Ensemble: Rename function to disctribution.

src/Model/Ensembles/Distribution.py

@@ -0,0 +1,232 @@
+# Distribution.py -- Pamhyr
+# Copyright (C) 2026  INRAE
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+# -*- coding: utf-8 -*-
+
+import struct
+import logging
+import itertools
+import numpy as np
+
+from tools import logger_exception
+
+from Model.Except import NotImplementedMethodeError
+from Model.Tools.PamhyrDB import SQLSubModel
+from Model.Scenario import Scenario
+
+logger = logging.getLogger()
+
+
+class Distribution(SQLSubModel):
+    _sub_classes = []
+
+    def __init__(self, id: int = -1,
+                 name: str = "", script: str = "",
+                 status=None):
+        super(Distribution, self).__init__(
+            id=id, status=status,
+            owner_scenario=0
+        )
+
+        self._name = name
+        self._type = "generic"
+        self._script = script
+        self._labels = []
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def labels(self):
+        return self._labels
+
+    @classmethod
+    def _db_create(cls, execute, ext=""):
+        execute(f"""
+          CREATE TABLE ensemble_distribution{ext} (
+            {cls.create_db_add_pamhyr_id()},
+            deleted BOOLEAN NOT NULL DEFAULT FALSE,
+            name TEXT NOT NULL,
+            script TEXT NOT NULL,
+            PRIMARY KEY(pamhyr_id)
+          )
+        """)
+
+        if ext == "_tmp":
+            return True
+
+        return cls._create_submodel(execute)
+
+    @classmethod
+    def _db_update(cls, execute, version, data=None):
+        major, minor, release = version.strip().split(".")
+        if major == "0" and int(minor) < 2:
+            cls._db_create(execute)
+        elif major == "0" and int(minor) == 2:
+            if int(release) < 8:
+                cls._db_create(execute)
+
+        return cls._update_submodel(execute, version, data)
+
+    @classmethod
+    def _db_load(cls, execute, data=None):
+        new = []
+        scenario = data["scenario"]
+        loaded = data['loaded_pid']
+
+        table = execute(
+            "SELECT pamhyr_id, deleted, name, script " +
+            "FROM ensemble_distribution " +
+            f"WHERE pamhyr_id NOT IN ({', '.join(map(str, loaded))})"
+        )
+
+        for row in table:
+            it = iter(row)
+
+            id = next(it)
+            deleted = (next(it) == 1)
+            name = next(it)
+            script = next(it)
+
+            new_distribution = cls(
+                id, name=name, script=script,
+                status=data["status"]
+            )
+            if deleted:
+                new_distribution.set_as_deleted()
+
+            loaded.add(id)
+            new.append(new_distribution)
+
+        return new
+
+    def _db_save(self, execute, data=None):
+        # Only save custom distribution
+        if self._type != "custom":
+            return True
+
+        if not self.must_be_saved():
+            return True
+
+        execute(
+            "INSERT INTO " +
+            "ensemble_distribution(pamhyr_id, deleted, " +
+            "  name, script) " +
+            "VALUES (?, ?, ?, ?)",
+            self.pamhyr_id, self.is_deleted(),
+            self._name, self._script
+        )
+
+        return True
+
+    def _data_traversal(self,
+                        predicate=lambda obj, data: True,
+                        modifier=lambda obj, data: None,
+                        data={}):
+        if predicate(self, data):
+            modifier(self, data)
+
+    def random(self, r):
+        raise NotImplementedMethodeError(cls, cls._load)
+
+
+###########################
+# DISTRIBUTION IMPLEMENTATION #
+###########################
+
+class Uniform(Distribution):
+    _sub_classes = []
+
+    def __init__(self, id: int = -1,
+                 name: str = "uniform", script: str = "",
+                 status=None):
+        super(Uniform, self).__init__(
+            id=id, status=status,
+            name=name, script=script
+        )
+
+        self._labels = ["low", "high"]
+
+    def random(self, params, nb):
+        lst = params.copy()
+        lst.append(nb)
+
+        return np.random.uniform(*lst)
+
+
+class Normal(Distribution):
+    _sub_classes = []
+
+    def __init__(self, id: int = -1,
+                 name: str = "normal", script: str = "",
+                 status=None):
+        super(Normal, self).__init__(
+            id=id, status=status,
+            name=name, script=script
+        )
+
+        self._labels = ["loc", "scale"]
+
+    def random(self, params, nb):
+        lst = params.copy()
+        lst.append(nb)
+
+        return np.random.normal(*lst)
+
+
+class Poisson(Distribution):
+    _sub_classes = []
+
+    def __init__(self, id: int = -1,
+                 name: str = "poisson", script: str = "",
+                 status=None):
+        super(Poisson, self).__init__(
+            id=id, status=status,
+            name=name, script=script
+        )
+
+        self._labels = ["lam"]
+
+    def random(self, params, nb):
+        lst = params.copy()
+        lst.append(nb)
+
+        return np.random.poisson(*lst)
+
+
+class Custom(Distribution):
+    _sub_classes = []
+
+    def __init__(self, id: int = -1,
+                 name: str = "", script: str = "",
+                 status=None):
+        super(Custom, self).__init__(
+            id=id, status=status,
+            name=name, script=script
+        )
+
+        self._labels = ["custom"]
+        self._type = "custom"
+
+    def random(self, params, nb):
+        try:
+            # Run script to (re)define the sample distribution
+            f = eval(self._script)
+            return f(params, nb)
+        except Exception as e:
+            logger_exception(e)
+            return None         # TODO: Raise helpful exception