feat!: add EnIF and EnIF-MDA analyses

.github/workflows/tests.yml

@@ -37,7 +37,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10", 3.11, 3.12]
+        python-version: ["3.12", "3.13", "3.14"]
 
     steps:
     - uses: actions/checkout@v4

README.md

@@ -13,6 +13,9 @@ at NORCE Norwegian Research Centre AS.
 
 ## Installation
 
+PET requires Python 3.12 through 3.14. The standard installation includes EnIF
+and EnIF-MDA with their dependencies.
+
 Before installing ensure you have python3 pre-requisites. On a Debian system run:
 
 ```
@@ -139,8 +142,9 @@ Some basic plotting functionality is provided [here](https://github.com/Python-E
 
 ## Tutorials
 
-- A PIPT tutorial is found [here](https://python-ensemble-toolbox.github.io/PET/tutorials/pipt/TinyBox/tutorial_pipt)
-- A POPT tutorial is found [here](https://python-ensemble-toolbox.github.io/PET/tutorials/popt/5Spot/tutorial_popt)
+- A PIPT tutorial is found [here](https://python-ensemble-toolbox.github.io/PET/tutorials/pipt/tutorial_pipt)
+- A POPT tutorial is found [here](https://python-ensemble-toolbox.github.io/PET/tutorials/popt/tutorial_popt)
+- [EnIF and EnIF-MDA](docs/tutorials/enif.md): installation, analysis settings and parameter graphs.
 
 ## Suggested readings:
 

docs/tutorials/README.md

@@ -4,8 +4,9 @@ Here are some tutorials.
 
 ## Running PIPT and POPT
 
-- [`tutorial_pipt.ipynb`](pipt/TinyBox/tutorial_pipt): Tutorial for running PIPT
-- [`tutorial_popt.ipynb`](popt/5Spot/tutorial_popt): Tutorial for running POPT
+- [`tutorial_pipt.ipynb`](pipt/tutorial_pipt): Tutorial for running PIPT
+- [`tutorial_pipt.ipynb`](popt/tutorial_popt): Tutorial for running POPT
+- [EnIF and EnIF-MDA](enif.md): Information-filter analyses and parameter graphs
 
 ## Data structures
 

docs/tutorials/enif.md

@@ -0,0 +1,118 @@
+# Ensemble information filter (EnIF)
+
+PET provides the original, single-update EnIF and an EnIF-MDA variant. Both
+use the sparse regression and precision estimation from ERT's
+[`_enif_update.py`](https://github.com/equinor/ert/blob/main/src/ert/analysis/_enif_update.py)
+through `graphite-maps`.
+
+## Installation
+
+Install PET from your checkout, including EnIF and its dependencies:
+
+```sh
+python -m pip install -e .
+```
+
+PET requires Python 3.12 through 3.14, matching its `graphite-maps` dependency.
+
+## Select the analysis
+
+Keep your existing ensemble, observation and simulator settings. For one EnIF
+update, set these entries in `dataassim`:
+
+```yaml
+daalg: [enif, enif]
+analysis: full
+```
+
+For EnIF-MDA, use:
+
+```yaml
+daalg: [enif, enif]
+analysis: mda
+mda:
+  tot_assim_steps: 3
+  inflation_param: [2, 4, 4]
+```
+
+The corresponding classes are `enif_full` and `enif_mda` in
+`pipt.update_schemes.enif`. PET's `pipt_init.init_da` loads them through the
+existing configuration interface.
+
+Both variants assimilate all selected `assimindex` entries together. Standard
+EnIF performs one update with inflation 1; it does not need `mda` settings.
+EnIF-MDA reruns the simulator and refits the regression and state precision
+after each update.
+
+MDA requires positive, finite inflation factors satisfying
+`sum(1 / alpha) = 1`. If you omit `inflation_param`, PET uses
+`tot_assim_steps` for each factor. A scalar factor repeats across the schedule.
+The schedule retains its original indexing on restart.
+
+## Parameter graphs
+
+EnIF estimates a separate prior precision block for each state in `idX`.
+By default:
+
+- A state with `grid` metadata in `prior_<state>` uses nearest-neighbour
+  connectivity. PET's prior parser converts `grid` to `nx`, `ny` and `nz`.
+- A state without grid metadata uses independent graph nodes.
+
+The regular-grid ordering matches PET's layered prior generator:
+`row = z * nx * ny + x * ny + y` (y varies fastest). For imported ensembles
+with a different ordering, reduced active-cell arrays or irregular geometry,
+provide a graph whose node numbers match the imported parameter rows.
+
+You can configure graphs and neighbourhood sizes under `enif`:
+
+```yaml
+enif:
+  parameter_graphs:
+    perm: perm_graph.npz
+  neighbourhood_expansion: 2
+  neighbor_propagation_order: 15
+```
+
+Write a graph file as a symmetric sparse adjacency array with
+`scipy.sparse.save_npz`. For example, for five parameters arranged in a chain:
+
+```python
+import networkx as nx
+from scipy import sparse
+
+graph = nx.path_graph(5)
+sparse.save_npz('perm_graph.npz', nx.to_scipy_sparse_array(graph, format='csc'))
+```
+
+Python configurations can also supply NetworkX graphs or SciPy sparse
+adjacency arrays directly in `parameter_graphs`. Use local node numbers
+`0` through `number_of_parameter_rows - 1` for each state. Graph weights do
+not affect the fit; EnIF uses connectivity.
+
+EnIF excludes rows containing non-finite values and rows with zero ensemble
+spread from estimation. It removes their graph nodes without connecting
+neighbours across the resulting gaps. The analysis gives those rows a zero
+increment, then applies PET's configured state limits.
+
+## Update and diagnostics
+
+EnIF uses PET's perturbed observations, random-number stream, state clipping,
+forecast loop and misfit reporting. It scales observation covariance by the
+current MDA factor once. It also estimates the unexplained response variance,
+as in ERT. For a correlated observation covariance, it whitens the observations,
+forecasts and perturbations before fitting the response map.
+
+The EnIF-specific update and helpers live in `pipt/update_schemes/enif.py`.
+The scheme inherits PET's `esmdaMixIn` lifecycle and returns an additive
+`step`, following the existing update-method interface. It uses the direct
+sparse solver, matching ERT's non-iterative transport setting.
+
+For `analysisdebug`, the scheme exposes `H`, `Prec_u`, `Prec_eps` and
+`Prec_posterior`. These matrices use standardized, retained state rows;
+`enif_active_rows` maps them back to the full state. With correlated observation
+errors, `H` and `Prec_eps` use whitened observation coordinates.
+
+This scheme requires at least two ensemble members and positive observation
+variances. It does not support PET's covariance localization, local analysis,
+multilevel ensembles or `emp_cov` sample input. Use parameter graphs to specify
+spatial dependence.

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "PET"
-version = "1.0"
+version = "0.2"
 description = "Python Ensemble Toolbox"
 authors = [
   { name = "Data assimilation and optimization group" }
@@ -15,15 +15,15 @@ maintainers = [
 ]
 license = { file = "LICENSE" }
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.12,<3.15"
 classifiers = [
     "Development Status :: 4 - Beta",
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
-    "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [
@@ -33,7 +33,10 @@ dependencies = [
     "h5py",
     "tqdm",
     "PyWavelets",
-    "geostat @ git+https://github.com/Python-Ensemble-Toolbox/Geostatistics@3f9f0c876815db140fae3404d322892190cb6728",
+    "psutil",
+    "geostat @ git+https://github.com/Python-Ensemble-Toolbox/Geostatistics@main",
+    "graphite-maps>=0.0.11,<0.1",
+    "pytest",
     "pandas",
     "p_tqdm",
     "tomli",