Update notebooks in prep for PyCBC PR #5388

tutorial/inference_4_bbh_example/IntroToPyCBCInference.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Introduction to PyCBC Inference 2: Analyzing a gravitational wave\n",
+    "# Introduction to PyCBC Inference: Analyzing a gravitational wave\n",
     "### Collin Capano and Alex Nitz"
    ]
   },
@@ -31,7 +31,7 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "!{sys.executable} -m pip install pycbc ligo-common emcee==2.2.1 --no-cache-dir\n",
+    "!{sys.executable} -m pip install pycbc ligo-common \"emcee>3.0\" ptemcee --no-cache-dir\n",
     "\n",
     "# This is needed to access the executables on sciserver. On a personal machine this should be ignore.\n",
     "path = %env PATH\n",
@@ -57,7 +57,7 @@
     "\n",
     "To analyze a gravitational wave, we need to provide a prior, a model, and data settings in our configuration file. These settings can all be put into a single file, or split up into multiple files, all of which are provided via the `--config-file` option. In this example, we split the configuration up into 3 files:\n",
     "  * [gw150914_like.ini](gw150914_like.ini): specifies the model we will use, and the parameters that will be varied and their priors. This is the configuration file you would use to analyze signals like GW150914.\n",
-    "  * [emcee_pt-gw150914_like.ini](emcee_pt-gw150914_like.ini): specifies sampler settings. In this case, we are using the `emcee_pt` sampler.\n",
+    "  * [ptemcee-gw150914_like.ini](ptemcee-gw150914_like.ini): specifies sampler settings. In this case, we are using the `ptemcee` sampler.\n",
     "  * [data.ini](data.ini): specifies the settings for loading the gravitational-wave data.\n",
     "\n",
     "In the following sections, we look at the various sections of thes configuration files in detail."
@@ -145,28 +145,28 @@
    "source": [
     "## The sampler\n",
     "\n",
-    "The sampler settings are in [emcee_pt-gw150914_like.ini](emcee_pt-gw150914_like.ini). Here we are using the `emcee_pt` sampler. This is a parallel tempered sampler, so it requires a number of temperatures to be set. Note a few other differences from the analytic example here:\n",
+    "The sampler settings are in [ptemcee-gw150914_like.ini](ptemcee-gw150914_like.ini). Here we are using the `ptemcee` sampler. This is a parallel tempered sampler, so it requires a number of temperatures to be set. Note a few other differences from the analytic example here:\n",
     "  * The `burn-in-test` is set to `nacl & max_posterior`.\n",
     "  * Instead of an `niterations` option, we have `effective-nsamples = 1000`.\n",
     "  * A checkpoint interval is set: `checkpoint-interval = 2000`\n",
     "  * There is a `max-samples-per-chain` option.\n",
     "\n",
-    "These are all options specific to MCMC samplers like `emcee_pt`. Details:\n",
+    "These are all options specific to MCMC samplers like `ptemcee`. Details:\n",
     "\n",
     "#### The burn in option\n",
     "Multiple burn in tests may be combined using standard boolean operators like `&` and `|`. In this example, we will consider the sampler to be burned in when it has passed the `nacl` test *and* the `max_posterior` test. This means:\n",
     "\n",
     " * `nacl`: The second half of the chain must be longer than 5 times the ACL. If so, the samlper is considered burned-in at the halfway point.\n",
     " * `max_posterior`: All of the walkers must find a point that has a log posterior greater than `maxP - ndim/2`, where `maxP` is the maximum posterior value found over all the walkers and `ndim` is the number of variable parameters. The first iteration for which all the walkers pass this test is the burn in iteration.\n",
     " \n",
-    "By doing `&`, we take the larger iteration of these two tests. This combination of tests has worked well for `emcee_pt`.\n",
+    "By doing `&`, we take the larger iteration of these two tests. This combination of tests has worked well for `ptemcee`.\n",
     "\n",
     "#### Checkpointing\n",
     "When a `checkpoint-interval` is set, `pycbc_inference` will dump the results to a checkpoint file after every `checkpoint-interval` iterations. The checkpoint file has the same name as the output, but with `.checkpoint` added on to it. \n",
     "\n",
     "While ``pycbc_inference`` is running it will create a checkpoint file which\n",
     "is named ``{output-file}.checkpoint``, where ``{output-file}`` was the name\n",
-    "of the file you specified with the ``--output-file`` command. If a `checkpoint-interval` is set, `pycbc_inference` will checkpoint after the given number of iterations. For `emcee_pt`, this means that it will dump the current samples to this file; when finished, the file is\n",
+    "of the file you specified with the ``--output-file`` command. If a `checkpoint-interval` is set, `pycbc_inference` will checkpoint after the given number of iterations. For `ptemcee`, this means that it will dump the current samples to this file; when finished, the file is\n",
     "renamed to ``{output-file}``.\n",
     "\n",
     "A ``{output-file}.bkup`` is also created, which is a copy of the checkpoint file. This is kept in case the checkpoint file gets corrupted during writing. The ``.bkup`` file is deleted at the end of the run, unless ``--save-backup`` is turned on.\n",
@@ -390,7 +390,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.13.14"
   }
  },
  "nbformat": 4,

tutorial/inference_4_bbh_example/emcee_pt-gw150914_like.ini → tutorial/inference_4_bbh_example/ptemcee-gw150914_like.ini

@@ -1,5 +1,5 @@
 [sampler]
-name = emcee_pt
+name = ptemcee
 ; for GW problems, its usually best to use 200 walkers and 20 temps;
 ; here, we've reduced that just for a test
 nwalkers = 100

tutorial/inference_4_bbh_example/run.sh

@@ -3,7 +3,7 @@
 # configuration files
 PRIOR_CONFIG=gw150914_like.ini
 DATA_CONFIG=data.ini
-SAMPLER_CONFIG=emcee_pt-gw150914_like.ini
+SAMPLER_CONFIG=ptemcee-gw150914_like.ini
 
 OUTPUT_PATH=bbh_results.hdf