diff --git a/auxdir/CFIS/tiles_202510/tiles_P1.txt b/auxdir/CFIS/tiles_202510/tiles_P1.txt
new file mode 100644
index 00000000..fbf9a7f4
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P1.txt
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P10.txt b/auxdir/CFIS/tiles_202510/tiles_P10.txt
new file mode 100644
index 00000000..3153ed87
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P10.txt
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P2.txt b/auxdir/CFIS/tiles_202510/tiles_P2.txt
new file mode 100644
index 00000000..564121a7
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P2.txt
@@ -0,0 +1,1514 @@
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P3.txt b/auxdir/CFIS/tiles_202510/tiles_P3.txt
new file mode 100644
index 00000000..8be695dd
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P3.txt
@@ -0,0 +1,1268 @@
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P4.txt b/auxdir/CFIS/tiles_202510/tiles_P4.txt
new file mode 100644
index 00000000..88f570ea
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P4.txt
@@ -0,0 +1,3196 @@
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P5.txt b/auxdir/CFIS/tiles_202510/tiles_P5.txt
new file mode 100644
index 00000000..644c04db
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P5.txt
@@ -0,0 +1,3087 @@
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P6.txt b/auxdir/CFIS/tiles_202510/tiles_P6.txt
new file mode 100644
index 00000000..2d8dd52c
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P6.txt
@@ -0,0 +1,1773 @@
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P7.txt b/auxdir/CFIS/tiles_202510/tiles_P7.txt
new file mode 100644
index 00000000..535084a2
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P7.txt
@@ -0,0 +1,231 @@
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P8.txt b/auxdir/CFIS/tiles_202510/tiles_P8.txt
new file mode 100644
index 00000000..b46d5908
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P8.txt
@@ -0,0 +1,6263 @@
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P9.txt b/auxdir/CFIS/tiles_202510/tiles_P9.txt
new file mode 100644
index 00000000..22a14113
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P9.txt
@@ -0,0 +1,2257 @@
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diff --git a/auxdir/CFIS/tiles_202510/tiles_P9_not_complete.txt b/auxdir/CFIS/tiles_202510/tiles_P9_not_complete.txt
new file mode 100644
index 00000000..70aad219
--- /dev/null
+++ b/auxdir/CFIS/tiles_202510/tiles_P9_not_complete.txt
@@ -0,0 +1,1581 @@
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diff --git a/docs/source/pipeline_canfar.md b/docs/source/pipeline_canfar.md
index cfba3d2f..ee4c6199 100644
--- a/docs/source/pipeline_canfar.md
+++ b/docs/source/pipeline_canfar.md
@@ -1,159 +1,505 @@
-patch="P7"
+# Running `ShapePipe` processing and post-processing pipelines on CANFAR
+
+Documentation to create ShapePipe output products for catalogues v1.x.
+
+## Initial Setup
+
+### CANFAR Login
+
+Login to the canfar system with
+
+```bash
+canfar auth login
+```
+
+This can be done from at notebook or terminal within the canfar science portal,
+or any remote terminal that has the canfar library installed.
+
+Check authentication status with
+
+```bash
+canfar auth list
+```
+
+If not on "default", run
+
+```bash
+canfar auth switch default
+```
+
+### Set variables (optional)
+
+Set the current patch in the shell as
+
+```bash
+patch=P[1-9]
+```
+
+For convenience, the current PSF model can be set as environment variable, e.g.:
+
+```bash
 psf="psfex"
+```
+
+Allowed are `psfex` and `mccd`.
 
-# Terminal title
+Setting the terminal title to display the patch can be useful for long jobs, to keep track of which terminal
+runs which patch:
+
+```bash
 echo -ne "\033]0;$patch\007"
+```
+
+### Prepare run directory
+
+First, go to the dedicated directory with
+
+```bash
+cd /path/to/version/$patch
+```
 
-# Run directory
-dir=~/cosmostat/v2/pre_v2/$psf/$patch
-cd $dir
+Next, set links to the tile number list and configuration directory:
 
-# Get tile number list
+```bash
 ln -s ~/shapepipe/auxdir/CFIS/tiles_202106/tiles_$patch.txt tile_numbers.txt
+ln -s ~/shapepipe/example/cfis
+```
 
+Create output and debug log directories
 
-# Get images
+```bash
+mkdir -p output
+mkdir -p debug
+```
 
-## Download and link separately
+Finally, create and link to central image storage directories for tiles and exposures:
 
-### Download
-### Create and link to central image storage directory
+```bash
 mkdir -p ~/cosmostat/v2/data_tiles/$patch
 ln -s ~/cosmostat/v2/data_tiles/$patch data_tiles
 mkdir -p ~/cosmostat/v2/data_exp/$patch
 ln -s ~/cosmostat/v2/data_tiles/$patch data_exp
+```
 
-### Download and move tiles 
-ln -s ~/shapepipe/example/cfis
-mkdir -p output
+## `ShapePipe` processing
+
+Now, everything should be ready to start running `ShapePipe` for the weak lensing processing. The following
+details all necessary steps.
+
+### Get Images
+
+We first download images, and in a second run create symbolic links with the proper pipeine naming scheme.
+
+#### Download and move tiles
+
+When running the main `ShapePipe` script `shapepipe_run`, the following env variable needs to point
+to the current working directory
+
+```bash
 export SP_RUN=`pwd`
+```
 
+Now we run the first module (`get_images_runner`) to download the tile images together with the weight files.
+This run can get interrupted by VOSpace I/O or connection errors. In that case, 
+we move new files to the image storage directory, remove the previous (now void of images) run directory,
+and update the run log file. We also check the number of previous and new tiles.
+
+```bash
 shapepipe_run -c cfis/config_Git_vos.ini
-ls -l data_tiles/ | wc; mv -i output/run_sp_Git_*/get_images_runner/output/CFIS.???.???.*fits* data_tiles; ls -l data_tiles/ | wc
-rm -rf output/run_sp_Git_*; update_runs_log_file.py
-# repeat the above block
+ls -l data_tiles/ | wc
+mv -i output/run_sp_Git_*/get_images_runner/output/CFIS.???.???.*fits* data_tiles
+ls -l data_tiles/ | wc
+rm -rf output/run_sp_Git_*
+update_runs_log_file.py
+```
 
-### Find exposures; this run can be stopped after Fe
+Repeat the above block as needed.
+
+### Find Exposures
+
+With all tile images (= stacks) downloaded, we can inquire their headers to identify the exposures that were used
+to create the stacks. This call to the pipeline also creates the symbolic links to the downloaded tile images.
+
+```bash
 shapepipe_run -c cfis/config_GitFe_symlink.ini
-# You can also run Fe alone
+```
+
+(One could also run `Fe` alone.)
 
-### Download and move exposures
+### Download and Move Exposures
 
+The last module create exposure lists on output. These are now used to download all exposures. As for the tile downloads,
+we have to account for VOSpace errors.
+
+```bash
 shapepipe_run -c cfis/config_Gie_vos.ini
 mv -i output/run_sp_Gie_*/get_images_runner/output/*.fits*fz data_exp
 rm -rf output/run_sp_Gie_*
 update_runs_log_file.py
-# repeat the above; or:
-while true; do shapepipe_run -c cfis/config_Gie_vos.ini; ls -l data_exp/ | wc; mv -i output/run_sp_Gie_*/get_images_runner/output/*.fits*fz data_exp;  ls -l data_exp/ | wc; rm -rf output/run_sp_Gie_*; update_runs_log_file.py; done
-# Make sure that after all images are downloaded there is no Gie run. This would
-# mess up later modules since last:get_image_runner could point to this run.
+```
+
+Repeat the above by hand, or peform it in an automatic loop:
+
+```bash
+while true; do
+  shapepipe_run -c cfis/config_Gie_vos.ini
+  ls -l data_exp/ | wc
+  mv -i output/run_sp_Gie_*/get_images_runner/output/*.fits*fz data_exp
+  ls -l data_exp/ | wc
+  rm -rf output/run_sp_Gie_*
+  update_runs_log_file.py
+done
+```
+
+**Note:** Make sure that after all images are downloaded there is no `Gie` run in the output directory.
+This would mess up later modules since `last:get_image_runner` could point to this run.
 
-### Create links (and re-run Fe, not necessary)
+### Create tile links again (necessary?)
+
+If necessary, e.g. because a previous `Git` run is no longer valid, re-create the symbolic links to the downloaded tiles with
+
+```bash
 job_sp_canfar.bash -p $psf `cat tile_numbers.txt` -j 1 -r symlink
+```
+
+### Uncompress tile weights
+
+The downloaded tile weights are compressed. The following call uncompresses all.
 
-# Get single-HDU single-exposure IDs file (from missing 32 job) 
-~/shapepipe/scripts/python/summary_run.py P$patch [32]
+```bash
+shapepipe_run -c cfis/config_tile_Uz.ini
+```
 
-# Mask tiles
+### Mask tiles
 
-## Run repeatedly if necessary
+This step is done globally for all tiles. There might be job failures or interruptions. The following
+command to the `ShapePipe` job script can be run repeatedly; already created masks will be skipped.
+
+```bash
 job_sp_canfar.bash -p $psf -n $OMP_NUM_THREADS -j 4
+```
+
+If masks were created in more than one run, i.e. situated in more than one output directory, these have to be
+combined for subsequent pipeline module runs. This is done by creating a new output directory with symbolic
+links, using the script
 
-## Combine all runs
+```bash
 combine_runs.bash -c flag_tile
+```
+
+## Tile detection
+
+We can finally run our first module using the canfar submission system. First, determine the number of optimal jobs such that at a given
+time the allowed maximum of 512 running jobs is not exceeded.
+Set as `N_PAR` (number of parallel jobs) a number between 1 and 8.
 
-# Tile detection
-curl_canfar_local.sh -j 16 -f tile_numbers.txt -p $psf -N $OMP_NUM_THREADS
+```bash
+canfar_submit_job -j 16 -f tile_numbers.txt -P N_PAR -v -s
+```
 
-# Option 0, global split and exp masks: sp_local=0
-# Todo: split Uz and SpMh
+Now, run the previous command with that number `JMAX`
 
-# For sp_local=- both mh_local (0, 1) are ok
+```bash
+canfar_submit_job -j 16 -f tile_numbers.txt -P N_PAR -v -J JMAX
+```
+
+### Exposure Processing
+
+#### Option 0: Global split and exp masks (deprecated; used for earlier v1.x patch runs)
+
+For this option, set `sp_local=0`.
+
+**TODO: ** Split Uz and SpMh
+
+For `sp_local=-` both `mh_local` (0, 1) are ok:
+
+```bash
 export mh_local=0
-#export mh_local=1
+```
 
-## Uncompress weights,  split exposures into single HDUs
-job_sp_canfar.bash -p $psf -n $OMP_NUM_THREADS -j 2
+#### Option 0: Mask exposures (deprecated)
 
-# Mask exposures
+Run repeatedly if necessary:
 
-## Run repeatedly if necessary
+```bash
 job_sp_canfar.bash -p $psf -n $OMP_NUM_THREADS -j 8
+```
 
-# Combine all runs
+Combine all runs:
+
+```bash
 combine_runs.bash -c flag_exp
+```
+
+### Option 1: Local split and mask exposures (recommended)
 
-# Option 1: sp_local=1, local split and mask exp
+Optional: Enable flags for local split processing and merge header runs as
+
+```bash
+export sp_local=1
 export mh_local=1
+```
+
+These flags are automatically set to 1 in the new job scripts.
+
+
+Get single-HDU single-exposure IDs file (from missing 32 job):
+
+```bash
+summary_run P$patch 32
+cp summary/missing_job_32_all.txt exp_shdu.txt
+```
+
+### Split exposures
+
+First, determine the number of maximum jobs with the option `-s` (see above). Then, submit with
 
-# Split exposures
-curl_canfar_local.sh -j 2 -f all.txt -p $psf -N $OMP_NUM_THREADS
+```bash
+canfar_submit_job -j 2 -v -f exp_shdu.txt -v -P N_PAR -J JMAX
+```
 
-# Mask exposures
-curl_canfar_local.sh -j 8 -f all.txt -p $psf -N $OMP_NUM_THREADS
+### Mask exposures
 
-# Exposure detection
+```bash
+canfar_submit_job -j 8 -f exp_shdu.txt -v -P N_PAR -J JMAX
+```
 
-cp summary/missing_job_32_sextractor.txt all.txt
-curl_canfar_local.sh -j 32 -m $mh_local -f all.txt -p $psf -N $OMP_NUM_THREADS
+### Exposure detection
 
-# Tile preparation
-curl_canfar_local.sh -j 64 -f tile_numbers.txt -p $psf -N $OMP_NUM_THREADS
+```bash
+canfar_submit_job -j 32 -f exp_shdu.txt -v -P N_PAR -J JMAX
+```
 
-# Tile shape measurement
-curl_canfar_local.sh -j 128 -f tile_numbers.txt -p $psf -N 8
+### Tile preparation
 
-# Merge subcatalogues
-curl_canfar_local.sh -j 256 -f tile_numbers.txt -p $psf -N 8
+```bash
+canfar_submit_job -j 64 -f tile_numbers.txt
+```
 
-# Create final cat
-curl_canfar_local.sh -j 512 -f tile_numbers.txt -p $psf -N $OMP_NUM_THREADS
-# Run in parallel
-cat mc.txt | xargs -I {} -P 16 bash -c 'init_run_exclusive_canfar.sh -p psfex -j 512 -e {} --n_smp 1'
+### Tile shape measurement
 
-# Combine all final cats in common output dir as links
-combine_runs.bash -c final -p psfex
+```bash
+canfar_submit_job -j 128 -f tile_numbers.txt
+```
 
-# Merge all final cats
-# (W3: 140GB RAM)
-# in /path/to/$psf
+### Merge sub-catalogues
+
+```bash
+canfar_submit_job -j 256 -f tile_numbers.txt
+```
+
+### Create final catalogues
+
+```bash
+canfar_submit_job -j 512 -f tile_numbers.txt
+```
+
+This was the last `ShapePipe` module to run for main processing.
+
+
+### Merge all final catalogues
+
+The last step of `ShapePipe` processing is, per patch, to merget all final catalogues. This is done via a python script, as follows.
+First, change to parent directory `/path/to/version` and run the following command for all patches
+
+```bash
 patchnum=`tr $patch P ''`
-create_final_cat.py -m final_cat_$patch.hdf5 -i . -p $patch/cfis/final_cat.param -P $patchnum -o $patch/n_tiles_final.txt -v
+create_final_cat.py -m final_cat_$patch.hdf5 -i . -p $patch/cfis/final_cat.param \
+    -P $patchnum -o $patch/n_tiles_final.txt -v
+```
+
+## Additional `ShapePipe` processing
+
+
+### Create star Catalogue
+
+We can additionaly create a combined star catalogue, with star shapes projecte from detector to world coordinates.
+This is useful for validation and galaxy-PSF/star correlation diagnostics.
+
+#### Combine all PSF runs
+
+In each patch directory /path/to/version/$patch, run
+
+```bash
+combine_runs.bash -p $psf -c psf
+```
+
+to create a single output directory of PSF files (symbolic links).
 
-# Star catalogue
-combine_runs.bash  -p $psf -c psf
+Optionally, to create and plot results for this patch only:
+
+```bash
 shapepipe_run -c $SP_CONFIG/config_Ms_$psf.ini
 shapepipe_run -c $SP_CONFIG/config_Pl_$psf.ini
+```
+
+#### Convert star catalogue to wCS
+
+Convert all input validation PSF files and create directories per patch `P?`.
+Create files `validation_psf_conv-<patchnum>-<idx>.fits` (for the v1.4 setup only one file):
+
+```bash
+cd /path/to/version
+mkdir stat_car
+cd star_cat
+```
 
-# Convert star cat to WCS
-## Convert all input validation psf files and create directories par patch
-## psf_conv_all/P?
-cd ../star_cat
+For each patch run
 
-# Create files validation_psf_conv-<patchnum>-<idx>.fits
-# (for the v1.4 setup only one file)
- convert_psf_pix2world.py -i .. -P $patchnum -v
+```bash
+convert_psf_pix2world.py -i .. -P $patchnum -v
+```
 
-# Combine previously created files as links within one SP run dir
-# (for the v1.4 setup only one link
-cd P$patch
+Combine previously created files as links within one ShapePipe run directory (for the v1.4 setup only one link).
+First (and optiohnal), create a subdir for a run and link to the input patches:
+
+```bash
+cd /path/to/version/star_cat
+mkdir v1.6
+ln -s ../P1
+ln -s ../P2
+...
+```
+
+Next, create links to all `validation_conv` runs:
+
+```bash
 combine_runs.bash -p psfex -c psf_conv
+```
+
+Merge all converted star catalogues and create `final-starcat.fits`:
 
-# Merge all converted star catalogues and create final-starcat.fits
+```bash
 export SP_RUN=`pwd`
 shapepipe_run -c ~/shapepipe/example/cfis/config_Ms_psfex_conv.ini
+```
+
+Rename to general PSF and star catalogue used for all ("a") sub-versions:
+
+
+```bash
+cp output/run_sp_Ms/merge_starcat_runner/output/full_starcat-0000000.fits \
+    unions_shapepipe_psf_2024_v1.6.a.fits 
+```
+
+The FITS file `CATTYPE` (newer version) should be `validation_psf_conf`.
+
+## Post-processing
+
+The following post-processing steps are performed with the library `sp_validation`.
+
+### Extract Information
+
+First, we extract all information from the final catalogue, per patch. We copy
+the parameter file and set links to the catalogues and `ShapePipe` config directory.
+
+```bash
+cd /path/to/version/$patch
+cp ~/astro/repositories/github/sp_validation/notebooks/params.py .
+ln -s ~/v1.3.x/final_cat_$patchnum3.hdf5  # not ../final_cat_P$patchnum.hdf5 !
+ln -s output/run_sp_MsPl/mccd_merge_starcat_runner/output/full_starcat-0000000.fits
+ln -s ~/astro/repositories/github/shapepipe/example/cfis
+```
+
+Then edit `params.py`: Set patch name; set `wrap_ra` for P2.
+
+Now we can run the script, recommended via job submission on candide. For large patches,
+this requies a job with a large memory, e.g. with `mem=380000`
+ 
+
+```bash
+[squeue] python ~/astro/repositories/github/sp_validation/notebooks/extract_info.py
+```
+
+This creates a patch-wise comprehensive catalogue.
+
+### Create global comprehensive catalogues
+
+```bash
+cd /patch/to/version
+[squeue] python ~/astro/repositories/github/sp_validation/scripts/create_joint_comprehensive_cat.py \
+    -v v1.6.c -v -p P1+P2+P3+P4+P5+P6+P7+P8+P9
+```
+
+This creates the file `unions_shapepipe_comprehensive_2024_v1.6.c.hdf5`.
+
+
+### Apply structural masks
+
+First, edit the Python script `~/astro/repositories/github/sp_validation/notebooks/demo_apply_hsp_masks.py`
+to match catalogue name. Check the coverage mask input file (see below).
+Run the script to apply the healsparse structural masks:
+
+```bash
+[squeue] python ~/astro/repositories/github/sp_validation/notebooks/demo_apply_hsp_masks.py
+```
+
+This creates the file `unions_shapepipe_comprehensive_struct_2024_v1.6.c.hdf5`.
+
+
+### Define sample, calibrate catalogue
+
+We are close to finally perform the last post-processing step, which is the calibration. First, the final galaxy sample
+in question needs to be defined, with masks and cuts to apply from a `yaml` config file. A number of pre-defined files
+can be found in `~/astro/repositories/github/sp_validation/calibration`.
+
+For example, to create `v1.6.6`, the steps are:
+
+```bash
+cd /path/to/version
+mkdir -p v1.6.6
+cd v1.6.6
+ln -s ~/astro/repositories/github/sp_validation/calibration/mask_v1.X.6.yaml config_mask.yaml
+ln -s ..//unions_shapepipe_comprehensive_struct_2024_v1.6.c.hdf5 unions_shapepipe_comprehensive_struct_2024_v1.X.c.hdf5
+[squeue] python ~/astro/repositories/github/sp_validation/calibrate_comprehensive_cat.py
+```
+
+calibrate_comprehensive
+
+### Create coverage mask
+
+First, on canfar, move to the directory that has the patch subdirectories.
+
+```bash
+cd /path/to/version
+```
+
+#### Get exposure numbers
+
+If the file `$patch/exp_numbers.txt` does not exist for a given patch, create it with the summary program
+
+```bash
+summary_run $patch 1
+```
+
+Now, create the list of CCDs that have PSF information with
+
+```bash
+get_ccds_with_psf -v -V v1.6
+```
+
+Next, download exposures headers
+
+```bash
+download_headers -i ccds_with_psfs_v1.6.txt -o headers_v1.6 -v
+```
+
+From the headers, the CCD corner coordinates are extracted with
+```bash
+extract_field_corners -i headers_v1.6 -v
+```
 
+Then, build the healsparse coverage mask file as
+```bash
+build_coverage_map
+```
 
-# Extra stuff
+Use `plot_coverage_map` to create plots of the coverage mask.
 
-## Delete jobs
-SSL=~/.ssl/cadcproxy.pem
-SESSION=https://ws-uv.canfar.net/skaha/v0/session
-for ID in `cat session_IDs.txt`; do echo $ID; curl -X DELETE -E $SSL $SESSION/$ID; done
+## Extra Utilities
 
-## Run in terminal in parallel (-e needs to be last arg)
-cat all.txt | xargs -P 16 -n 1  init_run_exclusive_canfar.sh -j 64 -p psfex -n -e
+### Run in Terminal in Parallel
 
-## Get missing jobs that are not currently running
-stats_jobs_canfar.sh
-grep -F -v -f jobs_running.txt summary/missing_job_128_ngmix_runner_3.txt > all3.txt
+```bash
+cat IDs.txt | xargs -I {} -P 16 bash -c 'init_run_exclusive_canfar.sh -j 512 -e {}'
+```
diff --git a/environment.yml b/environment.yml
index ebb04d2c..bec9facf 100644
--- a/environment.yml
+++ b/environment.yml
@@ -9,11 +9,12 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - python=3.9
+  - python=3.10
   - pip>=21.2.4
   - astromatic-psfex==3.21.1
   - astromatic-source-extractor==2.25.0
   - astropy==5.2
+  - astroquery
   - automake
   - autoconf
   - cmake
diff --git a/example/cfis/config_Git_vos.ini b/example/cfis/config_Git_vos.ini
index 26edf3ef..f1e59291 100644
--- a/example/cfis/config_Git_vos.ini
+++ b/example/cfis/config_Git_vos.ini
@@ -66,7 +66,7 @@ NUMBERING_SCHEME =
 
 # Input path where original images are stored. Can be local path or vos url.
 # Single string or list of strings
-INPUT_PATH = vos:cfis/tiles_DR5, vos:cfis/tiles_DR5
+INPUT_PATH = vos:cfis/tiles_DR6, vos:cfis/tiles_DR6
 
 # Input file pattern including tile number as dummy template
 INPUT_FILE_PATTERN = CFIS.000.000.r, CFIS.000.000.r.weight
diff --git a/pyproject.toml b/pyproject.toml
index bd83613c..b20220ec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,11 +9,17 @@ authors = [
     { name = "Martin Kilbinger", email = "martin.kilbinger@cea.fr" }
 ]
 license = { "file" = "LICENSE" }
-requires-python = ">=3.11"
+requires-python = ">=3.10"
 dependencies = [
     "joblib>=0.13",
     "modopt>=1.2",
-    "numpy>=1.14"
+    "numpy>=1.14",
+    "pandas",
+    "canfar",
+    "sf_tools",
+    "h5py",
+    "healsparse",
+    "skyproj",
 ]
 
 
@@ -29,6 +35,10 @@ lint = [
     "black",
     "isort"
 ]
+plot = [
+    "skyproj",
+    "matplotlib"
+]
 release = [
     "build",
     "twine",
@@ -39,11 +49,21 @@ test = [
     "pytest-pycodestyle",
     "pytest-pydocstyle"
 ]
-dev = ["shapepipe[doc,lint,release,test]"]
+dev = ["shapepipe[doc,lint,plot,release,test]"]
 
 [project.scripts]
 shapepipe_run = "shapepipe.shapepipe_run:main"
+summary_run = "shapepipe.summary_run:main"
+canfar_submit_job = "shapepipe.canfar_run:run_job"
+canfar_monitor = "shapepipe.canfar_run:run_log"
+canfar_monitor_log = "shapepipe.canfar_run:run_monitor_log"
+get_ccd_with_psfs = "shapepipe.get_ccds_run:run_ccd_psf_handler"
+download_headers = "shapepipe.coverage_run:run_download_headers"
+extract_field_corners = "shapepipe.coverage_run:run_extract_corners"
+build_coverage_map = "shapepipe.coverage_run:run_build_coverage"
+plot_coverage_map = "shapepipe.coverage_run:run_plot_coverage"
+coverage_pipeline = "shapepipe.coverage_run:run_pipeline"
 
 [tool.pytest.ini_options]
 addopts = "--verbose --cov=shapepipe"
-testpaths = ["shapepipe"]
\ No newline at end of file
+testpaths = ["shapepipe"]
diff --git a/scripts/python/clear_ngmix_prev.py b/scripts/python/clear_ngmix_prev.py
new file mode 100644
index 00000000..108b04ca
--- /dev/null
+++ b/scripts/python/clear_ngmix_prev.py
@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+"""
+Clean up previous ngmix run directories for completed tiles.
+Removes *_prev directories when the current run has finished.
+"""
+
+import argparse
+import os
+import re
+import shutil
+import glob
+from pathlib import Path
+from astropy.io import fits
+
+
+def get_fits_info(fits_file):
+    """Get number of objects (NAXIS2 in HDU 1) and number of HDUs."""
+    try:
+        with fits.open(fits_file) as hdul:
+            n_hdus = len(hdul)
+            # Get NAXIS2 from HDU 1 (second HDU, index 1)
+            if len(hdul) > 1:
+                n_objects = hdul[1].header.get("NAXIS2", "N/A")
+            else:
+                n_objects = "N/A (no HDU 1)"
+            return n_objects, n_hdus
+    except Exception as e:
+        return f"Error: {e}", "N/A"
+
+
+def check_finished(log_file):
+    """Check if 'finished' appears in the log file."""
+    try:
+        with open(log_file, "r") as f:
+            content = f.read()
+            return "finished" in content
+    except Exception as e:
+        print(f"Warning: Could not read {log_file}: {e}")
+        return False
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Clean up previous ngmix run directories for completed tiles."
+    )
+    parser.add_argument(
+        "-n", "--dry-run",
+        action="store_true",
+        help="Show what would be removed without actually removing files"
+    )
+    args = parser.parse_args()
+
+    if args.dry_run:
+        print("dry run mode: No files will be removed\n")
+
+    base_dir = Path("tile_runs")
+
+    # Pattern to match tile IDs like 123.456
+    tile_id_pattern = re.compile(r"^\d+\.\d+$")
+
+    # Find all tile ID directories
+    if not base_dir.exists():
+        print(f"Error: {base_dir} does not exist")
+        return
+
+    tile_dirs = [
+        d
+        for d in base_dir.iterdir()
+        if d.is_dir() and tile_id_pattern.match(d.name)
+    ]
+
+    print(f"Found {len(tile_dirs)} tile directories")
+
+    removed_count = 0
+    finished_count = 0
+
+    for tile_dir in sorted(tile_dirs):
+        tile_id = tile_dir.name
+
+        # Construct path to ngmix_runner directory
+        ngmix_dir = (
+            tile_dir / "output" / "run_sp_tile_ngmix_Ng1u" / "ngmix_runner"
+        )
+
+        if not ngmix_dir.exists():
+            continue
+
+        # Check for process-*.log files
+        log_pattern = str(ngmix_dir / "logs" / "process-*.log")
+        log_files = glob.glob(log_pattern)
+
+        if not log_files:
+            continue
+
+        # Check if any log file contains "finished"
+        is_finished = any(check_finished(log_file) for log_file in log_files)
+
+        if is_finished:
+            finished_count += 1
+
+            # Path to the _prev directory to remove
+            prev_dir = tile_dir / "output" / "run_sp_tile_ngmix_Ng1u_prev"
+
+            if prev_dir.exists():
+
+                # File info of new
+                ngmix_out_dir = ngmix_dir / "output"
+                fits_files = list(ngmix_out_dir.glob("ngmix-*.fits"))
+                if fits_files:
+                    for fits_file in fits_files:
+                        n_objects, n_hdus = get_fits_info(fits_file)
+                else:
+                    n_objects, n_hdus = (-1, -1)
+
+                # File info of new
+                prev_out_dir = prev_dir / "output"
+                fits_files = list(ngmix_out_dir.glob("ngmix-*.fits"))
+                if fits_files:
+                    for fits_file in fits_files:
+                        n_objects_prev, n_hdus_prev = get_fits_info(fits_file)
+                else:
+                    n_objects_prev, n_hdus_prev = (-1, -1)
+
+                action = "Would remove" if args.dry_run else "Removing"
+                print(
+                    f"Tile {tile_id}: {action} {prev_dir} (new: {n_objects}"
+                    + f" {n_hdus}) (prev: {n_objects_prev} {n_hdus_prev})"
+                )
+
+                if n_objects < n_objects_prev:
+                    print(f"Warning: {tile_id}")
+
+                if not args.dry_run:
+                    try:
+                        shutil.rmtree(prev_dir)
+                        removed_count += 1
+                    except Exception as e:
+                        print(f"Error removing {prev_dir}: {e}")
+                else:
+                    removed_count += 1
+            else:
+                pass
+        else:
+            print(f"Tile {tile_id}: Not finished")
+
+    print(f"\nSummary:")
+    print(f"  Tiles with finished runs: {finished_count}")
+    if args.dry_run:
+        print(f"  Previous directories that would be removed: {removed_count}")
+    else:
+        print(f"  Previous directories removed: {removed_count}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/python/distribute_tiles.py b/scripts/python/distribute_tiles.py
new file mode 100755
index 00000000..54dec894
--- /dev/null
+++ b/scripts/python/distribute_tiles.py
@@ -0,0 +1,289 @@
+#!/arc/home/kilbinger/.conda/envs/shapepipe/bin/python3.10
+"""
+distribute_tiles.py
+
+Uses canfar.helpers.distributed.chunk to automatically distribute tiles
+across replicas, then processes each tile assigned to this replica.
+"""
+
+import os
+import sys
+import subprocess
+from multiprocessing import Pool
+import fcntl
+from canfar.helpers.distributed import chunk
+
+
+def parse_arguments(args):
+    """
+    Extract the file IDs from command line arguments.
+
+    Args:
+        args (list of str): Original command line arguments (e.g., sys.argv[1:])
+
+    Returns:
+        str or None: The value passed with -f, or None if not present
+    """
+    # Default values
+    parsed = {"dry_run": 0, "parallel_jobs": 1}
+
+    i = 0
+    while i < len(args):
+        if args[i] == '-f' and i + 1 < len(args):
+            parsed['file_ids'] = args[i + 1]
+            i += 2
+        elif args[i] == '--batch_num' and i + 1 < len(args):
+            parsed['batch_num'] = int(args[i + 1])
+            i += 2
+        elif args[i] == '--batch_tot' and i + 1 < len(args):
+            parsed['batch_tot'] = int(args[i + 1])
+            i += 2
+        elif args[i] == '--batch_size' and i + 1 < len(args):
+            parsed['batch_size'] = int(args[i + 1])
+            i += 2
+        elif args[i] in ('-n', '--dry_run') and i + 1 < len(args):
+            parsed['dry_run'] = int(args[i + 1])
+            i += 2
+        elif args[i] == '--parallel_jobs' and i + 1 < len(args):
+            parsed['parallel_jobs'] = int(args[i + 1])
+            i += 2
+        elif args[i] == "--debug_out" and i + 1 < len(args):
+            parsed["debug_out"] = args[i + 1]
+            i += 2
+        else:
+            i += 1
+
+    return parsed
+
+
+def get_my_tiles(all_tiles, batch_num, batch_tot, batch_size):
+      """
+      Calculate which tiles this replica should process based on global position.
+      Args:
+          all_tiles: List of all tile IDs
+          batch_num: Current batch number (1-indexed)
+          batch_tot: Total number of batches
+          batch_size: Number of replicas per batch
+      Returns:
+          list: Tiles assigned to this replica
+
+      """
+      # Get local replica info from environment
+      local_replica_id = int(os.environ.get('REPLICA_ID', 1))
+
+      start_idx = (batch_num - 1) * batch_size
+      end_idx = min(batch_num * batch_size, len(all_tiles))
+      
+      batch_tiles = all_tiles[start_idx:end_idx]
+      print(f"Batch {batch_num}/{batch_tot}, local replica {local_replica_id}/{batch_size}")
+      print(f"Batch processes tiles {start_idx + 1} to {end_idx} ({len(batch_tiles)} tiles)")
+      
+      # Use chunk() to distribute this batch's tiles among local replicas
+      # chunk() will use REPLICA_ID and REPLICA_COUNT automatically
+      my_tiles = list(chunk(batch_tiles))
+      
+      return my_tiles
+
+def get_tile_list(file_ids):
+    """Read all tile IDs from file.
+    
+    Args:
+        file_ids: Path to file containing tile IDs
+        
+    Returns:
+        list: List of tile ID strings
+    """
+    with open(file_ids, 'r') as f:
+        tiles = [line.strip() for line in f if line.strip()]
+    return tiles
+
+
+def build_process_command(tile_id, original_args):
+    """
+    Build command for a single tile by replacing -f <file_ids> with -e <tile_id>.
+
+    Args:
+        tile_id (str): Tile ID to process
+        original_args (list of str): Original CLI arguments
+
+    Returns:
+        list of str: Command ready for subprocess
+    """
+    cmd = [f"{os.environ['HOME']}/shapepipe/scripts/sh/init_run_exclusive_canfar.sh"]
+
+    # Transform arguments: replace -f <file_ids> with -e <tile_id>,
+    # skip batch arguments, parallel_jobs, and dry_run if not 0, 1
+    i = 0
+    while i < len(original_args):
+        if original_args[i] == '-f' and i + 1 < len(original_args):
+            cmd.extend(['-e', tile_id])
+            i += 2
+        elif original_args[i] in ('--batch_num', '--batch_tot', '--batch_size', '--parallel_jobs'):
+            i += 2
+        elif original_args[i] in ('-n', '--dry_run'):
+            if original_args[i] in (0, 1):
+                cmd.extend(["-n", original_args[i]])
+            i += 2
+        else:
+            cmd.append(original_args[i])
+            i += 1
+
+    return cmd
+
+
+def process_single_tile(args_tuple):
+    """
+    Process a single tile (designed for multiprocessing).
+
+    Args:
+        args_tuple: Tuple of (tile_id, tile_num, total_tiles, original_args, dry_run, msg_batch)
+
+    Returns:
+        tuple: (tile_id, success, error_message)
+    """
+    tile_id, tile_num, total_tiles, original_args, dry_run, msg_batch = args_tuple
+
+    print(f"{'='*5} Processing {msg_batch}tile num/total={tile_num}/{total_tiles}: ID={tile_id} {'='*5}")
+
+    # Build command
+    cmd = build_process_command(tile_id, original_args)
+    print(f"Command: {' '.join(cmd)}")
+
+    # Execute command
+    try:
+        if dry_run != 2:
+            result = subprocess.run(cmd, check=True, capture_output=True, text=True)
+            print(f"✓ Successfully processed tile {tile_id}")
+            return (tile_id, True, None)
+        else:
+            print(f"dry_run=2")
+            return (tile_id, True, None)
+    except subprocess.CalledProcessError as e:
+        error_msg = f"Failed to process tile {tile_id}: {e}"
+        print(f"✗ {error_msg}", file=sys.stderr)
+        return (tile_id, False, error_msg)
+    except Exception as e:
+        error_msg = f"Unexpected error processing tile {tile_id}: {e}"
+        print(f"✗ {error_msg}", file=sys.stderr)
+        return (tile_id, False, error_msg)
+
+
+def print_debug(pat, tile_list, out_path, verbose=False):
+
+    local_replica_id = int(os.environ.get('REPLICA_ID', 1))
+
+    with open(out_path, "a") as f:
+        # Exclusive lock for save parallel use
+        fcntl.flock(f.fileno(), fcntl.LOCK_EX)
+        try:
+            # Build output string
+            output = f"{pat} distribute_tiles REPLICA_ID={local_replica_id}, tiles="
+            output += " ".join(tile_list) + "\n"
+
+            # Write to file
+            f.write(output)
+
+            # Optionally write to stdout
+            if verbose:
+                print(output, end="")
+        finally:
+            # Unlock
+            fcntl.flock(f.fileno(), fcntl.LOCK_UN)
+
+
+def main():
+
+    # Debug file line pattern
+    pat = "- "
+
+    # Parse arguments
+    args = parse_arguments(sys.argv[1:])
+
+    if not "file_ids" in args:
+        print("Error: -f <file_ids> must be provided", file=sys.stderr)
+        sys.exit(1)
+    else:
+        file_ids = args["file_ids"]
+    
+    # Read all tiles
+    print(f"Reading tile list from {file_ids}")
+    all_tiles = get_tile_list(file_ids)
+    print(f"Total tiles in file: {len(all_tiles)}")
+    
+    print(
+        "REPLICA_ID, REPLICA_COUNT=",
+        os.environ.get('REPLICA_ID'),
+        os.environ.get('REPLICA_COUNT')
+    )
+
+
+    # Check if we're in multi-batch mode
+    if "batch_num" in args and "batch_tot" in args and "batch_size" in args:
+        # Multi-batch mode: calculate global distribution
+        my_tile_list = get_my_tiles(
+            all_tiles,
+            args['batch_num'],
+            args['batch_tot'],
+            args['batch_size']
+        )
+        msg_batch = f"batch {args['batch_num']}/{args['batch_tot']} "
+
+    else:
+        # Use chunk() to get tiles for this replica
+        print("Using chunk() to determine tiles for this replica...")
+        my_tile_list = list(chunk(all_tiles))
+        msg_batch = ""
+    
+    if "debug_out" in args:
+        print_debug(f"{pat}{msg_batch}", my_tile_list, args["debug_out"], verbose=True)
+
+    print(f"This replica assigned {len(my_tile_list)} tiles")
+    print(f"Parallel jobs: {args['parallel_jobs']}")
+
+    if len(my_tile_list) > 0:
+        print(f"First tile: {my_tile_list[0]}")
+        if len(my_tile_list) > 1:
+            print(f"Last tile: {my_tile_list[-1]}")
+        else:
+            print("Only one tile")
+
+    # Process each tile assigned to this replica
+    success_count = 0
+    failure_count = 0
+
+    # Prepare arguments for parallel processing
+    process_args = [
+        (tile_id, i, len(my_tile_list), sys.argv[1:], args["dry_run"], msg_batch)
+        for i, tile_id in enumerate(my_tile_list, 1)
+    ]
+
+    if args['parallel_jobs'] > 1:
+        # Parallel processing
+        print(f"Processing tiles in parallel with {args['parallel_jobs']} workers")
+        with Pool(processes=args['parallel_jobs']) as pool:
+            results = pool.map(process_single_tile, process_args)
+    else:
+        # Sequential processing (original behavior)
+        print("Processing tiles sequentially")
+        results = [process_single_tile(arg) for arg in process_args]
+
+    # Count successes and failures
+    for tile_id, success, error_msg in results:
+        if success:
+            success_count += 1
+        else:
+            failure_count += 1
+
+    # Summary
+    print(
+        f"{'='*5} Processing completed tot/suc/fail="
+        + f"{len(my_tile_list)}/{success_count}/{failure_count}"
+    )
+
+    # Exit with error if any failures
+    if failure_count > 0:
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/python/get_ccds_with_psf.py b/scripts/python/get_ccds_with_psf.py
old mode 100644
new mode 100755
index 5f917ad0..e284e264
--- a/scripts/python/get_ccds_with_psf.py
+++ b/scripts/python/get_ccds_with_psf.py
@@ -9,192 +9,50 @@
 
 """
 
-
 import sys
-import numpy as np
-
-from shapepipe.utilities import summary
-
-
-def get_lines(fname):
-    """Get Lines.
-
-    Return list of lines read from a text file.
-
-    Parameters
-    ----------
-    fname: str
-        input file name
 
-    Returns:
-    list
-        IDs
+from shapepipe.utilities.ccd_psf_handler import CcdPsfHandler
 
-    """
-    IDs = []
-    with open(fname) as f:
-        lines = f.readlines()
-        for line in lines:
-            IDs.append(line.rstrip())
-
-    return IDs
 
+def run_ccd_psf_handler(args=None):
+    """Run CCD PSF Handler.
 
-def get_exp_shdu_missing(patches):
-    """Get Exp Shdu Missing.
-
-    Returns set of missing CCDs (single-exposure single-HDU IDs) from a list of patches.
+    Create instance and run the CCD PSF handler.
 
     Parameters
     ----------
-    patches: list
-        input patches
+    args : list, optional
+        command line arguments
 
     Returns
     -------
-    set
-       missing CCD IDs
+    int
+        exit code
 
     """
-    exp_shdu_missing_all = set()
-    
-    for patch in patches:
-
-        path_exp_shdu_missing = f"{patch}/summary/missing_job_32_all.txt"
-        exp_shdu_missing = get_lines(path_exp_shdu_missing)
-
-        print(f"Patch {patch}: Found {len(exp_shdu_missing)} missing ccds", end="; ")
-
-        exp_shdu_missing_all.update(exp_shdu_missing)
-
-        print(f"cumulative {len(exp_shdu_missing_all)} missing ccds")
-
-    print()
-    
-    return exp_shdu_missing_all
-
-
-def get_exp(patches):
-    """Get Exp.
-
-    Return set of exposures from a list of patches.
-
-    Parameters
-    ----------
-    patches: list
-        input patches
-
-    Returns
-    -------
-    set
-       exposure IDs
-
-    """
-    exp_all = set()
-    
-    for patch in patches:
-
-        path_exp = f"{patch}/exp_numbers.txt"
-        exp = get_lines(path_exp)
-
-        print(f"Patch {patch}: Found {len(exp)} exposures", end="; ")
-
-        exp_all.update(exp)
-
-        print(f"cumulative {len(exp_all)} exposures")
+    # Create instance
+    obj = CcdPsfHandler()
 
-    print()
+    return obj.run(args=args)
 
-    return exp_all
 
+def main(argv=None):
+    """Main.
 
-def get_ccds_with_psf(patches, n_CCD=40):
-    """Get CCDs With PSF.
-
-    Return set of CCDs from list of patches.
+    Main program.
 
     Parameters
     ----------
-    patches: list
-        input patches
+    argv : list, optional
+        command line arguments
 
     Returns
     -------
-    set
-       CCD IDs
+    int
+        exit code
 
     """
-    # Get missing CCDs
-    print("=== get missing CCDs ===")
-    exp_shdu_missing_all = get_exp_shdu_missing(patches)
-
-    # Get all exposures used in tiles
-    print("=== get exposures ===")
-    exp_all = get_exp(patches)
-
-    # Turn exposures into exposure-single-HDU names (CCDs)
-    exp_shdu_all = summary.get_all_shdus(exp_all, n_CCD)
-
-    print(f"Found {len(exp_shdu_all)} CCDs")
-
-    return exp_shdu_all
-
-
-def get_ccds_with_psf_method_2(patches, n_CCD=40):
-
-    for patch in patches:
-        directory = f"{patch}/exp_runs"
-
-    for entry in os.scandir(directory):
-        pass
-
-def save(IDs, path):
-    """Save.
-
-    Save list of IDs to text file.
-
-    Parameters
-    ----------
-    IDs: set
-        input IDs
-
-    path: str
-        output file name
-
-    """
-    with open(path, "w") as f_out:
-        for ID in IDs:
-            print(ID, file=f_out)
-
-def main(argv):
-    """Main.
-
-    Main program.
-
-    """
-    version = "v1.5"
-
-    if version == "v1.4":
-        n_patch = 7
-    elif version == "v1.5":
-        n_patch = 8
-    else:
-        raise ValueError(f"Invalid version {version}")
-        
-    patches = [f'P{x}' for x in np.arange(n_patch) + 1]
-
-    print(f"=== get_ccds_with_psf for version {version}, patches {patches} ===")
-
-    print("=== method 1: exp_list - missing === ")
-    exp_shdu_all = get_ccds_with_psf(patches)
-
-    save(exp_shdu_all, f"ccds_with_psf_{version}.txt")
-
-    #print("=== method 2: star cats === ")
-    #exp_shdu_all_method_2 = get_ccds_with_psf_method_2(patches)
-    #save(exp_shdu_all_method_2, f"ccds_with_psf_{version}_method_2.txt")
-
-    return 0
+    return run_ccd_psf_handler(args=argv)
 
 
 if __name__ == "__main__":
diff --git a/scripts/python/update_runs_log_file.py b/scripts/python/update_runs_log_file.py
index 6cd1fde6..debbe9c8 100644
--- a/scripts/python/update_runs_log_file.py
+++ b/scripts/python/update_runs_log_file.py
@@ -26,7 +26,7 @@ def matching_subdirs(base_dir, pattern, tail=False):
                     path = tail
 
                 # Remove postfix in case of multiple runs of same module
-                path = re.sub("_run_.\d?", "", path)
+                path = re.sub(r"_run_.\d?", "", path)
 
                 # Append to result
                 subdirs.append(path)
diff --git a/scripts/sh/canfar_async_job.sh b/scripts/sh/canfar_async_job.sh
new file mode 100755
index 00000000..7128e773
--- /dev/null
+++ b/scripts/sh/canfar_async_job.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+
+source $HOME/shapepipe/scripts/sh/functions.sh
+
+
+# 1. Extract the line from -f file
+# Save original arguments
+ARGS=("$@")
+
+# Find -f argument and get its value; remove -f and -S <anything>
+FILE_IDS=""
+NEW_ARGS=()
+i=0
+while [[ $i -lt $# ]]; do
+    arg="${ARGS[i]}"
+    case "$arg" in
+        -f)
+            ((i++))
+            FILE_IDS="${ARGS[i]}"
+            ;;
+        -S)
+            ((i++))  # skip next argument (value of -S)
+            ;;
+        *)
+            NEW_ARGS+=("$arg")
+            ;;
+    esac
+    ((i++))
+done
+
+if [[ -z "$FILE_IDS" ]]; then
+    echo "Error: -f <file_IDs> must be provided"
+    exit 1
+fi
+
+# Get the line for this replica
+LINE=$(get_line_from_file -f "$FILE_IDS")
+
+# 2. Call init_run.sh with remaining args + -e line
+$HOME/shapepipe/scripts/sh/init_run_exclusive_canfar.sh "${NEW_ARGS[@]}" -e "$LINE"
diff --git a/scripts/sh/functions.sh b/scripts/sh/functions.sh
index 02f6544b..68d1f2fa 100644
--- a/scripts/sh/functions.sh
+++ b/scripts/sh/functions.sh
@@ -10,6 +10,49 @@ pat="---- "
 STOP=0
 
 
+get_line_from_file() {
+    local file=""
+    local OPTIND opt
+
+    # Parse options: look for -f <filename>
+    while getopts ":f:" opt; do
+        case $opt in
+            f)
+                file="$OPTARG"
+                ;;
+            \?)
+                echo "Unknown option: -$OPTARG", ", continuing"
+                #return 1
+                ;;
+            :)
+                echo "Option -$OPTARG requires an argument."
+                return 1
+                ;;
+        esac
+    done
+
+    # Check that file and REPLICA_ID are set
+    if [[ -z "$file" ]]; then
+        echo "Error: -f option not provided."
+        return 1
+    fi
+    if [[ -z "$REPLICA_ID" ]]; then
+        echo "Error: REPLICA_ID not set."
+        return 1
+    fi
+
+    # Check file exists
+    if [[ ! -f "$file" ]]; then
+        echo "Error: file '$file' not found."
+        return 1
+    fi
+
+    # Extract the line corresponding to REPLICA_ID (1-based index)
+    sed -n "${REPLICA_ID}p" "$file"
+}
+
+
+
 # Add session and image IDs to log files
 function update_session_logs() {
   echo $my_session >> session_IDs.txt
diff --git a/scripts/sh/init_run_exclusive_canfar.sh b/scripts/sh/init_run_exclusive_canfar.sh
index 67f62797..cb7c9af6 100755
--- a/scripts/sh/init_run_exclusive_canfar.sh
+++ b/scripts/sh/init_run_exclusive_canfar.sh
@@ -17,18 +17,18 @@ sm=1
 
 # mh_local is 0 (1) if merge_header_runner is run on all exposures,
 # which is standard so far (run on exposures of given tile only; new)
-mh_local=0
+mh_local=1
 
 # sp_local is 0 (1) is split_headers_runner and mask_runner is run
 # on all exposures (locally). Not 100% automatic yet. 
-sp_local=0
+sp_local=1
 VERBOSE=1
 
 pat="-- "
 
 
 ## Help string
-usage="Usage: $(basename "$0") -j JOB -e ID -k KIND [OPTIONS]
+usage="Usage: $(basename "$0") -j JOB -e ID [OPTIONS]
 \n\nOptions:\n
    -h\tthis message\n
    -j, --job JOB\tRUnning JOB, bit-coded\n
@@ -39,7 +39,7 @@ usage="Usage: $(basename "$0") -j JOB -e ID -k KIND [OPTIONS]
    -m, --mh_local MH\n
    \tmerge header file local (MH=1) or global (MH=0); default is $mh_local\n
    -s, --sp_local SP\n
-   \tsplit local run local (SP=1) or global (SP=r0wwdefault is $sp_local\n
+   \tsplit local run local (SP=1) or global (SP=0); default is $sp_local\n
    --sm SM\n
    \tWith (SM=1; default) or without (SM=0) spread model input\n
    -N, --N_SMP N_SMOp\n
@@ -49,13 +49,13 @@ usage="Usage: $(basename "$0") -j JOB -e ID -k KIND [OPTIONS]
    -S, --scratch\n
     \tprocessing scratch directory, default is None ($scratch)\n
    -F, --fix FIX\n
-    \tfix missing data (re-download tile, unzip) for FIX=1; default is $FIX\n
+    \tfix missing data (re-download tile, unzip) for FIX=1; default is $fix\n
    -n, --dry_run LEVEL\n
     \tdry run (LEVEL=1), no actual processing; default is $dry_run\n
    --debug_out PATH\n
    \tdebug output file PATH, default not used\n
    --test\n
-   \ttest mode, no processind\n
+   \ttest mode, no processing\n
 "
 
 ## Help if no arguments                                                         
@@ -148,6 +148,7 @@ function message() {
 # Init message
 message "test=$test_only" $debug_out -1
 if [ "$test_only" == "1" ]; then
+  echo "MKDEBUG test"
   msg="init_run_exclusive.py script test mode, exiting."
   ex=0
 else
@@ -189,10 +190,10 @@ fi
 
 source $HOME/shapepipe/scripts/sh/functions.sh
 
-msg="Starting $(basename "$0")"
+msg="Starting $(basename "$0") `date` ID=$ID"
 message "$msg" $debug_out -1
-message "`date`" $debug_out -1
-message "ID=$ID" $debug_out -1
+#message "`date`" $debug_out -1
+#message "ID=$ID" $debug_out -1
 
 # Set kind
 kind=$(get_kind_from_job $job)
@@ -353,11 +354,7 @@ fi
 
 (( do_job = $job & 2 ))
 if [ $do_job != 0 ] && [ "$sp_local" == "1" ]; then
-#if [ ! -e "run_exp_Sp_shdu" ] && [ "$sp_local" == "1" ]; then
-  # run local Sp if not done already; works only with mh_local=1; this step needs to be done
-  # before following mh_local=1 steps 
   message "run local sp" $debug_out -1
-  #command "rm -rf run_sp_GitFeGie*/get_images_runner_run_2" $dry_run
   command "rm -rf run_sp_Gie*" $dry_run
   command "rm -rf run_sp_exp_Sp*" $dry_run
 
@@ -404,16 +401,18 @@ if [ $do_job != 0 ] && [ "$sp_local" == "1" ]; then
   cd output
 
   if [ "$job" == "2" ]; then
-    exit 0
+    msg="Finishing $(basename "$0") after job=2 `date` ID=$ID"
+    message "$msg" $debug_out 0
   fi
 
 fi
 
 if [ "$kind" == "tile" ] && [ "$sp_local" == "1" ]; then
+  echo "New (for P9): skipping link_to_exp_for_tile.py" 
   cd ../../..
-  command "link_to_exp_for_tile.py -t $ID -i tile_runs -I exp_runs -s $sp_local" $dry_run
+  #command "link_to_exp_for_tile.py -t $ID -i tile_runs -I exp_runs -s $sp_local" $dry_run
   cd tile_runs/$ID
-  command "combine_runs.bash -p psfex -c shdu" $dry_run
+  #command "combine_runs.bash -p psfex -c shdu" $dry_run
   cd output
 fi
 
@@ -486,18 +485,6 @@ fi
 (( do_job = $job & 64 ))
 if [[ $do_job != 0 ]]; then
   if [ "$kind" == "tile" ]; then
-    cd ../../..
-    command "link_to_exp_for_tile.py -t $ID -i tile_runs -I exp_runs -s $sp_local" $dry_run
-    cd ${kind}_runs/$ID/output
-
-    # Remove duplicate job-16 runs (tile detection)
-    # New (P8) commented
-    #n_16=`ls -rt1d run_sp_tile_Sx_* | wc -l`
-    #if [ "$n_16" != "1" ]; then
-      #n_remove="$(($n_16-1))"
-      #echo "removing $n_remove duplicate old job-16 runs"
-      #command "rm -rf `ls -rt1d run_sp_tile_Sx_* | head -$n_remove`" $dry_run
-    #fi
 
     # Remove previous runs of this job
     rm -rf run_sp_tile_PsViSmVi*
@@ -513,6 +500,16 @@ if [[ $do_job != 0 ]]; then
     dir_ngmix_prev="run_sp_tile_ngmix_Ng1u_prev/ngmix_runner/output"
     cat_ngmix_prev="$dir_ngmix_prev/ngmix-*.fits"
 
+    # Remove if empty
+    if [ ! -s $cat_ngmix ]; then
+      echo "Removing empty file $cat_ngmix"
+      rm $cat_ngmix
+    fi
+    if [ ! -s $cat_ngmix_prev ]; then
+      echo "Removing empty file $cat_ngmix_prev"
+      rm $cat_ngmix_prev
+    fi
+
     # Check whether ngmix output exists
     if [ -e $cat_ngmix ]; then
         message "ngmix output catalogue exists" $debug_out -1
@@ -552,7 +549,7 @@ fi
 if [[ $do_job != 0 ]]; then
 
   # Remove previous runs of this job
-  rm -rf run_sp_Ms_20??_*
+  rm -rf run_sp_Ms_20??-*
 
 fi
 
@@ -560,7 +557,7 @@ fi
 if [[ $do_job != 0 ]]; then
 
   # Remove previous runs of this job
-  rm -rf run_sp_Mc_20??_*
+  rm -rf run_sp_Mc_20??-*
 
 fi
 
@@ -614,8 +611,7 @@ fi
 
 cd $dir
 
-msg="End $(basename "$0")"
-echo $msg
-if [ "$debug_out" != "-1" ]; then
-  echo $pat$msg >> $debug_out-
-fi
+#msg="End $(basename "$0")"
+
+msg="Finished $(basename "$0") `date` ID=$ID"
+message "$msg" $debug_out -1
diff --git a/scripts/sh/remove_duplicates_tiles.sh b/scripts/sh/remove_duplicates_tiles.sh
old mode 100644
new mode 100755
index 2c774823..6f789fe6
--- a/scripts/sh/remove_duplicates_tiles.sh
+++ b/scripts/sh/remove_duplicates_tiles.sh
@@ -1,16 +1,55 @@
 #!/usr/bin/env bash
 
+# Script to remove duplicate tile runs, keeping only the newest
+# Run from directory containing P?/tile_runs subdirectories
+
 count=0
-for dir in tile_runs/*; do
-  if compgen -G "$dir/output//run_sp_tile_PsViSmVi*" > /dev/null; then
-    n=`ls -rdtl $dir/output/run_sp_tile_PsViSmVi* | wc -l`
-    if [ "$n" != "2" ]; then
-      ((n_remove=n-1))
-      echo  $dir $n $n_remove
-      rm -rf `ls -rdt1 $dir/output/run_sp_tile_PsViSmVi* | head -n $n_remove`
-      (count=(count+1))
-    fi
+total_removed=0
+
+echo "Starting duplicate tile run removal..."
+echo "Looking for patches in P?/tile_runs structure..."
+echo ""
+
+# Process each patch directory
+for patch_dir in P*/tile_runs; do
+  if [ ! -d "$patch_dir" ]; then
+    continue
   fi
+
+  patch_name=$(dirname "$patch_dir")
+  echo "Processing patch: $patch_name"
+
+  # Process each tile directory within the patch
+  for dir in "$patch_dir"/*; do
+    if [ ! -d "$dir" ]; then
+      continue
+    fi
+
+    # Check each of the three run types
+    for run_type in run_sp_tile_PsViSmVi run_sp_tile_Mc run_sp_tile_Ms; do
+      if compgen -G "$dir/output/${run_type}*" > /dev/null; then
+        n=$(ls -dt "$dir/output/${run_type}"* 2>/dev/null | wc -l)
+        if [ "$n" -gt 1 ]; then
+          ((n_remove=n-1))
+          echo "  Found $n copies of $run_type in $(basename "$dir"), removing $n_remove oldest"
+
+          # Remove all but the newest
+          ls -dt "$dir/output/${run_type}"* | tail -n "$n_remove" | while read old_run; do
+            echo "    Removing: $(basename "$old_run")"
+            rm -rf "$old_run"
+            ((total_removed++))
+          done
+
+          ((count++))
+        fi
+      fi
+    done
+  done
+  echo ""
 done
 
-echo $count
+echo "========================================"
+echo "Summary:"
+echo "  Processed tile directories: $count"
+echo "  Total run directories removed: $total_removed"
+echo "========================================"
diff --git a/src/shapepipe/canfar/canfar_log_monitor.py b/src/shapepipe/canfar/canfar_log_monitor.py
new file mode 100644
index 00000000..ee6fe5db
--- /dev/null
+++ b/src/shapepipe/canfar/canfar_log_monitor.py
@@ -0,0 +1,251 @@
+# canfar_log_monitor.py
+
+# Monitor CANFAR job logs and save non-empty outputs
+
+import os
+import sys
+import time
+import subprocess
+from datetime import datetime
+from pathlib import Path
+
+from canfar.sessions import Session
+
+from cs_util import args as cs_args
+from cs_util import logging
+
+
+class LogMonitor:
+    """Monitor CANFAR job logs continuously."""
+
+    def __init__(self):
+        self.params_default()
+        self._tracked_jobs = {}  # Track which jobs we've seen
+
+    def params_default(self):
+        """Set default parameters."""
+        self._params = {
+            "status": "Running",
+            "interval": 180,
+            "output_dir": "canfar_logs",
+            "kind": "headless",
+            "verbose": False,
+        }
+
+        self._short_options = {
+            "status": "-s",
+            "interval": "-i",
+            "output_dir": "-o",
+            "kind": "-k",
+        }
+
+        self._types = {
+            "interval": "int",
+        }
+
+        self._help_strings = {
+            "status": "job status to monitor (Running, Completed, or both); default is {}",
+            "interval": "polling interval in seconds; default is {}",
+            "output_dir": "directory to save log files; default is {}",
+            "kind": "session kind (headless, notebook, etc.); default is {}",
+        }
+
+    def set_params_from_command_line(self, args):
+        """Set parameters from command line."""
+        options = cs_args.parse_options(
+            self._params,
+            self._short_options,
+            self._types,
+            self._help_strings,
+        )
+        self._params = options
+
+        # Save calling command
+        logging.log_command(args)
+
+    def get_job_ids(self):
+        """Fetch job IDs based on status filter."""
+        session = Session()
+
+        kind = self._params["kind"] if self._params["kind"] != "all" else None
+
+        try:
+            info = session.fetch(kind=kind)
+        except Exception as e:
+            print(f"Error fetching sessions: {e}")
+            return []
+
+        if not info:
+            return []
+
+        # Filter by status
+        status_filter = self._params["status"]
+        job_ids = []
+
+        for job in info:
+            job_status = job.get("status", "")
+            job_id = job.get("id", "")
+            job_name = job.get("name", "")
+
+            # Handle "both" or comma-separated statuses
+            if status_filter.lower() == "both":
+                if job_status in ["Running", "Completed"]:
+                    job_ids.append((job_id, job_name, job_status))
+            elif "," in status_filter:
+                statuses = [s.strip() for s in status_filter.split(",")]
+                if job_status in statuses:
+                    job_ids.append((job_id, job_name, job_status))
+            else:
+                if job_status == status_filter:
+                    job_ids.append((job_id, job_name, job_status))
+
+        return job_ids
+
+    def fetch_logs(self, job_id):
+        """Fetch logs for a specific job ID using canfar logs command."""
+        try:
+            result = subprocess.run(
+                ["canfar", "logs", job_id],
+                capture_output=True,
+                text=True,
+                timeout=30,
+            )
+            return result.stdout
+        except subprocess.TimeoutExpired:
+            print(f"  Timeout fetching logs for {job_id}")
+            return None
+        except Exception as e:
+            if self._params["verbose"]:
+                print(f"  Error fetching logs for {job_id}: {e}")
+            return None
+
+    def is_log_empty(self, log_content):
+        """Check if log content is just the header (3 lines with no real content)."""
+        if not log_content:
+            return True
+
+        lines = log_content.strip().split('\n')
+
+        # If only 3 or fewer lines, and they're mostly empty, consider it empty
+        if len(lines) <= 3:
+            non_empty_lines = [line for line in lines if line.strip() and "Logs for session" not in line]
+            return len(non_empty_lines) == 0
+
+        return False
+
+    def save_log(self, job_id, job_name, log_content, job_status=None):
+        """Save log content to a file."""
+        output_dir = Path(self._params["output_dir"])
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        # Use job name if available, otherwise use ID
+        filename = f"{job_name}_{job_id}.log" if job_name else f"{job_id}.log"
+        filepath = output_dir / filename
+
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        with open(filepath, 'w') as f:
+            f.write(f"# Captured at {timestamp}\n")
+            f.write(f"# Job ID: {job_id}\n")
+            f.write(f"# Job Name: {job_name}\n\n")
+            f.write(log_content)
+
+            # Add status line if job is completed
+            if job_status == "Completed":
+            # Ensure there's a newline before the status line
+                if not log_content.endswith('\n'):
+                    f.write('\n')
+                    f.write('status=Completed\n')
+        return filepath
+
+    def monitor_loop(self):
+        """Main monitoring loop."""
+        print(f"Starting CANFAR log monitor...")
+        print(f"  Status filter: {self._params['status']}")
+        print(f"  Polling interval: {self._params['interval']} seconds")
+        print(f"  Output directory: {self._params['output_dir']}")
+        print(f"  Kind: {self._params['kind']}")
+        print()
+
+        iteration = 0
+
+        try:
+            while True:
+                iteration += 1
+                timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+                print(f"[{timestamp}] Poll #{iteration}")
+
+                # Get current job IDs
+                job_ids = self.get_job_ids()
+
+                if not job_ids:
+                    print(f"  No jobs found with status '{self._params['status']}'")
+                else:
+                    print(f"  Found {len(job_ids)} job(s)")
+
+                    for job_id, job_name, job_status in job_ids:
+                        print(f"  Checking {job_id} ({job_name}, {job_status})...", end=" ", flush=True)
+
+                        # Fetch logs
+                        log_content = self.fetch_logs(job_id)
+
+                        if log_content is None:
+                            print("failed to fetch")
+                            continue
+
+                        # Check if logs have content
+                        if self.is_log_empty(log_content):
+                            print("empty")
+                            # Track that we've seen this job
+                            if job_id not in self._tracked_jobs:
+                                self._tracked_jobs[job_id] = {"name": job_name, "has_logs": False, "num_lines": 0}
+                        else:
+                            # Count number of lines in current log
+                            current_num_lines = len(log_content.strip().split('\n'))
+
+                            # Get previous number of lines
+                            if job_id in self._tracked_jobs:
+                                previous_num_lines = self._tracked_jobs[job_id].get("num_lines", 0)
+                            else:
+                                previous_num_lines = 0
+
+                            # Only save if there's new content
+                            if current_num_lines > previous_num_lines:
+                                print(f"has content! ({current_num_lines} lines, +{current_num_lines - previous_num_lines})", end=" ")
+
+                                # Save the logs
+                                filepath = self.save_log(job_id, job_name, log_content)
+                                print(f"saved to {filepath}")
+
+                                # Update tracking
+                                self._tracked_jobs[job_id] = {
+                                    "name": job_name,
+                                    "has_logs": True,
+                                    "num_lines": current_num_lines
+                                }
+                            else:
+                                print(f"no new content ({current_num_lines} lines)")
+                                # Update tracking even if no new content
+                                if job_id not in self._tracked_jobs:
+                                    self._tracked_jobs[job_id] = {
+                                        "name": job_name,
+                                        "has_logs": True,
+                                        "num_lines": current_num_lines
+                                    }
+
+                print()
+                time.sleep(self._params['interval'])
+
+        except KeyboardInterrupt:
+            print("\n\nMonitoring stopped by user")
+            print(f"Total jobs tracked: {len(self._tracked_jobs)}")
+            jobs_with_logs = sum(1 for j in self._tracked_jobs.values() if j["has_logs"])
+            print(f"Jobs with logs saved: {jobs_with_logs}")
+
+    def run(self, args=None):
+        """Main entry point."""
+        if args is None:
+            args = sys.argv
+
+        self.set_params_from_command_line(args)
+        self.monitor_loop()
diff --git a/src/shapepipe/canfar/canfar_monitor.py b/src/shapepipe/canfar/canfar_monitor.py
new file mode 100644
index 00000000..5edc9e7a
--- /dev/null
+++ b/src/shapepipe/canfar/canfar_monitor.py
@@ -0,0 +1,282 @@
+# canfar_monitor.py
+
+# Montiot job submitted with the canfar client
+
+import os
+import sys
+
+from requests.exceptions import ReadTimeout
+from httpx import HTTPStatusError
+from pydantic import ValidationError
+from canfar.sessions import Session
+
+import pandas as pd
+
+from cs_util import args as cs_args
+from cs_util import logging
+
+
+class Log(object):
+
+    def __init__(self):
+
+        self.params_default()
+
+    def set_params_from_command_line(self, args):
+        """Set Params From Command line.
+
+        Only use when calling using python from command line.
+        Does not work from ipython or jupyter.
+
+        """
+        # Read command line options
+        options = cs_args.parse_options(
+            self._params,
+            self._short_options,
+            self._types,
+            self._help_strings,
+        )
+        self._params = options
+
+        # Save calling command
+        logging.log_command(args)
+
+    def params_default(self):
+
+        self._params = {
+            "kind": "headless",
+            "status": "all",
+            "destroy": False,
+            "bulk": 1,
+            "quiet": False,
+        }
+
+        self._short_options = {
+            "kind": "-k",
+            "status": "-s",
+            "destroy": "-d",
+            "bulk": "-b",
+            "quiet": "-q",
+        }
+
+        self._types = {
+            "destroy": "bool",
+            "bulk": "int",
+            "quiet": "bool",
+        }
+
+        self._help_strings = {
+            "kind": "session kind, allowed are all, headless, notebook, ...; default is {}",
+            "status": "procesing status, allowed are Pending, Running, Completed, all; default is {}",
+            "destroy": "if True, destroy all displayed jobs; default is {}",
+            "bulk": "destroy mode, allowed are 2, 1, 0; default is {}",
+            "quiet": "quiet output",
+        }
+
+    def update_params(self):
+        """Update Params.
+
+        Update parameters.
+        """
+        pass
+
+    def check_params(self):
+        pass
+
+    def print_info(self, df):
+
+        if not self._params["quiet"]:
+            # Prepare header and rows with left-justified formatting
+            header = f"{'type':<10} {'status':<10} {'date':<12} {'time':<10} {'name':<20} {'id':<10}"
+            print("=" * len(header))
+            print(header)
+            print("=" * len(header))
+
+            for _, row in df.iterrows():
+                print(f"{row['type']:<10} {row['status']:<10} {row['date']:<12} {row['time']:<10} {row['name']:<20} {row['id']:<10}")
+
+            print("=" * len(header))
+
+        print(f"{len(df)} jobs found")
+
+    def filter(self):
+
+        df = pd.DataFrame(self._info)
+
+        if len(self._info) == 0:
+            return df
+
+        # Extract clean date/time
+        try:
+            # Remove 'T' and 'Z' to make it a proper datetime string
+            dt_series = (
+                df['startTime']
+                    .str.replace('T', ' ', regex=False)
+                    .str.replace('Z', '', regex=False)
+                )
+            # Extract date and time separately
+            df['date'] = dt_series.str.split(' ').str[0]
+            df['time'] = dt_series.str.split(' ').str[1]
+        except KeyError:
+            if self._params["verbose"]:
+                print(
+                    f"Column 'startTime' not found among {df.columns.tolist()},"
+                    + f" setting dummy columns for date and time."
+                )
+            df['date'] = '1975-12-06'
+            df['time'] = '12:04:00'
+        except Exception as e:
+            estr = f": {e}" if self._params["verbose"] else ""
+            raise RunTimeError(
+                f"An error occurred while processing dates: {estr}"
+            ) from e
+
+        # Select columns of interest
+        cols = ['type', 'status', 'date', 'time', 'name', 'id']
+        for col in cols:
+            if not col in df:
+                raise IndexError(f"Column {col} not in job info, which contains {df.columns.tolist()}") 
+        df = df[['type', 'status', 'date', 'time', 'name', 'id']]
+
+        # Get input status or None if missing
+        status_filter = self._params.get("status", None)
+
+        # If not None and not "all": filter
+        if status_filter and status_filter != "all":
+            df = df[df["status"] == status_filter]
+
+        return df
+
+    def run(self, args=None):
+
+        if args is None:
+            args = sys.argv
+
+        obj = self
+   
+        obj.set_params_from_command_line(args)
+        obj.update_params()
+        obj.check_params()
+
+        session = self.get_session()
+        
+        df_filtered = self.filter()
+
+        self.print_info(df_filtered)
+
+        if self._params["destroy"]:
+            self.destroy(df_filtered, session)
+
+    def get_kind(self):
+        """Get Kind.
+
+        Return kind for communication with canfar.
+        In particular, returns None if kind in parametesr is "all".
+
+        """
+        return (
+            self._params["kind"]
+            if self._params["kind"] != "all"
+            else None
+        )
+
+    def destroy(self, df, session):
+
+        bulk = self._params["bulk"]
+
+        if bulk == 2:
+            kind = self._get_kind()
+
+            if self._params["status"] != "all":
+                status = self._params["status"]
+            else:
+                status = None
+
+            try:
+                print("Bulk destroy")
+                result = session.destroy_with(
+                    prefix="sp-",
+                    kind=kind,
+                    status=status,
+                )
+            except Exception as e:
+                if self._params.get("verbose"):
+                    print(f"Failed to destroy jobs: {e}")
+            else:
+                print("Failed to destroy jobs")
+                # Optionally exit or return gracefully
+                return False
+        
+            print("Success")
+            return
+
+        elif bulk == 1:
+            # Recommended
+            ids = df['id'].tolist()
+            try:
+                print("List destroy")
+                result = session.destroy(ids)
+                print(f"{len(ids)} sessions done")
+            except Exception as e:
+                estr = f": {e}" if self._params["verbose"] else ""
+                print("failed{estr}")
+
+        else:
+            print("Loop destroy")
+            for _, row in df.iterrows():
+                session_id = row["id"]
+                name = row.get("name", "<unknown>")
+                print(f"Destroying {session_id} {name}", end=" ", flush=True)
+                try:
+                    result = session.destroy(session_id)
+                    print(f"{result[session_id]} done")
+                except Exception as e:
+                    estr = f": {e}" if self._params["verbose"] else ""
+                    print("failed{estr}")
+
+
+    def get_session(self):
+
+        # Initialize session manager
+        session = Session()
+
+        # Fetch information
+        try:
+            if not self._params["quiet"]:
+                print(
+                    f"Retreiving session info of kind={self._params['kind']},"
+                    + f" status={self._params['status']}"
+                )
+            kind = self.get_kind()
+            self._info = session.fetch(kind=kind)
+            if len(self._info) == 0:
+                print("No session fetched")
+                return []
+        except ReadTimeout as e:
+            estr = f": {e}" if self._params["verbose"] else ""
+            raise ReadTimeout(
+                f"ReadTimeout occurred while fetching session info{estr}"
+            ) from e
+        except ValidationError as e:
+            estr = f": {e}" if self._params["verbose"] else ""
+            raise ValueError(
+                f"Validation error occurred; incorrect kind{estr}"
+            ) from e
+        except HTTPStatusError as e:
+            estr = f": {e}" if self._params["verbose"] else ""
+            raise ValueError(
+                f"HTTPStatusError occurred while fetching session info{estr}"
+            ) from None
+        except Exception as e:
+            # Handle any other exceptions
+            print(f"An unexpected error occurred: {e}")
+            if self._params["verbose"]:
+                raise
+            else:
+                sys.exit(2)
+        else:
+            # Runs only if no exception occurred
+            if not self._params["quiet"]:
+                print(f"Successfully fetched kind '{self._params['kind']}'")
+
+        return session
diff --git a/src/shapepipe/canfar/canfar_submit.py b/src/shapepipe/canfar/canfar_submit.py
new file mode 100644
index 00000000..7c0bb5be
--- /dev/null
+++ b/src/shapepipe/canfar/canfar_submit.py
@@ -0,0 +1,461 @@
+# canfar_submit_job.py
+
+# Submit job with the canfar client
+
+import os
+import sys
+import asyncio
+import math
+
+from canfar.sessions import Session
+from canfar.sessions import AsyncSession
+from datetime import datetime
+
+from cs_util import args as cs_args
+from cs_util import logging
+
+
+class Job(object):
+
+    def __init__(self):
+
+        self.params_default()
+
+        self._patch = os.environ["patch"] if "patch" in os.environ else "P0"
+
+        # Set job parameters
+        version = "1.1"
+        self._image = f"images.canfar.net/unions/shapepipe:{version}"
+
+        # Maximum replicas per batch (CANFAR limit is 512)
+        self._max_replicas_per_batch = 512
+        if self._max_replicas_per_batch != 512:
+            print(f"Setting the maximum number of replicas to the non-standard {self._max_replicas_per_batch}")
+
+        # Default number of cores, can be overwritten by -c or -P (parallel jobs) options
+        self._cores_default = 2
+
+
+    def set_params_from_command_line(self, args):
+        """Set Params From Command line.
+
+        Only use when calling using python from command line.
+        Does not work from ipython or jupyter.
+
+        """
+        # Read command line options
+        options = cs_args.parse_options(
+            self._params,
+            self._short_options,
+            self._types,
+            self._help_strings,
+        )
+        self._params = options
+
+        # Save calling command
+        logging.log_command(args)
+
+    def params_default(self):
+
+        self._params = {
+            "job": -1,
+            "exclusive": None,
+            "file_IDs": None,
+            "psf": "psfex",
+            "n": "0",
+            "debug_out": None,
+            "dry_run": 0,
+            "log": "log_jobs.txt",
+            "sync": "async",
+            "cores": 2,
+            "ram": 4,
+            "parallel_jobs": 1,
+            "jobs_per_session": 0,
+            "stats": False,
+        }
+
+        self._short_options = {
+            "job": "-j",
+            "exclusive": "-e",
+            "file_IDs": "-f",
+            "psf": "-p",
+            "dry_run": "-n",
+            "log": "-l",
+            "sync": "-S",
+            "cores": "-c",
+            "ram": "-r",
+            "parallel_jobs": "-P",
+            "jobs_per_session": "-J",
+            "stats": "-s",
+        }
+
+        self._types = {
+            "job": "int",
+            "cores": "int",
+            "ram": "int",
+            "parallel_jobs": "int",
+            "jobs_per_session": "int",
+            "stats": "bool",
+        }
+
+        self._help_strings = {
+            "job": "Running JOB, bit-coded, default is {}",
+            "exclusive": "Run exclusively given image",
+            "file_IDs": "file containing IDs",
+            "psf": "PSF model, allowed are 'psfex' and 'mccd'; default is {}",
+            "dry_run": "if dry run > 0 no actual processing; allowed are 2, 1, 0; default is {}",
+            "debug_out": "debug output file path, default:set automatically",
+            "log": "output log file with job and tile IDs",
+            "sync": "job submission mode, allowed are async, sync; default is {}",
+            "cores": "number of CPU cores per replica; default is {} or overwritten by -P",
+            "ram": "RAM in GB per replica; default is {}",
+            "parallel_jobs": "number of tiles to process in parallel per replica; default is {}",
+            "jobs_per_session": (
+                "number of jobs (tiles) per session; if 0, create one session per job (default is {});"
+                + " Number of replicas is #FILE_IDs / JOBS_PER_SESSSION"
+            ),
+            "stats": "print stats of sessions/batches/replicas and exit",
+        }
+
+    def update_params(self):
+        """Update Params.
+
+        Update parameters.
+        """
+
+        # If not given, set number of cores to number of parallel jobs;
+        # else set to default. 
+        if self._params["cores"] == -1:
+            if self._params["parallel_jobs"] != 1:
+                self._params["cores"] = self._params["parallel_jobs"]
+            else:
+                self._params["cores"] = self._cores_default
+        pass
+
+    def check_params(self):
+
+        if self._params["job"] == -1:
+            raise ValueError(f"need to specify job (option -j)")
+
+        if self._params["sync"] == "async" and not self._params["file_IDs"]:
+            raise ValueError("asynchronous mode only possible when tile_IDs file given (-f)")
+
+        if not self._params["exclusive"] and not self._params["file_IDs"]:
+            raise ValueError("No image ID(s) indicated, use option -e ID or -f file_IDs")
+
+    def set_options_base(self):
+
+        opt = {}
+
+        cwd = os.getcwd()
+        
+        # asynchronous mode
+        if self._params["sync"] == "async":
+            opt["-f"] = f"-f {cwd}/{self._params['file_IDs']}"
+
+        # Options
+        opt["j"] = f"-j {self._params['job']}"
+        opt["p"] = f"-p {self._params['psf']}"
+
+        # Set identifier for debug file name
+        if self._params["exclusive"]:
+            deb_ID = self._params["exclusive"]
+        else:
+            deb_ID = os.path.basename(self._params["file_IDs"])
+      
+        if self._params['debug_out']:
+            opt["debut_out"] = f"--debug_out {cwd}/debug/{self._params['debug_out']}"
+        else:
+            opt["debut_out"] = f"--debug_out {cwd}/debug/debug_{deb_ID}.txt"
+
+        opt["d"] = f"-d {cwd}"
+        opt["m_s"] = "-m 1 -s 1" if self._patch in ("P8", "P9") else ""
+        if self._params["dry_run"] != 0:
+            opt["n"] = f"-n {self._params['dry_run']}"
+        if self._params["parallel_jobs"] > 1:
+            opt["P"] = f"--parallel_jobs {self._params['parallel_jobs']}"
+
+        options = ""
+        for key in opt:
+            options = f"{options} {opt[key]}"
+
+        self._options_base = options
+
+    def set_job_name(self, suf=None):
+
+        if suf is None:
+            suf = self._params["exclusive"]
+
+        suf1 = suf.replace(".", "-")
+        
+        job_name = f"sp-{self._patch}-j{self._params['job']}-{suf1}"
+
+        return job_name
+
+    def set_command(self, mode):
+
+        if mode == "async_single":
+            self._cmd = f"{os.environ['HOME']}/shapepipe/scripts/sh/canfar_async_job.sh"
+        elif mode == "async_bulk":
+            self._cmd = f"{os.environ['HOME']}/shapepipe/scripts/python/distribute_tiles.py"
+        elif mode == "sync":
+            self._cmd = f"{os.environ['HOME']}/shapepipe/scripts/sh/init_run_exclusive_canfar.sh"
+
+    def get_tile_IDs(self):
+
+        if self._params["exclusive"]:
+            tile_IDs = [self._params["exclusive"]]
+            suf = None
+        else:
+            with open(self._params["file_IDs"], "r") as f:
+                tile_IDs = [line.strip() for line in f]
+            suf = ""
+
+        return tile_IDs, len(tile_IDs), suf
+
+    async def run_async(self):
+        async with AsyncSession() as session:
+
+            _, total_n, suf = self.get_tile_IDs()
+
+            # Calculate number of replicas (sessions) based on jobs_per_session
+            if self._params["jobs_per_session"] > 0:
+                # Each replica processes multiple jobs
+                num_replicas = math.ceil(total_n / self._params["jobs_per_session"])
+                print(f"Distributing {total_n} jobs across {num_replicas} sessions")
+                print(f"Each session will process ~{self._params['jobs_per_session']} jobs")
+            else:
+                # Old behavior: one replica per job
+                num_replicas = total_n
+                print(f"Creating {num_replicas} sessions (one per job)")
+
+            # Calculate number of batches needed
+            num_batches = math.ceil(num_replicas / self._max_replicas_per_batch)
+
+            if num_batches == 1:
+                # Submit single batch
+                self.set_command(mode="async_bulk")
+                print(f"Submitting {num_replicas} replicas in a single batch")
+                return await self._submit_single_batch(num_replicas, total_n)
+            else:
+                # Submit multiple batches as chunks
+                self.set_command(mode="async_bulk")
+                print(f"Splitting into {num_batches} batches (max {self._max_replicas_per_batch} replicas per batch)")
+                return await self._submit_multiple_batches(num_replicas, total_n, num_batches)
+
+
+    async def _submit_single_batch(self, num_replicas, total_n):
+        """Submit a single batch of jobs.
+
+        Args:
+            num_replicas: Number of replicas (sessions) to create
+            total_n: Total number of jobs (tiles) to process
+        """
+        print(f"Submitting {num_replicas} replicas to process {total_n} jobs")
+
+        job_name = self.set_job_name(suf="")
+        options = self._options_base.lstrip()
+        print(f"Running '{self._cmd} {options}'")
+
+        async with AsyncSession() as session:
+            try:
+                sessions = await session.create(
+                    name=job_name,
+                    image=self._image,
+                    cmd=self._cmd,
+                    args=options,
+                    replicas=num_replicas,
+                    cores=self._params["cores"],
+                    ram=self._params["ram"],
+                )
+                print(f"✓ Batch submitted successfully: {len(sessions)} sessions created")
+                print(f"Each session will process ~{math.ceil(total_n / num_replicas)} jobs using chunk()")
+                print("Sessions = ", sessions)
+            except Exception as e:
+                print(f"❌ CANFAR session.create() failed: {type(e).__name__}: {e}")
+                raise
+
+        return sessions
+
+    async def _submit_multiple_batches(self, num_replicas, total_n, num_batches):
+        """Submit multiple batches of jobs.
+
+        Args:
+            num_replicas: Total number of replicas (sessions) to create
+            total_n: Total number of jobs (tiles) to process
+            num_batches: Number of batches to split replicas into
+
+        Each batch creates a subset of replicas. chunk() automatically
+        distributes all tiles across all replicas.
+        """
+        all_sessions = []
+
+        async with AsyncSession() as session:
+            for batch_num in range(1, num_batches + 1):
+                # Calculate batch size (number of replicas in this batch)
+                if batch_num < num_batches:
+                    batch_size = self._max_replicas_per_batch
+                else:
+                    # Last batch gets the remainder
+                    batch_size = num_replicas - (num_batches - 1) * self._max_replicas_per_batch
+
+                print(f"\n--- Batch {batch_num}/{num_batches} ---")
+                print(f"Submitting {batch_size} replicas")
+                print(f"Total {num_replicas} replicas will process {total_n} jobs")
+                print(f"chunk() will distribute jobs across all replicas")
+
+                job_name = self.set_job_name(suf=f"b{batch_num}")
+                options = (
+                    f"{self._options_base} --batch_num {batch_num}"
+                    + f" --batch_tot {num_batches}"
+                    + f" --batch_size {batch_size}"
+                )
+                options = options.lstrip()
+                print(f"Running '{self._cmd} {options}'")
+
+                try:
+                    sessions = await session.create(
+                        name=job_name,
+                        image=self._image,
+                        cmd=self._cmd,
+                        args=options,
+                        replicas=batch_size,
+                        cores=self._params["cores"],
+                        ram=self._params["ram"],
+
+                    )
+                    print(f"✓ Batch {batch_num} submitted: {len(sessions)} sessions created")
+                    print(sessions)
+                    all_sessions.extend(sessions)
+
+                    # Small delay between batches to avoid overwhelming the system
+                    if batch_num < num_batches:
+                        await asyncio.sleep(1)
+
+                except Exception as e:
+                    print(f"❌ Batch {batch_num} failed: {type(e).__name__}: {e}")
+                    # Continue with other batches even if one fails
+                    continue
+
+        print(f"\n=== Submission complete ===")
+        print(f"Total sessions created: {len(all_sessions)} replicas (for {total_n} jobs)")
+        print(f"Each replica will process ~{math.ceil(total_n / num_replicas)} jobs")
+
+        return all_sessions
+
+    def run_no_async(self):
+
+        session = Session()
+
+        self.set_command(mode="sync")
+
+        tile_IDs, _, _ = self.get_tile_IDs()
+
+        job_ids = []
+        for tile_ID in tile_IDs:
+
+            job_name = self.set_job_name(tile_ID)
+
+            options = f"{self._options_base} -e {tile_ID}"
+
+            # Remove leading whitespace, problem in passing args below
+            options = options.lstrip()
+
+            print(f"Running '{self._cmd} {options}'")
+
+            # Submit flexible job (default - auto-scaling)
+            job_id = session.create(
+                name=job_name,
+                image=self._image,
+                cmd=self._cmd,
+                args=options,
+            )
+
+            print(f"Submitted job: {job_id}")
+            job_ids.append(job_id)
+
+        return job_ids
+
+    def write_job_tile_IDs(self, job_ids):
+
+        tile_IDs, _, _ = self.get_tile_IDs()
+
+        cwd = os.getcwd()
+        log_path = f"{cwd}/{self._params['log']}"
+
+        print(f"Writing job and tile ID log file {log_path}")
+        with open(log_path, "w") as log:
+            for job_id, tile_ID in zip(job_ids, tile_IDs):
+                print(job_id, tile_ID, file=log)
+
+    def print_stats(self):
+
+        # Total number of jobs (= length of input ID file)
+        _, total_n, _ = self.get_tile_IDs()
+
+        if self._params["jobs_per_session"] == 0:
+            # Compute max jobs per session to fit in single batch
+            self._params["jobs_per_session"] = math.ceil(total_n / self._max_replicas_per_batch)
+            print(f"Max jobs per session (-J) = {self._params['jobs_per_session']}")
+
+        # Number of sessions
+        n_session = math.ceil(total_n / self._params["jobs_per_session"])
+
+        # Number of batches
+        n_batch = math.ceil(n_session / self._max_replicas_per_batch)
+
+        # Number of jobs per session
+        n_jobs = math.ceil(self._params["jobs_per_session"] / n_batch)
+        
+        n_jobs_serial = max(
+            math.ceil(n_jobs / self._params["parallel_jobs"]),
+            1,
+        )
+
+        print(
+            f"Number of jobs             = {total_n}\t\t(#{self._params['file_IDs']})")
+        print(
+            f"Number of sessions         = {n_session:5d}\t\t({total_n} /"
+            + f" {self._params['jobs_per_session']})"
+        )
+        print(
+            f"Number of batches          = {n_batch:5d}\t\t({n_session} /"
+            + f" {self._max_replicas_per_batch})"
+        )
+        print(
+            f"Number of jobs per session = {n_jobs:5d}"
+            + f"\t\t({self._params['jobs_per_session']} / {n_batch}"
+        )
+        print(
+            f"Number of serial jobs      = {n_jobs_serial:5d}\t\t({n_jobs}"
+            + f" / {self._params['parallel_jobs']})"
+        )
+
+    def run(self, args=None):
+
+        obj = self
+
+        if args is None:
+            args = sys.argv
+        obj.set_params_from_command_line(args)
+        obj.update_params()
+        obj.check_params()
+
+        obj.set_options_base()
+
+        if self._params["stats"]:
+            obj.print_stats()
+            sys.exit(0)
+
+        # Initialize session manager
+        self._session = Session()
+
+        if obj._params["sync"] == "async":
+            print("Async mode")
+            job_ids = asyncio.run(obj.run_async())
+        else:
+            print("Sync mode")
+            job_ids = obj.run_no_async()
+        print(f"Submitting jobs: done")
+
+        obj.write_job_tile_IDs(job_ids)
diff --git a/src/shapepipe/canfar_run.py b/src/shapepipe/canfar_run.py
new file mode 100644
index 00000000..f7303983
--- /dev/null
+++ b/src/shapepipe/canfar_run.py
@@ -0,0 +1,36 @@
+import sys
+from shapepipe.canfar import canfar_submit, canfar_monitor, canfar_log_monitor
+
+def run_job(args=None):
+
+    # Create instance
+    obj = canfar_submit.Job()
+
+    obj.run(args=args)
+
+
+def run_log(args=None):
+
+    # Create instance
+    obj = canfar_monitor.Log()
+    try:
+        sys.exit(obj.run(args=args))
+    except ValueError as e:
+        print(e)
+        sys.exit(1)
+
+
+def run_monitor_log(args=None):
+
+    # Create instance
+    obj = canfar_log_monitor.LogMonitor()
+    obj.run(args=args)
+
+
+def main(argv=None):
+    """Main
+
+    Main program
+    """
+    # Scripts to call canfar classes are created by pyproject.toml
+    return 0
diff --git a/src/shapepipe/coverage_run.py b/src/shapepipe/coverage_run.py
new file mode 100644
index 00000000..3b30ce04
--- /dev/null
+++ b/src/shapepipe/coverage_run.py
@@ -0,0 +1,149 @@
+"""COVERAGE_RUN
+
+Call coverage processing classes.
+
+Author: Martin Kilbinger <martin.kilbinger@cea.fr>
+
+"""
+
+import sys
+
+from shapepipe.utilities.header_downloader import HeaderDownloader
+from shapepipe.utilities.field_corners_extractor import FieldCornersExtractor
+from shapepipe.utilities.coverage_map_builder import CoverageMapBuilder
+from shapepipe.utilities.coverage_plotter import CoveragePlotter
+
+
+def run_download_headers(args=None):
+    """Run Download Headers.
+
+    Download FITS headers from VOSpace for exposures in a CCD list.
+
+    Parameters
+    ----------
+    args : list, optional
+        command line arguments
+
+    Returns
+    -------
+    int
+        exit code
+
+    """
+    obj = HeaderDownloader()
+    return obj.run(args=args)
+
+
+def run_extract_corners(args=None):
+    """Run Extract Corners.
+
+    Extract field corner coordinates from FITS headers.
+
+    Parameters
+    ----------
+    args : list, optional
+        command line arguments
+
+    Returns
+    -------
+    int
+        exit code
+
+    """
+    obj = FieldCornersExtractor()
+    return obj.run(args=args)
+
+
+def run_build_coverage(args=None):
+    """Run Build Coverage.
+
+    Build HealSparse coverage maps from field corner coordinates.
+
+    Parameters
+    ----------
+    args : list, optional
+        command line arguments
+
+    Returns
+    -------
+    int
+        exit code
+
+    """
+    obj = CoverageMapBuilder()
+    return obj.run(args=args)
+
+
+def run_plot_coverage(args=None):
+    """Run Plot Coverage.
+
+    Plot HealSparse coverage map.
+
+    Parameters
+    ----------
+    args : list, optional
+        command line arguments
+
+    Returns
+    -------
+    int
+        exit code
+
+    """
+    obj = CoveragePlotter()
+    return obj.run(args=args)
+
+
+def run_pipeline(args=None):
+    """Run Pipeline.
+
+    Run the complete coverage map pipeline:
+    1. Download FITS headers
+    2. Extract field corners
+    3. Build coverage map
+
+    Parameters
+    ----------
+    args : list, optional
+        command line arguments
+
+    Returns
+    -------
+    int
+        exit code
+
+    """
+    print("Step 1: Downloading FITS headers")
+    ret = run_download_headers(args)
+    if ret != 0:
+        print("Failed to download headers")
+        return ret
+
+    print()
+    print("Step 2: Extracting field corners")
+    ret = run_extract_corners(args)
+    if ret != 0:
+        print("Failed to extract field corners")
+        return ret
+
+    print()
+    print("Step 3: Building coverage map")
+    ret = run_build_coverage(args)
+    if ret != 0:
+        print("Failed to build coverage map")
+        return ret
+
+    print()
+    print("Pipeline completed successfully")
+
+    return 0
+
+
+def main(argv=None):
+    """Main.
+
+    Main program.
+
+    """
+    # Scripts to call coverage classes are created by pyproject.toml
+    return 0
diff --git a/src/shapepipe/get_ccds_run.py b/src/shapepipe/get_ccds_run.py
new file mode 100644
index 00000000..fb18b0a7
--- /dev/null
+++ b/src/shapepipe/get_ccds_run.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+
+"""GET_CCDS_WITH_PSF
+
+Obtain list of CCDs (single-exposure single-HDU files) for which valid PSF information
+is available. This can serve to create a footprint coverage mask.
+
+Author: Martin Kilbinger <martin.kilbinger@cea.fr>
+
+"""
+
+import sys
+
+from shapepipe.utilities.ccd_psf_handler import CcdPsfHandler
+
+
+def run_ccd_psf_handler(args=None):
+    """Run CCD PSF Handler.
+
+    Create instance and run the CCD PSF handler.
+
+    Parameters
+    ----------
+    args : list, optional
+        command line arguments
+
+    Returns
+    -------
+    int
+        exit code
+
+    """
+    # Create instance
+
+    obj = CcdPsfHandler()
+
+    return obj.run(args=args)
+
+
+def main(argv=None):
+    """Main.
+    """
+    # A scripts to call the ccd psf class is created by pyproject.toml
+    return 0
diff --git a/src/shapepipe/modules/make_cat_package/make_cat.py b/src/shapepipe/modules/make_cat_package/make_cat.py
index 4130a226..b86ace30 100644
--- a/src/shapepipe/modules/make_cat_package/make_cat.py
+++ b/src/shapepipe/modules/make_cat_package/make_cat.py
@@ -187,7 +187,7 @@ def save_sm_data(
 
         sm = np.copy(sexcat_sm_file.get_data()["SPREAD_MODEL"])
         sm_err = np.copy(sexcat_sm_file.get_data()["SPREADERR_MODEL"])
-                         
+
         sexcat_sm_file.close()
 
     else:
@@ -341,12 +341,16 @@ def _save_ngmix_data(self, ngmix_cat_path, moments=False):
         ngmix_cat_file.open()
 
         ngmix_n_epoch = ngmix_cat_file.get_data()["n_epoch_model"]
-        if len(ngmix_n_epoch) / self._cat_size_target < 0.9:
+        # Low number of ngmix objects could be due to
+        # (1) shape measurement failures (e.g. missing PSF): ok, continue
+        # (2) previous processing errors, e.g. premature run of
+        # merge_sep_cats_runner: raise error
+        if len(ngmix_n_epoch) / self._cat_size_target < 0.0:
             ngmix_cat_file.close()
             err_msg = (
-                f"Merged shape catalogue {ngmix_cat_path} has very different size"       
-                + f" ({len(ngmix_n_epoch)}) than target size"           
-                + f" {self._cat_size_target})"                             
+                f"Merged shape catalogue {ngmix_cat_path} has very different"
+                + f" size ({len(ngmix_n_epoch)}) compared to target size"
+                + f" {self._cat_size_target})"
             )
             return err_msg
 
diff --git a/src/shapepipe/modules/mask_package/mask.py b/src/shapepipe/modules/mask_package/mask.py
index 36b553da..e749f557 100644
--- a/src/shapepipe/modules/mask_package/mask.py
+++ b/src/shapepipe/modules/mask_package/mask.py
@@ -14,6 +14,8 @@
 from astropy.coordinates import SkyCoord
 from astropy.io import fits
 
+from astroquery.vizier import Vizier
+
 from shapepipe.pipeline import file_io
 from shapepipe.pipeline.config import CustomParser
 from shapepipe.pipeline.execute import execute
@@ -119,6 +121,16 @@ def __init__(
         # Set error flag
         self._err = False
 
+        # Guide Star Catalogue parameters
+        #self._CDS_cat_ID = "I/271/out"   # GSC 2.2, does not have Fmag
+        self._CDS_cat_ID = "I/305/out"  # GSC 2.3
+
+        # Keys in CDS astroquery result
+        self._cds_keys = ["GSC2.3", "RAJ2000", "DEJ2000", "Fmag", "jmag", "Vmag", "Nmag", "Class"]
+
+        # Minimal scaling for halo and spike polygon templates
+        self._scaling_min = 0.1
+
     def _get_config(self):
         """Get Config.
 
@@ -460,7 +472,7 @@ def find_stars(self, position, radius):
         Returns
         -------
         dict
-          Star dicotionnary for GSC objects in the field
+          Star dictionnary for GSC objects in the field
 
         Raises
         ------
@@ -468,37 +480,28 @@ def find_stars(self, position, radius):
             For invalid configuration options
 
         """
-        if "CDSclient" in self._config["PATH"]:
-            ra = position[0]
-            dec = position[1]
-
-            if dec > 0.0:
-                sign = "+"
-            else:
-                sign = ""
-
-            cmd_line = (
-                f'{self._config["PATH"]["CDSclient"]} {ra} {sign}{dec} '
-                + f"-r {radius} -n 1000000"
-            )
-
-            self._CDS_stdout, self._CDS_stderr = execute(cmd_line)
-
-        elif "star_cat" in self._config["PATH"]:
+        if "star_cat" in self._config["PATH"]:
             f = open(self._config["PATH"]["star_cat"], "r")
             self._CDS_stdout = f.read()
-            self._CDS_stderr = ""
             f.close()
-
         else:
-            raise ValueError(
-                "Either [PROGRAM_PATH]:CDSCLIENT_PATH in the mask config file "
-                + " or a star catalogue as module input needs to be present"
-            )
-
-        if self._CDS_stderr != "":
-            self._err = True
-            return None
+            # For some exposures, Vizier returned empty star list if input position
+            # is not single precision 
+            p = np.array(position, dtype='single')
+
+            coord = SkyCoord(ra=p[0] * units.deg, dec=p[1] * units.deg, frame="icrs")
+
+            Vizier.ROW_LIMIT = -1  # no row limit
+            result = Vizier.query_region(coord, radius=radius*units.arcmin, catalog=self._CDS_cat_ID)
+            if len(result) == 0:
+                raise IndexError(
+                    "Vizier astroquer returned empty list at ",
+                    coord,
+                    radius
+                )
+            self._CDS_stdout = result[0]
+        
+        self._CDS_stderr = ""
 
         return self._make_star_cat(self._CDS_stdout)
 
@@ -803,12 +806,12 @@ def _get_image_radius(self, center=None):
     def _make_star_cat(self, CDSclient_output):
         """Make Star Catalogue.
 
-        Make a dictionary from findgsc2.2 output.
+        Create a dictionary from an astroquery request.
 
         Parameters
         ----------
         CDSclient_output : str
-            Output of findgsc2.2
+            Output astroquery
 
         Returns
         -------
@@ -819,37 +822,8 @@ def _make_star_cat(self, CDSclient_output):
         header = []
         stars = {}
 
-        # get header
-        for key in CDSclient_output.splitlines()[3].split(" "):
-            if (key != "") and (key != ";"):
-                # cleaning output
-                key = key.replace(" ", "")
-                for key_split in re.split(",|#|;", key):
-                    if key_split != "":
-                        key = key_split
-                header.append(key)
-                stars[key] = []
-
-        # get data
-        for elem in range(4, len(CDSclient_output.splitlines()) - 5):
-            idx = 0
-            for key in CDSclient_output.splitlines()[elem].split(" "):
-                if (key != "") and (key != ";"):
-                    # cleaning output
-                    key = key.replace(" ", "")
-                    for key_split in re.split(",|#|;", key):
-                        if key_split != "":
-                            key = key_split
-                    # handle missing data
-                    try:
-                        key = float(key)
-                        stars[header[idx]].append(key)
-                    except Exception:
-                        if key == "---":
-                            stars[header[idx]].append(None)
-                        else:
-                            stars[header[idx]].append(key)
-                    idx += 1
+        for key in self._cds_keys:
+            stars[key] = CDSclient_output[key]
 
         return stars
 
@@ -907,6 +881,7 @@ def _create_mask(
 
         stars_used = [[], [], []]
 
+        """
         star_zip = zip(
             stars["RA(J2000)"],
             stars["Dec(J2000)"],
@@ -916,11 +891,17 @@ def _create_mask(
             stars["Nmag"],
             stars["Clas"],
         )
+        """
+
+        # Get keys without object name
+        keys_to_use = self._cds_keys[1:]
+        star_zip = zip(*(stars[k] for k in keys_to_use))
 
         for ra, dec, Fmag, Jmag, Vmag, Nmag, clas in star_zip:
             mag = 0.0
             idx = 0.0
 
+            # Compute mean magnitude
             if Fmag is not None:
                 mag += Fmag
                 idx += 1.0
@@ -946,6 +927,8 @@ def _create_mask(
             ):
                 if (mag < mag_limit) and (clas == 0):
                     scaling = 1.0 - scale_factor * (mag - mag_pivot)
+                    if scaling < self._scaling_min:
+                        scaling = self._scaling_min
                     pos = self._wcs.all_world2pix(ra, dec, 0)
                     stars_used[0].append(pos[0])
                     stars_used[1].append(pos[1])
@@ -1014,6 +997,7 @@ def _exec_WW(self, types="HALO"):
                 self._WW_stdout, self._WW_stderr = execute(cmd)
                 self._rm_reg_stdout, self._rm_reg_stderr = execute(f"rm {reg}")
 
+
             else:
                 reg = self._config[types]["reg_file"]
 
@@ -1032,10 +1016,10 @@ def _exec_WW(self, types="HALO"):
 
                 self._WW_stdout, self._WW_stderr = execute(cmd)
 
+
         elif types == "ALL":
 
             default_reg = [
-                (f'{self._config["PATH"]["temp_dir"]}' + f"halo{self._img_number}.reg"),
                 (
                     f'{self._config["PATH"]["temp_dir"]}'
                     + f"halo{self._img_number}.reg"
@@ -1072,7 +1056,6 @@ def _exec_WW(self, types="HALO"):
                 self._rm_reg_stdout, self._rm_reg_stderr = execute(
                     f"rm {reg[0]} {reg[1]}"
                 )
-
             else:
                 reg = [
                     self._config["HALO"]["reg_file"],
diff --git a/src/shapepipe/modules/mask_runner.py b/src/shapepipe/modules/mask_runner.py
index 0cbec20e..8cfd9375 100644
--- a/src/shapepipe/modules/mask_runner.py
+++ b/src/shapepipe/modules/mask_runner.py
@@ -2,7 +2,7 @@
 
 Module runner for ``mask``.
 
-:Author: Axel Guinot
+:Author: Axel Guinot, Martin Kilbinger
 
 """
 
@@ -15,7 +15,7 @@
     file_pattern=["image", "weight", "flag"],
     file_ext=[".fits", ".fits", ".fits"],
     depends=["numpy", "astropy"],
-    executes=["ww", "findgsc2.2"],
+    executes=["ww"],
     numbering_scheme="_0",
 )
 def mask_runner(
diff --git a/src/shapepipe/modules/sextractor_package/sextractor_script.py b/src/shapepipe/modules/sextractor_package/sextractor_script.py
index 22d5106c..3238edf4 100644
--- a/src/shapepipe/modules/sextractor_package/sextractor_script.py
+++ b/src/shapepipe/modules/sextractor_package/sextractor_script.py
@@ -114,7 +114,9 @@ def make_post_process(cat_path, f_wcs_path, pos_params, ccd_size):
                 )
             w = f_wcs[exp][idx_j]["WCS"]
             pix_tmp = w.all_world2pix(
-                cat.get_data()[pos_params[0]], cat.get_data()[pos_params[1]], 0
+                cat.get_data()[pos_params[0]], cat.get_data()[pos_params[1]],
+                0,
+                quiet=True,
             )
             ind = (
                 (pix_tmp[0] > int(ccd_size[0]))
diff --git a/src/shapepipe/modules/uncompress_fits_package/uncompress_fits.py b/src/shapepipe/modules/uncompress_fits_package/uncompress_fits.py
index 55f3d4d7..85627d13 100644
--- a/src/shapepipe/modules/uncompress_fits_package/uncompress_fits.py
+++ b/src/shapepipe/modules/uncompress_fits_package/uncompress_fits.py
@@ -7,6 +7,7 @@
 """
 
 from astropy.io import fits
+import fitsio
 
 
 class Uncompress(object):
@@ -54,9 +55,29 @@ def process(self):
         for idx in range(len(self._input_file_list)):
 
             # Get data and header
-            data = fits.getdata(self._input_file_list[idx], self._data_hdu)
-            header = fits.getheader(self._input_file_list[idx], self._data_hdu)
-
+            # 2025: Bug while reading data (Invalid TFORM2: 1PE(625)
+            try:
+                data = fits.getdata(self._input_file_list[idx], self._data_hdu)
+                header = fits.getheader(self._input_file_list[idx], self._data_hdu)
+            except:
+                hdu = fitsio.FITS(self._input_file_list[idx])[self._data_hdu]
+                data = hdu.read()
+                header_fitsio = hdu.read_header()
+
+                # Transform header to astropy.io.fits
+                header = fits.Header()
+                for name in header_fitsio.keys():
+                    # Skip empty keys (padding in header)
+                    if name:
+                        value = header_fitsio[name]
+                        comment = header_fitsio.get_comment(name)
+
+                    # Optional: fix invalid TFORMn
+                    if name.startswith('TFORM') and isinstance(value, str) and 'PE' in value:
+                        value = '625E'
+
+                    header[name] = (value, comment)
+            
             # Create and write new FITS file with that HDU only
             hdu = fits.PrimaryHDU(data, header)
             hdul = fits.HDUList([hdu])
diff --git a/scripts/python/summary_run.py b/src/shapepipe/summary_run.py
similarity index 83%
rename from scripts/python/summary_run.py
rename to src/shapepipe/summary_run.py
index f24d6574..3057a5b9 100755
--- a/scripts/python/summary_run.py
+++ b/src/shapepipe/summary_run.py
@@ -5,19 +5,22 @@
 
 from shapepipe.utilities.summary import *
 
-from summary_params_pre_v2 import *
+from shapepipe.utilities.summary_params_pre_v2 import *
 
 
-def main(argv=None):
+def run(*args):
 
-    patch = argv[1]
+    patch = args[0]
 
-    if len(argv) == 3:
-        job_exclusive = argv[2]
+    if len(args) >= 2:
+        job_exclusive = args[1]
     else:
         job_exclusive = None
 
-    verbose = False
+    if len(args) == 3:
+        verbose = True
+    else:
+        verbose = False
 
     jobs, list_tile_IDs_dot = set_jobs_v2_pre_v2(patch, verbose)
 
@@ -73,5 +76,12 @@ def main(argv=None):
     return 0
 
 
+def main(args=None):
+
+    if args is None:
+        args = sys.argv[1:]  # skip script name
+    run(*args)
+
+
 if __name__ == "__main__":
-    sys.exit(main(sys.argv))
+    sys.exit(main())
diff --git a/src/shapepipe/utilities/ccd_psf_handler.py b/src/shapepipe/utilities/ccd_psf_handler.py
new file mode 100644
index 00000000..3ee29303
--- /dev/null
+++ b/src/shapepipe/utilities/ccd_psf_handler.py
@@ -0,0 +1,378 @@
+"""CCD_PSF_HANDLER
+
+Obtain list of CCDs (single-exposure single-HDU files) for which valid PSF
+information is available. This can serve to create a footprint coverage mask.
+
+Author: Mike Hudson, Martin Kilbinger <martin.kilbinger@cea.fr>
+
+"""
+
+import glob
+import os
+import re
+import sys
+
+import numpy as np
+
+from cs_util import args as cs_args
+from cs_util import logging
+
+from shapepipe.utilities import summary
+
+
+class CcdPsfHandler(object):
+    """CCD PSF Handler Class.
+
+    Handles extraction of CCDs with valid PSF information from shapepipe
+    patches.
+    """
+
+    def __init__(self):
+        """Initialize the handler."""
+        self.params_default()
+
+    def params_default(self):
+        """Set default parameters and command line options."""
+
+        self._params = {
+            "version_cat": "v1.6",
+            "n_CCD": 40,
+            "output": None,
+        }
+
+        self._short_options = {
+            "version_cat": "-V",
+            "n_CCD": "-n",
+            "output": "-o",
+        }
+
+        self._types = {
+            "n_CCD": "int",
+        }
+
+        self._help_strings = {
+            "version_cat": "catalogue major version, allowed are v1.3, v1.4, v1.5, v1.6; default is {}",
+
+            "n_CCD": "number of CCDs per exposure; default is {}",
+            "output": "output file path; default is ccds_with_psf_<version>.txt",
+        }
+
+    def set_params_from_command_line(self, args):
+        """Set Params From Command line.
+
+        Only use when calling using python from command line.
+        Does not work from ipython or jupyter.
+
+        Parameters
+        ----------
+        args : list
+            command line arguments
+
+        """
+        # Read command line options
+        options = cs_args.parse_options(
+            self._params,
+            self._short_options,
+            self._types,
+            self._help_strings,
+            args=args,
+        )
+        self._params = options
+
+        # Save calling command
+        logging.log_command(args)
+
+    def update_params(self):
+        """Update parameters.
+
+        Set derived parameters based on input parameters.
+        """
+        # Determine number of patches based on version
+        version = self._params["version_cat"]
+        if version in ("v1.3", "v1.4"):
+            n_patch = 7
+        elif version == "v1.5":
+            n_patch = 8
+        elif version == "v1.6":
+            n_patch = 9
+        else:
+            raise ValueError(f"Invalid version {version}")
+
+        self._params["n_patch"] = n_patch
+        self._params["patches"] = [f"P{x}" for x in np.arange(n_patch) + 1]
+
+        # Set output file if not specified
+        if self._params["output"] is None:
+            self._params["output"] = f"ccds_with_psf_{version}.txt"
+
+    def check_params(self):
+        """Check parameters for validity."""
+        # Add any parameter validation logic here
+        pass
+
+    def get_lines(self, fname):
+        """Get Lines.
+
+        Return list of lines read from a text file.
+
+        Parameters
+        ----------
+        fname : str
+            input file name
+
+        Returns
+        -------
+        list
+            IDs
+
+        """
+        IDs = []
+        with open(fname) as f:
+            lines = f.readlines()
+            for line in lines:
+                IDs.append(line.rstrip())
+
+        return IDs
+
+    def get_exp_shdu_missing(self, patches):
+        """Get Exp Shdu Missing.
+
+        Returns set of missing CCDs (single-exposure single-HDU IDs) from a list of patches.
+
+        Parameters
+        ----------
+        patches : list
+            input patches
+
+        Returns
+        -------
+        set
+           missing CCD IDs
+
+        """
+        exp_shdu_missing_all = set()
+
+        for patch in patches:
+
+            path_exp_shdu_missing = f"{patch}/summary/missing_job_32_all.txt"
+            exp_shdu_missing = self.get_lines(path_exp_shdu_missing)
+
+            print(
+                f"Patch {patch}: Found {len(exp_shdu_missing)} missing ccds",
+                end="; ",
+            )
+
+            exp_shdu_missing_all.update(exp_shdu_missing)
+
+            print(f"cumulative {len(exp_shdu_missing_all)} missing ccds")
+
+        print()
+
+        return exp_shdu_missing_all
+
+    def get_exp(self, patches):
+        """Get Exp.
+
+        Return set of exposures from a list of patches.
+
+        Parameters
+        ----------
+        patches : list
+            input patches
+
+        Returns
+        -------
+        set
+           exposure IDs
+
+        """
+        exp_all = set()
+
+        for patch in patches:
+
+            path_exp = f"{patch}/exp_numbers.txt"
+            exp = self.get_lines(path_exp)
+
+            print(f"Patch {patch}: Found {len(exp)} exposures", end="; ")
+
+            exp_all.update(exp)
+
+            print(f"cumulative {len(exp_all)} exposures")
+
+        print()
+
+        return exp_all
+
+    def get_ccds_with_psf(self, patches, n_CCD=40):
+        """Get CCDs With PSF.
+
+        Return set of CCDs from list of patches.
+
+        Parameters
+        ----------
+        patches : list
+            input patches
+        n_CCD : int
+            number of CCDs per exposure
+
+        Returns
+        -------
+        set
+           CCD IDs
+
+        """
+        # Get missing CCDs
+        print("=== get missing CCDs ===")
+        exp_shdu_missing_all = self.get_exp_shdu_missing(patches)
+
+        # Get all exposures used in tiles
+        print("=== get exposures ===")
+        exp_all = self.get_exp(patches)
+
+        # Turn exposures into exposure-single-HDU names (CCDs)
+        exp_shdu_all = summary.get_all_shdus(exp_all, n_CCD)
+
+        print(f"Found {len(exp_shdu_all)} CCDs")
+
+        return exp_shdu_all
+
+    def get_ccds_with_psf_method_v1_3(self, patches, n_CCD=40):
+        """Get CCDs With PSF Method v1.3.
+
+        Return set of CCDs with valid PSF by scanning run directories.
+        Finds star_selection files and checks corresponding stat files
+        for valid (non-nan) FWHM values.
+
+        Parameters
+        ----------
+        patches : list
+            input patches
+        n_CCD : int
+            number of CCDs per exposure (unused, kept for API consistency)
+
+        Returns
+        -------
+        set
+           CCD IDs with valid PSF
+
+        """
+        exp_shdu_all = set()
+
+        for patch in patches:
+            # Find all star_selection files
+            mask_pattern = (
+                f"{patch}/output/run_sp_exp_SxSePs*/"
+                f"setools_runner/output/mask/star_selection-*.fits"
+            )
+            mask_files = glob.glob(mask_pattern)
+
+            print(
+                f"Patch {patch}: Found {len(mask_files)} star_selection FITS"
+                + " files"
+            )
+
+            for mask_file in mask_files:
+                # Extract exp_shdu from filename
+                basename = os.path.basename(mask_file)
+                match = re.match(r"star_selection-(.+)\.fits", basename)
+                if not match:
+                    print(f"Warning: Non-matching file name {basename}")
+                    continue
+                exp_shdu = match.group(1)
+
+                # Get corresponding stat file path
+                # Replace mask dir with stat/stat dir in the path
+                stat_file = mask_file.replace(
+                    "/mask/star_selection-",
+                    "/stat/star_stat-",
+                ).replace(".fits", ".txt")
+
+                #print("MKDEBUG ", exp_shdu, stat_file)
+
+                # Check if stat file exists and has valid FWHM
+                if not os.path.exists(stat_file):
+                    print("MKDEBUG stat file ", stat_file, " does not exist")
+                    continue
+
+                # Read stat file and check for nan in "Mean star fwhm selected"
+                has_nan = False
+                with open(stat_file) as f:
+                    for line in f:
+                        if "Mean star fwhm selected" in line:
+                            if "nan" in line.lower():
+                                has_nan = True
+                            break
+
+                if not has_nan:
+                    #print("MKDEBUG append", exp_shdu)
+                    exp_shdu_all.add(exp_shdu)
+
+            print(f"After patch {patch}: {len(exp_shdu_all)} CCDs with valid PSF") 
+
+        print(f"Found {len(exp_shdu_all)} CCDs with valid PSF")
+
+        return exp_shdu_all
+
+    def save(self, IDs, path):
+        """Save.
+
+        Save list of IDs to text file.
+
+        Parameters
+        ----------
+        IDs : set
+            input IDs
+        path : str
+            output file name
+
+        """
+        with open(path, "w") as f_out:
+            for ID in IDs:
+                print(ID, file=f_out)
+
+    def run(self, args=None):
+        """Run.
+
+        Main execution method.
+
+        Parameters
+        ----------
+        args : list, optional
+            command line arguments
+
+        Returns
+        -------
+        int
+            exit code (0 for success)
+
+        """
+        if args is None:
+            args = sys.argv
+
+        # Set parameters from command line
+        self.set_params_from_command_line(args)
+        self.update_params()
+        self.check_params()
+
+        # Get parameters
+        patches = self._params["patches"]
+        version = self._params["version_cat"]
+        n_CCD = self._params["n_CCD"]
+        output = self._params["output"]
+
+        print(
+            f"=== get_ccds_with_psf for version {version}, patches {patches} ==="
+        )
+
+        if self._params["version_cat"] == "v1.3":
+            print("=== method for 1.3: exp with PSF star sample === ")
+            exp_shdu_all = self.get_ccds_with_psf_method_v1_3(patches, n_CCD)
+        else:
+            print("=== method for >= 1.4: exp_list - missing === ")
+            exp_shdu_all = self.get_ccds_with_psf(patches, n_CCD)
+
+        self.save(exp_shdu_all, output)
+
+        print(f"Results saved to {output}")
+
+        return 0
diff --git a/src/shapepipe/utilities/coverage_map_builder.py b/src/shapepipe/utilities/coverage_map_builder.py
new file mode 100644
index 00000000..152598e1
--- /dev/null
+++ b/src/shapepipe/utilities/coverage_map_builder.py
@@ -0,0 +1,325 @@
+"""COVERAGE_MAP_BUILDER
+
+Build HealSparse coverage maps from field corner coordinates.
+
+Author: Mike Hudson, Martin Kilbinger <martin.kilbinger@cea.fr>
+
+"""
+
+import sys
+from os.path import exists
+
+import numpy as np
+import healsparse as hsp
+import hpgeom as hpg
+
+from cs_util import args as cs_args
+from cs_util import logging
+
+from shapepipe.utilities.coverage_plotter import CoveragePlotter
+
+
+class CoverageMapBuilder(object):
+    """Coverage Map Builder Class.
+
+    Builds HealSparse coverage maps from field corner coordinates.
+    """
+
+    def __init__(self):
+        """Initialize the builder."""
+        self.params_default()
+
+    def params_default(self):
+        """Set default parameters and command line options."""
+
+        self._params = {
+            "input_file": "exp_ra_dec.txt",
+            "output_file": "coverage.hsp",
+            "nside_coverage": 32,
+            "nside": 2048,
+            "apply_median_filter": False,
+            "n_median_iterations": 2,
+            "create_boolean": False,
+            "boolean_threshold": 3,
+            "boolean_output": None,
+            "create_plot": False,
+            "plot_output": None,
+            "plot_region": None,
+        }
+
+        self._short_options = {
+            "input_file": "-i",
+            "output_file": "-o",
+            "nside_coverage": "-c",
+            "nside": "-n",
+            "apply_median_filter": "-m",
+            "n_median_iterations": "-N",
+            "create_boolean": "-b",
+            "boolean_threshold": "-t",
+            "boolean_output": "-B",
+            "create_plot": "-p",
+            "plot_output": "-P",
+            "plot_region": "-g",
+        }
+
+        self._types = {
+            "nside_coverage": "int",
+            "nside": "int",
+            "apply_median_filter": "bool",
+            "n_median_iterations": "int",
+            "create_boolean": "bool",
+            "boolean_threshold": "int",
+            "create_plot": "bool",
+        }
+
+        self._help_strings = {
+            "input_file": "input file with field corners; default is {}",
+            "output_file": "output HealSparse map file; default is {}",
+            "nside_coverage": "HealSparse coverage nside; default is {}",
+            "nside": "HealSparse map nside; default is {}",
+            "apply_median_filter": "apply median filter to smooth map; default is {}",
+            "n_median_iterations": "number of median filter iterations; default is {}",
+            "create_boolean": "create boolean coverage map; default is {}",
+            "boolean_threshold": "threshold value for boolean map; default is {}",
+            "boolean_output": "output file for boolean map; default is <output_file>_bool.hsp",
+            "create_plot": "create plot of coverage map; default is {}",
+            "plot_output": "output file for plot; default is <output_file>.png",
+            "plot_region": "predefined region for plot (NGC, SGC, fullsky); default is {}",
+        }
+
+    def set_params_from_command_line(self, args):
+        """Set Params From Command line.
+
+        Only use when calling using python from command line.
+        Does not work from ipython or jupyter.
+
+        Parameters
+        ----------
+        args : list
+            command line arguments
+
+        """
+        # Read command line options
+        options = cs_args.parse_options(
+            self._params,
+            self._short_options,
+            self._types,
+            self._help_strings,
+            args=args,
+        )
+        self._params = options
+
+        # Save calling command
+        logging.log_command(args)
+
+    def update_params(self):
+        """Update parameters.
+
+        Set derived parameters based on input parameters.
+        """
+        # Set boolean output filename if not specified
+        if self._params["boolean_output"] is None:
+            output_file = self._params["output_file"]
+            if output_file.endswith(".hsp"):
+                base = output_file[:-4]
+            else:
+                base = output_file
+            self._params["boolean_output"] = f"{base}_bool.hsp"
+
+        # Set plot output filename if not specified
+        if self._params["plot_output"] is None:
+            output_file = self._params["output_file"]
+            if output_file.endswith(".hsp"):
+                base = output_file[:-4]
+            else:
+                base = output_file
+            self._params["plot_output"] = f"{base}.png"
+
+    def check_params(self):
+        """Check parameters for validity."""
+        if not exists(self._params["input_file"]):
+            raise FileNotFoundError(
+                f"Input file not found: {self._params['input_file']}"
+            )
+
+        # Check nside values are powers of 2
+        nside_coverage = self._params["nside_coverage"]
+        nside = self._params["nside"]
+
+        if not (nside_coverage & (nside_coverage - 1) == 0):
+            raise ValueError(
+                f"nside_coverage must be a power of 2, got {nside_coverage}"
+            )
+
+        if not (nside & (nside - 1) == 0):
+            raise ValueError(f"nside must be a power of 2, got {nside}")
+
+    def median_filter(self, hsp_map):
+        """Median Filter.
+
+        Apply median filter to HealSparse map using neighbors.
+
+        Parameters
+        ----------
+        hsp_map : healsparse.HealSparseMap
+            input map
+
+        Returns
+        -------
+        healsparse.HealSparseMap
+            filtered map
+
+        """
+        nside = hsp_map.nside_sparse
+
+        new_hsp = hsp_map + 0
+        pixs = hsp_map.valid_pixels
+        n = hpg.neighbors(nside, pixs)
+        n = np.where(n >= 0, n, 0)
+        pix2 = pixs.reshape(len(pixs), 1)
+        n = np.hstack([n, pix2])
+        mn = hsp_map[n]
+        new = np.median(mn, axis=1).astype(np.uint16)
+        new_hsp[pixs] = new
+
+        return new_hsp
+
+    def run(self, args=None):
+        """Run.
+
+        Main execution method.
+
+        Parameters
+        ----------
+        args : list, optional
+            command line arguments
+
+        Returns
+        -------
+        int
+            exit code (0 for success)
+
+        """
+        if args is None:
+            args = sys.argv
+
+        # Set parameters from command line
+        self.set_params_from_command_line(args)
+        self.update_params()
+        self.check_params()
+
+        # Get parameters
+        input_file = self._params["input_file"]
+        output_file = self._params["output_file"]
+        nside_coverage = self._params["nside_coverage"]
+        nside = self._params["nside"]
+        apply_median_filter = self._params["apply_median_filter"]
+        n_median_iterations = self._params["n_median_iterations"]
+        create_boolean = self._params["create_boolean"]
+        boolean_threshold = self._params["boolean_threshold"]
+        boolean_output = self._params["boolean_output"]
+        create_plot = self._params["create_plot"]
+        plot_output = self._params["plot_output"]
+        plot_region = self._params["plot_region"]
+        verbose = self._params["verbose"]
+
+        if verbose:
+            print(f"Reading field corners from: {input_file}")
+
+        # Load field corner data
+        dtype = np.dtype(
+            [
+                ("expnum", int),
+                ("tlra", float),
+                ("trra", float),
+                ("brra", float),
+                ("blra", float),
+                ("tldec", float),
+                ("trdec", float),
+                ("brdec", float),
+                ("bldec", float),
+            ]
+        )
+        rows = np.loadtxt(input_file, dtype=dtype)
+
+        print(f"Loaded {len(rows)} exposure field corners")
+
+        if verbose:
+            print(
+                f"Creating HealSparse map (nside_coverage={nside_coverage}, nside={nside})"
+            )
+
+        # Create empty map
+        m = hsp.HealSparseMap.make_empty(nside_coverage, nside, np.uint16)
+
+        # Add polygons for each exposure
+        if verbose:
+            print("Adding polygons to map")
+
+        for i, r in enumerate(rows):
+            ra = [r["tlra"], r["trra"], r["brra"], r["blra"]]
+            dec = [r["tldec"], r["trdec"], r["brdec"], r["bldec"]]
+            poly = hsp.Polygon(ra=ra, dec=dec, value=1)
+            m += poly
+
+            if verbose and i % 1000 == 0:
+                print(f"{i:6d} / {len(rows):6d}")
+
+        print(f"Added {len(rows)} polygons to map")
+
+        # Apply median filter if requested
+        if apply_median_filter:
+            if verbose:
+                print(
+                    f"Applying median filter ({n_median_iterations} iterations)"
+                )
+
+            for i in range(n_median_iterations):
+                m = self.median_filter(m)
+                if verbose:
+                    print(f"  Iteration {i+1}/{n_median_iterations} complete")
+
+        # Write main coverage map
+        if verbose:
+            print(f"Writing coverage map to: {output_file}")
+
+        m.write(output_file, clobber=True)
+        print(f"Coverage map saved to {output_file}")
+
+        # Create and write boolean map if requested
+        if create_boolean:
+            if verbose:
+                print(
+                    f"Creating boolean map (threshold={boolean_threshold})"
+                )
+
+            c = hsp.HealSparseMap.make_empty(nside_coverage, nside, bool)
+            c[m.valid_pixels] = m[m.valid_pixels] >= boolean_threshold
+
+            if verbose:
+                print(f"Writing boolean map to: {boolean_output}")
+
+            c.write(boolean_output, clobber=True)
+            print(f"Boolean map saved to {boolean_output}")
+
+        # Create plot if requested
+        if create_plot:
+            if verbose:
+                print(f"Creating plot of coverage map")
+
+            try:
+                plotter = CoveragePlotter()
+                plotter.plot_coverage_map(
+                    m,
+                    plot_output,
+                    region=plot_region,
+                    vmax=boolean_threshold if create_boolean else 3,
+                    colorbar=True,
+                    colorbar_label="Coverage depth",
+                )
+                print(f"Plot saved to {plot_output}")
+            except ImportError as e:
+                print(f"Warning: Could not create plot: {e}")
+                print("Install sp_validation package for plotting support: pip install -e /path/to/sp_validation")
+
+        return 0
diff --git a/src/shapepipe/utilities/coverage_plotter.py b/src/shapepipe/utilities/coverage_plotter.py
new file mode 100644
index 00000000..14f84fc6
--- /dev/null
+++ b/src/shapepipe/utilities/coverage_plotter.py
@@ -0,0 +1,343 @@
+"""COVERAGE_PLOTTER
+
+Plot HealSparse coverage maps.
+
+Author: Mike Hudson, Martin Kilbinger <martin.kilbinger@cea.fr>
+
+"""
+
+import sys
+from os.path import exists
+
+import numpy as np
+import healsparse as hsp
+import matplotlib
+import matplotlib.pyplot as plt
+
+try:
+    from cs_util.plots import FootprintPlotter
+    HAS_FOOTPRINT_PLOTTER = True
+except ImportError:
+    HAS_FOOTPRINT_PLOTTER = False
+
+from cs_util import args as cs_args
+from cs_util import logging
+
+
+class CoveragePlotter(object):
+    """Coverage Plotter Class.
+
+    Plots HealSparse coverage maps.
+    """
+
+    def __init__(self):
+        """Initialize the plotter."""
+        self.params_default()
+
+    def params_default(self):
+        """Set default parameters and command line options."""
+
+        self._params = {
+            "input_file": None,
+            "output_file": "coverage_plot.png",
+            "region": None,
+            "ralo": 100.0,
+            "rahi": 270.0,
+            "declo": 28.0,
+            "dechi": 85.0,
+            "vmin": 0,
+            "vmax": 3,
+            "cmap": "rainbow",
+            "draw_milky_way": True,
+            "colorbar": True,
+            "colorbar_label": "Coverage depth",
+            "figsize_x": 12,
+            "figsize_y": 8,
+            "dpi": 200,
+        }
+
+        self._short_options = {
+            "input_file": "-i",
+            "output_file": "-o",
+            "region": "-g",
+            "ralo": "-R",
+            "rahi": "-r",
+            "declo": "-D",
+            "dechi": "-d",
+            "vmin": "-m",
+            "vmax": "-M",
+            "cmap": "-c",
+            "draw_milky_way": "-w",
+            "colorbar": "-C",
+            "colorbar_label": "-L",
+            "figsize_x": "-x",
+            "figsize_y": "-y",
+            "dpi": "-p",
+        }
+
+        self._types = {
+            "ralo": "float",
+            "rahi": "float",
+            "declo": "float",
+            "dechi": "float",
+            "vmin": "int",
+            "vmax": "int",
+            "draw_milky_way": "bool",
+            "colorbar": "bool",
+            "figsize_x": "float",
+            "figsize_y": "float",
+            "dpi": "int",
+        }
+
+        self._help_strings = {
+            "input_file": "input HealSparse map file; required",
+            "output_file": "output plot file (png, pdf, etc.); default is {}",
+            "region": "predefined region (NGC, SGC, fullsky); overrides RA/Dec limits if specified; default is {}",
+            "ralo": "minimum RA for plot extent; default is {}",
+            "rahi": "maximum RA for plot extent; default is {}",
+            "declo": "minimum Dec for plot extent; default is {}",
+            "dechi": "maximum Dec for plot extent; default is {}",
+            "vmin": "minimum value for colormap; default is {}",
+            "vmax": "maximum value for colormap; default is {}",
+            "cmap": "matplotlib colormap name; default is {}",
+            "draw_milky_way": "draw Milky Way outline; default is {}",
+            "colorbar": "add colorbar to plot; default is {}",
+            "colorbar_label": "colorbar label; default is {}",
+            "figsize_x": "figure width in inches; default is {}",
+            "figsize_y": "figure height in inches; default is {}",
+            "dpi": "figure DPI; default is {}",
+        }
+
+    def set_params_from_command_line(self, args):
+        """Set Params From Command line.
+
+        Only use when calling using python from command line.
+        Does not work from ipython or jupyter.
+
+        Parameters
+        ----------
+        args : list
+            command line arguments
+
+        """
+        # Read command line options
+        options = cs_args.parse_options(
+            self._params,
+            self._short_options,
+            self._types,
+            self._help_strings,
+            args=args,
+        )
+        self._params = options
+
+        # Save calling command
+        logging.log_command(args)
+
+    def update_params(self):
+        """Update parameters.
+
+        Set derived parameters based on input parameters.
+        """
+        pass
+
+    def check_params(self):
+        """Check parameters for validity."""
+        if self._params["input_file"] is None:
+            raise ValueError("Input file is required (use -i or --input_file)")
+
+        if not exists(self._params["input_file"]):
+            raise FileNotFoundError(
+                f"Input file not found: {self._params['input_file']}"
+            )
+
+        if not HAS_FOOTPRINT_PLOTTER:
+            raise ImportError(
+                "cs_util package with FootprintPlotter is required for plotting. "
+                "Install with: pip install -e /path/to/cs_util"
+            )
+
+        # Check region validity
+        region = self._params["region"]
+        if region is not None:
+            valid_regions = list(FootprintPlotter._regions.keys())
+            if region not in valid_regions:
+                raise ValueError(
+                    f"Invalid region '{region}'. Valid regions are: {', '.join(valid_regions)}"
+                )
+
+    def plot_coverage_map(
+        self,
+        hsp_map,
+        output_file,
+        region=None,
+        ralo=100.0,
+        rahi=270.0,
+        declo=28.0,
+        dechi=85.0,
+        vmin=0,
+        vmax=3,
+        title=None,
+        colorbar=True,
+        colorbar_label="Coverage depth",
+        figsize=(10, 10),
+        dpi=200,
+    ):
+        """Plot Coverage Map.
+
+        Create a plot of the HealSparse coverage map using FootprintPlotter.
+
+        Parameters
+        ----------
+        hsp_map : healsparse.HealSparseMap
+            coverage map to plot
+        output_file : str
+            output file path
+        region : str, optional
+            predefined region name (NGC, SGC, fullsky)
+        ralo : float
+            minimum RA for plot extent (used if region is None)
+        rahi : float
+            maximum RA for plot extent (used if region is None)
+        declo : float
+            minimum Dec for plot extent (used if region is None)
+        dechi : float
+            maximum Dec for plot extent (used if region is None)
+        vmin : int
+            minimum value for colormap
+        vmax : int
+            maximum value for colormap
+        title : str, optional
+            plot title
+        colorbar : bool
+            add colorbar to plot
+        colorbar_label : str
+            colorbar label
+        figsize : tuple
+            figure size (width, height) in inches
+        dpi : int
+            figure DPI
+
+        """
+        # Set up matplotlib parameters
+        font = {"size": 16}
+        matplotlib.rc("font", **font)
+        matplotlib.rcParams["savefig.dpi"] = dpi
+        matplotlib.rcParams["figure.dpi"] = dpi
+        matplotlib.rcParams["figure.figsize"] = figsize
+
+        # Create FootprintPlotter instance
+        plotter = FootprintPlotter(
+            nside_coverage=hsp_map.nside_coverage,
+            nside_map=hsp_map.nside_sparse
+        )
+
+        # Plot based on region or manual extent
+        if region is not None:
+            # Use predefined region
+            region_params = FootprintPlotter._regions[region]
+            plotter.plot_region(
+                hsp_map,
+                region_params,
+                outpath=output_file,
+                title=title,
+                colorbar=colorbar,
+                colorbar_label=colorbar_label
+            )
+        else:
+            # Use manual RA/Dec limits
+            # Calculate ra_0 for projection
+            if ralo < rahi:
+                ra_0 = (ralo + rahi) / 2.0
+            else:
+                ra_0 = 130.0
+
+            extend = [ralo, rahi, declo, dechi]
+            plotter.plot_area(
+                hsp_map,
+                ra_0=ra_0,
+                extend=extend,
+                vmax=vmax,
+                outpath=output_file,
+                title=title,
+                colorbar=colorbar,
+                colorbar_label=colorbar_label
+            )
+
+    def run(self, args=None):
+        """Run.
+
+        Main execution method.
+
+        Parameters
+        ----------
+        args : list, optional
+            command line arguments
+
+        Returns
+        -------
+        int
+            exit code (0 for success)
+
+        """
+        if args is None:
+            args = sys.argv
+
+        # Set parameters from command line
+        self.set_params_from_command_line(args)
+        self.update_params()
+        self.check_params()
+
+        # Get parameters
+        input_file = self._params["input_file"]
+        output_file = self._params["output_file"]
+        region = self._params["region"]
+        ralo = self._params["ralo"]
+        rahi = self._params["rahi"]
+        declo = self._params["declo"]
+        dechi = self._params["dechi"]
+        vmin = self._params["vmin"]
+        vmax = self._params["vmax"]
+        colorbar = self._params["colorbar"]
+        colorbar_label = self._params["colorbar_label"]
+        figsize_x = self._params["figsize_x"]
+        figsize_y = self._params["figsize_y"]
+        dpi = self._params["dpi"]
+        verbose = self._params["verbose"]
+
+        if verbose:
+            print(f"Reading coverage map from: {input_file}")
+
+        # Load HealSparse map
+        hsp_map = hsp.HealSparseMap.read(input_file)
+
+        print(f"Loaded map with nside={hsp_map.nside_sparse}")
+        print(
+            f"Valid pixels: {len(hsp_map.valid_pixels)} / {hsp_map.nside_sparse**2 * 12}"
+        )
+
+        if verbose:
+            if region:
+                print(f"Creating plot for region: {region}")
+            else:
+                print(f"Creating plot with extent: RA=[{ralo}, {rahi}], Dec=[{declo}, {dechi}]")
+
+        # Create plot
+        self.plot_coverage_map(
+            hsp_map,
+            output_file,
+            region=region,
+            ralo=ralo,
+            rahi=rahi,
+            declo=declo,
+            dechi=dechi,
+            vmin=vmin,
+            vmax=vmax,
+            colorbar=colorbar,
+            colorbar_label=colorbar_label,
+            figsize=(figsize_x, figsize_y),
+            dpi=dpi,
+        )
+
+        print(f"Plot saved to {output_file}")
+
+        return 0
diff --git a/src/shapepipe/utilities/field_corners_extractor.py b/src/shapepipe/utilities/field_corners_extractor.py
new file mode 100644
index 00000000..f0765a04
--- /dev/null
+++ b/src/shapepipe/utilities/field_corners_extractor.py
@@ -0,0 +1,405 @@
+"""FIELD_CORNERS_EXTRACTOR
+
+Extract field corner coordinates from FITS headers.
+
+Author: Mike Hudson, Martin Kilbinger <martin.kilbinger@cea.fr>
+
+"""
+
+import sys
+import os
+import glob
+import re
+from os.path import exists
+from multiprocessing import Pool, cpu_count
+
+import numpy as np
+from astropy import wcs
+from astropy.io.fits import Header
+
+from cs_util import args as cs_args
+from cs_util import logging
+
+
+class FieldCornersExtractor(object):
+    """Field Corners Extractor Class.
+
+    Extracts RA/Dec coordinates of field corners from FITS headers.
+    """
+
+    def __init__(self):
+        """Initialize the extractor."""
+        self.params_default()
+
+    def params_default(self):
+        """Set default parameters and command line options."""
+
+        self._params = {
+            "input_dir": "header",
+            "output_file": "exp_ra_dec.txt",
+            "resume": False,
+            "n_processes": 1,
+        }
+
+        self._short_options = {
+            "input_dir": "-i",
+            "output_file": "-o",
+            "resume": "-r",
+            "n_processes": "-n",
+        }
+
+        self._types = {
+            "resume": "bool",
+            "n_processes": "int",
+        }
+
+        self._help_strings = {
+            "input_dir": "input directory containing header files; default is {}",
+            "output_file": "output file for field corners; default is {}",
+            "resume": "resume from existing output file; default is {}",
+            "n_processes": f"number of parallel processes (1=serial, 0=auto={cpu_count()}); default is {{}}",
+        }
+
+    def set_params_from_command_line(self, args):
+        """Set Params From Command line.
+
+        Only use when calling using python from command line.
+        Does not work from ipython or jupyter.
+
+        Parameters
+        ----------
+        args : list
+            command line arguments
+
+        """
+        # Read command line options
+        options = cs_args.parse_options(
+            self._params,
+            self._short_options,
+            self._types,
+            self._help_strings,
+            args=args,
+        )
+        self._params = options
+
+        # Save calling command
+        logging.log_command(args)
+
+    def update_params(self):
+        """Update parameters.
+
+        Set derived parameters based on input parameters.
+        """
+        # Ensure input directory ends without trailing slash
+        if self._params["input_dir"].endswith("/"):
+            self._params["input_dir"] = self._params["input_dir"][:-1]
+
+    def check_params(self):
+        """Check parameters for validity."""
+        if not exists(self._params["input_dir"]):
+            raise FileNotFoundError(
+                f"Input directory not found: {self._params['input_dir']}"
+            )
+
+        # Set n_processes to cpu_count if 0
+        if self._params["n_processes"] == 0:
+            self._params["n_processes"] = cpu_count()
+
+        if self._params["n_processes"] < 0:
+            raise ValueError(
+                f"n_processes must be >= 0, got {self._params['n_processes']}"
+            )
+
+    @staticmethod
+    def process_single_header(path_and_verbose):
+        """Process Single Header.
+
+        Worker function to process a single header file.
+        Static method so it can be pickled for multiprocessing.
+
+        Parameters
+        ----------
+        path_and_verbose : tuple
+            (path, verbose) where path is header file path and verbose is bool
+
+        Returns
+        -------
+        tuple or None
+            (expnum, ra_list, dec_list) on success, None on failure
+
+        """
+        path, verbose = path_and_verbose
+
+        # Extract exposure number from filename
+        end = path.find(".txt")
+        expnum = int(path[end - 6 : end])
+
+        try:
+            # Parse header and extract WCS
+            with open(path, "r") as f:
+                string = f.read()
+                tokens = re.split(r"^(END\s+)", string, flags=re.MULTILINE)
+                n = len(tokens)
+                w = []
+                for i in range(2, n - 1, 2):
+                    h1 = Header.fromstring(tokens[i] + tokens[i + 1], sep="\n")
+                    w.append(wcs.WCS(h1))
+
+            # Calculate field corners
+            tl = w[0].pixel_to_world(2079, 0)
+            tr = w[8].pixel_to_world(32, 0)
+            br = w[35].pixel_to_world(2079, 0)
+            bl = w[27].pixel_to_world(32, 0)
+
+            ra = [tl.ra.deg, tr.ra.deg, br.ra.deg, bl.ra.deg]
+            dec = [tl.dec.deg, tr.dec.deg, br.dec.deg, bl.dec.deg]
+
+            return (expnum, ra, dec)
+
+        except Exception as e:
+            if verbose:
+                print(f"Failed to process {expnum}: {e}")
+            return None
+
+    def get_wcs_from_header(self, ftext):
+        """Get WCS From Header.
+
+        Parse header text file and extract WCS for all HDUs.
+
+        Parameters
+        ----------
+        ftext : str
+            path to header text file
+
+        Returns
+        -------
+        list or None
+            list of WCS objects, one per HDU, or None if parsing failed
+
+        """
+        try:
+            with open(ftext, "r") as f:
+                string = f.read()
+                tokens = re.split(r"^(END\s+)", string, flags=re.MULTILINE)
+                n = len(tokens)
+                h = []
+                w = []
+                for i in range(2, n - 1, 2):
+                    h1 = Header.fromstring(tokens[i] + tokens[i + 1], sep="\n")
+                    h.append(h1)
+                    w.append(wcs.WCS(h1))
+            return w
+        except Exception as e:
+            if self._params["verbose"]:
+                print(f"Problem opening {ftext}: {e}")
+            return None
+
+    def get_megacam_field(self, w):
+        """Get MegaCam Field.
+
+        Calculate RA/Dec of the 4 corners of a MegaCam field.
+
+        Parameters
+        ----------
+        w : list
+            list of WCS objects for all CCDs
+
+        Returns
+        -------
+        tuple
+            (ra_list, dec_list) where each is a list of 4 corner coordinates
+
+        """
+        # MegaCam field corners from specific chips and pixels
+        # Note: chips are flipped, so bottom right becomes top left, etc.
+        tl = w[0].pixel_to_world(2079, 0)  # top left
+        tr = w[8].pixel_to_world(32, 0)  # top right
+        br = w[35].pixel_to_world(2079, 0)  # bottom right
+        bl = w[27].pixel_to_world(32, 0)  # bottom left
+
+        return (
+            [tl.ra.deg, tr.ra.deg, br.ra.deg, bl.ra.deg],
+            [tl.dec.deg, tr.dec.deg, br.dec.deg, bl.dec.deg],
+        )
+
+    def get_done_exposures(self):
+        """Get Done Exposures.
+
+        Read exposures already processed from output file.
+
+        Returns
+        -------
+        set
+            set of exposure numbers already processed
+
+        """
+        output_file = self._params["output_file"]
+
+        if not exists(output_file):
+            return set()
+
+        try:
+            done = np.loadtxt(output_file, usecols=(0), dtype=int)
+            return set(done)
+        except Exception as e:
+            if self._params["verbose"]:
+                print(f"Could not read existing output file: {e}")
+            return set()
+
+    def run(self, args=None):
+        """Run.
+
+        Main execution method.
+
+        Parameters
+        ----------
+        args : list, optional
+            command line arguments
+
+        Returns
+        -------
+        int
+            exit code (0 for success)
+
+        """
+        if args is None:
+            args = sys.argv
+
+        # Set parameters from command line
+        self.set_params_from_command_line(args)
+        self.update_params()
+        self.check_params()
+
+        # Get parameters
+        input_dir = self._params["input_dir"]
+        output_file = self._params["output_file"]
+        resume = self._params["resume"]
+        verbose = self._params["verbose"]
+
+        # Find all header files
+        paths = glob.glob(f"{input_dir}/*.txt")
+        n = len(paths)
+
+        if n == 0:
+            print(f"No header files found in {input_dir}/")
+            return 1
+
+        print(f"{n} header files found")
+
+        # Get list of already processed exposures if resuming
+        done = set()
+        if resume:
+            done = self.get_done_exposures()
+            print(f"{len(done)} already done")
+
+        # Build todo list
+        todo = []
+        for p in paths:
+            end = p.find(".txt")
+            expnum = int(p[end - 6 : end])
+            if expnum not in done:
+                todo.append(p)
+
+        n_todo = len(todo)
+        print(f"{n_todo} to process")
+
+        if n_todo == 0:
+            print("Nothing to do")
+            return 0
+
+        # Get n_processes
+        n_processes = self._params["n_processes"]
+
+        # Process headers
+        if n_processes == 1:
+            # Serial processing
+            results = self._process_serial(todo, verbose)
+        else:
+            # Parallel processing
+            print(f"Using {n_processes} parallel processes")
+            results = self._process_parallel(todo, n_processes, verbose)
+
+        # Filter out failed results (None values)
+        results = [r for r in results if r is not None]
+
+        # Sort results by exposure number
+        results.sort(key=lambda x: x[0])
+
+        # Write results to file
+        mode = "a" if resume else "w"
+        with open(output_file, mode, buffering=1) as f:
+            for expnum, ra, dec in results:
+                f.write(f"{expnum:6d} ")
+                np.savetxt(f, ra, fmt="%9.5f", newline=" ")
+                np.savetxt(f, dec, fmt="%9.5f", newline=" ")
+                f.write("\n")
+
+        n_success = len(results)
+        n_failed = n_todo - n_success
+
+        print(f"Processed {n_success} exposures")
+        if n_failed > 0:
+            print(f"Failed to process {n_failed} exposures")
+
+        print(f"Results written to {output_file}")
+
+        return 0
+
+    def _process_serial(self, todo, verbose):
+        """Process Serial.
+
+        Process headers serially.
+
+        Parameters
+        ----------
+        todo : list
+            list of header file paths to process
+        verbose : bool
+            verbose output
+
+        Returns
+        -------
+        list
+            list of results (expnum, ra, dec) tuples
+
+        """
+        results = []
+        n_todo = len(todo)
+
+        for i, p in enumerate(todo):
+            result = self.process_single_header((p, verbose))
+            results.append(result)
+
+            if verbose and i % 100 == 0:
+                print(f"{i:6d} / {n_todo:6d}")
+
+        return results
+
+    def _process_parallel(self, todo, n_processes, verbose):
+        """Process Parallel.
+
+        Process headers in parallel using multiprocessing.
+
+        Parameters
+        ----------
+        todo : list
+            list of header file paths to process
+        n_processes : int
+            number of parallel processes
+        verbose : bool
+            verbose output
+
+        Returns
+        -------
+        list
+            list of results (expnum, ra, dec) tuples
+
+        """
+        # Prepare arguments for worker function
+        args = [(p, verbose) for p in todo]
+
+        # Create pool and process
+        with Pool(processes=n_processes) as pool:
+            results = pool.map(self.process_single_header, args)
+
+        return results
diff --git a/src/shapepipe/utilities/header_downloader.py b/src/shapepipe/utilities/header_downloader.py
new file mode 100644
index 00000000..e7cb70e6
--- /dev/null
+++ b/src/shapepipe/utilities/header_downloader.py
@@ -0,0 +1,247 @@
+"""HEADER_DOWNLOADER
+
+Download FITS headers from VOSpace for exposures listed in a CCD file.
+
+Author: Mike Hudson, Martin Kilbinger <martin.kilbinger@cea.fr>
+
+"""
+
+import sys
+import os
+from os.path import exists
+
+import numpy as np
+import vos
+
+from cs_util import args as cs_args
+from cs_util import logging
+
+
+class HeaderDownloader(object):
+    """Header Downloader Class.
+
+    Downloads FITS headers from VOSpace for exposures in a CCD list.
+    """
+
+    def __init__(self):
+        """Initialize the downloader."""
+        self.params_default()
+
+    def params_default(self):
+        """Set default parameters and command line options."""
+
+        self._params = {
+            "input_file": None,
+            "output_dir": "header",
+            "vospace_path": "vos:cfis/pitcairn",
+            "overwrite": False,
+        }
+
+        self._short_options = {
+            "input_file": "-i",
+            "output_dir": "-o",
+            "vospace_path": "-p",
+            "overwrite": "-O",
+        }
+
+        self._types = {
+            "overwrite": "bool",
+        }
+
+        self._help_strings = {
+            "input_file": "input CCD list file (txt or csv); required",
+            "output_dir": "output directory for headers; default is {}",
+            "vospace_path": "VOSpace base path; default is {}",
+            "overwrite": "overwrite existing header files; default is {}",
+        }
+
+    def set_params_from_command_line(self, args):
+        """Set Params From Command line.
+
+        Only use when calling using python from command line.
+        Does not work from ipython or jupyter.
+
+        Parameters
+        ----------
+        args : list
+            command line arguments
+
+        """
+        # Read command line options
+        options = cs_args.parse_options(
+            self._params,
+            self._short_options,
+            self._types,
+            self._help_strings,
+            args=args,
+        )
+        self._params = options
+
+        # Save calling command
+        logging.log_command(args)
+
+    def update_params(self):
+        """Update parameters.
+
+        Set derived parameters based on input parameters.
+        """
+        # Ensure output directory ends without trailing slash
+        if self._params["output_dir"].endswith("/"):
+            self._params["output_dir"] = self._params["output_dir"][:-1]
+
+    def check_params(self):
+        """Check parameters for validity."""
+        if self._params["input_file"] is None:
+            raise ValueError("Input file is required (use -i or --input_file)")
+
+        if not exists(self._params["input_file"]):
+            raise FileNotFoundError(
+                f"Input file not found: {self._params['input_file']}"
+            )
+
+        # Create output directory if it doesn't exist
+        if not exists(self._params["output_dir"]):
+            os.makedirs(self._params["output_dir"])
+            if self._params["verbose"]:
+                print(f"Created output directory: {self._params['output_dir']}")
+
+    def get_exposures(self, ccd_list_file):
+        """Get Exposures.
+
+        Extract unique exposure numbers from CCD list file.
+
+        Parameters
+        ----------
+        ccd_list_file : str
+            path to CCD list file (txt or csv)
+
+        Returns
+        -------
+        np.array
+            unique exposure numbers
+
+        """
+        exps = []
+
+        # Check if CSV format
+        if ccd_list_file.endswith(".csv"):
+            import astropy.table
+
+            t = astropy.table.Table.read(ccd_list_file)
+            expccd = t["CCD"].data
+            r = np.char.split(expccd, sep="-")
+
+            for i, r1 in enumerate(r):
+                exp = int(r1[0])
+                exps.append(exp)
+        else:
+            # Text format
+            f = np.loadtxt(ccd_list_file, dtype="str", encoding="ascii")
+            r = np.char.split(f, sep="-")
+
+            for i, r1 in enumerate(r):
+                exp = int(r1[0])
+                exps.append(exp)
+
+        exps = np.array(exps)
+        uniq = np.unique(exps)
+
+        return uniq
+
+    def get_fits_header(self, expnum, client):
+        """Get FITS Header.
+
+        Download FITS header from VOSpace.
+
+        Parameters
+        ----------
+        expnum : int
+            exposure number
+        client : vos.Client
+            VOSpace client
+
+        Returns
+        -------
+        bool
+            True if successful, False otherwise
+
+        """
+        vospace_path = self._params["vospace_path"]
+        output_dir = self._params["output_dir"]
+        overwrite = self._params["overwrite"]
+
+        source = f"{vospace_path}/{expnum:d}p.fits.fz"
+        dest = f"{output_dir}/{expnum:d}.txt"
+
+        if exists(dest) and not overwrite:
+            return True
+
+        try:
+            client.copy(source, dest, head=True)
+            return True
+        except Exception as e:
+            print(f"Could not copy {source}: {e}")
+            return False
+
+    def run(self, args=None):
+        """Run.
+
+        Main execution method.
+
+        Parameters
+        ----------
+        args : list, optional
+            command line arguments
+
+        Returns
+        -------
+        int
+            exit code (0 for success)
+
+        """
+        if args is None:
+            args = sys.argv
+
+        # Set parameters from command line
+        self.set_params_from_command_line(args)
+        self.update_params()
+        self.check_params()
+
+        # Get parameters
+        input_file = self._params["input_file"]
+        output_dir = self._params["output_dir"]
+        verbose = self._params["verbose"]
+
+        if verbose:
+            print(f"Reading CCD list from: {input_file}")
+
+        # Extract unique exposures
+        exps = self.get_exposures(input_file)
+
+        print(f"Found {len(exps)} unique exposures")
+
+        if verbose:
+            print(f"Downloading headers to: {output_dir}/")
+
+        # Initialize VOSpace client once
+        client = vos.Client()
+
+        # Download headers
+        n_success = 0
+        n_failed = 0
+
+        for i, exp in enumerate(exps):
+            success = self.get_fits_header(exp, client)
+            if success:
+                n_success += 1
+            else:
+                n_failed += 1
+
+            if verbose and i % 100 == 0:
+                print(f"{i:6d} / {len(exps):6d}")
+
+        print(f"Downloaded {n_success} headers")
+        if n_failed > 0:
+            print(f"Failed to download {n_failed} headers")
+
+        return 0
diff --git a/src/shapepipe/utilities/summary.py b/src/shapepipe/utilities/summary.py
index 46ad69bd..069354cb 100755
--- a/src/shapepipe/utilities/summary.py
+++ b/src/shapepipe/utilities/summary.py
@@ -211,6 +211,11 @@ def check_special_one(module, path):
                     code = 5
                     msg = "No object detected (weight might be 0 everywhere)"
                     return msg, code
+                m = re.search("astropy\.wcs\.wcs\.NoConvergence", line)
+                if m:
+                    code = 8
+                    msg = "WCS world2pix did not converge"
+                    return mgs, code
 
             if module == "ngmix_runner":
                 m = re.search("finished", line)
@@ -516,12 +521,13 @@ def output_missing(
         # Count image IDs in names that were found earlier
 
         # Get file name pattern
-        if module != "split_exp_runner" or self._bit != "2":
+        if module != "split_exp_runner" or (self._bit != 2 and self._bit != 4096):
             pattern = re.compile(r"(?:\d{3}-\d{3}|\d{7}-\d+|\d{7})")
         else:
             # split_exp_runner with sp_local=0: input is exp, output is shdu
             # (images) and exp (header); ignore hdu number.
             # If sp_local=1 set bit to != 2
+            # Update 11/2025: No longer working for P9 4096. Solution: set n_mult=3.
             pattern = re.compile(
                 r"(?:\d{3}-\d{3}|\d{7})"
             )
diff --git a/scripts/python/summary_params_pre_v2.py b/src/shapepipe/utilities/summary_params_pre_v2.py
similarity index 97%
rename from scripts/python/summary_params_pre_v2.py
rename to src/shapepipe/utilities/summary_params_pre_v2.py
index 10118039..e8f7bb8b 100644
--- a/scripts/python/summary_params_pre_v2.py
+++ b/src/shapepipe/utilities/summary_params_pre_v2.py
@@ -58,7 +58,7 @@ def set_jobs_v2_pre_v2(patch, verbose):
         verbose=verbose,
     )
 
-    if patch == "P8":
+    if patch in ("P8", "P9"):
         jobs["2"] = job_data(
             2,
             ["run_sp_Uz", "run_sp_exp_Sp_shdu"],
@@ -105,7 +105,7 @@ def set_jobs_v2_pre_v2(patch, verbose):
         verbose=verbose,
     )
 
-    if patch != "P8":
+    if patch not in ("P8", "P9"):
         jobs["8"] = job_data(
             8,
             ["run_sp_Ma_exp"],
@@ -197,7 +197,7 @@ def set_jobs_v2_pre_v2(patch, verbose):
         verbose=verbose,
     )
 
-    if patch in ("P2", "P5", "P8"):
+    if patch in ("P2", "P5", "P8", "P9"):
         n_sh = 1
     else:
         n_sh = 8
@@ -239,8 +239,8 @@ def set_jobs_v2_pre_v2(patch, verbose):
 
     jobs["512"] = job_data(
         "512",
-        "run_sp_Mc",
-        "make_cat_runner",
+        ["run_sp_Mc"],
+        ["make_cat_runner"],
         "tile_IDs",
         path_main=path_main,
         path_left="tile_runs",