Full test suite

.github/actions/test/action.yml

@@ -15,9 +15,9 @@ runs:
       shell: bash
       working-directory: test_suite
       run: |
-        wget https://ipag.osug.fr/~pintec/mcfost/test_data_ee572a652377a58197232071980657f7f7fbc3f9.tar.gz
+        wget -nv https://ipag.osug.fr/~pintec/mcfost/test_data_0c484a1a905c13bea4c3bc0494ba288daddbe779.tgz
         rm -rf test_data
-        tar xzf test_data_ee572a652377a58197232071980657f7f7fbc3f9.tar.gz
+        tar xzf test_data_0c484a1a905c13bea4c3bc0494ba288daddbe779.tgz
 
     # Only do this if not in manylinux docker container
     - uses: actions/setup-python@v4

src/Voronoi.f90

@@ -232,7 +232,7 @@ subroutine Voronoi_tesselation(n_points, particle_id, x,y,z,h, vx,vy,vz, is_ghos
     real(kind=dp), parameter :: threshold = 3 ! defines at how many h cells will be cut
     character(len=2) :: unit
 
-    logical, parameter :: lrandom = .true.
+    logical :: lrandom
     integer, dimension(:), allocatable :: order,SPH_id2,SPH_original_id2
     real(kind=dp), dimension(:), allocatable :: x_tmp2,y_tmp2,z_tmp2,h_tmp2
 
@@ -299,6 +299,10 @@ subroutine Voronoi_tesselation(n_points, particle_id, x,y,z,h, vx,vy,vz, is_ghos
     n_cells = icell
     n_cells_per_cpu = (1.0*n_cells) / nb_proc_voro + 1
 
+
+    lrandom = .true.
+    if (lnot_random_Voronoi) lrandom = .false.
+
     ! Randomizing particles
     if (lrandom) then
        allocate(x_tmp2(n_cells), y_tmp2(n_cells), z_tmp2(n_cells), h_tmp2(n_cells), &

src/init_mcfost.f90

@@ -213,6 +213,7 @@ subroutine set_default_variables()
   llinear_rgrid = .false.
   image_offset_centre(:) = (/0.0,0.0,0.0/)
   loverwrite_s12 = .false.
+  lnot_random_Voronoi = .false.
 
   tmp_dir = "./"
 
@@ -1459,6 +1460,9 @@ subroutine initialisation_mcfost()
         call get_command_argument(i_arg,s)
         read(s,*) v_syst
         i_arg = i_arg + 1
+     case("-not_random_Voronoi")
+        i_arg = i_arg + 1
+        lnot_random_Voronoi = .true.
       case default
         write(*,*) "Error: unknown option: "//trim(s)
         write(*,*) "Use 'mcfost -h' to get list of available options"
@@ -2068,6 +2072,7 @@ subroutine display_help()
   write(*,*) "        : -SPH_amax <size> [mum] : force the grain size that follow the dust"
   write(*,*) "                                   (only works with 1 grain size dump)"
   write(*,*) "        : -force_Mgas : force the gas mass to be the value given the mcfost parameter file"
+  write(*,*) "        : -not_random_Voronoi : force the particle order to remain the same"
   write(*,*) ""
   write(*,*) "You can find the full documentation at:"
   write(*,*) trim(doc_webpage)

src/parameters.f90

@@ -88,7 +88,7 @@ module parametres
   					   !if limit_mem == 1: the continua are stored on a small frequency grid and interpolated locally on the full grid.
   					   !					This approach is faster than computing the continua for each wavelength point, and is relatively cheap in ram.
   					   !					-> good trade-off between 0 and 2.
-  					   !if limit_mem == 2: everything is computed locally on the full grid. Slow but cheap in memory. 
+  					   !if limit_mem == 2: everything is computed locally on the full grid. Slow but cheap in memory.
 
   ! Decomposition image
   logical :: lsepar_contrib, lsepar_pola, lonly_capt_interet, lsepar_ori
@@ -162,7 +162,7 @@ module parametres
   logical :: ldelete_inside_rsph, ldelete_outside_rsph, ldelete_above_theta
   real(kind=dp) :: ufac_implicit,scale_length_units_factor,scale_mass_units_factor,correct_density_factor_elongated_cells
   real(kind=dp) :: SPH_amin, SPH_amax, fluffyness, gap_factor, rsph_min, rsph_max
-  logical :: lupdate_velocities, lno_vr, lno_vz, lvphi_Kep, lfluffy
+  logical :: lupdate_velocities, lno_vr, lno_vz, lvphi_Kep, lfluffy, lnot_random_Voronoi
   integer :: isink_centre
 
   ! Disk parameters

test_suite/compute_test_suite.sh

@@ -1,7 +1,10 @@
 #!/bin/bash
+
+# results to be uploaded to ipag-nfs.u-ga.fr:webpage/mcfost/
 #export mcfost=$MCFOST_INSTALL/../src/mcfost
 export OMP_NUM_THREADS=1
 export mcfost=$(pwd)/../src/mcfost
+export MCFOST_UTILS=$(pwd)/../utils
 
 for dir in test_data/*; do
     param=`basename "$dir".para`
@@ -11,9 +14,9 @@ for dir in test_data/*; do
     pwd
     echo "---------------------------------------------"
 
-    rm -rf data_*
+    rm -rf data_* *.tmp
     if [ "$param" == "discF_00500.para" ]; then
-        opt="-phantom discF_00500"
+        opt="-phantom discF_00500 -not_random_Voronoi"
     else
         opt=""
     fi

test_suite/get_test_data.sh

@@ -0,0 +1,3 @@
+wget -nv https://ipag.osug.fr/~pintec/mcfost/test_data_b17a7fd4bce16a7c2befe713f6dd91773c057740.tar.gz
+rm -rf test_data
+tar xzf test_data_b17a7fd4bce16a7c2befe713f6dd91773c057740.tar.gz

test_suite/test_data/debris/debris.para

@@ -27,7 +27,7 @@
   1	                  1=Mie, 2=hg (2 implies the loss of polarizarion)
 
 #Symetries
-  T	                  image symmetry
+  F	                  image symmetry
   T	                  central symmetry
   T	                  axial symmetry (important only if N_phi > 1)
 

test_suite/test_data/discF_00500/discF_00500.para

@@ -1,4 +1,4 @@
-2.20                      mcfost version
+3.00                      mcfost version
 
 #Number of photon packages
   1e7                  nbr_photons_eq_th  : T computation
@@ -17,7 +17,6 @@
 
 #Maps
   301 301 900.           grid (nx,ny), size [AU]
-  10   10                 MC : N_bin_incl, N_bin_az
   0  90.  2  F          RT: imin, imax, n_incl, centered ?
   0    180.   2             RT: az_min, az_max, n_az angles
   140.0			  distance (pc)
@@ -50,11 +49,6 @@
   1.125                   flaring exponent, unused for envelope
   -0.5  0.0    	          surface density exponent (or -gamma for tappered-edge disk or volume density for envelope), usually < 0, -gamma_exp (or alpha_in & alpha_out for debris disk)
 
-#Cavity : everything is empty above the surface
- F	  	     	  cavity ?
- 15. 50.		  height, reference radius (AU)
- 1.5 			  flaring exponent
-
 #Grain properties
   1  Number of species
   Mie  1 2  0.0  1.0  0.9 Grain type (Mie or DHS), N_components, mixing rule (1 = EMT or 2 = coating),  porosity, mass fraction, Vmax (for DHS)
@@ -68,13 +62,13 @@
   2                       nmol
 
   [email protected] 6           molecular data filename, level_max
-  15.0 100     	  	  vmax (km.s^-1), n_speed
+  15.0 25     	  	  vmax (km.s^-1), n_speed
   T 1.e-4 abundance.fits.gz   cst molecule abundance ?, abundance, abundance file
-  T  2                       ray tracing ?,  number of lines in ray-tracing
-  1 3	 		  transition numbers
+  T  1                       ray tracing ?,  number of lines in ray-tracing
+  3	 		  transition numbers
 
   [email protected]  6
-  15.0 150     	  	  vmax (km.s^-1), n_speed
+  15.0 25     	  	  vmax (km.s^-1), n_speed
   T 1e-9 abundance.fits.gz   cst molecule abundance ?, abundance, abundance file
   T  1                      ray tracing ?,  number of lines in ray-tracing
   4	 		  transition numbers
@@ -87,8 +81,3 @@
   3360.0	2.31	0.3	  1.875252612114377E+01  -1.635726683116375E+01  -1.433567792771137E+00  T Temp, radius (solar radius),M (solar mass),x,y,z (AU), is a blackbody?
   lte4000-3.5.NextGen.fits.gz
   0.0	2.2  fUV, slope_fUV
-
-Executed command line : ./mcfost disc_00130.para -phantom /Users/cpinte/disc_00130 -max_mem 0
-Fri Jun 17 15:27:32 AEST 2016
-Darwin MacBook-Pro-de-Christophe.local 15.5.0 Darwin Kernel Version 15.5.0: Tue Apr 19 18:36:36 PDT 2016; root:xnu-3248.50.21~8/RELEASE_X86_64 x86_64
-sha = 6b533cb7ad9d31eeec10691e4f065e7c1c285b47

test_suite/test_data/ref3.0_multi/ref3.0_multi.para

@@ -16,7 +16,7 @@
   100 70 1 20             n_rad (log distribution), nz (or n_theta), n_az, n_rad_in
 
 #Maps
-  101 101 3000.           grid (nx,ny), size [AU]
+  101 101 700.           grid (nx,ny), size [AU]
   0.  90.  5  F          RT: imin, imax, n_incl, centered ?
   0    0.   1             RT: az_min, az_max, n_az angles
   140.0			  distance (pc)

test_suite/test_mcfost.py

@@ -6,35 +6,42 @@
 import glob
 import os
 
-# Reference tests are computed with db94244c41cad9c1517363b1607f8596af1c55c0
 _mcfost_bin = "../src/mcfost"
 
 # Get list of models using directory names
 model_list = glob.glob1("test_data/","*")
 
 # If running on CI, only test ref3.0
 if os.environ.get('CI', None) == 'true':
-    model_list = ["ref3.0"]
+    model_list = ["ref3.0"]#["ref3.0","ref3.0_multi","debris"]
 
 wl_list = ["1.0","10","100","1000"]
 wl_list_pola = ["1.0","1000"]
 wl_list_contrib = ["1.0","100","1000"]
 
 
+# flag to skip the calculations and set-up the thresholds in a easy way
+compute_models = True
+
+# change this to test results downloaded from other machines
+test_dir = "."
+
+if test_dir != ".":
+    compute_models = False
+
 def mcfost(filename,opt=""):
     cmd = _mcfost_bin+" "+filename+" "+opt
     result = subprocess.call(cmd.split())
 
 def clean_results(model_name):
-    #cmd = ["rm","-rf"]+glob.glob("data_*")
-    cmd = ["rm","-rf",model_name]
+    cmd = ["rm","-rf",model_name," *.tmp"]
     result = subprocess.call(cmd)
 
 def all_almost_equal(x,y,threshold=0.01):
     # test if all the values of two arrays are almost equal
     return (abs((x-y)) < threshold * x).all()
 
-def MC_similar(x,y,threshold=0.01,mask_threshold=1e-25):
+def MC_similar(x,y,threshold=0.01,mask_threshold=1e-24):
     # test if two arrays have the same at the 75% percentile
     # ignoring values that are very small as they are very noisy
 
@@ -44,7 +51,7 @@ def MC_similar(x,y,threshold=0.01,mask_threshold=1e-25):
     y_ma = np.ma.masked_where(mask, y)
 
     #return (abs((x_ma-y_ma)/x_ma).mean() < threshold)
-    return ( np.percentile(abs((x_ma-y_ma)/x_ma), 75)  < threshold )
+    return ( np.percentile(abs((x_ma-y_ma)/x_ma).compressed(), 75)  < threshold )
 
 def test_mcfost_bin():
     # We first test if the mcfost binary actually exists and runs
@@ -56,19 +63,20 @@ def test_mcfost_bin():
 
 @pytest.mark.parametrize("model_name", model_list)
 def test_Temperature(model_name):
-    clean_results(model_name) # removing all previous calculations
+    if compute_models:
+        clean_results(model_name) # removing all previous calculations
 
-    # Run the mcfost model
-    filename = "test_data/"+model_name+"/"+model_name+".para"
-    if (model_name == "discF_00500"):
-        opt=" -phantom test_data/"+model_name+"/"+model_name
-    else:
-        opt=""
-    mcfost(filename,opt="-mol -root_dir "+model_name+opt)
+        # Run the mcfost model
+        filename = "test_data/"+model_name+"/"+model_name+".para"
+        if (model_name == "discF_00500"):
+            opt=" -phantom test_data/"+model_name+"/"+model_name+" -not_random_Voronoi"
+        else:
+            opt=""
+        mcfost(filename,opt="-mol -root_dir "+model_name+opt)
 
     # Read the results
     T_name = model_name+"/data_th/Temperature.fits.gz"
-    T = fits.getdata(T_name)
+    T = fits.getdata(test_dir+"/"+T_name)
     T_ref = fits.getdata("test_data/"+T_name)
 
     print("Maximum T difference", (abs(T-T_ref)/(T_ref+1e-30)).max())
@@ -84,7 +92,7 @@ def test_SED(model_name):
     SED_name = model_name+"/data_th/sed_rt.fits.gz"
     if (not os.path.isfile(SED_name)):
         pytest.skip("No SED")
-    SED = fits.getdata(SED_name)
+    SED = fits.getdata(test_dir+"/"+SED_name)
     SED_ref = fits.getdata("test_data/"+SED_name)
 
     print("Maximum SED difference", (abs(SED-SED_ref)/(SED_ref+1e-30)).max())
@@ -99,29 +107,31 @@ def test_mol_map(model_name):
 
     # Read the results
     image_name = model_name+"/data_CO/lines.fits.gz"
-    image = fits.getdata(image_name)
+    image = fits.getdata(test_dir+"/"+image_name)
     image_ref = fits.getdata("test_data/"+image_name)
 
     print("Maximum mol map difference", (abs(image-image_ref)/(image_ref+1e-30)).max())
     print("Mean mol map difference   ", (abs(image-image_ref)/(image_ref+1e-30)).mean())
 
-    assert MC_similar(image_ref,image,threshold=0.05)
+    assert MC_similar(image_ref,image,threshold=0.1)
 
 
 @pytest.mark.parametrize("model_name", model_list)
 @pytest.mark.parametrize("wl", wl_list)
 def test_image(model_name, wl):
-    # Run the mcfost model
-    filename = "test_data/"+model_name+"/"+model_name+".para"
-    if (model_name == "discF_00500"):
-        opt=" -phantom test_data/"+model_name+"/"+model_name
-    else:
-        opt=""
-    mcfost(filename,opt="-img "+wl+" -root_dir "+model_name+opt)
+
+    if compute_models:
+        # Run the mcfost model
+        filename = "test_data/"+model_name+"/"+model_name+".para"
+        if (model_name == "discF_00500"):
+            opt=" -phantom test_data/"+model_name+"/"+model_name
+        else:
+            opt=""
+        mcfost(filename,opt="-img "+wl+" -root_dir "+model_name+opt)
 
     # Read the results
     image_name = model_name+"/data_"+wl+"/RT.fits.gz"
-    image = fits.getdata(image_name)
+    image = fits.getdata(test_dir+"/"+image_name)
     image_ref = fits.getdata("test_data/"+image_name)
 
     # We just keep intensity
@@ -141,7 +151,7 @@ def test_pola(model_name, wl):
 
     # Read the results
     image_name = model_name+"/data_"+wl+"/RT.fits.gz"
-    image = fits.getdata(image_name)
+    image = fits.getdata(test_dir+"/"+image_name)
     if ((image.shape[0] != 4) & (image.shape[0] != 8)):
         pytest.skip("No pola")
     image_ref = fits.getdata("test_data/"+image_name)
@@ -150,24 +160,24 @@ def test_pola(model_name, wl):
     image = image[[1,2],:,:,:,:]
     image_ref = image_ref[[1,2],:,:,:,:]
 
-    print("Maximum image difference", (abs(image-image_ref)/(image_ref+1e-30)).max())
-    print("Mean image difference   ", (abs(image-image_ref)/(image_ref+1e-30)).mean())
+    print("Maximum pola difference", (abs(image-image_ref)/(image_ref+1e-36)).max())
+    print("Mean pola difference   ", (abs(image-image_ref)/(image_ref+1e-36)).mean())
+
+    if model_name == "debris":
+        mask_threshold = 1e-32
+    else:
+        mask_threshold = 1e-21
 
-    assert MC_similar(image_ref,image,threshold=0.05)
+    assert MC_similar(image_ref,image,threshold=0.1,mask_threshold=mask_threshold)
 
 @pytest.mark.parametrize("model_name", model_list)
 @pytest.mark.parametrize("wl", wl_list_contrib)
 def test_contrib(model_name, wl):
     # Re-use previous calculation
 
-    # Skip test on CI because it currently fails
-    # TODO: fix this test failure
-    if os.environ.get('CI', None) == 'true':
-        pytest.skip("CI")
-
     # Read the results
     image_name = model_name+"/data_"+wl+"/RT.fits.gz"
-    image = fits.getdata(image_name)
+    image = fits.getdata(test_dir+"/"+image_name)
     if ((image.shape[0] != 5) & (image.shape[0] != 8)):
         pytest.skip("No contrib")
     image_ref = fits.getdata("test_data/"+image_name)
@@ -176,7 +186,12 @@ def test_contrib(model_name, wl):
     image = image[[4,5,6,7],:,:,:,:]
     image_ref = image_ref[[4,5,6,7],:,:,:,:]
 
-    print("Maximum image difference", (abs(image-image_ref)/(image_ref+1e-30)).max())
-    print("Mean image difference   ", (abs(image-image_ref)/(image_ref+1e-30)).mean())
+    print("Maximum contrib difference", (abs(image-image_ref)/(image_ref+1e-30)).max())
+    print("Mean contrib difference   ", (abs(image-image_ref)/(image_ref+1e-30)).mean())
+
+    if model_name == "ref3.0_multi":
+        mask_threshold=1e-20
+    else:
+        mask_threshold=1e-23
 
-    assert MC_similar(image_ref,image,threshold=0.05)
+    assert MC_similar(image_ref,image,threshold=0.1,mask_threshold=mask_threshold)