diff --git a/.exercises/README.md b/.exercises/README.md
new file mode 100644
index 0000000..bf08c07
--- /dev/null
+++ b/.exercises/README.md
@@ -0,0 +1,6 @@
+# .exercises
+
+In this directory you will find scripts that will exercise the Sharrow library.
+The initial exercises are `mtc` and `sandag`, which include bash scripts that
+will reproduce the respective example model's unit tests that are otherwise run
+using GitHub Actions. This allows the user to easily test those examples locally.
diff --git a/.exercises/mtc/run_mtc.sh b/.exercises/mtc/run_mtc.sh
new file mode 100644
index 0000000..12d58ec
--- /dev/null
+++ b/.exercises/mtc/run_mtc.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+
+set -euxo pipefail
+
+# This script runs the MTC example model with sharrow, mirroring the GitHub Actions workflow.
+
+cd $(dirname "$0")
+
+for repo in "driftlesslabs/activitysim" "ActivitySim/activitysim-prototype-mtc"; do
+ dir=$(basename "$repo")
+ if [ ! -d "$dir" ] || [ -z "$(ls -A "$dir" 2>/dev/null)" ]; then
+ gh repo clone "$repo" -- --depth 1
+ else
+ git -C "$dir" pull --ff-only || git -C "$dir" pull
+ fi
+done
+
+uv venv
+source .venv/bin/activate
+uv pip install -e ../.. # install sharrow in editable mode
+uv pip install ./activitysim
+uv pip install pytest nbmake
+
+cd activitysim-prototype-mtc
+python -m pytest ./test
diff --git a/.exercises/sandag/run_sandag.sh b/.exercises/sandag/run_sandag.sh
new file mode 100644
index 0000000..b0a636f
--- /dev/null
+++ b/.exercises/sandag/run_sandag.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+
+set -euxo pipefail
+
+# This script runs the MTC example model with sharrow, mirroring the GitHub Actions workflow.
+
+cd $(dirname "$0")
+
+for repo in "driftlesslabs/activitysim" "ActivitySim/sandag-abm3-example"; do
+ dir=$(basename "$repo")
+ if [ ! -d "$dir" ] || [ -z "$(ls -A "$dir" 2>/dev/null)" ]; then
+ gh repo clone "$repo" -- --depth 1
+ else
+ git -C "$dir" pull --ff-only || git -C "$dir" pull
+ fi
+done
+
+uv venv
+source .venv/bin/activate
+uv pip install -e ../.. # install sharrow in editable mode
+uv pip install ./activitysim
+uv pip install pytest nbmake
+
+cd sandag-abm3-example
+python -m pytest ./test
diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml
index a75b213..763e7e8 100644
--- a/.github/workflows/run-tests.yml
+++ b/.github/workflows/run-tests.yml
@@ -7,8 +7,6 @@ on:
- 'v[0-9]+.[0-9]+**'
pull_request:
branches: [ main, develop ]
- tags:
- - 'v[0-9]+.[0-9]+**'
workflow_dispatch:
jobs:
@@ -21,16 +19,12 @@ jobs:
shell: bash -l {0}
steps:
- uses: actions/checkout@v4
- - uses: actions/setup-python@v5
+ - name: Code Quality Check with Ruff
+ # code quality check, stop the build for any errors
+ uses: astral-sh/ruff-action@v3
with:
- python-version: '3.11'
- - name: Install Ruff
- run: |
- python -m pip install ruff
- - name: Lint with Ruff
- run: |
- # code quality check, stop the build for any errors
- ruff check . --show-fixes --exit-non-zero-on-fix
+ version: "latest"
+ args: "check . --show-fixes --exit-non-zero-on-fix"
test-minimal:
needs: fmt
@@ -41,32 +35,23 @@ jobs:
shell: bash -l {0}
steps:
- uses: actions/checkout@v4
- - uses: actions/setup-python@v5
+ - uses: astral-sh/setup-uv@v7
with:
+ version: "0.9.6"
+ enable-cache: true
+ cache-dependency-glob: "uv.lock"
python-version: '3.11'
- - name: Install pytest
- run: |
- python -m pip install pytest pytest-cov pytest-regressions pytest-xdist nbmake
- - name: Install sharrow
- run: |
- python -m pip install .
- name: Initial simple tests
# tests that sharrow can be imported and that categorical tests can be run
run: |
- python -m pytest sharrow/tests/test_categorical.py
- - name: Install openmatrix
- run: |
- python -m pip install openmatrix
+ uv run pytest sharrow/tests/test_categorical.py
- name: Dataset tests
# tests that the datasets can be read and that the tests can be run
run: |
- python -m pytest sharrow/tests/test_datasets.py
- - name: Install zarr and dask-diagnostics
- run: |
- python -m pip install zarr "dask[diagnostics]"
+ uv run pytest sharrow/tests/test_datasets.py
- name: More complete test with pytest
run: |
- python -m pytest -v --disable-warnings sharrow/tests
+ uv run pytest -v --disable-warnings sharrow/tests
test:
needs: fmt
@@ -74,39 +59,37 @@ jobs:
runs-on: ${{ matrix.os }}
strategy:
matrix:
- os: ["ubuntu-latest", "macos-latest", "windows-latest"]
- python-version: ["3.10", "3.11", "3.12"]
+ os: ["ubuntu-latest", "windows-latest"]
+ python-version: ["3.10", "3.11", "3.12", "3.13"]
+ fail-fast: false
defaults:
run:
shell: bash -l {0}
steps:
- uses: actions/checkout@v4
- - name: Install Python and Dependencies
- uses: conda-incubator/setup-miniconda@v3
+ - uses: astral-sh/setup-uv@v7
with:
- miniforge-version: latest
- environment-file: envs/testing.yml
+ version: "0.9.6"
python-version: ${{ matrix.python-version }}
- activate-environment: testing-env
- auto-activate-base: false
- auto-update-conda: false
- - name: Install sharrow
- run: |
- python -m pip install .
- - name: Conda checkup
+ - name: File contents
run: |
- conda info -a
- conda list
- - name: Lint with Ruff
+ cat sharrow/example_data.py
+ - name: UV sync
run: |
- # code quality check
- # stop the build if there are Python syntax errors or undefined names
- ruff check . --select=E9,F63,F7,F82 --no-fix
- # stop the build for any other configured Ruff linting errors
- ruff check . --show-fixes --exit-non-zero-on-fix
+ uv self version
+ uv cache clean
+ uv sync --locked
+ - name: Syntax Check with Ruff
+ uses: astral-sh/ruff-action@v3
+ with:
+ args: "check . --select=E9,F63,F7,F82 --no-fix"
+ - name: Code Quality Check with Ruff
+ uses: astral-sh/ruff-action@v3
+ with:
+ args: "check . --show-fixes --exit-non-zero-on-fix"
- name: Test with pytest
run: |
- python -m pytest
+ uv run --locked pytest
deploy-docs:
needs: test
@@ -165,6 +148,7 @@ jobs:
with:
user: __token__
password: ${{ secrets.PYPI_API_TOKEN }}
+
activitysim-examples:
# test that updates to sharrow will not break the activitysim canonical examples
needs: fmt
@@ -177,17 +161,18 @@ jobs:
- region: ActivitySim 1-Zone Example (MTC)
region-org: ActivitySim
region-repo: activitysim-prototype-mtc
- region-branch: pandas2
+ region-branch: extended
- region: ActivitySim 2-Zone Example (SANDAG)
region-org: ActivitySim
region-repo: sandag-abm3-example
- region-branch: pandas2
+ region-branch: main
fail-fast: false
defaults:
run:
shell: bash -l {0}
name: ${{ matrix.region }}
runs-on: ubuntu-latest
+ timeout-minutes: 720 # Sets the timeout to 12 hours
steps:
- name: Checkout Sharrow
uses: actions/checkout@v4
@@ -201,45 +186,37 @@ jobs:
ref: 'main'
path: 'activitysim'
- - name: Setup Miniforge
- uses: conda-incubator/setup-miniconda@v3
+ - name: Setup UV
+ uses: astral-sh/setup-uv@v7
with:
- miniforge-version: latest
- activate-environment: asim-test
+ version: "0.9.6"
+ enable-cache: true
+ cache-dependency-glob: "uv.lock"
python-version: ${{ env.python-version }}
- name: Set cache date for year and month
run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV
- - uses: actions/cache@v4
- with:
- path: |
- ${{ env.CONDA }}/envs
- ~/.cache/ActivitySim
- key: ${{ env.label }}-conda-${{ hashFiles('activitysim/conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
- id: cache
-
- - name: Update environment
+ - name: Create Virtual Env
run: |
- conda env update -n asim-test -f activitysim/conda-environments/github-actions-tests.yml
- if: steps.cache.outputs.cache-hit != 'true'
-
- - name: Install sharrow
- # installing from source
- run: |
- python -m pip install ./sharrow
-
- - name: Install activitysim
- # installing without dependencies is faster, we trust that all needed dependencies
- # are in the conda environment defined above. Also, this avoids pip getting
- # confused and reinstalling tables (pytables).
- run: |
- python -m pip install ./activitysim --no-deps
-
- - name: Conda checkup
+ uv venv
+ source .venv/bin/activate
+ uv pip install "black==22.12.0" "coveralls==3.3.1" \
+ "cytoolz==0.12.2" "dask==2023.11.*" "isort==5.12.0" \
+ "multimethod<2.0" "nbmake==1.4.6" "numba==0.57.*" \
+ "numpy==1.24.*" "openmatrix==0.3.5.0" "orca==1.8" \
+ "pandera>=0.15,<0.18.1" "pandas==2.2.*" "platformdirs==3.2.*" \
+ "psutil==5.9.*" "pyarrow==11.*" "pydantic==2.6.*" "pypyr==5.8.*" \
+ "tables>=3.9" "pytest==7.2.*" "pytest-cov" "pytest-regressions" \
+ "pyyaml==6.*" "requests==2.28.*" "ruff" "scikit-learn==1.2.*" \
+ "sharrow>=2.9.1" "simwrapper>1.7" "sparse" "xarray==2025.01.*" \
+ "zarr>=2,<3" "zstandard" \
+ ./sharrow ./activitysim
+
+ - name: UV checkup
run: |
- conda info -a
- conda list
+ source .venv/bin/activate
+ uv pip list
- name: Checkout Example
uses: actions/checkout@v4
@@ -250,5 +227,6 @@ jobs:
- name: Test ${{ matrix.region }}
run: |
- cd ${{ matrix.region-repo }}/test
- python -m pytest .
+ source .venv/bin/activate
+ cd ${{ matrix.region-repo }}
+ python -m pytest ./test
diff --git a/docs/walkthrough/one-dim.ipynb b/docs/walkthrough/one-dim.ipynb
index 30c4417..86c4b39 100644
--- a/docs/walkthrough/one-dim.ipynb
+++ b/docs/walkthrough/one-dim.ipynb
@@ -1450,7 +1450,7 @@
"metadata": {},
"outputs": [],
"source": [
- "wide_logsums = wide_flow.logit_draws(b, logsums=1, compile_watch=\"simple\")[-1]"
+ "wide_logsums = wide_flow.logit_draws(b, logsums=1, compile_watch=True)[-1]"
]
},
{
@@ -1460,7 +1460,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%time wide_logsums = wide_flow.logit_draws(b, logsums=1, compile_watch=\"simple\")[-1]\n",
+ "%time wide_logsums = wide_flow.logit_draws(b, logsums=1, compile_watch=True)[-1]\n",
"wide_logsums"
]
},
diff --git a/pyproject.toml b/pyproject.toml
index 97f4094..2d570e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,10 +40,10 @@ packages = ["sharrow", "sharrow.utils"]
include-package-data = true
[tool.setuptools.package-data]
-sharrow = ["*"]
+sharrow = ["*", "example_data/*"]
[tool.setuptools_scm]
-fallback_version = "1999"
+fallback_version = "1999.0"
write_to = "sharrow/_version.py"
[tool.ruff]
@@ -51,6 +51,7 @@ fix = true
line-length = 88
target-version = "py39"
extend-include = ["*.ipynb"]
+required-version = ">=0.11,<0.15"
[tool.ruff.lint]
select = [
@@ -84,3 +85,15 @@ testpaths = [
"sharrow/tests",
"docs",
]
+
+[dependency-groups]
+dev = [
+ "ipykernel>=6.31.0",
+ "nbmake>=1.5.5",
+ "openmatrix>=0.3.5.0",
+ "pytest>=8.4.2",
+ "pytest-regressions>=2.8.3",
+ "scipy>=1.13.1",
+ "sparse>=0.15.5",
+ "zarr>=2.18.2",
+]
diff --git a/sharrow/_nested_logit_tree/__init__.py b/sharrow/_nested_logit_tree/__init__.py
new file mode 100644
index 0000000..bd26a35
--- /dev/null
+++ b/sharrow/_nested_logit_tree/__init__.py
@@ -0,0 +1,1318 @@
+from __future__ import annotations
+
+import heapq
+import warnings
+from collections import OrderedDict
+from typing import TYPE_CHECKING, Literal
+
+import networkx as nx
+import numpy as np
+
+from ._lazy import lazy
+
+if TYPE_CHECKING:
+ import pandas as pd
+
+
+def add_split_key(d, k, v):
+ if isinstance(k, (tuple, list)):
+ if len(k) == 0:
+ raise KeyError(f"empty {type(k)}")
+ elif len(k) == 1:
+ d[k[0]] = v
+ else:
+ if k[0] not in d:
+ d[k[0]] = {}
+ add_split_key(d[k[0]], k[1:], v)
+ else:
+ d[k] = v
+
+
+def split_keys(d):
+ out = {}
+ for k, v in d.items():
+ add_split_key(out, k, v)
+ return out
+
+
+class NestingTree(nx.DiGraph):
+ node_dict_factory = OrderedDict
+ adjlist_dict_factory = OrderedDict
+
+ def copy(self, as_view=False):
+ # copy operation needs to also copy the root_id and suggested_elemental_order
+ result = super().copy(as_view=as_view)
+ result._root_id = self._root_id
+ result._suggested_elemental_order = self._suggested_elemental_order
+ return result
+
+ def __get__(self, instance, owner):
+ # self : SubkeyStore
+ # instance : instance of parent class that has `self` as a member, or None
+ # owner : class of `instance`
+ if instance is None:
+ pass # print("GRR: no instance")
+ return self
+ newself = getattr(instance, self.private_name, None)
+ if newself is None:
+ pass # print(f"GRR No Current: {instance=} {owner=}")
+ try:
+ instance.initialize_graph()
+ except ValueError:
+ pass
+ newself = getattr(instance, self.private_name, None)
+ if newself is not None:
+ newself._instance = instance
+ pass # print(f"GRR: get {instance=} {newself=}")
+ return newself
+
+ def __set__(self, instance, value):
+ # self : NestingTree object
+ # instance : instance of parent class that has `self` as a member
+ # value : the new value that is trying to be assigned
+ assert isinstance(value, NestingTree) or value is None
+ if value is None:
+ self.__delete__(instance)
+ return
+ t = value.copy()
+ t._instance = instance
+ setattr(instance, self.private_name, t)
+ try:
+ t._instance.mangle()
+ except AttributeError:
+ pass # print(f"GRR: {err}")
+ else:
+ pass # print(f"GRR Mangle: {instance}")
+
+ def __delete__(self, instance):
+ setattr(instance, self.private_name, None)
+ try:
+ instance.mangle()
+ except AttributeError:
+ pass # print(f"GRR: {err}")
+ else:
+ pass # print(f"GRR Mangle: {instance}")
+
+ def __set_name__(self, owner, name):
+ self.name = name
+ self.private_name = "_private_" + name
+
+ def touch(self):
+ try:
+ self._instance.mangle()
+ except AttributeError:
+ pass # print("GRR: mangle failure")
+ else:
+ pass # print("GRR: mangle ok")
+
+ def __init__(self, *arg, root_id=0, suggested_elemental_order=(), **kwarg):
+ if len(arg) and isinstance(arg[0], NestingTree):
+ super().__init__(*arg, **kwarg)
+ self._root_id = arg[0]._root_id
+ if suggested_elemental_order != ():
+ self._suggested_elemental_order = suggested_elemental_order
+ else:
+ self._suggested_elemental_order = arg[0]._suggested_elemental_order
+ else:
+ super().__init__(*arg, **kwarg)
+ self._root_id = root_id
+ self._suggested_elemental_order = suggested_elemental_order
+ if self._root_id not in self.nodes:
+ self.add_node(root_id, name="_root_", root=True)
+ self._clear_caches()
+
+ def __eq__(self, other):
+ return (
+ self._adj == other._adj
+ and self._node == other._node
+ and self._root_id == other._root_id
+ and self._suggested_elemental_order == other._suggested_elemental_order
+ )
+
+ def suggest_elemental_order(self, order):
+ self._suggested_elemental_order = tuple(j for j in order if j in self.nodes)
+
+ @property
+ def root_id(self):
+ """Int : The code for the root node."""
+ return self._root_id
+
+ @root_id.setter
+ def root_id(self, x):
+ top_nests = list(self.successors(self._root_id))
+ top_attrs = [self.edges[self._root_id, t] for t in top_nests]
+ if self._root_id in self.nodes:
+ self.remove_node(self._root_id)
+ self._root_id = x
+ if self._root_id not in self.nodes:
+ self.add_node(self._root_id, name="_root_", root=True)
+ for t, a in zip(top_nests, top_attrs):
+ self.add_edge(self._root_id, t, **a, _clear_caches=False)
+ self._clear_caches()
+
+ def _clear_caches(self):
+ NestingTree.topological_sorted.invalidate(self, "topological_sorted")
+ NestingTree.topological_sorted_no_elementals.invalidate(
+ self, "topological_sorted_no_elementals"
+ )
+ NestingTree.standard_sort.invalidate(self, "standard_sort")
+ NestingTree.standard_sort.invalidate(self, "standard_slot_map")
+ NestingTree.elementals.invalidate(self, "elementals")
+ NestingTree.standard_competitive_edge_list.invalidate(
+ self, "standard_competitive_edge_list"
+ )
+ NestingTree.standard_competitive_edge_list_2.invalidate(
+ self, "standard_competitive_edge_list_2"
+ )
+ self._predecessor_slots = {}
+ self._successor_slots = {}
+ self.touch()
+
+ def add_edge(self, u, v, implied=False, _clear_caches=True, **kwarg):
+ """
+ Add an edge between u and v.
+
+ The nodes u and v will be automatically added if they are
+ not already in the graph.
+
+ Edge attributes can be specified with keywords.
+
+ Parameters
+ ----------
+ u, v : int
+ Nodes should be integer codes. The upstream node `u` is
+ a nest or the root node. Downsteam node `v` can be
+ a nest or elemental alternative.
+ implied : bool, default False
+ Implied edges are for connection of otherwise unconnected
+ nests to the root node.
+ _clear_caches : bool, default True
+ kwarg : keyword arguments, optional
+ Edge data (or labels or objects) can be assigned using
+ keyword arguments.
+ """
+ if not implied:
+ drops = []
+ for u_, v_, imp_ in self.in_edges(nbunch=[v], data="implied"):
+ if imp_:
+ drops.append([u_, v_])
+ for d in drops:
+ super().remove_edge(*d)
+ if _clear_caches:
+ self._clear_caches()
+ return super().add_edge(int(u), int(v), implied=implied, **kwarg)
+
+ def _remove_edge_no_implied(self, u, v, *arg, **kwarg):
+ result = super().remove_edge(u, v)
+ self._clear_caches()
+ return result
+
+ def remove_edge(self, u, v, *arg, **kwarg):
+ """
+ Remove the edge between u and v.
+
+ Parameters
+ ----------
+ u, v : int
+ Remove the edge between nodes u and v.
+
+ Raises
+ ------
+ NetworkXError
+ If there is not an edge between u and v.
+ """
+ result = super().remove_edge(u, v)
+ if self.in_degree(v) == 0 and v != self._root_id:
+ self.add_edge(self._root_id, v, implied=True, _clear_caches=False)
+ self._clear_caches()
+ return result
+
+ def add_node(
+ self,
+ code,
+ *,
+ children=(),
+ parent=None,
+ parents=None,
+ phi_parameters=None,
+ **kwarg,
+ ):
+ """
+ Add a single node `code` and update node attributes.
+
+ Parameters
+ ----------
+ code : int
+ Although the generic networkx.DiGraph allows a node
+ to be any hashable Python object except None, Larch
+ assumes that node codes are integers.
+ children : Collection
+ A collection of other node codes that are the children
+ of this new node. Links will be created from this node
+ to each child.
+ parent : int, optional
+ The parent of this new node. If not given, the root
+ node is assumed to be the parent of this node, and an
+ implied link is created. This implied link is removed
+ if the node is later made the child of some other node.
+ If the parent is set explicitly, the link is *not*
+ removed later.
+ parents : Collection, optional
+ Set multiple parent up-stream nodes.
+ phi_parameters : Mapping
+ Set phi parameters on graph links connecting to this
+ node, used in network GEV models. The keys of this mapping
+ indicate the node at the other end of the link, and the
+ values are parameter names.
+ kwarg : other keyword arguments, optional
+ Set or change node attributes using key=value.
+ """
+ if parents is not None and parent is not None:
+ raise TypeError("cannot give both parent and parents arguments")
+ super().add_node(code, **kwarg)
+ for child in children:
+ self.add_edge(code, child, _clear_caches=False)
+ if parent is not None:
+ self.add_edge(parent, code, _clear_caches=False)
+ elif parents is not None:
+ for p in parents:
+ self.add_edge(p, code, _clear_caches=False)
+ else:
+ if self.in_degree(code) == 0 and code != self._root_id:
+ self.add_edge(self._root_id, code, implied=True, _clear_caches=False)
+ if phi_parameters is not None:
+ for k, parametername in phi_parameters.items():
+ if (code, k) in self.edges:
+ self.edges[code, k]["parameter"] = str(parametername)
+ elif (k, code) in self.edges:
+ self.edges[k, code]["parameter"] = str(parametername)
+ else:
+ raise ValueError(
+ f"connected node {k} from phi_parameters not found"
+ )
+ self._clear_caches()
+
+ def new_node(self, *, code=None, **kwarg):
+ """
+ Add a new nesting node to this NestingTree.
+
+ A new unique code is automatically created and returned by
+ this method for creating new nests.
+
+ All arguments must be given as keyword parameters.
+
+ Parameters
+ ----------
+ parameter : str
+ The name of the parameter to associate with this nest.
+ children : Collection[int], optional
+ The code numbers for the children of this nest. These can be
+ elemental alternatives or other nests. If not given, no children
+ will be defined initially, but they can be added later.
+ parent : int, optional
+ The code number for the parent of this nest. If not given,
+ the parent is implied as the root node, unless and until set
+ to some other node.
+ name : str, optional
+ A human-readable name to associate with this nest.
+ code : int, optional
+ Use this code for the new nest. If this code already exists,
+ a ValueError is raised.
+
+ Returns
+ -------
+ int
+ The new code for this nest.
+
+ Raises
+ ------
+ ValueError
+ If a new code is given but it already exists in this tree.
+ """
+ if code is None:
+ proposed_code = len(self)
+ while proposed_code in self:
+ proposed_code += 1
+ else:
+ if code in self:
+ raise ValueError(f"code {code} already exists in this tree")
+ proposed_code = code
+ self.add_node(proposed_code, **kwarg)
+ return proposed_code
+
+ def add_nodes(self, codes, *arg, parent=None, **kwarg):
+ for code in codes:
+ self.add_node(code, *arg, parent=parent, **kwarg)
+
+ def remove_node(self, n):
+ """
+ Remove node n.
+
+ Removes the node n, reconnecting all outedges to the head node
+ of all inedges. Attempting to remove a non-existent node will
+ raise an exception.
+
+ Parameters
+ ----------
+ n : int
+ A node in the graph
+ """
+ replace_edges = {k: self.edges[k].copy() for k in self.edges(n)}
+ replace_heads = [k for k, _ in self.in_edges(n)]
+ super().remove_node(n)
+ for k, attrs in replace_edges.items():
+ for h in replace_heads:
+ super().add_edge(h, k[1], **attrs)
+ self._clear_caches()
+
+ @lazy
+ def topological_sorted(self):
+ return list(reverse_lexicographical_topological_sort(self))
+
+ @lazy
+ def topological_sorted_no_elementals(self):
+ try:
+ result = self.topological_sorted.copy()
+ except nx.NetworkXUnfeasible:
+ from networkx.algorithms.cycles import find_cycle
+
+ try:
+ cycle = find_cycle(self)
+ except Exception:
+ pass
+ else:
+ print("Found graph cycle:")
+ print(list(cycle))
+ raise
+
+ # collect zero out-degree (elemental) codes in a set
+ # to remove them in a batch, which is much faster than
+ # removing them from the list one at a time.
+ to_remove = set()
+ for code, out_degree in self.out_degree:
+ if not out_degree:
+ to_remove.add(code)
+ return [i for i in result if i not in to_remove]
+
+ @lazy
+ def standard_sort(self):
+ return self.elementals + tuple(self.topological_sorted_no_elementals)
+
+ def node_name(self, code):
+ return self.nodes[code].get("name", str(code))
+
+ @lazy
+ def standard_sort_names(self):
+ return [self.node_name(s) for s in self.standard_sort]
+
+ def node_names(self):
+ return {s: (self.node_name(s) or s) for s in self.standard_sort}
+
+ def elemental_names(self):
+ return {s: (self.node_name(s) or s) for s in self.elementals}
+
+ @lazy
+ def standard_slot_map(self):
+ return {i: n for n, i in enumerate(self.standard_sort)}
+
+ def predecessor_slots(self, code):
+ if code in self._predecessor_slots:
+ return self._predecessor_slots[code]
+ s = np.empty(self.in_degree(code), dtype=np.int32)
+ for n, i in enumerate(self.predecessors(code)):
+ s[n] = self.standard_slot_map[i]
+ self._predecessor_slots[code] = s
+ return s
+
+ def successor_slots(self, code):
+ if code in self._successor_slots:
+ return self._successor_slots[code]
+ s = np.empty(self.out_degree(code), dtype=np.int32)
+ for n, i in enumerate(self.successors(code)):
+ s[n] = self.standard_slot_map[i]
+ self._successor_slots[code] = s
+ return s
+
+ def __elementals_iter(self):
+ for code, out_degree in self.out_degree:
+ if not out_degree:
+ yield code
+
+ @lazy
+ def elementals(self):
+ result = []
+ found = set()
+ for e in self._suggested_elemental_order:
+ if self.out_degree(e) == 0:
+ result.append(e)
+ found.add(e)
+ for e in sorted(self.__elementals_iter()):
+ if e not in found:
+ result.append(e)
+ found.add(e)
+ return tuple(result)
+
+ def n_elementals(self):
+ return len(self.elementals)
+
+ def n_intermediate_nests(self):
+ return len(self.nodes) - self.n_elementals() - 1
+
+ def elemental_descendants_iter(self, code):
+ if not self.out_degree(code):
+ yield code
+ return
+ all_d = nx.descendants(self, code)
+ for dcode, dout_degree in self.out_degree(all_d):
+ if not dout_degree:
+ yield dcode
+
+ def elemental_descendants(self, code):
+ return [i for i in self.elemental_descendants_iter(code)]
+
+ @property
+ def n_edges(self):
+ return self.number_of_edges()
+
+ def edge_slot_arrays(self, alpha_locator=None):
+ s = self.n_edges
+ up = np.zeros(s, dtype=np.int32)
+ dn = np.zeros(s, dtype=np.int32)
+ first_visit = np.zeros(s, dtype=np.int32)
+ alloc_slot = np.full_like(first_visit, -1)
+ n = s
+ first_visit_found = set()
+ for upcode in reversed(self.standard_sort):
+ upslot = self.standard_slot_map[upcode]
+ for dnslot in reversed(self.successor_slots(upcode)):
+ n -= 1
+ up[n] = upslot
+ dn[n] = dnslot
+ for n in range(s):
+ if dn[n] not in first_visit_found:
+ first_visit[n] = 1
+ first_visit_found.add(dn[n])
+ if alpha_locator is not None:
+ for n in range(s):
+ alloc_slot[n] = alpha_locator.get(
+ (self.standard_sort[up[n]], self.standard_sort[dn[n]]), -1
+ )
+ return up, dn, first_visit, alloc_slot
+
+ def nodes_with_successors_iter(self):
+ for code, out_degree in self.out_degree:
+ if out_degree:
+ yield code
+
+ def nodes_with_multiple_predecessors_iter(self):
+ for code, in_degree in self.in_degree:
+ if in_degree > 1:
+ yield code
+
+ @lazy
+ def standard_competitive_edge_list(self):
+ alphas = []
+ for n in self.nodes_with_multiple_predecessors_iter():
+ predecessors = sorted(self.predecessors(n))
+ for k in predecessors:
+ alphas.append((k, n))
+ return alphas
+
+ @lazy
+ def standard_competitive_edge_list_2(self):
+ alphas = []
+ for n in self.nodes_with_multiple_predecessors_iter():
+ predecessors = sorted(self.predecessors(n))
+ alphas.append((predecessors, n))
+ return alphas
+
+ def __getstate__(self):
+ attr = {} # self.__dict__.copy()
+ no_pickle = (
+ "topological_sorted",
+ "topological_sorted_no_elementals",
+ "standard_sort",
+ "standard_slot_map",
+ #'_standard_elemental_sort',
+ "elementals",
+ "_predecessor_slots",
+ "_successor_slots",
+ "_touch",
+ "node_dict_factory",
+ "_instance",
+ )
+ for k, v in self.__dict__.items():
+ if k not in no_pickle:
+ attr[k] = v
+ return attr
+
+ def __setstate__(self, state):
+ self.__dict__ = state.copy()
+ self._predecessor_slots = {}
+ self._successor_slots = {}
+
+ def __xml__(self, use_viz=True, use_dot=True, output="svg", figsize=None, **format):
+ viz = None
+ dot = None
+
+ if output != "svg":
+ # viz only supports svg presently, always use dot for other formats
+ use_viz = False
+
+ if use_viz:
+ try:
+ import pygraphviz as viz
+ except ImportError:
+ if use_dot:
+ try:
+ import pydot as dot
+ except ImportError:
+ pass
+ elif use_dot:
+ try:
+ import pydot as dot
+ except ImportError:
+ pass
+
+ if viz is None and dot is None:
+ if use_viz and use_dot:
+ msg = "neither pydot nor pygraphviz modules are installed, unable to draw nesting tree"
+ elif use_viz:
+ msg = "pygraphviz module not installed, unable to draw nesting tree"
+ elif use_dot:
+ msg = "pydot module not installed, unable to draw nesting tree"
+ else:
+ msg = "no drawing module used, unable to draw nesting tree"
+ warnings.warn(msg, stacklevel=2)
+ raise NotImplementedError(msg)
+
+ if viz is not None:
+ existing_format_keys = list(format.keys())
+ for key in existing_format_keys:
+ if key.upper() != key:
+ format[key.upper()] = format[key]
+ if "SUPPRESSGRAPHSIZE" not in format:
+ if "GRAPHWIDTH" not in format:
+ format["GRAPHWIDTH"] = 6.5
+ if "GRAPHHEIGHT" not in format:
+ format["GRAPHHEIGHT"] = 4
+ if "UNAVAILABLE" not in format:
+ format["UNAVAILABLE"] = True
+ # x = XML_Builder("div", {'class':"nesting_graph larch_art"})
+ # x.h2("Nesting Structure", anchor=1, attrib={'class':'larch_art_xhtml'})
+ from io import BytesIO
+
+ if "SUPPRESSGRAPHSIZE" not in format:
+ G = viz.AGraph(
+ name="Tree",
+ directed=True,
+ size="{GRAPHWIDTH},{GRAPHHEIGHT}".format(**format),
+ )
+ else:
+ G = viz.AGraph(name="Tree", directed=True)
+ for n in self.nodes:
+ nname = self.nodes[n].get("name", n)
+ if nname == n:
+ G.add_node(
+ n,
+ label=f"<{nname}>",
+ style="rounded,solid",
+ shape="box",
+ )
+ else:
+ G.add_node(
+ n,
+ label=f'<{nname} ({n})>',
+ style="rounded,solid",
+ shape="box",
+ )
+ _eG = G.add_subgraph(
+ name="cluster_elemental",
+ nbunch=self.elementals,
+ color="#cccccc",
+ bgcolor="#eeeeee",
+ label="Elemental Alternatives",
+ labelloc="b",
+ style="rounded,solid",
+ )
+ # unavailable_nodes = set()
+ # if format['UNAVAILABLE']:
+ # if self.is_provisioned():
+ # try:
+ # for n, ncode in enumerate(self.alternative_codes()):
+ # if np.sum(self.Data('Avail'),axis=0)[n,0]==0: unavailable_nodes.add(ncode)
+ # except Exception: raise
+ # try:
+ # legible_avail = not isinstance(self.df.queries.avail, str)
+ # except Exception:
+ # legible_avail = False
+ # if legible_avail:
+ # for ncode,navail in self.df.queries.avail.items():
+ # try:
+ # if navail=='0': unavailable_nodes.add(ncode)
+ # except Exception: raise
+ # _eG.add_subgraph(
+ # name='cluster_elemental_unavailable', nbunch=unavailable_nodes,
+ # color='#bbbbbb', bgcolor='#dddddd',
+ # label='Unavailable Alternatives', labelloc='b', style='rounded,solid'
+ # )
+ G.add_node(self.root_id, label="Root")
+ up_nodes = set()
+ down_nodes = set()
+ for i, j in self.edges:
+ G.add_edge(i, j)
+ down_nodes.add(j)
+ up_nodes.add(i)
+ pyg_imgdata = BytesIO()
+ try:
+ G.draw(
+ pyg_imgdata, format=output, prog="dot"
+ ) # write postscript in k5.ps with neato layout
+ except ValueError as err:
+ if "in path" in str(err):
+ warnings.warn(
+ str(err) + "; unable to draw nesting tree in report",
+ stacklevel=2,
+ )
+ raise NotImplementedError() from err
+ if output == "svg":
+ import xml.etree.ElementTree as ET
+
+ ET.register_namespace("", "http://www.w3.org/2000/svg")
+ ET.register_namespace("xlink", "http://www.w3.org/1999/xlink")
+ return ET.fromstring(pyg_imgdata.getvalue().decode())
+ else:
+ raise NotImplementedError(f"output {output} with use_viz")
+ else:
+ pydot = dot
+
+ # set Graphviz graph type
+ if self.is_directed():
+ graph_type = "digraph"
+ else:
+ graph_type = "graph"
+ strict = nx.number_of_selfloops(self) == 0 and not self.is_multigraph()
+
+ name = self.name
+ graph_defaults = self.graph.get("graph", {})
+ if name == "":
+ P = pydot.Dot(
+ "", graph_type=graph_type, strict=strict, **graph_defaults
+ )
+ else:
+ P = pydot.Dot(
+ f'"{name}"',
+ graph_type=graph_type,
+ strict=strict,
+ **graph_defaults,
+ )
+ try:
+ P.set_node_defaults(**self.graph["node"])
+ except KeyError:
+ pass
+ try:
+ P.set_edge_defaults(**self.graph["edge"])
+ except KeyError:
+ pass
+
+ cluster_elemental = pydot.Cluster(
+ "elemental",
+ style="rounded",
+ bgcolor="lightgrey",
+ color="white",
+ rank="same",
+ rankdir="LR",
+ )
+
+ for n, nodedata in self.nodes(data=True):
+ str_nodedata = dict(
+ (k if k != "name" else "name_", '"' + str(v) + '"')
+ for k, v in nodedata.items()
+ )
+
+ if "parameter" in nodedata:
+ param_label = (
+ '
'
+ ''
+ "{}"
+ ""
+ ).format(nodedata["parameter"])
+ else:
+ param_label = ""
+
+ if "name" in nodedata and n != self.root_id:
+ name = nodedata["name"]
+ str_nodedata["label"] = (
+ "<"
+ f'({n}) '
+ f"{name}"
+ f"{param_label}>"
+ )
+
+ # Default styling for nodes can have been overridden
+ if n in self.elementals:
+ str_nodedata["style"] = str_nodedata.get("style", "filled")
+ str_nodedata["fillcolor"] = str_nodedata.get("fillcolor", "white")
+ elif n == self.root_id:
+ str_nodedata["shape"] = str_nodedata.get("shape", "invhouse")
+ else:
+ str_nodedata["style"] = str_nodedata.get("style", "rounded")
+ str_nodedata["shape"] = str_nodedata.get("shape", "rectangle")
+
+ p = pydot.Node(str(n), **str_nodedata)
+ P.add_node(p)
+ if n in self.elementals:
+ cluster_elemental.add_node(p)
+
+ P.add_subgraph(cluster_elemental)
+
+ if self.is_multigraph():
+ for u, v, key, edgedata in self.edges(data=True, keys=True):
+ str_edgedata = dict(
+ (k, str(v_)) for k, v_ in edgedata.items() if k != "key"
+ )
+ if v in self.elementals:
+ str_edgedata["constraint"] = "false"
+ edge = pydot.Edge(str(u), str(v), key=str(key), **str_edgedata)
+ P.add_edge(edge)
+
+ else:
+ for u, v, edgedata in self.edges(data=True):
+ str_edgedata = dict(
+ (k, '"' + str(v) + '"') for k, v in edgedata.items()
+ )
+ edge = pydot.Edge(str(u), str(v), **str_edgedata)
+ P.add_edge(edge)
+
+ ###
+ from xmle import Elem
+
+ prog = None
+ if output == "svg":
+ import xml.etree.ElementTree as ET
+
+ ET.register_namespace("", "http://www.w3.org/2000/svg")
+ ET.register_namespace("xlink", "http://www.w3.org/1999/xlink")
+ elif output == "png":
+ prog = [P.prog, "-Gdpi=300"]
+ if figsize is not None:
+ prog.append(rf"-Gsize={figsize[0]},{figsize[1]}\!")
+ e = Elem.from_any(P.create(prog=prog, format=output, **format))
+ e.attrib["dpi"] = (300, 300)
+ return e
+ return Elem.from_any(P.create(prog=prog, format=output, **format))
+
+ def _repr_html_(self):
+ from xmle import Elem
+
+ try:
+ xml = self.__xml__()
+ except FileNotFoundError:
+ return Elem("div", text="Graphviz not installed").tostring()
+ else:
+ x = Elem("div") << (xml)
+ return x.tostring()
+
+ def to_png(self, figsize=None, filename=None):
+ """
+ Output the graph visualization as a png.
+
+ Parameters
+ ----------
+ figsize : 2-tuple, optional
+ The (width, height) in inches.
+
+ Returns
+ -------
+ xmle.Elem
+ """
+ result = self.__xml__(output="png", use_viz=False, figsize=figsize)
+ if filename is not None:
+ import base64
+
+ if result.attrib["src"][:22] != "data:image/png;base64,":
+ raise ValueError(
+ "problem decoding png:{}".format(result.attrib["src"][:22])
+ )
+ with open(filename, "wb") as fh:
+ fh.write(base64.decodebytes(result.attrib["src"][22:].encode()))
+ return result
+
+ def partial_figure(
+ self, including_nodes=None, source=None, *, n=None, n_at_level=3, n_expand=1
+ ):
+ """
+ Generate a partial figure of the graph.
+
+ Parameters
+ ----------
+ including_nodes : iterable or None
+ An iterable containing node codes or names (or a mix).
+ source : nodecode, optional
+ All paths from this node to everything in `including_nodes` will be represented.
+ Defaults to `root_id`.
+ n : int
+ If including_nodes is None, select this number of nodes randomly
+
+ Returns
+ -------
+ Elem
+ """
+ if source is None:
+ source = self.root_id
+ from networkx.algorithms.simple_paths import all_simple_paths
+
+ shows = set()
+
+ if including_nodes is None and n is not None:
+ including_nodes = sorted(np.random.choice(self.nodes, n, replace=False))
+
+ if including_nodes is None and n_at_level is not None:
+ import itertools
+ from collections import deque
+
+ q = deque([self.root_id])
+ including_nodes = []
+ while q:
+ i = q.popleft()
+ take = tuple(itertools.islice(self.successors(i), n_at_level))
+ q.extend(take[:n_expand])
+ including_nodes.extend(take)
+
+ # Add every node in every path from the root to each `including_nodes`
+ for each_node in including_nodes:
+ if each_node in self.nodes:
+ for i in all_simple_paths(self, source, each_node):
+ for j in i:
+ shows.add(j)
+ else:
+ for each_node_ in self.get_nodes_by_name(each_node):
+ for i in all_simple_paths(self, source, each_node_):
+ for j in i:
+ shows.add(j)
+ s = self.subgraph(shows)
+ return graph_to_figure(s)
+
+ def get_nodes_by_name(self, name):
+ result = [k for k, v in self.nodes(data=True) if v.get("name") == name]
+ return result
+
+ def subgraph_from(self, node):
+ from collections import deque
+
+ Q = deque([node])
+ found = set()
+ while len(Q):
+ i = Q.popleft()
+ if i not in found:
+ found.add(i)
+ Q.extend(self.successors(i))
+ return NestingTree(self.subgraph(found), root_id=node)
+
+ def stats_summarize(self):
+ print("Graph Stats")
+ print(f" Overall: {len(self)} nodes")
+ tier = [self.root_id]
+ next_tier = list(self.successors(self.root_id))
+ n = 0
+ while len(next_tier):
+ tier = next_tier
+ n += 1
+ print(f" Tier {n}: {len(tier)} nodes")
+ next_tier = list()
+ for i in tier:
+ next_tier.extend(self.successors(i))
+
+ def node_slot_arrays(self, model, parameter_dict=None):
+ """
+ Return arrays for node parameters.
+
+ Parameters
+ ----------
+ model : Model or dtype
+
+ Returns
+ -------
+ muslots, start, num
+ """
+ if hasattr(model, "get_param_loc"):
+ muslots = np.full(
+ [
+ len(self),
+ ],
+ -1,
+ dtype=np.int32,
+ )
+ for child, childcode in enumerate(self.standard_sort):
+ # for parent in self.predecessor_slots(childcode):
+ # alpha[parent, child] = 1
+ pname = self.nodes[childcode].get("parameter", None)
+ if pname is None:
+ muslots[child] = -1
+ else:
+ muslots[child] = model.get_param_loc(pname)
+ else:
+ muslots = np.ones(
+ [
+ len(self),
+ ],
+ dtype=model,
+ )
+ for child, childcode in enumerate(self.standard_sort):
+ # for parent in self.predecessor_slots(childcode):
+ # alpha[parent, child] = 1
+ pname = self.nodes[childcode].get("parameter", None)
+ if parameter_dict is not None and isinstance(pname, str):
+ pname = parameter_dict.get(pname, pname)
+ if pname is not None:
+ muslots[child] = model(pname)
+ num = np.zeros(len(self.nodes), dtype=np.int32)
+ start = np.full(len(self.nodes), -1, dtype=np.int32)
+ n = self.n_edges
+ for upcode in reversed(self.standard_sort):
+ upslot = self.standard_slot_map[upcode]
+ for _dnslot in reversed(self.successor_slots(upcode)):
+ n -= 1
+ num[upslot] += 1
+ start[upslot] = n
+ return (
+ muslots,
+ start,
+ num,
+ )
+
+ def _get_simple_mu_and_alpha(self, model, holdfast_invalidates=True):
+ # alpha = np.zeros([len(self), len(self)], dtype=np.float64)
+ mu = np.ones(
+ [
+ len(self),
+ ],
+ dtype=np.float64,
+ )
+ muslots = np.full(
+ [
+ len(self),
+ ],
+ -1,
+ dtype=np.int32,
+ )
+ for child, childcode in enumerate(self.standard_sort):
+ # for parent in self.predecessor_slots(childcode):
+ # alpha[parent, child] = 1
+ pname = self.nodes[childcode].get("parameter", None)
+ mu[child] = model.get_value(pname, default=1.0)
+ muslots[child] = model.get_param_loc(pname)
+ if holdfast_invalidates:
+ if model.pholdfast[muslots[child]]:
+ muslots[child] = -1
+
+ s = self.n_edges
+ up = np.zeros(s, dtype=np.int32)
+ dn = np.zeros(s, dtype=np.int32)
+ val = np.zeros(s, dtype=np.float64)
+ num = np.zeros(len(self.nodes), dtype=np.int32)
+ start = np.full(len(self.nodes), -1, dtype=np.int32)
+ # first_visit = np.zeros(s, dtype=np.int32)
+ n = s
+ # first_visit_found = set()
+ for upcode in reversed(self.standard_sort):
+ upslot = self.standard_slot_map[upcode]
+ for dnslot in reversed(self.successor_slots(upcode)):
+ n -= 1
+ up[n] = upslot
+ dn[n] = dnslot
+ num[upslot] += 1
+ start[upslot] = n
+ val[n] = 1 / len(
+ self.predecessor_slots(self.standard_sort[dnslot])
+ ) # TODO make not always constant fraction
+ # for n in range(s):
+ # if dn[n] not in first_visit_found:
+ # first_visit[n] = 1
+ # first_visit_found.add(dn[n])
+
+ return mu, muslots, up, dn, num, start, val
+
+ def to_dict(self):
+ return {
+ "nodes": dict(self.nodes),
+ "edges": split_keys(self.edges),
+ "root_id": self.root_id,
+ "suggested_elemental_order": self._suggested_elemental_order,
+ }
+
+ @classmethod
+ def from_dict(cls, x):
+ self = cls(
+ root_id=x.get("root_id", None),
+ suggested_elemental_order=x.get("suggested_elemental_order", ()),
+ )
+ nodes = x.get("nodes", {})
+ for k, v in nodes.items():
+ self.add_node(k, **v)
+ edges = x.get("edges", {})
+ for i, jv in edges.items():
+ for j, v in jv.items():
+ self.add_edge(i, j, **v)
+ return self
+
+ def as_arrays(self, model=np.float32, trim=True, parameter_dict=None):
+ result = {}
+ result["n_nodes"] = len(self)
+ result["n_alts"] = n_alts = self.n_elementals()
+ up, dn, first_visit, alloc_slot = self.edge_slot_arrays()
+ result["edges_up"] = up
+ result["edges_dn"] = dn
+ result["edges_1st"] = first_visit
+ result["edges_alloc"] = alloc_slot
+ muslots, start, num = self.node_slot_arrays(
+ model=model, parameter_dict=parameter_dict
+ )
+ if trim:
+ muslots = muslots[n_alts:]
+ start = start[n_alts:]
+ num = num[n_alts:]
+ result["mu_params"] = muslots
+ result["start_slots"] = start
+ result["len_slots"] = num
+ return result
+
+ def description(self, which: Literal["nodes", "edges"] = "nodes") -> pd.DataFrame:
+ """
+ Generate a DataFrame that describe this graph.
+
+ Parameters
+ ----------
+ which : {'nodes','edges'}
+ Which type of description to return.
+
+ Returns
+ -------
+ DataFrame
+ Describing nodes or edges
+ """
+ from itertools import islice
+
+ import pandas as pd
+
+ if which in ("edge", "edges", "link", "links"):
+ ef = pd.DataFrame.from_dict(self.edges, orient="index")
+ ef.index.names = ["up", "down"]
+ return ef.fillna("")
+
+ nf = pd.DataFrame.from_dict(self.nodes, orient="index")
+ max_c = 8
+ max_p = 8
+ for n in nf.index:
+ children = [e[1] for e in islice(self.out_edges(n), max_c)]
+ if len(children) > max_c - 1:
+ children[-1] = "..."
+ if children:
+ nf.loc[n, "children"] = ", ".join(str(c) for c in children)
+ for n in nf.index:
+ parents = [e[0] for e in islice(self.in_edges(n), max_p)]
+ if len(parents) > max_p - 1:
+ parents[-1] = "..."
+ if parents:
+ nf.loc[n, "parents"] = ", ".join(str(c) for c in parents)
+ nf.drop(columns="root", errors="ignore", inplace=True)
+ return nf.fillna("")
+
+
+def graph_to_figure(graph, output_format="svg", **format):
+ try:
+ import pygraphviz as viz
+ except ImportError:
+ warnings.warn(
+ "pygraphviz module not installed, unable to draw nesting tree", stacklevel=2
+ )
+ raise NotImplementedError(
+ "pygraphviz module not installed, unable to draw nesting tree"
+ ) from None
+ existing_format_keys = list(format.keys())
+ for key in existing_format_keys:
+ if key.upper() != key:
+ format[key.upper()] = format[key]
+ if "SUPPRESSGRAPHSIZE" not in format:
+ if "GRAPHWIDTH" not in format:
+ format["GRAPHWIDTH"] = 6.5
+ if "GRAPHHEIGHT" not in format:
+ format["GRAPHHEIGHT"] = 4
+ if "UNAVAILABLE" not in format:
+ format["UNAVAILABLE"] = True
+ # x = XML_Builder("div", {'class':"nesting_graph larch_art"})
+ # x.h2("Nesting Structure", anchor=1, attrib={'class':'larch_art_xhtml'})
+ from io import BytesIO
+
+ if "SUPPRESSGRAPHSIZE" not in format:
+ G = viz.AGraph(
+ name="Tree",
+ directed=True,
+ size="{GRAPHWIDTH},{GRAPHHEIGHT}".format(**format),
+ )
+ else:
+ G = viz.AGraph(name="Tree", directed=True)
+ for n in graph.nodes:
+ nname = graph.nodes[n].get("name", n)
+ if nname == n:
+ G.add_node(n, label=f"<{nname}>", style="rounded,solid", shape="box")
+ else:
+ G.add_node(
+ n,
+ label=f'<{nname} ({n})>',
+ style="rounded,solid",
+ shape="box",
+ )
+ try:
+ graph.elementals # noqa: B018
+ except AttributeError:
+ pass
+ else:
+ _eG = G.add_subgraph(
+ name="cluster_elemental",
+ nbunch=graph.elementals,
+ color="#cccccc",
+ bgcolor="#eeeeee",
+ label="Elemental Alternatives",
+ labelloc="b",
+ style="rounded,solid",
+ )
+ # unavailable_nodes = set()
+ # if format['UNAVAILABLE']:
+ # if self.is_provisioned():
+ # try:
+ # for n, ncode in enumerate(self.alternative_codes()):
+ # if np.sum(self.Data('Avail'),axis=0)[n,0]==0: unavailable_nodes.add(ncode)
+ # except Exception: raise
+ # try:
+ # legible_avail = not isinstance(self.df.queries.avail, str)
+ # except Exception:
+ # legible_avail = False
+ # if legible_avail:
+ # for ncode,navail in self.df.queries.avail.items():
+ # try:
+ # if navail=='0': unavailable_nodes.add(ncode)
+ # except Exception: raise
+ # eG.add_subgraph(
+ # name='cluster_elemental_unavailable',
+ # nbunch=unavailable_nodes,
+ # color='#bbbbbb', bgcolor='#dddddd',
+ # label='Unavailable Alternatives', labelloc='b', style='rounded,solid'
+ # )
+ try:
+ G.add_node(graph.root_id, label="Root")
+ except AttributeError:
+ pass
+ up_nodes = set()
+ down_nodes = set()
+ for i, j in graph.edges:
+ G.add_edge(i, j)
+ down_nodes.add(j)
+ up_nodes.add(i)
+ pyg_imgdata = BytesIO()
+ try:
+ G.draw(
+ pyg_imgdata, format=output_format, prog="dot"
+ ) # write postscript in k5.ps with neato layout
+ except ValueError as err:
+ if "in path" in str(err):
+ warnings.warn(
+ str(err) + "; unable to draw nesting tree in report", stacklevel=2
+ )
+ raise NotImplementedError() from err
+ from xmle import Elem
+
+ if output_format == "svg":
+ import xml.etree.ElementTree as ET
+
+ ET.register_namespace("", "http://www.w3.org/2000/svg")
+ ET.register_namespace("xlink", "http://www.w3.org/1999/xlink")
+ result = ET.fromstring(pyg_imgdata.getvalue().decode())
+ else:
+ result = Elem(
+ "span",
+ attrib={"style": "color:red"},
+ text=f"Unable to render output_format '{output_format}'",
+ )
+ x = Elem("div") << result
+ return x
+
+
+def reverse_lexicographical_topological_sort(G, key=None):
+ """
+ Generate nodes in reverse lexicographically topologically sorted order.
+
+ A general topological sort is a nonunique permutation of the nodes such that
+ an edge from u to v implies that u appears before v in the topological sort
+ order.
+
+ The lexicographical topological sort breaks ties by ordering according to
+ node labels, so that the sorting becomes unique.
+
+ Parameters
+ ----------
+ G : NetworkX digraph
+ A directed acyclic graph (DAG)
+
+ key : function, optional
+ This function maps nodes to keys with which to resolve ambiguities in
+ the sort order. Defaults to the identity function.
+
+ Returns
+ -------
+ iterable
+ An iterable of node names in lexicographical topological sort order.
+
+ Raises
+ ------
+ NetworkXError
+ Topological sort is defined for directed graphs only. If the graph `G`
+ is undirected, a :exc:`NetworkXError` is raised.
+
+ NetworkXUnfeasible
+ If `G` is not a directed acyclic graph (DAG) no topological sort exists
+ and a :exc:`NetworkXUnfeasible` exception is raised. This can also be
+ raised if `G` is changed while the returned iterator is being processed
+
+ RuntimeError
+ If `G` is changed while the returned iterator is being processed.
+
+ """
+ if not G.is_directed():
+ msg = "Topological sort not defined on undirected graphs."
+ raise nx.NetworkXError(msg)
+
+ if key is None:
+
+ def key(node):
+ return node
+
+ nodeid_map = {n: i for i, n in enumerate(G)}
+
+ def create_tuple(node):
+ return key(node), nodeid_map[node], node
+
+ outdegree_map = {v: d for v, d in G.out_degree() if d > 0}
+ # These nodes have zero outdegree and ready to be returned.
+ zero_outdegree = [create_tuple(v) for v, d in G.out_degree() if d == 0]
+ heapq.heapify(zero_outdegree)
+
+ while zero_outdegree:
+ _, _, node = heapq.heappop(zero_outdegree)
+
+ if node not in G:
+ raise RuntimeError("Graph changed during iteration")
+ for parent, _child in G.in_edges(node):
+ try:
+ outdegree_map[parent] -= 1
+ except KeyError as e:
+ raise RuntimeError("Graph changed during iteration") from e
+ if outdegree_map[parent] == 0:
+ heapq.heappush(zero_outdegree, create_tuple(parent))
+ del outdegree_map[parent]
+
+ yield node
+
+ if zero_outdegree:
+ msg = "Graph contains a cycle or graph changed during iteration"
+ raise nx.NetworkXUnfeasible(msg)
diff --git a/sharrow/_nested_logit_tree/_lazy.py b/sharrow/_nested_logit_tree/_lazy.py
new file mode 100644
index 0000000..290a9d1
--- /dev/null
+++ b/sharrow/_nested_logit_tree/_lazy.py
@@ -0,0 +1,90 @@
+# from: https://github.com/stefanholek/lazy
+#
+# Copyright (c) 2011-2017 Stefan H. Holek
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# 1. Redistributions of source code must retain the above copyright
+# notice, this list of conditions and the following disclaimer.
+# 2. Redistributions in binary form must reproduce the above copyright
+# notice, this list of conditions and the following disclaimer in the
+# documentation and/or other materials provided with the distribution.
+#
+# THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
+# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
+# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
+# SUCH DAMAGE.
+
+
+"""Decorator to create lazy attributes."""
+
+from __future__ import annotations
+
+import functools
+
+
+class lazy:
+ """
+ Lazy descriptor.
+
+ Used as a decorator to create lazy attributes. Lazy attributes
+ are evaluated on first use.
+ """
+
+ def __init__(self, func):
+ self.__func = func
+ functools.wraps(self.__func)(self)
+
+ def __get__(self, inst, inst_cls):
+ if inst is None:
+ return self
+
+ if not hasattr(inst, "__dict__"):
+ raise AttributeError(
+ f"'{inst_cls.__name__}' object has no attribute '__dict__'"
+ )
+
+ name = self.__name__
+ if name.startswith("__") and not name.endswith("__"):
+ name = f"_{inst_cls.__name__}{name}"
+
+ value = self.__func(inst)
+ inst.__dict__[name] = value
+ return value
+
+ @classmethod
+ def invalidate(cls, inst, name):
+ """
+ Invalidate a lazy attribute.
+
+ This obviously violates the lazy contract. A subclass of lazy
+ may however have a contract where invalidation is appropriate.
+ """
+ # These commands from the original are not currently used by larch and
+ # are omitted for speed. They are left here in case they are needed
+ # for future compatibility.
+ #
+ # inst_cls = inst.__class__
+ #
+ # if not hasattr(inst, '__dict__'):
+ # raise AttributeError("'%s' object has no attribute '__dict__'" % (inst_cls.__name__,))
+ #
+ # if name.startswith('__') and not name.endswith('__'):
+ # name = '_%s%s' % (inst_cls.__name__, name)
+ #
+ # if not isinstance(getattr(inst_cls, name), cls):
+ # raise AttributeError("'%s.%s' is not a %s attribute"
+ # % (inst_cls.__name__, name, cls.__name__))
+
+ if name in inst.__dict__:
+ del inst.__dict__[name]
diff --git a/sharrow/aster.py b/sharrow/aster.py
index 24cbc2f..8b0b995 100755
--- a/sharrow/aster.py
+++ b/sharrow/aster.py
@@ -403,14 +403,25 @@ def _replacement(
topname = self.spacename
pref_topname = self.preferred_spacename or topname
+ missing_backstop = False
+ # this flag will indicate that we have a sparse variable with no backstop
+
if self.spacevars is not None:
if attr not in self.spacevars:
if self.get_default or (
topname == pref_topname and not self.swallow_errors
):
- raise KeyError(
- f"{topname}..{attr}\nexpression={self.original_expr}"
- )
+ # check if there is a blender for this attr
+ # if so, we are working with a sparse MAZ variable that has
+ # no backstop TAZ data. So we will set the result to zero
+ # and let the blender processing handle the rest below.
+ blender = self.blenders.get(attr, None)
+ if blender is not None:
+ missing_backstop = True
+ else:
+ raise KeyError(
+ f"{topname}..{attr}\nexpression={self.original_expr}"
+ )
# we originally raised a KeyError here regardless, but what if
# we just give back the original node, and see if other spaces,
# possibly fallback spaces, might work? If nothing works then
@@ -419,7 +430,8 @@ def _replacement(
# using the same rules, then circle back later to clean up
# errors, so that as-yet unprocessed Ast elements get the
# chance to be seen by the first pass.
- return original_node
+ if not missing_backstop:
+ return original_node
dim_slots = self.dim_slots
if isinstance(self.spacevars, dict):
@@ -554,23 +566,24 @@ def _maybe_transpose_first_two_args(_slice):
# inside the blender, the args will be maz-taz mapped, but we need the plain (i)maz too now
result_arg_ = [j.slice for j in result_args]
if len(result_args) == 2:
+ get_blended_2_args = [
+ result,
+ ast.Name(
+ id=f"__{pref_topname}___s_{attr}__indices", ctx=ast.Load()
+ ),
+ ast.Name(id=f"__{pref_topname}___s_{attr}__indptr", ctx=ast.Load()),
+ ast.Name(id=f"__{pref_topname}___s_{attr}__data", ctx=ast.Load()),
+ result_arg_[0 if not transpose_lead else 1],
+ result_arg_[1 if not transpose_lead else 0],
+ ]
+ max_blend_distance = blender.get("max_blend_distance")
+ if max_blend_distance is not None and max_blend_distance != np.inf:
+ get_blended_2_args.append(
+ ast_Constant(max_blend_distance), # blend_limit
+ )
result = ast.Call(
func=ast.Name("get_blended_2", cts=ast.Load()),
- args=[
- result,
- ast.Name(
- id=f"__{pref_topname}___s_{attr}__indices", ctx=ast.Load()
- ),
- ast.Name(
- id=f"__{pref_topname}___s_{attr}__indptr", ctx=ast.Load()
- ),
- ast.Name(
- id=f"__{pref_topname}___s_{attr}__data", ctx=ast.Load()
- ),
- result_arg_[0 if not transpose_lead else 1],
- result_arg_[1 if not transpose_lead else 0],
- ast_Constant(blender.get("max_blend_distance")), # blend_limit
- ],
+ args=get_blended_2_args,
keywords=[],
)
else:
diff --git a/sharrow/example_data.py b/sharrow/example_data.py
index 95aef53..10ec665 100644
--- a/sharrow/example_data.py
+++ b/sharrow/example_data.py
@@ -1,10 +1,27 @@
+import contextlib
import os
from importlib.resources import as_file, files
+from pathlib import Path
import numpy as np
import pandas as pd
+@contextlib.contextmanager
+def get_example_data_path(filename: str | None = None):
+ """Context manager that yields a path to a file in the example data directory."""
+ if filename:
+ with as_file(files("sharrow").joinpath("example_data").joinpath(filename)) as f:
+ if f.exists():
+ yield f
+ else:
+ # Fallback for when running from source
+ yield Path(__file__).parent.joinpath("example_data").joinpath(filename)
+ else:
+ with as_file(files("sharrow").joinpath("example_data")) as data_path:
+ yield data_path
+
+
def get_skims_filename() -> str:
"""Return the path to the example skims file."""
return os.path.join(os.path.dirname(__file__), "example_data", "skims.omx")
@@ -15,7 +32,7 @@ def get_skims_omx():
from . import dataset
- with as_file(files("sharrow").joinpath("example_data/skims.omx")) as filename:
+ with get_example_data_path("skims.omx") as filename:
skims = None
with openmatrix.open_file(str(filename)) as f:
skims = dataset.from_omx_3d(
@@ -32,8 +49,7 @@ def get_skims_omx():
def get_skims_zarr():
from . import dataset
- f = files("sharrow").joinpath("example_data/skims.zarr")
- with as_file(f) as zfile:
+ with get_example_data_path("skims.zarr") as zfile:
if zfile.exists():
skims = dataset.from_zarr(zfile, consolidated=False)
else:
@@ -44,8 +60,7 @@ def get_skims_zarr():
def get_skims():
from . import dataset
- f = files("sharrow").joinpath("example_data/skims.zarr")
- with as_file(f) as zfile:
+ with get_example_data_path("skims.zarr") as zfile:
if zfile.exists():
skims = dataset.from_zarr(zfile, consolidated=False)
else:
@@ -54,27 +69,27 @@ def get_skims():
def get_households():
- with as_file(files("sharrow").joinpath("example_data/households.csv.gz")) as f:
+ with get_example_data_path("households.csv.gz") as f:
return pd.read_csv(f, index_col="HHID")
def get_persons():
- with as_file(files("sharrow").joinpath("example_data/persons.csv.gz")) as f:
+ with get_example_data_path("persons.csv.gz") as f:
return pd.read_csv(f, index_col="PERID")
def get_land_use():
- with as_file(files("sharrow").joinpath("example_data/land_use.csv.gz")) as f:
+ with get_example_data_path("land_use.csv.gz") as f:
return pd.read_csv(f, index_col="TAZ")
def get_maz_to_taz():
- with as_file(files("sharrow").joinpath("example_data/maz_to_taz.csv")) as f:
+ with get_example_data_path("maz_to_taz.csv") as f:
return pd.read_csv(f, index_col="MAZ")
def get_maz_to_maz_walk():
- with as_file(files("sharrow").joinpath("example_data/maz_to_maz_walk.csv")) as f:
+ with get_example_data_path("maz_to_maz_walk.csv") as f:
return pd.read_csv(f)
diff --git a/sharrow/nested_logit.py b/sharrow/nested_logit.py
index 22b6ace..d80ac8d 100644
--- a/sharrow/nested_logit.py
+++ b/sharrow/nested_logit.py
@@ -160,7 +160,8 @@ def construct_nesting_tree(alternatives, nesting_settings):
try:
from larch.model.tree import NestingTree
except ImportError:
- raise ImportError("larch is required to construct nesting trees") from None
+ # if larch is not installed, import from local copy (not as well optimized)
+ from ._nested_logit_tree import NestingTree
if not isinstance(alternatives, Mapping):
alt_names = list(alternatives)
diff --git a/sharrow/relationships.py b/sharrow/relationships.py
index 1adcc10..b0cb96b 100644
--- a/sharrow/relationships.py
+++ b/sharrow/relationships.py
@@ -398,9 +398,9 @@ def shape(self):
else:
from .flows import presorted
- dim_order = presorted(self.root_dataset.dims, self.dim_order)
+ dim_order = presorted(self.root_dataset.sizes, self.dim_order)
return tuple(
- self.root_dataset.dims[i] for i in dim_order if i not in self.dim_exclude
+ self.root_dataset.sizes[i] for i in dim_order if i not in self.dim_exclude
)
@property
@@ -408,7 +408,7 @@ def root_dims(self):
from .flows import presorted
return tuple(
- presorted(self.root_dataset.dims, self.dim_order, self.dim_exclude)
+ presorted(self.root_dataset.sizes, self.dim_order, self.dim_exclude)
)
def __shallow_copy_extras(self):
diff --git a/sharrow/sparse.py b/sharrow/sparse.py
index db3d71a..7667a84 100644
--- a/sharrow/sparse.py
+++ b/sharrow/sparse.py
@@ -1,3 +1,4 @@
+import logging
import math
import numba as nb
@@ -141,7 +142,83 @@ def sparse_blender(
index=None,
i_dim="omaz",
j_dim="dmaz",
+ *,
+ backing_i_dim="otaz",
+ backing_j_dim="dtaz",
):
+ """
+ Add sparse matrix data to blend with dense backing array.
+
+ This method attaches sparse 2D array data to the dataset, which will be
+ blended with an existing dense backing array. The sparse data typically
+ represents more detailed zone-to-zone values (e.g., MAZ-to-MAZ skims),
+ while the dense backing array contains coarser zone values (e.g., TAZ-to-TAZ
+ skims). The blending behavior is controlled by max_blend_distance.
+
+ Parameters
+ ----------
+ name : str
+ Name of the variable to blend. Must match an existing variable in the
+ dataset, or a zero-filled backing array will be created.
+ i : array-like
+ Origin zone indices for sparse data points.
+ j : array-like
+ Destination zone indices for sparse data points.
+ data : array-like
+ Values at each (i, j) sparse data point.
+ shape : tuple of int, optional
+ Shape of the sparse matrix as (n_rows, n_cols). If not provided and
+ index is given, shape is inferred as (len(index), len(index)).
+ max_blend_distance : float, optional
+ Maximum distance threshold for blending. Sparse values exceeding this
+ threshold will fall back to dense backing values. If None or not
+ provided, defaults to infinity (no distance-based fallback).
+ blend_distance_name : str, optional
+ Name of a variable containing distances to use for blend calculations.
+ If provided, enables distance-weighted blending.
+ index : array-like, optional
+ Index mapping for zone IDs. Used to convert external zone IDs (i, j)
+ to internal 0-based positions in the sparse matrix.
+ i_dim : str, default "omaz"
+ Name of the origin dimension for the sparse data (typically the more
+ detailed zone system like MAZ).
+ j_dim : str, default "dmaz"
+ Name of the destination dimension for the sparse data.
+ backing_i_dim : str, default "otaz"
+ Name of the origin dimension in the dense backing array (typically the
+ coarser zone system like TAZ).
+ backing_j_dim : str, default "dtaz"
+ Name of the destination dimension in the dense backing array.
+
+ Notes
+ -----
+ The sparse data is stored in GCXS (Generalized Compressed Sparse) format
+ with compression along the first axis (rows) for efficient lookups.
+
+ If the named variable does not exist in the dataset, a zero-filled backing
+ array is automatically created with dimensions (backing_i_dim, backing_j_dim).
+
+ The blending behavior works as follows:
+ - If sparse data exists for a zone pair and is within max_blend_distance,
+ a weighted blend of sparse and dense values is used.
+ - If sparse data exceeds max_blend_distance, the dense backing value is used.
+ - If no sparse data exists for a zone pair, the dense backing value is used.
+
+ See Also
+ --------
+ is_blended : Check if a variable has sparse blending enabled.
+
+ Examples
+ --------
+ >>> skims.redirection.sparse_blender(
+ ... "DISTWALK",
+ ... maz_to_maz_walk.OMAZ,
+ ... maz_to_maz_walk.DMAZ,
+ ... maz_to_maz_walk.DISTWALK,
+ ... max_blend_distance=1.0,
+ ... index=maz_taz.index,
+ ... )
+ """
i = np.asarray(i)
j = np.asarray(j)
if index is not None and shape is None:
@@ -162,6 +239,20 @@ def apply_mapper(x):
if sparse is None:
raise ImportError("sparse is not installed")
+ if name not in self._obj:
+ # the TAZ level backing array does not exist yet, so create it with zeros
+ logging.getLogger(__name__).warning(
+ f"Creating zero backing array for sparse blender {name}"
+ )
+ backing_shape = (
+ self._obj.sizes[backing_i_dim],
+ self._obj.sizes[backing_j_dim],
+ )
+ self._obj[name] = xr.DataArray(
+ np.broadcast_to(np.zeros([1], dtype=data.dtype), backing_shape),
+ dims=(backing_i_dim, backing_j_dim),
+ )
+
sparse_data = sparse.GCXS(
sparse.COO(np.stack((i_, j_)), data, shape=shape), compressed_axes=(0,)
)
diff --git a/sharrow/tests/test_sparse.py b/sharrow/tests/test_sparse.py
new file mode 100644
index 0000000..40748de
--- /dev/null
+++ b/sharrow/tests/test_sparse.py
@@ -0,0 +1,378 @@
+import secrets
+
+import numpy as np
+import pandas as pd
+import pytest
+from pytest import approx
+
+import sharrow as sh
+
+
+@pytest.fixture
+def sparse_skims_setup():
+ """Create skims with sparse MAZ data.
+
+ Mirrors the setup from sparse.ipynb.
+ """
+ # Load example data
+ skims = sh.example_data.get_skims()
+ maz_taz = sh.example_data.get_maz_to_taz()
+ maz_to_maz_walk = sh.example_data.get_maz_to_maz_walk()
+
+ # Add DISTJUMP column
+ maz_to_maz_walk["DISTJUMP"] = maz_to_maz_walk["DISTWALK"] + 0.07
+
+ # Set redirection
+ skims.redirection.set(
+ maz_taz,
+ map_to="otaz",
+ name="omaz",
+ map_also={"dtaz": "dmaz"},
+ )
+
+ # Add sparse blender for DISTWALK
+ skims.redirection.sparse_blender(
+ "DISTWALK",
+ maz_to_maz_walk.OMAZ,
+ maz_to_maz_walk.DMAZ,
+ maz_to_maz_walk.DISTWALK,
+ max_blend_distance=1.0,
+ index=maz_taz.index,
+ )
+
+ # Create DISTJUMP variable as zeros
+
+ # skims = skims.assign(DISTJUMP=xr.DataArray(
+ # np.broadcast_to(np.zeros([1]), skims["DISTWALK"].shape),
+ # dims=skims["DISTWALK"].dims,
+ # coords=skims["DISTWALK"].coords,
+ # ))
+
+ # Add sparse blender for DISTJUMP, which has no backing in the TAZ data
+ skims.redirection.sparse_blender(
+ "DISTJUMP",
+ maz_to_maz_walk.OMAZ,
+ maz_to_maz_walk.DMAZ,
+ maz_to_maz_walk.DISTJUMP,
+ index=maz_taz.index,
+ )
+
+ return skims, maz_taz, maz_to_maz_walk
+
+
+@pytest.fixture
+def trips_data():
+ """Create test trips dataframe."""
+ return pd.DataFrame(
+ {
+ "orig_maz": [100, 100, 100, 200, 200],
+ "dest_maz": [100, 101, 103, 201, 202],
+ }
+ )
+
+
+def blend(s, d, max_s):
+ """Compute blended values from sparse and dense data.
+
+ Blends sparse and dense data based on max_s threshold.
+ """
+ out = np.zeros_like(d)
+ ratio = s / max_s
+ out = d * ratio + s * (1 - ratio)
+ out = np.where(s > max_s, d, out)
+ out = np.where(np.isnan(s), d, out)
+ return out
+
+
+def test_sparse_basic_flow(sparse_skims_setup, trips_data):
+ """Test basic flow with sparse blending."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+ trips = trips_data
+
+ # Create DataTree
+ tree = sh.DataTree(
+ base=trips,
+ skims=skims,
+ relationships=(
+ "base.orig_maz @ skims.omaz",
+ "base.dest_maz @ skims.dmaz",
+ ),
+ )
+
+ # Setup flow
+ flow = tree.setup_flow(
+ {
+ "plain_distance": "DISTWALK",
+ },
+ boundscheck=True,
+ )
+
+ # Test blending logic
+ sparse_dat = np.array([0.01, 0.2, np.nan, 3.2, np.nan])
+ dense_dat = np.array([0.12, 0.12, 0.12, 0.17, 0.17])
+
+ expected = blend(sparse_dat, dense_dat, 1.0)
+ result = flow.load().ravel()
+
+ assert result == approx(expected)
+
+
+def test_sparse_flow_with_transformations(sparse_skims_setup, trips_data):
+ """Test flow with transformations on sparse data."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+ trips = trips_data
+
+ tree = sh.DataTree(
+ base=trips,
+ skims=skims,
+ relationships=(
+ "base.orig_maz @ skims.omaz",
+ "base.dest_maz @ skims.dmaz",
+ ),
+ )
+
+ # Setup flow with multiple transformations
+ flow2 = tree.setup_flow(
+ {
+ "plain_distance": "DISTWALK",
+ "clip_distance": "DISTWALK.clip(upper=0.15)",
+ "square_distance": "DISTWALK**2",
+ }
+ )
+
+ expected = np.array(
+ [
+ [1.1100e-02, 1.1100e-02, 1.2321e-04],
+ [1.8400e-01, 1.5000e-01, 3.3856e-02],
+ [1.2000e-01, 1.2000e-01, 1.4400e-02],
+ [1.7000e-01, 1.5000e-01, 2.8900e-02],
+ [1.7000e-01, 1.5000e-01, 2.8900e-02],
+ ],
+ dtype=np.float32,
+ )
+
+ result = flow2.load_dataframe().values
+ assert result == approx(expected)
+
+
+def test_sparse_distjump(sparse_skims_setup, trips_data):
+ """Test DISTJUMP variable with infinite blend distance."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+ trips = trips_data
+
+ tree = sh.DataTree(
+ base=trips,
+ skims=skims,
+ relationships=(
+ "base.orig_maz @ skims.omaz",
+ "base.dest_maz @ skims.dmaz",
+ ),
+ )
+
+ flow3 = tree.setup_flow(
+ {
+ "jump_distance": "skims['DISTJUMP']",
+ },
+ boundscheck=True,
+ )
+
+ result = flow3.load()
+
+ # DISTJUMP should have sparse values + 0.07, or 0 for missing
+ sparse_dat = np.array([0.01, 0.2, np.nan, 3.2, np.nan]) + 0.07
+ dense_dat = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
+ expected = blend(sparse_dat, dense_dat, np.inf)
+
+ assert result.ravel() == approx(expected)
+
+
+def test_sparse_at_accessor(sparse_skims_setup, trips_data):
+ """Test the 'at' accessor with sparse data."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+ trips = trips_data
+
+ out = skims.at(
+ omaz=trips.orig_maz,
+ dmaz=trips.dest_maz,
+ _names=["DIST", "DISTWALK"],
+ _load=True,
+ )
+
+ # Check DIST values (dense data)
+ np.testing.assert_array_almost_equal(
+ out["DIST"].to_numpy(),
+ np.array([0.12, 0.12, 0.12, 0.17, 0.17], dtype=np.float32),
+ )
+
+ # Check DISTWALK values (blended sparse/dense)
+ np.testing.assert_array_almost_equal(
+ out["DISTWALK"].to_numpy(),
+ np.array([0.0111, 0.184, 0.12, 0.17, 0.17], dtype=np.float32),
+ )
+
+
+def test_sparse_at_accessor_with_time_period_raises(sparse_skims_setup, trips_data):
+ """Test that 'at' accessor raises NotImplementedError for 3D sparse."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+ trips = trips_data
+
+ with pytest.raises(NotImplementedError):
+ skims.at(
+ omaz=trips.orig_maz,
+ dmaz=trips.dest_maz,
+ time_period=["AM", "AM", "AM", "AM", "AM"],
+ _names=["DIST", "DISTWALK", "SOV_TIME"],
+ _load=True,
+ )
+
+
+def test_sparse_iat_accessor(sparse_skims_setup):
+ """Test the 'iat' accessor with sparse data."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+
+ out = skims.iat(
+ omaz=[0, 0, 0, 100, 100],
+ dmaz=[0, 1, 3, 101, 102],
+ _names=["DIST", "DISTWALK"],
+ _load=True,
+ )
+
+ # Check DIST values
+ np.testing.assert_array_almost_equal(
+ out["DIST"].to_numpy(),
+ np.array([0.12, 0.12, 0.12, 0.17, 0.17], dtype=np.float32),
+ )
+
+ # Check DISTWALK values
+ np.testing.assert_array_almost_equal(
+ out["DISTWALK"].to_numpy(),
+ np.array([0.0111, 0.184, 0.12, 0.17, 0.17], dtype=np.float32),
+ )
+
+
+def test_sparse_shared_memory(sparse_skims_setup, trips_data):
+ """Test that sparse data works correctly with shared memory."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+ trips = trips_data
+
+ # Create unique token for shared memory
+ token = "skims-with-sparse" + secrets.token_hex(5)
+
+ # Write to shared memory
+ readback0 = skims.shm.to_shared_memory(token)
+ assert readback0.attrs == skims.attrs
+
+ # Read back from shared memory
+ readback = sh.Dataset.shm.from_shared_memory(token)
+ assert readback.attrs == skims.attrs
+
+ # Test iat accessor on readback
+ out = readback.iat(
+ omaz=[0, 0, 0, 100, 100],
+ dmaz=[0, 1, 3, 101, 102],
+ _names=["DIST", "DISTWALK"],
+ _load=True,
+ )
+ np.testing.assert_array_almost_equal(
+ out["DIST"].to_numpy(),
+ np.array([0.12, 0.12, 0.12, 0.17, 0.17], dtype=np.float32),
+ )
+ np.testing.assert_array_almost_equal(
+ out["DISTWALK"].to_numpy(),
+ np.array([0.0111, 0.184, 0.12, 0.17, 0.17], dtype=np.float32),
+ )
+
+ # Test at accessor on readback
+ out = readback.at(
+ omaz=trips.orig_maz,
+ dmaz=trips.dest_maz,
+ _names=["DIST", "DISTWALK"],
+ _load=True,
+ )
+ np.testing.assert_array_almost_equal(
+ out["DIST"].to_numpy(),
+ np.array([0.12, 0.12, 0.12, 0.17, 0.17], dtype=np.float32),
+ )
+ np.testing.assert_array_almost_equal(
+ out["DISTWALK"].to_numpy(),
+ np.array([0.0111, 0.184, 0.12, 0.17, 0.17], dtype=np.float32),
+ )
+
+ # Check blenders attribute is preserved
+ assert readback.redirection.blenders == {
+ "DISTWALK": {"max_blend_distance": 1.0, "blend_distance_name": None},
+ "DISTJUMP": {"max_blend_distance": np.inf, "blend_distance_name": None},
+ }
+
+
+def test_sparse_blenders_attribute(sparse_skims_setup):
+ """Test that blenders attribute is set correctly."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+
+ assert skims.redirection.blenders == {
+ "DISTWALK": {"max_blend_distance": 1.0, "blend_distance_name": None},
+ "DISTJUMP": {"max_blend_distance": np.inf, "blend_distance_name": None},
+ }
+
+
+def test_sparse_reverse(sparse_skims_setup, trips_data):
+ """Test reverse operation on sparse data."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+ trips = trips_data
+
+ tree = sh.DataTree(
+ base=trips,
+ skims=skims,
+ relationships=(
+ "base.orig_maz @ skims.omaz",
+ "base.dest_maz @ skims.dmaz",
+ ),
+ )
+
+ flow3 = tree.setup_flow(
+ {
+ "plain_distance": "DISTWALK",
+ "reverse_distance": 'skims.reverse("DISTWALK")',
+ }
+ )
+
+ expected = np.array(
+ [[0.0111, 0.0111], [0.184, 0.12], [0.12, 0.12], [0.17, 0.17], [0.17, 0.17]],
+ dtype=np.float32,
+ )
+
+ result = flow3.load()
+ assert result == approx(expected)
+
+
+def test_sparse_reverse_iat(sparse_skims_setup):
+ """Test reverse lookup using iat accessor."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+
+ z = skims.iat(
+ omaz=[0, 1, 3, 101, 102],
+ dmaz=[0, 0, 0, 100, 100],
+ _names=["DIST", "DISTWALK"],
+ _load=True,
+ )
+
+ assert z["DISTWALK"].data == approx(np.array([0.0111, 0.12, 0.12, 0.17, 0.17]))
+ assert z["DIST"].data == approx(np.array([0.12, 0.12, 0.12, 0.17, 0.17]))
+
+
+def test_sparse_dense_lookup(sparse_skims_setup):
+ """Test direct dense dimension lookup (bypassing sparse)."""
+ skims, maz_taz, maz_to_maz_walk = sparse_skims_setup
+
+ # Lookup using dense TAZ dimensions should bypass sparse data
+ result = skims.at(
+ otaz=[1, 1, 1, 16, 16],
+ dtaz=[1, 1, 1, 16, 16],
+ _names=["DIST", "DISTWALK"],
+ _load=True,
+ )
+
+ # Should return dense data only, no sparse blending
+ assert result is not None
+ assert "DIST" in result
+ assert "DISTWALK" in result
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