will replace the spectral model by a node function with
+    ``enumbins`` nodes,
+    ``BINS`` will replace the spectral model by a bin function with
+    ``enumbins`` nodes,
+    and ``AUTO`` will automatically select the method based on the input
+    data. If ``AUTO`` is selected and there are only CTA observations
+    then ``SLICE`` will be used, otherwise ``NODES`` will be used.
 ``ebinalg <FILE|LIN|LOG|POW> [string]``
     Algorithm for defining energy bins. For ``FILE``, the energy bins are defined
@@ -151,6 +166,10 @@ General parameters
 ``(fix_bkg = no) [boolean]``
     Fix background model parameters?
+``(bingamma = -2.0) [real]``
+    Spectral index for ``BINS`` method. Within each spectral bin a power law
+    will be assumed with the specified spectral index.
+
 ``(dll_sigstep = 0) [real]``
     Step size in standard deviations for log-like profiles. Note this value 
     determines the number of steps to use.
@@ -2,7 +2,7 @@
 # ==========================================================================
 # Display spectrum generated by csspec
 #
-# Copyright (C) 2015-2020 Juergen Knoedlseder
+# Copyright (C) 2015-2021 Juergen Knoedlseder
 #
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU General Public License as published by
@@ -18,6 +18,7 @@
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
 # ==========================================================================
+import os
 import sys
 try:
     import matplotlib.pyplot as plt
@@ -70,7 +71,7 @@ def get_spectrum_fits(fits):
     ----------
     fits : `~gammalib.GFits`
         Spectral GFits object
-    
+
     Returns
     -------
     spec : dict
@@ -168,7 +169,7 @@ def get_spectrum_fits(fits):
 # ========================= #
 # Plot delta log-likelihood #
 # ========================= #
-def plot_dloglike(spec):
+def plot_dloglike(ax, spec):
     """
     Plot delta log-likelihood
@@ -187,7 +188,7 @@ def plot_dloglike(spec):
     # Compute the logarithmic center of each bin
     fluxpnts = 10 ** ((y_bounds[1:]+y_bounds[:-1]) / 2.0)
-    
+
     # Scale the boundaries on the grid
     y_bounds = 10 ** y_bounds
@@ -201,12 +202,12 @@ def plot_dloglike(spec):
     dll_hist = [[row[i] for row in dll_hist] for i in range(steps)]
     # Plot the likelihood profiles
-    plt.pcolormesh(np.array(spec['engs_scan']), y_bounds,
-                   np.array(dll_hist), vmin=-10.0, vmax=0.0,
-                   cmap='Reds', alpha=0.9)
-    cbar = plt.colorbar()
+    c = ax.pcolormesh(np.array(spec['engs_scan']), y_bounds,
+                      np.array(dll_hist), vmin=-10.0, vmax=0.0,
+                      cmap='Reds', alpha=0.9)
+    cbar = plt.colorbar(c)
     cbar.set_label(r'$\Delta$ log-likelihood')
-    plt.grid()
+    ax.grid()
     # Return
     return
@@ -229,27 +230,48 @@ def plot_spectrum(filename, plotfile):
     # Get spectrum parameters
     spec = get_spectrum_file(filename)
+    # Set energy scale of spectrum
+    if max(spec['energies']+spec['ul_energies']) < 1.0e-3:
+        xlabel = 'Energy (MeV)'
+        conv   = 1.0e6
+    elif max(spec['energies']) < 1.0:
+        xlabel = 'Energy (GeV)'
+        conv   = 1.0e3
+    else:
+        xlabel = 'Energy (TeV)'
+        conv   = 1.0
+    if conv != 1.0:
+        spec['energies']    = [e * 1.0e6 for e in spec['energies']]
+        spec['ed_engs']     = [e * 1.0e6 for e in spec['ed_engs']]
+        spec['eu_engs']     = [e * 1.0e6 for e in spec['eu_engs']]
+        spec['ul_energies'] = [e * 1.0e6 for e in spec['ul_energies']]
+        spec['ul_ed_engs']  = [e * 1.0e6 for e in spec['ul_ed_engs']]
+        spec['ul_eu_engs']  = [e * 1.0e6 for e in spec['ul_eu_engs']]
+
     # Create the plot
-    plt.figure()
-    plt.loglog()
-    plt.grid()
-    plt.xlabel('Energy (TeV)')
-    plt.ylabel(r'E$^2$ $\times$ dN/dE (erg cm$^{-2}$ s$^{-1}$)')
+    fig = plt.figure()
+    fig.subplots_adjust(left=0.17, right=0.98, top=0.92, bottom=0.10)
+    ax = fig.add_subplot(111)
+    ax.loglog()
+    ax.grid()
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(r'E$^2$ $\times$ dN/dE (erg cm$^{-2}$ s$^{-1}$)')
+    ax.set_title(os.path.basename(filename))
     # Plot the spectrum
-    plt.errorbar(spec['energies'], spec['flux'], 
+    ax.errorbar(spec['energies'], spec['flux'],
                  yerr=spec['e_flux'], xerr=[spec['ed_engs'], spec['eu_engs']],
                  fmt='ro')
     # Plot upper limits
     if len(spec['ul_energies'])	> 0:
-        plt.errorbar(spec['ul_energies'], spec['ul_flux'], yerr=spec['yerr'],
+        ax.errorbar(spec['ul_energies'], spec['ul_flux'], yerr=spec['yerr'],
                      xerr=[spec['ul_ed_engs'], spec['ul_eu_engs']],
                      uplims=True, fmt='ro')
     # Plot log-likelihood profiles
     if len(spec['dll_scan']) > 0:
-        plot_dloglike(spec)
+        plot_dloglike(ax, spec)
     # Show figure
     if len(plotfile) > 0:
@@ -21,7 +21,7 @@ sonar.cxx.missingIncludeWarnings=False
 # Exclusions
 sonar.exclusions=src/template/cscript_test.py,src/template/ctool_test.py,cscripts/tests/**,pyext/build/**,pyext/ctools/tools.py,pyext/ctools/*_wrap.cpp,pyext/ctools/tests/**,dev/**,doc/**,test/dev/**
-sonar.coverage.exclusions=src/template/**,test/science_verification.py,test/test_examples.py,test/test_mputils.py,pyext/setup.py
+sonar.coverage.exclusions=src/template/**,test/science_verification.py,test/test_examples.py,test/test_mputils.py,test/tutorials.py,pyext/setup.py
 sonar.cpd.exclusions=examples/**,test/**,pyext/ctools/tools.py
 # C++ configuration
@@ -2,7 +2,7 @@
 # ==========================================================================
 # This scripts performs unit tests for the cttsmap tool
 #
-# Copyright (C) 2014-2018 Juergen Knoedlseder
+# Copyright (C) 2014-2021 Juergen Knoedlseder
 #
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU General Public License as published by
@@ -175,12 +175,13 @@ class Test(test):
         # Execute copy of cttsmap tool again, now with a higher chatter
         # level than before
-        cpy_tsmap['binmin']  = 0
-        cpy_tsmap['binmax']  = 1
-        cpy_tsmap['outmap']  = 'cttsmap_py2.fits'
-        cpy_tsmap['logfile'] = 'cttsmap_py2.log'
-        cpy_tsmap['chatter'] = 3
-        cpy_tsmap['publish'] = True
+        cpy_tsmap['binmin']   = 0
+        cpy_tsmap['binmax']   = 1
+        cpy_tsmap['fix_spat'] = True
+        cpy_tsmap['outmap']   = 'cttsmap_py2.fits'
+        cpy_tsmap['logfile']  = 'cttsmap_py2.log'
+        cpy_tsmap['chatter']  = 3
+        cpy_tsmap['publish']  = True
         cpy_tsmap.logFileOpen()  # Needed to get a new log file
         cpy_tsmap.execute()