minor changes due to updates to pymc

.github/workflows/client-R-git-package.yml

@@ -25,7 +25,7 @@ jobs:
        -
         name: APT Dependencies
         run: |
-          apt update && DEBIAN_FRONTEND="noninteractive" apt install -y r-base libcurl4-gnutls-dev libxml2-dev libfontconfig1-dev libfreetype6 libharfbuzz-dev libfribidi-dev libtiff-dev libssl-dev
+          apt update && DEBIAN_FRONTEND="noninteractive" apt install -y r-base libcurl4-gnutls-dev libxml2-dev libfontconfig1-dev libfreetype6 libharfbuzz-dev libfribidi-dev libtiff-dev libssl-dev libgit2-dev pkg-config libuv1-dev
        -
         name: R Dependencies
         run: |

.github/workflows/client-python-pymc.yml

@@ -28,7 +28,7 @@ jobs:
           apt update && DEBIAN_FRONTEND="noninteractive" apt install -y python3-pip python3-venv
           python3 -m venv venv
           . venv/bin/activate
-          pip3 install numpy arviz pymc umbridge
+          pip3 install numpy arviz pymc umbridge matplotlib
        -
         name: Build and run
         run: |

.github/workflows/client-python-tinyDA.yml

@@ -25,10 +25,15 @@ jobs:
        -
         name: Dependencies
         run: |
-          apt update && DEBIAN_FRONTEND="noninteractive" apt install -y python3-pip python3-venv
-          python3 -m venv venv
+          # Pinned to Python 3.12: tinyDA pins arviz<0.19, whose use of the
+          # matplotlib.style.core internal API only works with old matplotlib
+          # versions
+          apt update && DEBIAN_FRONTEND="noninteractive" apt install -y software-properties-common
+          add-apt-repository -y ppa:deadsnakes/ppa
+          apt update && DEBIAN_FRONTEND="noninteractive" apt install -y python3.12 python3.12-venv
+          python3.12 -m venv venv
           . venv/bin/activate
-          pip3 install papermill umbridge tinyDA ipykernel ray
+          pip3 install papermill umbridge tinyDA ipykernel ray "matplotlib<3.9"
        -
         name: Build and run
         run: |

clients/python/pymc-client.py

@@ -34,7 +34,9 @@
     with pm.Model() as model:
         # UM-Bridge models with a single 1D output implementing a PDF
         # may be used as a PyMC density that in turn may be sampled
-        posterior = pm.DensityDist('posterior',logp=op,shape=input_dim)
+        def umbridge_logp(value):
+            return op(value)
+        posterior = pm.CustomDist('posterior', logp=umbridge_logp, shape=input_dim)
 
         map_estimate = pm.find_MAP()
         print(f"MAP estimate of posterior is {map_estimate['posterior']}")