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--- soft:ciao [2024/12/01 23:45] – asad
+++ soft:ciao [2025/07/01 03:38] (current) – asad
@@ Line 1: / Line 1: @@
 ====== CIAO ======
-===== Quick start =====
-  - [[https://cxc.cfa.harvard.edu/ciao/ahelp/blanksky_image.html|blanksky_image]] bkgfile='_background_clean.evt' outroot='_blank' imgfile='_0.5-7_thresh.img' tmpdir=./ clobber=yes
-  - [[https://cxc.cfa.harvard.edu/ciao/ahelp/merge_obs.html|merge_obs]]
 ===== Install on Ubuntu 24.04 LTS using conda =====
-After installing [[https://docs.anaconda.com/anaconda/install/linux/|anaconda3 in Ubuntu]] 18.04 LTS, follow https://cxc.cfa.harvard.edu/ciao/download/conda.html.
+<code bash [enable_line_numbers="true"]>
+conda create -n ciao -c https://cxc.cfa.harvard.edu/conda/ciao -c conda-forge ciao pyciao sherpa ds9 ciao-contrib caldb marx
-Here are the steps:
+conda activate ciao
+pip uninstall pillow
-. Run the following code in a terminal to install ciao and caldb and some associated software in a conda environment named "ciao-4.14".
+pip install --no-cache-dir pillow
+pip install astropy tqdm
-<code>conda create -n ciao-4.14 -c https://cxc.cfa.harvard.edu/conda/ciao -c conda-forge ciao sherpa ds9 ciao-contrib caldb marx</code>
+git clone https://github.com/bcalden/ClusterPyXT
+cd ~/ClusterPyXT
-. Activate the new conda environment: ''conda activate ciao-4.12''
+python clusterpyxt.py --continue
+</code>
-. Then install ''astropy'', ''jupyter'', ''ipykernel'' inside the ciao environment as:
-''conda install -n ciao-4.12 jupyter ipykernel astropy''
-. Install git on Ubuntu: ''sudo apt-get install git''
-. Download ClusterPyXT: ''git clone https://github.com/bcalden/ClusterPyXT''
-. Go inside ClusterPyXT: ''cd ~/ClusterPyXT''
-. Initialize the system: run ''python clusterpyxt.py'' and it will ask two things: (1) CIAO installation directory (''~/anaconda3/envs/ciao-4.12''), (2) data output directory.
-. Initialize a cluster: run ''python clusterpyxt.py'' again, and answer the questions; type y/n if asked. Give the values of nH (Hydrogen column density), z, metal abundance (use 0.3 for solar abundance), and S/N (signal-to-noise ratio, use 5).
-. Download and process the cluster data: <code>python clusterpyxt.py --continue</code>.
+===== Brightness map =====
+<code bash>
+blanksky_image bkgfile='_background_clean.evt' outroot='_blank' imgfile='_0.5-7_thresh.img' tmpdir=./ clobber=yes
+merge_obs
+</code>