diff --git a/.all-contributorsrc b/.all-contributorsrc index b2da537f7..7f9a01b02 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -62,7 +62,8 @@ "profile": "https://www.brosaplanella.xyz", "contributions": [ "review", - "code" + "code", + "example" ] }, { @@ -97,7 +98,7 @@ { "login": "IntelLiGent", "name": "Horizon Europe IntelLiGent Consortium", - "avatar_url": "assets/logo-farger.pdf", + "avatar_url": "assets/logo-farger.svg", "profile": "https://heuintelligent.eu/", "contributions": [ "financial" @@ -111,6 +112,24 @@ "contributions": [ "code" ] + }, + { + "login": "MarkBlyth", + "name": "MarkBlyth", + "avatar_url": "https://avatars.githubusercontent.com/u/20501619?v=4", + "profile": "https://github.com/MarkBlyth", + "contributions": [ + "code" + ] + }, + { + "login": "f-g-r-i-m-m", + "name": "f-g-r-i-m-m", + "avatar_url": "https://avatars.githubusercontent.com/u/137511310?v=4", + "profile": "https://github.com/f-g-r-i-m-m", + "contributions": [ + "example" + ] } ], "contributorsPerLine": 7, diff --git a/.github/workflows/lychee_links.yaml b/.github/workflows/lychee_links.yaml new file mode 100644 index 000000000..9d5037dad --- /dev/null +++ b/.github/workflows/lychee_links.yaml @@ -0,0 +1,61 @@ +# Lychee Link Checking + +name: Links +on: + workflow_dispatch: + pull_request: + push: + branches: + - main + schedule: + - cron: '0 6 * * 0' # Run weekly on Sundays at 06:00 UTC + +jobs: + Lychee: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Restore lychee cache + uses: actions/cache@v4 + with: + path: .lycheecache + key: cache-lychee-${{ github.sha }} + restore-keys: cache-lychee- + + - name: Set up Lychee + uses: lycheeverse/lychee-action@v1.10.0 + with: + args: >- + --cache + --no-progress + --max-cache-age 2d + --timeout 10 + --max-retries 5 + --skip-missing + --exclude-loopback + --accept 200,429 + --exclude "https://tiles.stadiamaps.com/*|https://b.tile.openstreetmap.org/*" + --exclude "https://cartodb-basemaps-c.global.ssl.fastly.net/*" + --exclude "https://events.mapbox.com/*|https://events.mapbox.cn/*|https://api.mapbox.cn/*" + --exclude "https://github.com/mikolalysenko/glsl-read-float/*" + --exclude "https://fonts.openmaptiles.org/*" + --exclude "https://a.tile.openstreetmap.org/*" + --exclude "https://openstreetmap.org/*|https://www.openstreetmap.org/*" + --exclude "https://cdn.plot.ly/*" + --exclude "http://www.w3.org/*|https://www.w3.org/*" + --exclude "https://doi.org/*" + --exclude "https://raw.githubusercontent.com/paramm-team/pybamm-param/develop/pbparam/input/data/" + --exclude-path ./CHANGELOG.md + --exclude-path asv.conf.json + --exclude-path docs/conf.py + './**/*.rst' + './**/*.md' + './**/*.py' + './**/*.ipynb' + './**/*.json' + './**/*.toml' + fail: true + jobSummary: true + format: markdown diff --git a/.github/workflows/test_on_push.yaml b/.github/workflows/test_on_pull_request.yaml similarity index 84% rename from .github/workflows/test_on_push.yaml rename to .github/workflows/test_on_pull_request.yaml index e2b6d19d9..2f74bbe32 100644 --- a/.github/workflows/test_on_push.yaml +++ b/.github/workflows/test_on_pull_request.yaml @@ -105,6 +105,37 @@ jobs: run: | nox -s examples + # Quick benchmarks on macos-14 + benchmarks: + needs: style + runs-on: macos-14 + strategy: + fail-fast: false + name: Benchmarks + + steps: + - name: Check out PyBOP repository + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - name: Set up Python 3.12 + id: setup-python + uses: actions/setup-python@v4 + with: + python-version: 3.12 + + - name: Install dependencies + shell: bash + run: | + python -m pip install --upgrade pip asv[virtualenv] + + - name: Run quick benchmarks + shell: bash + run: | + asv machine --machine "GitHubRunner" + asv run --machine "GitHubRunner" --quick --show-stderr + # Runs only on macos-14 with Python 3.12 check_coverage: needs: style diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 47ef467c5..f063b3d4d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ ci: repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.5.0" + rev: "v0.6.4" hooks: - id: ruff args: [--fix, --show-fixes] @@ -32,3 +32,9 @@ repos: - id: rst-backticks - id: rst-directive-colons - id: rst-inline-touching-normal + + - repo: https://github.com/kynan/nbstripout + rev: 0.7.1 + hooks: + - id: nbstripout + args: ['--keep-output', '--drop-empty-cells'] diff --git a/CHANGELOG.md b/CHANGELOG.md index 616a26aeb..7668f65f6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,14 +6,52 @@ ## Breaking Changes +# [v24.9.0](https://github.com/pybop-team/PyBOP/tree/v24.9.0) - 2024-09-10 + +## Features + +- [#462](https://github.com/pybop-team/PyBOP/pull/462) - Enables multidimensional learning rate for `pybop.AdamW` with updated (more robust) integration testing. Fixes bug in `Minkowski` and `SumofPower` cost functions for gradient-based optimisers. +- [#411](https://github.com/pybop-team/PyBOP/pull/411) - Updates notebooks with README in `examples/` directory, removes kaleido dependency and moves to nbviewer rendering, displays notebook figures with `notebook_connected` plotly renderer +- [#6](https://github.com/pybop-team/PyBOP/issues/6) - Adds Monte Carlo functionality, with methods based on Pints' algorithms. A base class is added `BaseSampler`, in addition to `PintsBaseSampler`. +- [#353](https://github.com/pybop-team/PyBOP/issues/353) - Allow user-defined check_params functions to enforce nonlinear constraints, and enable SciPy constrained optimisation methods +- [#222](https://github.com/pybop-team/PyBOP/issues/222) - Adds an example for performing and electrode balancing. +- [#441](https://github.com/pybop-team/PyBOP/issues/441) - Adds an example for estimating constants within a `pybamm.FunctionalParameter`. +- [#405](https://github.com/pybop-team/PyBOP/pull/405) - Adds frequency-domain based EIS prediction methods via `model.simulateEIS` and updates to `problem.evaluate` with examples and tests. +- [#460](https://github.com/pybop-team/PyBOP/pull/460) - Notebook example files added for ECM and folder structure updated. +- [#450](https://github.com/pybop-team/PyBOP/pull/450) - Adds support for IDAKLU with output variables, and corresponding examples, tests. +- [#364](https://github.com/pybop-team/PyBOP/pull/364) - Adds the MultiFittingProblem class and the multi_fitting example script. +- [#444](https://github.com/pybop-team/PyBOP/issues/444) - Merge `BaseModel` `build()` and `rebuild()` functionality. +- [#435](https://github.com/pybop-team/PyBOP/pull/435) - Adds SLF001 linting for private members. +- [#418](https://github.com/pybop-team/PyBOP/issues/418) - Wraps the `get_parameter_info` method from PyBaMM to get a dictionary of parameter names and types. +- [#413](https://github.com/pybop-team/PyBOP/pull/413) - Adds `DesignCost` functionality to `WeightedCost` class with additional tests. +- [#357](https://github.com/pybop-team/PyBOP/pull/357) - Adds `Transformation()` class with `LogTransformation()`, `IdentityTransformation()`, and `ScaledTransformation()`, `ComposedTransformation()` implementations with corresponding examples and tests. +- [#427](https://github.com/pybop-team/PyBOP/issues/427) - Adds the nbstripout pre-commit hook to remove unnecessary metadata from notebooks. +- [#327](https://github.com/pybop-team/PyBOP/issues/327) - Adds the `WeightedCost` subclass, defines when to evaluate a problem and adds the `spm_weighted_cost` example script. +- [#393](https://github.com/pybop-team/PyBOP/pull/383) - Adds Minkowski and SumofPower cost classes, with an example and corresponding tests. +- [#403](https://github.com/pybop-team/PyBOP/pull/403/) - Adds lychee link checking action. + +## Bug Fixes + +- [#473](https://github.com/pybop-team/PyBOP/pull/473) - Bugfixes for transformation class, adds optional `apply_transform` arg to `BaseCost.__call__()`, adds `log_update()` method to `BaseOptimiser` +- [#464](https://github.com/pybop-team/PyBOP/issues/464) - Fix order of design `parameter_set` updates and refactor `update_capacity`. +- [#468](https://github.com/pybop-team/PyBOP/issue/468) - Renames `quick_plot.py` to `standard_plots.py`. +- [#454](https://github.com/pybop-team/PyBOP/issue/454) - Fixes benchmarking suite. +- [#421](https://github.com/pybop-team/PyBOP/issues/421) - Adds a default value for the initial SOC for design problems. + +## Breaking Changes + +- [#483](https://github.com/pybop-team/PyBOP/pull/483) - Replaces `pybop.MAP` with `pybop.LogPosterior` with an updated call args and bugfixes. +- [#436](https://github.com/pybop-team/PyBOP/pull/436) - **API Change:** The functionality from `BaseCost.evaluate/S1` & `BaseCost._evaluate/S1` is represented in `BaseCost.__call__` & `BaseCost.compute`. `BaseCost.compute` directly acts on the predictions, while `BaseCost.__call__` calls `BaseProblem.evaluate/S1` before `BaseCost.compute`. `compute` has optional args for gradient cost calculations. +- [#424](https://github.com/pybop-team/PyBOP/issues/424) - Replaces the `init_soc` input to `FittingProblem` with the option to pass an initial OCV value, updates `BaseModel` and fixes `multi_model_identification.ipynb` and `spm_electrode_design.ipynb`. + # [v24.6.1](https://github.com/pybop-team/PyBOP/tree/v24.6.1) - 2024-07-31 ## Features + - [#313](https://github.com/pybop-team/PyBOP/pull/313/) - Fixes for PyBaMM v24.5, drops support for PyBaMM v23.9, v24.1 ## Bug Fixes - ## Breaking Changes # [v24.6](https://github.com/pybop-team/PyBOP/tree/v24.6) - 2024-07-08 diff --git a/CITATION.cff b/CITATION.cff index 6583447f3..0b6552e6e 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -11,5 +11,5 @@ authors: family-names: Courtier - given-names: David family-names: Howey -version: "24.6.1" # Update this when you release a new version +version: "24.9.0" # Update this when you release a new version repository-code: 'https://www.github.com/pybop-team/pybop' diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 727c5c510..274ba954e 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -21,7 +21,7 @@ pip install -e .[all,dev] Before you commit any code, please perform the following checks using [Nox](https://nox.thea.codes/en/stable/index.html): -- [All tests pass](#testing): `$ nox -s unit` +- [All tests pass](#testing): `$ nox -s quick` ### Installing and using pre-commit @@ -62,9 +62,9 @@ You now have everything you need to start making changes! ### B. Writing your code -6. PyBOP is developed in [Python](https://en.wikipedia.org/wiki/Python_(programming_language)), and makes heavy use of [NumPy](https://en.wikipedia.org/wiki/NumPy) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](http://blog.hackerearth.com/how-can-r-users-learn-python-for-data-science)). +6. PyBOP is developed in [Python](https://en.wikipedia.org/wiki/Python_(programming_language)), and makes heavy use of [NumPy](https://en.wikipedia.org/wiki/NumPy) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](https://rebeccabarter.com/blog/2023-09-11-from_r_to_python)). 7. Make sure to follow our [coding style guidelines](#coding-style-guidelines). -8. Commit your changes to your branch with [useful, descriptive commit messages](https://chris.beams.io/posts/git-commit/): Remember these are publicly visible and should still make sense a few months ahead in time. While developing, you can keep using the GitHub issue you're working on as a place for discussion. [Refer to your commits](https://stackoverflow.com/questions/8910271/how-can-i-reference-a-commit-in-an-issue-comment-on-github) when discussing specific lines of code. +8. Commit your changes to your branch with [useful, descriptive commit messages](https://chris.beams.io/posts/git-commit/): Remember these are publicly visible and should still make sense a few months ahead in time. While developing, you can keep using the GitHub issue you're working on as a place for discussion. Refer to your commits when discussing specific lines of code. This is achieved by referencing the SHA-hash in the comment. An example of this looks like: `the commit 3e5c1e6 solved the issue...` 9. If you want to add a dependency on another library, or re-use code you found somewhere else, have a look at [these guidelines](#dependencies-and-reusing-code). ### C. Merging your changes with PyBOP @@ -83,16 +83,20 @@ PyBOP follows the [PEP8 recommendations](https://www.python.org/dev/peps/pep-000 ### Ruff -We use [ruff](https://github.com/charliermarsh/ruff) to check our PEP8 adherence. To try this on your system, navigate to the PyBOP directory in a console and type +We use [ruff](https://github.com/charliermarsh/ruff) to lint and ensure adherence to Python PEP standards. To manually trigger `ruff`, navigate to the PyBOP directory in a console and type ```bash python -m pip install pre-commit pre-commit run ruff ``` -ruff is configured inside the file `pre-commit-config.yaml`, allowing us to ignore some errors. If you think this should be added or removed, please submit an [issue](https://guides.github.com/features/issues/). +ruff is configured inside the file `pyproject.toml`, allowing us to ignore some errors. If you think a rule should be added or removed, please submit an [issue](https://guides.github.com/features/issues/). -When you commit your changes they will be checked against ruff automatically (see [Pre-commit checks](#pre-commit-checks)). +When you commit your changes they will be checked against ruff automatically (see [Pre-commit checks](#pre-commit-checks)). If you are having issues getting your commit to pass the linting, it +is possible to skip linting for single lines (this should only be done as a **last resort**) by adding a line comment of `#noqa: $ruff_rule` where the `$ruff_rule` is replaced with the rule in question. +It is also possible to skip linting altogether by committing your changes by using the +`--no-verify` command-line flag. +These rules can be found in the ruff configuration in `pyproject.toml` or in the failed pre-commit output. Please note the lint skipping in the pull request for reviewers. ### Naming @@ -105,7 +109,7 @@ Class names are CamelCase, and start with an upper case letter, for example `MyO While it's a bad idea for developers to "reinvent the wheel", it's important for users to get a _reasonably sized download and an easy install_. In addition, external libraries can sometimes cease to be supported, and when they contain bugs it might take a while before fixes become available as automatic downloads to PyBOP users. For these reasons, all dependencies in PyBOP should be thought about carefully and discussed on GitHub. -Direct inclusion of code from other packages is possible, as long as their license permits it and is compatible with ours, but again should be considered carefully and discussed in the group. Snippets from blogs and [stackoverflow](https://stackoverflow.com/) can often be included but must include attribution to the original by commenting with a link in the source code. +Direct inclusion of code from other packages is possible, as long as their license permits it and is compatible with ours, but again should be considered carefully and discussed in the group. Snippets from blogs and stackoverflow can often be included but must include attribution to the original by commenting with a link in the source code. ### Separating dependencies diff --git a/README.md b/README.md index 99f7a031e..5814f7202 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ [![Contributors](https://img.shields.io/github/contributors/pybop-team/PyBOP)](https://github.com/pybop-team/PyBOP/graphs/contributors) [![Last Commit](https://img.shields.io/github/last-commit/pybop-team/PyBOP/develop?color=purple)](https://github.com/pybop-team/PyBOP/commits/develop) [![Python Versions from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fpybop-team%2FPyBOP%2Fdevelop%2Fpyproject.toml&label=Python)](https://pypi.org/project/pybop/) - [![Forks](https://img.shields.io/github/forks/pybop-team/PyBOP?style=flat)](https://github.com/pybop-team/PyBOPe/network/members) + [![Forks](https://img.shields.io/github/forks/pybop-team/PyBOP?style=flat)](https://github.com/pybop-team/PyBOP/network/members) [![Stars](https://img.shields.io/github/stars/pybop-team/PyBOP?style=flat&color=gold)](https://github.com/pybop-team/PyBOP/stargazers) [![Codecov](https://codecov.io/gh/pybop-team/PyBOP/branch/develop/graph/badge.svg)](https://codecov.io/gh/pybop-team/PyBOP) [![Open Issues](https://img.shields.io/github/issues/pybop-team/PyBOP)](https://github.com/pybop-team/PyBOP/issues/) @@ -74,7 +74,7 @@ Additional script-based examples can be found in the [examples directory](https: - [Unscented Kalman filter parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_UKF.py) - [Import and export parameters using Faraday's BPX format](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py) - [Maximum a posteriori parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py) -- [Gradient based parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_adam.py) +- [Gradient based parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_AdamW.py) ### Supported Methods The table below lists the currently supported [models](https://github.com/pybop-team/PyBOP/tree/develop/pybop/models), [optimisers](https://github.com/pybop-team/PyBOP/tree/develop/pybop/optimisers), and [cost functions](https://github.com/pybop-team/PyBOP/tree/develop/pybop/costs) in PyBOP. @@ -120,14 +120,16 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d NicolaCourtier
NicolaCourtier
💻 👀 💡 ⚠️ David Howey
David Howey
🤔 🧑‍🏫 Martin Robinson
Martin Robinson
🤔 🧑‍🏫 👀 💻 ⚠️ - Ferran Brosa Planella
Ferran Brosa Planella
👀 💻 + Ferran Brosa Planella
Ferran Brosa Planella
👀 💻 💡 Agriya Khetarpal
Agriya Khetarpal
💻 🚇 👀 + Faraday Institution
Faraday Institution
💵 - Faraday Institution
Faraday Institution
💵 UK Research and Innovation
UK Research and Innovation
💵 - EU IntelLiGent Project
IntelLiGent Consortium
💵 + Horizon Europe IntelLiGent Consortium
Horizon Europe IntelLiGent Consortium
💵 Muhammed Nedim Sogut
Muhammed Nedim Sogut
💻 + MarkBlyth
MarkBlyth
💻 + f-g-r-i-m-m
f-g-r-i-m-m
💡 diff --git a/asv.conf.json b/asv.conf.json index fdd830cea..e6b508e06 100644 --- a/asv.conf.json +++ b/asv.conf.json @@ -8,7 +8,7 @@ "python -m build --wheel -o {build_cache_dir} {build_dir}" ], "default_benchmark_timeout": 180, - "branches": ["develop"], + "branches": ["HEAD"], "environment_type": "virtualenv", "matrix": { "req":{ diff --git a/benchmarks/benchmark_model.py b/benchmarks/benchmark_model.py index 843b03bcc..2941cba88 100644 --- a/benchmarks/benchmark_model.py +++ b/benchmarks/benchmark_model.py @@ -1,8 +1,7 @@ import numpy as np import pybop - -from .benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class BenchmarkModel: @@ -27,19 +26,20 @@ def setup(self, model, parameter_set): self.model = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set)) # Define fitting parameters - parameters = [ + parameters = pybop.Parameters( pybop.Parameter( "Current function [A]", prior=pybop.Gaussian(0.4, 0.02), bounds=[0.2, 0.7], initial_value=0.4, ) - ] + ) # Generate synthetic data sigma = 0.001 self.t_eval = np.arange(0, 900, 2) - values = self.model.predict(t_eval=self.t_eval) + self.init_state = {"Initial SoC": 0.5} + values = self.model.predict(t_eval=self.t_eval, initial_state=self.init_state) corrupt_values = values["Voltage [V]"].data + np.random.normal( 0, sigma, len(self.t_eval) ) @@ -59,7 +59,7 @@ def setup(self, model, parameter_set): # Create fitting problem self.problem = pybop.FittingProblem( - model=self.model, dataset=dataset, parameters=parameters, init_soc=0.5 + model=self.model, dataset=dataset, parameters=parameters ) def time_model_predict(self, model, parameter_set): @@ -70,7 +70,9 @@ def time_model_predict(self, model, parameter_set): model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. """ - self.model.predict(inputs=self.inputs, t_eval=self.t_eval) + self.model.predict( + inputs=self.inputs, t_eval=self.t_eval, initial_state=self.init_state + ) def time_model_simulate(self, model, parameter_set): """ @@ -80,7 +82,7 @@ def time_model_simulate(self, model, parameter_set): model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. """ - self.problem._model.simulate(inputs=self.inputs, t_eval=self.t_eval) + self.problem.model.simulate(inputs=self.inputs, t_eval=self.t_eval) def time_model_simulateS1(self, model, parameter_set): """ @@ -90,4 +92,4 @@ def time_model_simulateS1(self, model, parameter_set): model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. """ - self.problem._model.simulateS1(inputs=self.inputs, t_eval=self.t_eval) + self.problem.model.simulateS1(inputs=self.inputs, t_eval=self.t_eval) diff --git a/benchmarks/benchmark_optim_construction.py b/benchmarks/benchmark_optim_construction.py index fee5f0789..0849b4f38 100644 --- a/benchmarks/benchmark_optim_construction.py +++ b/benchmarks/benchmark_optim_construction.py @@ -1,8 +1,7 @@ import numpy as np import pybop - -from .benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class BenchmarkOptimisationConstruction: @@ -29,7 +28,7 @@ def setup(self, model, parameter_set, optimiser): model_instance = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set)) # Define fitting parameters - parameters = [ + parameters = pybop.Parameters( pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.6, 0.02), @@ -42,7 +41,7 @@ def setup(self, model, parameter_set, optimiser): bounds=[0.375, 0.625], initial_value=0.51, ), - ] + ) # Generate synthetic data sigma = 0.001 @@ -76,7 +75,7 @@ def time_optimisation_construction(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. - optimiser (pybop.Optimiser): The optimizer class being used. + optimiser (pybop.Optimiser): The optimiser class being used. """ self.optim = pybop.Optimisation(self.cost, optimiser=optimiser) @@ -87,6 +86,6 @@ def time_cost_evaluate(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. - optimiser (pybop.Optimiser): The optimizer class being used. + optimiser (pybop.Optimiser): The optimiser class being used. """ self.cost([0.63, 0.51]) diff --git a/benchmarks/benchmark_parameterisation.py b/benchmarks/benchmark_parameterisation.py index a64116a48..9615f7876 100644 --- a/benchmarks/benchmark_parameterisation.py +++ b/benchmarks/benchmark_parameterisation.py @@ -1,8 +1,7 @@ import numpy as np import pybop - -from .benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class BenchmarkParameterisation: @@ -30,7 +29,7 @@ def setup(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class to be benchmarked. parameter_set (str): The name of the parameter set to be used. - optimiser (pybop.Optimiser): The optimizer class to be used. + optimiser (pybop.Optimiser): The optimiser class to be used. """ # Set random seed set_random_seed() @@ -46,7 +45,7 @@ def setup(self, model, parameter_set, optimiser): model_instance = model(parameter_set=params) # Define fitting parameters - parameters = [ + parameters = pybop.Parameters( pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.55, 0.03), @@ -57,7 +56,7 @@ def setup(self, model, parameter_set, optimiser): prior=pybop.Gaussian(0.55, 0.03), bounds=[0.375, 0.7], ), - ] + ) # Generate synthetic data sigma = 0.003 @@ -111,7 +110,7 @@ def time_parameterisation(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class being benchmarked (unused). parameter_set (str): The name of the parameter set being used (unused). - optimiser (pybop.Optimiser): The optimizer class being used (unused). + optimiser (pybop.Optimiser): The optimiser class being used (unused). """ self.optim.run() diff --git a/benchmarks/benchmark_track_parameterisation.py b/benchmarks/benchmark_track_parameterisation.py index 9180ffecb..134adaf3d 100644 --- a/benchmarks/benchmark_track_parameterisation.py +++ b/benchmarks/benchmark_track_parameterisation.py @@ -1,8 +1,7 @@ import numpy as np import pybop - -from .benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class BenchmarkTrackParameterisation: @@ -46,7 +45,7 @@ def setup(self, model, parameter_set, optimiser): model_instance = model(parameter_set=params) # Define fitting parameters - parameters = [ + parameters = pybop.Parameters( pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.55, 0.03), @@ -57,7 +56,7 @@ def setup(self, model, parameter_set, optimiser): prior=pybop.Gaussian(0.55, 0.03), bounds=[0.375, 0.7], ), - ] + ) # Generate synthetic data sigma = 0.003 diff --git a/docs/_extension/gallery_directive.py b/docs/_extension/gallery_directive.py index 3579ffcd8..4ab88d996 100644 --- a/docs/_extension/gallery_directive.py +++ b/docs/_extension/gallery_directive.py @@ -12,7 +12,7 @@ """ from pathlib import Path -from typing import Any, Dict, List +from typing import Any from docutils import nodes from docutils.parsers.rst import directives @@ -68,7 +68,7 @@ class GalleryGridDirective(SphinxDirective): "class-card": directives.unchanged, } - def run(self) -> List[nodes.Node]: + def run(self) -> list[nodes.Node]: """Create the gallery grid.""" if self.arguments: # If an argument is given, assume it's a path to a YAML file @@ -129,7 +129,7 @@ def run(self) -> List[nodes.Node]: return [container.children[0]] -def setup(app: Sphinx) -> Dict[str, Any]: +def setup(app: Sphinx) -> dict[str, Any]: """Add custom configuration to sphinx app. Args: diff --git a/docs/_static/switcher.json b/docs/_static/switcher.json index 8985f8ee1..96ffb5291 100644 --- a/docs/_static/switcher.json +++ b/docs/_static/switcher.json @@ -4,11 +4,16 @@ "url": "https://pybop-docs.readthedocs.io/en/latest/" }, { - "name": "v24.6 (stable)", - "version": "v24.6", - "url": "https://pybop-docs.readthedocs.io/en/v24.6.1/", + "name": "v24.9.0 (stable)", + "version": "v24.9.0", + "url": "https://pybop-docs.readthedocs.io/en/v24.9.0/", "preferred": true }, + { + "name": "v24.6", + "version": "v24.6", + "url": "https://pybop-docs.readthedocs.io/en/v24.6.1/" + }, { "name": "v24.3", "version": "v24.3", diff --git a/docs/conf.py b/docs/conf.py index df93a8fa4..d7c54b116 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -7,11 +7,11 @@ from pathlib import Path sys.path.append(str(Path(".").resolve())) -from pybop._version import __version__ # noqa: E402 +from pybop._version import __version__ # -- Project information ----------------------------------------------------- project = "PyBOP" -copyright = "2023, The PyBOP Team" +copyright = "2023, The PyBOP Team" # noqa A001 author = "The PyBOP Team" release = f"v{__version__}" diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 000000000..913f1e4cf --- /dev/null +++ b/examples/README.md @@ -0,0 +1,43 @@ +# Examples + +This directory contains example notebooks and scripts demonstrating how to use PyBOP. + +## Directory Structure + +- `notebooks/`: Jupyter notebooks of example functionality with explanations +- `scripts/`: Python scripts for quick reference and command-line usage +- `standalone/`: Example scripts for using standalone classes + +## Notebooks + +The `notebooks/` directory contains Jupyter notebooks that provide detailed, interactive examples of various features and use cases. These notebooks include explanations, code snippets, and visualisations. + +To view the notebooks with interactive figures without downloading the repository, please use nbviewer: + +
+ +[Notebooks on nbviewer](https://nbviewer.org/github/pybop-team/PyBOP/tree/develop/examples/notebooks/) + +
+ +## Scripts + +The `scripts/` directory contains standalone Python scripts that demonstrate specific tasks or workflows. These scripts are designed for quick reference and can be run directly from the command line. + +## Getting Started + +1. Clone the repository: `git clone https://github.com/pybop-team/pybop.git` +2. Navigate to the examples directory: `cd pybop/examples` +3. Explore the notebooks and scripts in their respective directories. +4. To run the Jupyter notebooks locally: + - Install Jupyter: `pip install jupyter` + - Start Jupyter Notebook: `jupyter notebook` + - Navigate to the `notebooks/` directory and open the desired notebook + +5. To run the Python scripts: + - Install PyBOP: `pip install pybop` + - Run a script using Python: `python scripts/script_name.py` + +## Contributing + +If you have additional examples or improvements to existing ones, please feel free to submit a pull request. We appreciate your contributions! diff --git a/examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb b/examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb index 9fd084dd6..91b44943f 100644 --- a/examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb +++ b/examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "markdown", - "id": "00940c64-4748-4b08-9a35-ea98ce311e71", + "id": "0", "metadata": {}, "source": [ "## LG M50 Single Pulse Parameter Identification\n", "\n", - "This example presents an experimental parameter identification method for a two-RC circuit model. The data for this notebook is located within the same directory and was obtained from [[1]](https://github.com/WDWidanage/Simscape-Battery-Library/tree/main/Examples/parameterEstimation_TECMD/Data).\n", + "This example presents an experimental parameter identification method for a two-RC circuit model. The data for this notebook is located within the same directory and was obtained from WDWidanage/Simscape-Battery-Library [[1]](https://github.com/WDWidanage/Simscape-Battery-Library/tree/a3842b91b3ccda006bc9be5d59c8bcbd167ceef7/Examples/parameterEstimation_TECMD/Data).\n", "\n", "\n", "### Setting up the Environment\n", @@ -17,75 +17,43 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "dd0e1a20-1ba3-4ff5-8f6a-f9c6f25c2a4a", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:35.622247Z", - "iopub.status.busy": "2024-04-14T18:57:35.621773Z", - "iopub.status.idle": "2024-04-14T18:57:40.672493Z", - "shell.execute_reply": "2024-04-14T18:57:40.671959Z" - } - }, + "execution_count": null, + "id": "1", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (24.0)\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (8.1.2)\r\n" + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: comm>=0.1.3 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from ipywidgets) (0.2.2)\r\n", - "Requirement already satisfied: ipython>=6.1.0 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from ipywidgets) (8.23.0)\r\n", - "Requirement already satisfied: traitlets>=4.3.1 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from ipywidgets) (5.14.2)\r\n", - "Requirement already satisfied: widgetsnbextension~=4.0.10 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from ipywidgets) (4.0.10)\r\n", - "Requirement already satisfied: jupyterlab-widgets~=3.0.10 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from ipywidgets) (3.0.10)\r\n", - 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"Requirement already satisfied: pexpect>4.3 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\r\n" + "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.4)\r\n", - "Requirement already satisfied: ptyprocess>=0.5 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\r\n", - "Requirement already satisfied: wcwidth in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\r\n", - "Requirement already satisfied: executing>=1.2.0 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\r\n", - "Requirement already satisfied: asttokens>=2.1.0 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\r\n", - "Requirement already satisfied: pure-eval in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\r\n", - "Requirement already satisfied: six>=1.12.0 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\r\n" + "zsh:1: no matches found: pybop[plot]\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Note: you may need to restart the kernel to use updated packages.\n", - "zsh:1: no matches found: pybop[plot]\r\n" + "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" ] }, { @@ -97,14 +65,14 @@ } ], "source": [ - "%pip install --upgrade pip ipywidgets\n", + "%pip install --upgrade pip ipywidgets -q\n", "%pip install pybop[plot] -q\n", "%pip install pandas -q" ] }, { "cell_type": "markdown", - "id": "90efc3d3-bf00-423d-ba81-246e4763b499", + "id": "2", "metadata": {}, "source": [ "### Importing Libraries\n", @@ -114,29 +82,43 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "d6afb8f9-3872-4a7e-a76d-0b50855fe089", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:40.675505Z", - "iopub.status.busy": "2024-04-14T18:57:40.675195Z", - "iopub.status.idle": "2024-04-14T18:57:46.231644Z", - "shell.execute_reply": "2024-04-14T18:57:46.230956Z" - } - }, + "execution_count": null, + "id": "3", + "metadata": {}, "outputs": [], "source": [ + "import numpy as np\n", "import pandas as pd\n", - "import plotly.graph_objects as go\n", "import pybamm\n", "from scipy.io import loadmat\n", "\n", - "import pybop" + "import pybop\n", + "\n", + "go = pybop.PlotlyManager().go\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" ] }, { "cell_type": "markdown", - "id": "a976f817-f0b3-421e-8cd3-a49be9128068", + "id": "6", "metadata": {}, "source": [ "## Importing Data\n", @@ -153,16 +135,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "705e7986", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.250744Z", - "iopub.status.busy": "2024-04-14T18:57:46.250383Z", - "iopub.status.idle": "2024-04-14T18:57:46.294482Z", - "shell.execute_reply": "2024-04-14T18:57:46.294078Z" - } - }, + "execution_count": null, + "id": "7", + "metadata": {}, "outputs": [], "source": [ "ocp = loadmat(\"data/LGM50_5Ah_OCV.mat\", simplify_cells=True, mat_dtype=False)\n", @@ -172,7 +147,7 @@ }, { "cell_type": "markdown", - "id": "609b0fc7", + "id": "8", "metadata": {}, "source": [ "### Convert to Dataframes\n", @@ -184,16 +159,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "cc48c662", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.296068Z", - "iopub.status.busy": "2024-04-14T18:57:46.295926Z", - "iopub.status.idle": "2024-04-14T18:57:46.302754Z", - "shell.execute_reply": "2024-04-14T18:57:46.302555Z" - } - }, + "execution_count": null, + "id": "9", + "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(pulse_data[\"LGM50_5Ah_Pulse\"][\"T0\"][\"SoC9\"][\"Cell19\"][\"data\"])\n", @@ -203,7 +171,7 @@ }, { "cell_type": "markdown", - "id": "34aeb1d2", + "id": "10", "metadata": {}, "source": [ "A plot of time vs voltage confirms the data looks correct for fitting. In this situation, we would prefer to have additional samples from the relaxation, but as we will show below, PyBOP is still able to identify parameter values that fit this system." @@ -211,16 +179,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "c4189ca7", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.304020Z", - "iopub.status.busy": "2024-04-14T18:57:46.303922Z", - "iopub.status.idle": "2024-04-14T18:57:46.726277Z", - "shell.execute_reply": "2024-04-14T18:57:46.725777Z" - } - }, + "execution_count": null, + "id": "11", + "metadata": {}, "outputs": [ { "data": { @@ -230,15 +191,10 @@ " if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n", " if (typeof require !== 'undefined') {\n", " require.undef(\"plotly\");\n", - " define('plotly', function(require, exports, module) {\n", - " /**\n", - "* plotly.js v2.30.0\n", - "* Copyright 2012-2024, Plotly, Inc.\n", - "* All rights reserved.\n", - "* Licensed under the MIT license\n", - "*/\n", - "/*! 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n,i,a,o=e[t],s=w.scopeDefaults[e.scope];\"lonaxis\"===t?(n=s.lonaxisRange,i=s.lataxisRange,a=function(t,e){return[t,e]}):\"lataxis\"===t&&(n=s.lataxisRange,i=s.lonaxisRange,a=function(t,e){return[e,t]});var l={type:\"linear\",range:[n[0],n[1]-1e-6],tick0:o.tick0,dtick:o.dtick};v.setConvert(l,r);var u=v.calcTicks(l);e.isScoped||\"lonaxis\"!==t||u.pop();for(var c=u.length,f=new Array(c),h=0;h-1&&b(n.event,i,[r.xaxis],[r.yaxis],r.id,c),s.indexOf(\"event\")>-1&&p.click(i,n.event))}))}function f(t){return r.projection.invert([t[0]+r.xaxis._offset,t[1]+r.yaxis._offset])}},S.makeFramework=function(){var t=this,e=t.graphDiv,r=e._fullLayout,i=\"clip\"+r._uid+t.id;t.clipDef=r._clips.append(\"clipPath\").attr(\"id\",i),t.clipRect=t.clipDef.append(\"rect\"),t.framework=n.select(t.container).append(\"g\").attr(\"class\",\"geo \"+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},v.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var e,r=t.center||{},n=t.projection,i=n.rotation||{};this.viewInitial={fitbounds:t.fitbounds,\"projection.scale\":n.scale},e=t._isScoped?{\"center.lon\":r.lon,\"center.lat\":r.lat}:t._isClipped?{\"projection.rotation.lon\":i.lon,\"projection.rotation.lat\":i.lat}:{\"center.lon\":r.lon,\"center.lat\":r.lat,\"projection.rotation.lon\":i.lon},u.extendFlat(this.viewInitial,e)},S.render=function(t){this._hasMarkerAngles&&t?this.plot(this._geoCalcData,this._fullLayout,[],!0):this._render()},S._render=function(){var t,e=this.projection,r=e.getPath();function n(t){var r=e(t.lonlat);return r?c(r[0],r[1]):null}function i(t){return e.isLonLatOverEdges(t.lonlat)?\"none\":null}for(t in this.basePaths)this.basePaths[t].attr(\"d\",r);for(t in this.dataPaths)this.dataPaths[t].attr(\"d\",(function(t){return r(t.geojson)}));for(t in this.dataPoints)this.dataPoints[t].attr(\"display\",i).attr(\"transform\",n)}},10816:function(t,e,r){\"use strict\";var n=r(84888).KY,i=r(3400).counterRegex,a=r(43520),o=\"geo\",s=i(o),l={};l[o]={valType:\"subplotid\",dflt:o,editType:\"calc\"},t.exports={attr:o,name:o,idRoot:o,idRegex:s,attrRegex:s,attributes:l,layoutAttributes:r(40384),supplyLayoutDefaults:r(86920),plot:function(t){for(var e=t._fullLayout,r=t.calcdata,i=e._subplots[o],s=0;s0&&P<0&&(P+=360);var O,I,D,z=(C+P)/2;if(!p){var R=d?f.projRotate:[z,0,0];O=r(\"projection.rotation.lon\",R[0]),r(\"projection.rotation.lat\",R[1]),r(\"projection.rotation.roll\",R[2]),r(\"showcoastlines\",!d&&x)&&(r(\"coastlinecolor\"),r(\"coastlinewidth\")),r(\"showocean\",!!x&&void 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r.on(\"zoomstart\",(function(){n.select(this).style(l)})).on(\"zoom\",(function(){e.scale(n.event.scale).translate(n.event.translate),t.render(!0);var r=e.invert(t.midPt);t.graphDiv.emit(\"plotly_relayouting\",{\"geo.projection.scale\":e.scale()/t.fitScale,\"geo.center.lon\":r[0],\"geo.center.lat\":r[1]})})).on(\"zoomend\",(function(){n.select(this).style(u),f(t,e,i)})),r}function p(t,e){var r,i,a,o,s,h,p,d,v,g=c(0,e);function y(t){return e.invert(t)}function m(r){var n=e.rotate(),i=e.invert(t.midPt);r(\"projection.rotation.lon\",-n[0]),r(\"center.lon\",i[0]),r(\"center.lat\",i[1])}return g.on(\"zoomstart\",(function(){n.select(this).style(l),r=n.mouse(this),i=e.rotate(),a=e.translate(),o=i,s=y(r)})).on(\"zoom\",(function(){if(h=n.mouse(this),function(t){var r=y(t);if(!r)return!0;var n=e(r);return Math.abs(n[0]-t[0])>2||Math.abs(n[1]-t[1])>2}(r))return g.scale(e.scale()),void 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r=v(e,M=b);h.of(this,arguments)({type:\"zoom\"})})),A=h.of(this,arguments),p++||A({type:\"zoomstart\"})})).on(\"zoomend\",(function(){var r;n.select(this).style(u),d.call(a,\"zoom\",null),r=h.of(this,arguments),--p||r({type:\"zoomend\"}),f(t,e,y)})).on(\"zoom.redraw\",(function(){t.render(!0);var r=e.rotate();t.graphDiv.emit(\"plotly_relayouting\",{\"geo.projection.scale\":e.scale()/t.fitScale,\"geo.projection.rotation.lon\":-r[0],\"geo.projection.rotation.lat\":-r[1]})})),n.rebind(a,h,\"on\")}function v(t,e){var r=t.invert(e);return r&&isFinite(r[0])&&isFinite(r[1])&&function(t){var e=t[0]*o,r=t[1]*o,n=Math.cos(r);return[n*Math.cos(e),n*Math.sin(e),Math.sin(r)]}(r)}function g(t,e,r,n){var i=y(r-t),a=y(n-e);return Math.sqrt(i*i+a*a)}function y(t){return(t%360+540)%360-180}function m(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),u=Math.sin(n);return i[a]=t[a]*l-t[s]*u,i[s]=t[s]*l+t[a]*u,i}function x(t,e){for(var r=0,n=0,i=t.length;nMath.abs(s)?(u.boxEnd[1]=u.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),u.boxEnd[1]l[3]&&(u.boxEnd[1]=l[3],u.boxEnd[0]=u.boxStart[0]+(l[3]-u.boxStart[1])/Math.abs(_))):(u.boxEnd[0]=u.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),u.boxEnd[0]l[2]&&(u.boxEnd[0]=l[2],u.boxEnd[1]=u.boxStart[1]+(l[2]-u.boxStart[0])*Math.abs(_)))}}else u.boxEnabled?(a=u.boxStart[0]!==u.boxEnd[0],s=u.boxStart[1]!==u.boxEnd[1],a||s?(a&&(g(0,u.boxStart[0],u.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,u.boxStart[1],u.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),u.boxEnabled=!1,u.boxInited=!1):u.boxInited&&(u.boxInited=!1);break;case\"pan\":u.boxEnabled=!1,u.boxInited=!1,e?(u.panning||(u.dragStart[0]=n,u.dragStart[1]=i),Math.abs(u.dragStart[0]-n).999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},346:function(t,e,r){\"use strict\";var n=r(86140),i=r(86968).u,a=r(92880).extendFlat,o=r(3400).counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}t.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 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r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},87152:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if(\"auto\"===c.tickmode){c.tickmode=\"linear\";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var p=n.calcTicks(c,{msUTC:!0}),d=0;d/g,\" 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a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,h=e.glplot.selection,v=0;v\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),E.push(\"value: \"+k.valueLabel),h.textLabel&&E.push(h.textLabel),x=E.join(\"
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R=[1,1,1];for(o=0;o<3;++o){var F=x[u=(l=c[M[o]]).type];R[o]=Math.pow(F.acc,1/F.count)/p[o]}g=\"data\"===D||Math.max.apply(null,R)/Math.min.apply(null,R)<=4?R:[1,1,1]}c.aspectratio.x=f.aspectratio.x=g[0],c.aspectratio.y=f.aspectratio.y=g[1],c.aspectratio.z=f.aspectratio.z=g[2],n.glplot.setAspectratio(c.aspectratio),n.viewInitial.aspectratio||(n.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),n.viewInitial.aspectmode||(n.viewInitial.aspectmode=c.aspectmode);var B=c.domain||null,N=e._size||null;if(B&&N){var j=n.container.style;j.position=\"absolute\",j.left=N.l+B.x[0]*N.w+\"px\",j.top=N.t+(1-B.y[1])*N.h+\"px\",j.width=N.w*(B.x[1]-B.x[0])+\"px\",j.height=N.h*(B.y[1]-B.y[0])+\"px\"}n.glplot.redraw()}},k.destroy=function(){var t=this;t.glplot&&(t.camera.mouseListener.enabled=!1,t.container.removeEventListener(\"wheel\",t.camera.wheelListener),t.camera=null,t.glplot.dispose(),t.container.parentNode.removeChild(t.container),t.glplot=null)},k.getCamera=function(){var t,e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),{up:{x:(t=e.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},k.setViewport=function(t){var e,r=this,n=t.camera;r.camera.lookAt.apply(this,[[(e=n).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),r.glplot.setAspectratio(t.aspectratio),\"orthographic\"===n.projection.type!==r.camera._ortho&&(r.glplot.redraw(),r.glplot.clearRGBA(),r.glplot.dispose(),r.initializeGLPlot())},k.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},k.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},k.saveLayout=function(t){var e,r,n,i,a,o,s=this,l=s.fullLayout,u=s.isCameraChanged(t),h=s.isAspectChanged(t),p=u||h;if(p){var d={};u&&(e=s.getCamera(),n=(r=f.nestedProperty(t,s.id+\".camera\")).get(),d[s.id+\".camera\"]=n),h&&(i=s.glplot.getAspectratio(),o=(a=f.nestedProperty(t,s.id+\".aspectratio\")).get(),d[s.id+\".aspectratio\"]=o),c.call(\"_storeDirectGUIEdit\",t,l._preGUI,d),u&&(r.set(e),f.nestedProperty(l,s.id+\".camera\").set(e)),h&&(a.set(i),f.nestedProperty(l,s.id+\".aspectratio\").set(i),s.glplot.redraw())}return p},k.updateFx=function(t,e){var r=this,n=r.camera;if(n)if(\"orbit\"===t)n.mode=\"orbit\",n.keyBindingMode=\"rotate\";else if(\"turntable\"===t){n.up=[0,0,1],n.mode=\"turntable\",n.keyBindingMode=\"rotate\";var i=r.graphDiv,a=i._fullLayout,o=r.fullSceneLayout.camera,s=o.up.x,l=o.up.y,u=o.up.z;if(u/Math.sqrt(s*s+l*l+u*u)<.999){var h=r.id+\".camera.up\",p={x:0,y:0,z:1},d={};d[h]=p;var v=i.layout;c.call(\"_storeDirectGUIEdit\",v,a._preGUI,d),o.up=p,f.nestedProperty(v,h).set(p)}}else n.keyBindingMode=t;r.fullSceneLayout.hovermode=e},k.toImage=function(t){var e=this;t||(t=\"png\"),e.staticMode&&e.container.appendChild(n),e.glplot.redraw();var r=e.glplot.gl,i=r.drawingBufferWidth,a=r.drawingBufferHeight;r.bindFramebuffer(r.FRAMEBUFFER,null);var o=new Uint8Array(i*a*4);r.readPixels(0,0,i,a,r.RGBA,r.UNSIGNED_BYTE,o),function(t,e,r){for(var n=0,i=r-1;n0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(o,i,a);var s=document.createElement(\"canvas\");s.width=i,s.height=a;var l,u=s.getContext(\"2d\",{willReadFrequently:!0}),c=u.createImageData(i,a);switch(c.data.set(o),u.putImageData(c,0,0),t){case\"jpeg\":l=s.toDataURL(\"image/jpeg\");break;case\"webp\":l=s.toDataURL(\"image/webp\");break;default:l=s.toDataURL(\"image/png\")}return e.staticMode&&e.container.removeChild(n),l},k.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[M[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},k.make4thDimension=function(){var t=this,e=t.graphDiv._fullLayout;t._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(t._mockAxis,e)},t.exports=T},52094:function(t){\"use strict\";t.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;aOpenStreetMap contributors',o=['© Carto',a].join(\" \"),s=['Map tiles by Stamen Design','under CC BY 3.0',\"|\",'Data by OpenStreetMap contributors','under ODbL'].join(\" 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n=r(3480),i=r(3400),a=i.strTranslate,o=i.strScale,s=r(84888).KY,l=r(9616),u=r(33428),c=r(43616),f=r(72736),h=r(14440),p=\"mapbox\",d=e.constants=r(47552);function v(t){return\"string\"==typeof t&&(-1!==d.styleValuesMapbox.indexOf(t)||0===t.indexOf(\"mapbox://\")||0===t.indexOf(\"stamen\"))}e.name=p,e.attr=\"subplot\",e.idRoot=p,e.idRegex=e.attrRegex=i.counterRegex(p),e.attributes={subplot:{valType:\"subplotid\",dflt:\"mapbox\",editType:\"calc\"}},e.layoutAttributes=r(5232),e.supplyLayoutDefaults=r(5976),e.plot=function(t){var e=t._fullLayout,r=t.calcdata,a=e._subplots[p];if(n.version!==d.requiredVersion)throw new Error(d.wrongVersionErrorMsg);var o=function(t,e){var r=t._fullLayout;if(\"\"===t._context.mapboxAccessToken)return\"\";for(var n=[],a=[],o=!1,s=!1,l=0;l1&&i.warn(d.multipleTokensErrorMsg),n[0]):(a.length&&i.log([\"Listed mapbox access token(s)\",a.join(\",\"),\"but did not use a Mapbox map style, ignoring token(s).\"].join(\" \")),\"\")}(t,a);n.accessToken=o;for(var l=0;lw/2){var T=m.split(\"|\").join(\"
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e,r=t.sourcetype,n=t.source,a={type:r};return\"geojson\"===r?e=\"data\":\"vector\"===r?e=\"string\"==typeof n?\"url\":\"tiles\":\"raster\"===r?(e=\"tiles\",a.tileSize=256):\"image\"===r&&(e=\"url\",a.coordinates=t.coordinates),a[e]=n,t.sourceattribution&&(a.attribution=i(t.sourceattribution)),a}(t);e.addSource(this.idSource,r)}},l.findFollowingMapboxLayerId=function(t){if(\"traces\"===t)for(var e=this.subplot.getMapLayers(),r=0;r1)for(r=0;r-1&&g(e.originalEvent,n,[r.xaxis],[r.yaxis],r.id,t),i.indexOf(\"event\")>-1&&u.click(n,e.originalEvent)}}},b.updateFx=function(t){var e=this,r=e.map,n=e.gd;if(!e.isStatic){var a,o=t.dragmode;a=function(t,r){r.isRect?(t.range={})[e.id]=[u([r.xmin,r.ymin]),u([r.xmax,r.ymax])]:(t.lassoPoints={})[e.id]=r.map(u)};var s=e.dragOptions;e.dragOptions=i.extendDeep(s||{},{dragmode:t.dragmode,element:e.div,gd:n,plotinfo:{id:e.id,domain:t[e.id].domain,xaxis:e.xaxis,yaxis:e.yaxis,fillRangeItems:a},xaxes:[e.xaxis],yaxes:[e.yaxis],subplot:e.id}),r.off(\"click\",e.onClickInPanHandler),h(o)||f(o)?(r.dragPan.disable(),r.on(\"zoomstart\",e.clearOutline),e.dragOptions.prepFn=function(t,r,n){p(t,r,n,e.dragOptions,o)},l.init(e.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",e.clearOutline),e.div.onmousedown=null,e.div.ontouchstart=null,e.div.removeEventListener(\"touchstart\",e.div._ontouchstart),e.onClickInPanHandler=e.onClickInPanFn(e.dragOptions),r.on(\"click\",e.onClickInPanHandler))}function u(t){var r=e.map.unproject(t);return[r.lng,r.lat]}},b.updateFramework=function(t){var 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y=E.findPolygonOffset(v,L[0],L[1],C);B+=A+y[0],N+=M+y[1]}switch(l){case\"zoom\":Y.clickFn=st,u||(Y.moveFn=C?it:rt,Y.doneFn=at,function(){j=null,U=null,V=s.pathSubplot(),q=!1;var t=c._fullLayout[s.id];H=i(t.bgcolor).getLuminance(),(G=g.makeZoombox(h,H,T,k,V)).attr(\"fill-rule\",\"evenodd\"),W=g.makeCorners(h,T,k),w(c)}());break;case\"select\":case\"lasso\":b(t,n,a,Y,l)}},y.init(Y)},N.updateRadialDrag=function(t,e,r){var i=this,u=i.gd,c=i.layers,f=i.radius,h=i.innerRadius,p=i.cx,d=i.cy,v=i.radialAxis,m=S.radialDragBoxSize,x=m/2;if(v.visible){var b,_,T,M=R(i.radialAxisAngle),E=v._rl,L=E[0],C=E[1],P=E[r],O=.75*(E[1]-E[0])/(1-i.getHole(e))/f;r?(b=p+(f+x)*Math.cos(M),_=d-(f+x)*Math.sin(M),T=\"radialdrag\"):(b=p+(h-x)*Math.cos(M),_=d-(h-x)*Math.sin(M),T=\"radialdrag-inner\");var I,D,z,B=g.makeRectDragger(c,T,\"crosshair\",-x,-x,m,m),N={element:B,gd:u};!1===t.dragmode&&(N.dragmode=!1),V(n.select(B),v.visible&&h0==(r?z>L:zn?function(t){return t<=0}:function(t){return t>=0};t.c2g=function(r){var n=t.c2l(r)-e;return(s(n)?n:0)+o},t.g2c=function(r){return t.l2c(r+e-o)},t.g2p=function(t){return t*a},t.c2p=function(e){return t.g2p(t.c2g(e))}}}(t,e);break;case\"angularaxis\":!function(t,e){var r=t.type;if(\"linear\"===r){var i=t.d2c,s=t.c2d;t.d2c=function(t,e){return function(t,e){return\"degrees\"===e?a(t):t}(i(t),e)},t.c2d=function(t,e){return s(function(t,e){return\"degrees\"===e?o(t):t}(t,e))}}t.makeCalcdata=function(e,r){var n,i,a=e[r],o=e._length,s=function(r){return t.d2c(r,e.thetaunit)};if(a)for(n=new Array(o),i=0;i0?1:0}function r(t){var e=t[0],r=t[1];if(!isFinite(e)||!isFinite(r))return[1,0];var n=(e+1)*(e+1)+r*r;return[(e*e+r*r-1)/n,2*r/n]}function n(t,e){var r=e[0],n=e[1];return[r*t.radius+t.cx,-n*t.radius+t.cy]}function i(t,e){return e*t.radius}t.exports={smith:r,reactanceArc:function(t,e,a,o){var s=n(t,r([a,e])),l=s[0],u=s[1],c=n(t,r([o,e])),f=c[0],h=c[1];if(0===e)return[\"M\"+l+\",\"+u,\"L\"+f+\",\"+h].join(\" \");var p=i(t,1/Math.abs(e));return[\"M\"+l+\",\"+u,\"A\"+p+\",\"+p+\" 0 0,\"+(e<0?1:0)+\" \"+f+\",\"+h].join(\" \")},resistanceArc:function(t,a,o,s){var l=i(t,1/(a+1)),u=n(t,r([a,o])),c=u[0],f=u[1],h=n(t,r([a,s])),p=h[0],d=h[1];if(e(o)!==e(s)){var v=n(t,r([a,0]));return[\"M\"+c+\",\"+f,\"A\"+l+\",\"+l+\" 0 0,\"+(00){for(var n=[],i=0;i=c&&(h.min=0,d.min=0,g.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}function v(t,e,r,n){var i=h[e._name];function o(r,n){return a.coerce(t,e,i,r,n)}o(\"uirevision\",n.uirevision),e.type=\"linear\";var p=o(\"color\"),d=p!==i.color.dflt?p:r.font.color,v=e._name.charAt(0).toUpperCase(),g=\"Component 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n=r(33428),i=r(49760),a=r(24040),o=r(3400),s=o.strTranslate,l=o._,u=r(76308),c=r(43616),f=r(78344),h=r(92880).extendFlat,p=r(7316),d=r(54460),v=r(86476),g=r(93024),y=r(72760),m=y.freeMode,x=y.rectMode,b=r(81668),_=r(22676).prepSelect,w=r(22676).selectOnClick,T=r(22676).clearOutline,k=r(22676).clearSelectionsCache,A=r(33816);function M(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}t.exports=M;var S=M.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=this,n=e[r.id],i=e._size;r._hasClipOnAxisFalse=!1;for(var a=0;aE*b?i=(a=b)*E:a=(i=x)/E,o=y*i/x,l=m*a/b,r=e.l+e.w*v-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[v-o/2,v+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var S=p.baxis=h({},t.baxis,{range:[_-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});f(S,p.graphDiv._fullLayout),S.setScale();var L=p.caxis=h({},t.caxis,{range:[_-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(L,p.graphDiv._fullLayout),L.setScale();var C=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",C),p.layers.plotbg.select(\"path\").attr(\"d\",C);var P=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",P);var O=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",O),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var I=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",I),p.layers.bgrid.attr(\"transform\",I);var D=s(r+i/2,n)+\"rotate(30)\"+s(0,-M._offset);p.layers.aaxis.attr(\"transform\",D),p.layers.agrid.attr(\"transform\",D);var z=s(r+i/2,n)+\"rotate(-30)\"+s(0,-L._offset);p.layers.caxis.attr(\"transform\",z),p.layers.cgrid.attr(\"transform\",z),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(u.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(u.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",L.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(u.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),c.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var e=this,r=e.graphDiv,n=e.id.substr(7)+\"title\",i=e.layers,a=e.aaxis,o=e.baxis,s=e.caxis;if(e.drawAx(a),e.drawAx(o),e.drawAx(s),t){var u=Math.max(a.showticklabels?a.tickfont.size/2:0,(s.showticklabels?.75*s.tickfont.size:0)+(\"outside\"===s.ticks?.87*s.ticklen:0)),c=(o.showticklabels?o.tickfont.size:0)+(\"outside\"===o.ticks?o.ticklen:0)+3;i[\"a-title\"]=b.draw(r,\"a\"+n,{propContainer:a,propName:e.id+\".aaxis.title\",placeholder:l(r,\"Click to enter Component A title\"),attributes:{x:e.x0+e.w/2,y:e.y0-a.title.font.size/3-u,\"text-anchor\":\"middle\"}}),i[\"b-title\"]=b.draw(r,\"b\"+n,{propContainer:o,propName:e.id+\".baxis.title\",placeholder:l(r,\"Click to enter Component B title\"),attributes:{x:e.x0-c,y:e.y0+e.h+.83*o.title.font.size+c,\"text-anchor\":\"middle\"}}),i[\"c-title\"]=b.draw(r,\"c\"+n,{propContainer:s,propName:e.id+\".caxis.title\",placeholder:l(r,\"Click to enter Component C title\"),attributes:{x:e.x0+e.w+c,y:e.y0+e.h+.83*s.title.font.size+c,\"text-anchor\":\"middle\"}})}},S.drawAx=function(t){var e,r=this,n=r.graphDiv,i=t._name,a=i.charAt(0),s=t._id,l=r.layers[i],u=a+\"tickLayout\",c=(e=t).ticks+String(e.ticklen)+String(e.showticklabels);r[u]!==c&&(l.selectAll(\".\"+s+\"tick\").remove(),r[u]=c),t.setScale();var f=d.calcTicks(t),h=d.clipEnds(t,f),p=d.makeTransTickFn(t),v=d.getTickSigns(t)[2],g=o.deg2rad(30),y=v*(t.linewidth||1)/2,m=v*t.ticklen,x=r.w,b=r.h,_=\"b\"===a?\"M0,\"+y+\"l\"+Math.sin(g)*m+\",\"+Math.cos(g)*m:\"M\"+y+\",0l\"+Math.cos(g)*m+\",\"+-Math.sin(g)*m,w={a:\"M0,0l\"+b+\",-\"+x/2,b:\"M0,0l-\"+x/2+\",-\"+b,c:\"M0,0l-\"+b+\",\"+x/2}[a];d.drawTicks(n,t,{vals:\"inside\"===t.ticks?h:f,layer:l,path:_,transFn:p,crisp:!1}),d.drawGrid(n,t,{vals:h,layer:r.layers[a+\"grid\"],path:w,transFn:p,crisp:!1}),d.drawLabels(n,t,{vals:f,layer:l,transFn:p,labelFns:d.makeLabelFns(t,0,30)})};var L=A.MINZOOM/2+.87,C=\"m-0.87,.5h\"+L+\"v3h-\"+(L+5.2)+\"l\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l2.6,1.5l-\"+L/2+\",\"+.87*L+\"Z\",P=\"m0.87,.5h-\"+L+\"v3h\"+(L+5.2)+\"l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-2.6,1.5l\"+L/2+\",\"+.87*L+\"Z\",O=\"m0,1l\"+L/2+\",\"+.87*L+\"l2.6,-1.5l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-\"+(L/2+2.6)+\",\"+(.87*L+4.5)+\"l2.6,1.5l\"+L/2+\",-\"+.87*L+\"Z\",I=!0;function D(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearOutline=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,y,b,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),L=M.graphDiv,z=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var r=L._fullLayout.clickmode;D(L),2===t&&(L.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",L,R({a:0,b:0,c:0}))),r.indexOf(\"select\")>-1&&1===t&&w(e,L,[M.xaxis],[M.yaxis],M.id,M.dragOptions),r.indexOf(\"event\")>-1&&g.click(L,e,M.id)}function B(t,e){return 1-e/M.h}function N(t,e){return 1-(t+(M.h-e)/Math.sqrt(3))/M.w}function j(t,e){return(t-(M.h-e)/Math.sqrt(3))/M.w}function U(i,a){var o=r+i*t,s=n+a*e,l=Math.max(0,Math.min(1,B(0,n),B(0,s))),u=Math.max(0,Math.min(1,N(r,n),N(o,s))),c=Math.max(0,Math.min(1,j(r,n),j(o,s))),v=(l/2+c)*M.w,g=(1-l/2-u)*M.w,m=(v+g)/2,x=g-v,_=(1-l)*M.h,w=_-x/E;x.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),k.transition().style(\"opacity\",1).duration(200),b=!0),L.emit(\"plotly_relayouting\",R(p))}function V(){D(L),p!==f&&(a.call(\"_guiRelayout\",L,R(p)),I&&L.data&&L._context.showTips&&(o.notifier(l(L,\"Double-click to zoom back out\"),\"long\"),I=!1))}function q(t,e){var r=t/M.xaxis._m,n=e/M.yaxis._m,i=[(p={a:f.a-n,b:f.b+(r+n)/2,c:f.c-(r-n)/2}).a,p.b,p.c].sort(o.sorterAsc),a=i.indexOf(p.a),l=i.indexOf(p.b),u=i.indexOf(p.c);i[0]<0&&(i[1]+i[0]/2<0?(i[2]+=i[0]+i[1],i[0]=i[1]=0):(i[2]+=i[0]/2,i[1]+=i[0]/2,i[0]=0),p={a:i[a],b:i[l],c:i[u]},e=(f.a-p.a)*M.yaxis._m,t=(f.c-p.c-f.b+p.b)*M.xaxis._m);var h=s(M.x0+t,M.y0+e);M.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",h);var d=s(-t,-e);M.clipDefRelative.select(\"path\").attr(\"transform\",d),M.aaxis.range=[p.a,M.sum-p.b-p.c],M.baxis.range=[M.sum-p.a-p.c,p.b],M.caxis.range=[M.sum-p.a-p.b,p.c],M.drawAxes(!1),M._hasClipOnAxisFalse&&M.plotContainer.select(\".scatterlayer\").selectAll(\".trace\").call(c.hideOutsideRangePoints,M),L.emit(\"plotly_relayouting\",R(p))}function H(){a.call(\"_guiRelayout\",L,R(p))}this.dragOptions={element:S,gd:L,plotinfo:{id:M.id,domain:L._fullLayout[M.id].domain,xaxis:M.xaxis,yaxis:M.yaxis},subplot:M.id,prepFn:function(a,l,c){M.dragOptions.xaxes=[M.xaxis],M.dragOptions.yaxes=[M.yaxis],t=L._fullLayout._invScaleX,e=L._fullLayout._invScaleY;var v=M.dragOptions.dragmode=L._fullLayout.dragmode;m(v)?M.dragOptions.minDrag=1:M.dragOptions.minDrag=void 0,\"zoom\"===v?(M.dragOptions.moveFn=U,M.dragOptions.clickFn=F,M.dragOptions.doneFn=V,function(t,e,a){var l=S.getBoundingClientRect();r=e-l.left,n=a-l.top,L._fullLayout._calcInverseTransform(L);var c=L._fullLayout._invTransform,v=o.apply3DTransform(c)(r,n);r=v[0],n=v[1],f={a:M.aaxis.range[0],b:M.baxis.range[1],c:M.caxis.range[1]},p=f,h=M.aaxis.range[1]-f.a,d=i(M.graphDiv._fullLayout[M.id].bgcolor).getLuminance(),y=\"M0,\"+M.h+\"L\"+M.w/2+\", 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Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*A})).attr(\"height\",(function(t){return t.thickness*A}))}(i=k(t,c.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],u=y.t+y.h,i.visible&&(d.drawTicks(t,i,{vals:\"inside\"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,u,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,u)}));var C=[v].concat(c.gauge.steps),P=f.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(T),P.exit().remove();var O=f.selectAll(\"g.value-bullet\").data([c.gauge.bar]);O.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),O.select(\"rect\").attr(\"height\",M).attr(\"y\",(A-M)/2).call(T),w(b)?O.select(\"rect\").transition().duration(b.duration).ease(b.easing).each(\"end\",(function(){_&&_()})).each(\"interrupt\",(function(){_&&_()})).attr(\"width\",Math.max(0,i.c2p(Math.min(c.gauge.axis.range[1],r[0].y)))):O.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(c.gauge.axis.range[1],r[0].y))):0),O.exit().remove();var I=r.filter((function(){return c.gauge.threshold.value||0===c.gauge.threshold.value})),D=f.selectAll(\"g.threshold-bullet\").data(I);D.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),D.select(\"line\").attr(\"x1\",i.c2p(c.gauge.threshold.value)).attr(\"x2\",i.c2p(c.gauge.threshold.value)).attr(\"y1\",(1-c.gauge.threshold.thickness)/2*A).attr(\"y2\",(1-(1-c.gauge.threshold.thickness)/2)*A).call(m.stroke,c.gauge.threshold.line.color).style(\"stroke-width\",c.gauge.threshold.line.width),D.exit().remove();var z=f.selectAll(\"g.gauge-outline\").data([g]);z.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),z.select(\"rect\").call(L).call(T),z.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var K=I.selectAll(\"text.title\").data(e);K.exit().remove(),K.enter().append(\"text\").classed(\"title\",!0),K.attr(\"text-anchor\",(function(){return R?x.right:x[O.title.align]})).text(O.title.text).call(f.font,O.title.font).call(p.convertToTspans,t),K.attr(\"transform\",(function(){var t,e=B.l+B.w*b[O.title.align],r=h.titlePadding,n=f.bBox(K.node());return D?(z&&(t=O.gauge.axis.visible?f.bBox(Y.node()).top-r-n.bottom:B.t+B.h/2-U/2-n.bottom-r),R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)):t=O._numbersTop-r-n.bottom,l(e,t)}))}))}},50048:function(t,e,r){\"use strict\";var n=r(49084),i=r(29736).axisHoverFormat,a=r(21776).Ks,o=r(52948),s=r(45464),l=r(92880).extendFlat,u=r(67824).overrideAll,c=t.exports=u(l({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:a(),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),valuehoverformat:i(\"value\",1),showlegend:l({},s.showlegend,{dflt:!1})},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),\"calc\",\"nested\");c.flatshading.dflt=!0,c.lighting.facenormalsepsilon.dflt=0,c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 0},62624:function(t,e,r){\"use strict\";var n=r(47128),i=r(3832).processGrid,a=r(3832).filter;t.exports=function(t,e){e._len=Math.min(e.x.length,e.y.length,e.z.length,e.value.length),e._x=a(e.x,e._len),e._y=a(e.y,e._len),e._z=a(e.z,e._len),e._value=a(e.value,e._len);var r=i(e);e._gridFill=r.fill,e._Xs=r.Xs,e._Ys=r.Ys,e._Zs=r.Zs,e._len=r.len;for(var o=1/0,s=-1/0,l=0;l0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n-1}function R(t,e){return null===t?e:t}function F(e,r,n){C();var i,a,o,l=[r],u=[n];if(s>=1)l=[r],u=[n];else if(s>0){var c=function(t,e){var r=t[0],n=t[1],i=t[2],a=function(t,e,r){for(var n=[],i=0;i-1?n[p]:L(d,v,y);h[p]=x>-1?x:O(d,v,y,R(e,m))}i=h[0],a=h[1],o=h[2],t._meshI.push(i),t._meshJ.push(a),t._meshK.push(o),++g}}function B(t,e,r,n){var i=t[3];in&&(i=n);for(var a=(t[3]-i)/(t[3]-e[3]+1e-9),o=[],s=0;s<4;s++)o[s]=(1-a)*t[s]+a*e[s];return o}function N(t,e,r){return t>=e&&t<=r}function j(t){var e=.001*(E-S);return t>=S-e&&t<=E+e}function U(e){for(var r=[],n=0;n<4;n++){var i=e[n];r.push([t._x[i],t._y[i],t._z[i],t._value[i]])}return r}var V=3;function q(t,e,r,n,i,a){a||(a=1),r=[-1,-1,-1];var o=!1,s=[N(e[0][3],n,i),N(e[1][3],n,i),N(e[2][3],n,i)];if(!s[0]&&!s[1]&&!s[2])return!1;var l=function(t,e,r){return j(e[0][3])&&j(e[1][3])&&j(e[2][3])?(F(t,e,r),!0):aMath.abs(L-M)?[A,L]:[L,M];d=!0,Q(r,C[0],C[1]),d=!1}}var I=[[Math.min(S,M),Math.max(S,M)],[Math.min(A,E),Math.max(A,E)]];[\"x\",\"y\",\"z\"].forEach((function(r){for(var n=[],i=0;i0&&(f.push(d.id),\"x\"===r?h.push([d.distRatio,0,0]):\"y\"===r?h.push([0,d.distRatio,0]):h.push([0,0,d.distRatio]))}else c=nt(1,\"x\"===r?b-1:\"y\"===r?_-1:w-1);f.length>0&&(n[a]=\"x\"===r?tt(e,f,o,s,h,n[a]):\"y\"===r?et(e,f,o,s,h,n[a]):rt(e,f,o,s,h,n[a]),a++),c.length>0&&(n[a]=\"x\"===r?K(e,c,o,s,n[a]):\"y\"===r?J(e,c,o,s,n[a]):$(e,c,o,s,n[a]),a++)}var v=t.caps[r];v.show&&v.fill&&(D(v.fill),n[a]=\"x\"===r?K(e,[0,b-1],o,s,n[a]):\"y\"===r?J(e,[0,_-1],o,s,n[a]):$(e,[0,w-1],o,s,n[a]),a++)}})),0===g&&P(),t._meshX=n,t._meshY=i,t._meshZ=a,t._meshIntensity=o,t._Xs=y,t._Ys=m,t._Zs=x}(),t}t.exports={findNearestOnAxis:u,generateIsoMeshes:p,createIsosurfaceTrace:function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new c(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}}},70548:function(t,e,r){\"use strict\";var n=r(3400),i=r(24040),a=r(50048),o=r(27260);function s(t,e,r,n,a){var s=a(\"isomin\"),l=a(\"isomax\");null!=l&&null!=s&&s>l&&(e.isomin=null,e.isomax=null);var u=a(\"x\"),c=a(\"y\"),f=a(\"z\"),h=a(\"value\");u&&u.length&&c&&c.length&&f&&f.length&&h&&h.length?(i.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\",\"z\"],n),a(\"valuehoverformat\"),[\"x\",\"y\",\"z\"].forEach((function(t){a(t+\"hoverformat\");var e=\"caps.\"+t;a(e+\".show\")&&a(e+\".fill\");var r=\"slices.\"+t;a(r+\".show\")&&(a(r+\".fill\"),a(r+\".locations\"))})),a(\"spaceframe.show\")&&a(\"spaceframe.fill\"),a(\"surface.show\")&&(a(\"surface.count\"),a(\"surface.fill\"),a(\"surface.pattern\")),a(\"contour.show\")&&(a(\"contour.color\"),a(\"contour.width\")),[\"text\",\"hovertext\",\"hovertemplate\",\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lighting.vertexnormalsepsilon\",\"lighting.facenormalsepsilon\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"flatshading\",\"opacity\"].forEach((function(t){a(t)})),o(t,e,n,a,{prefix:\"\",cLetter:\"c\"}),e._length=null):e.visible=!1}t.exports={supplyDefaults:function(t,e,r,i){s(t,e,0,i,(function(r,i){return n.coerce(t,e,a,r,i)}))},supplyIsoDefaults:s}},6296:function(t,e,r){\"use strict\";t.exports={attributes:r(50048),supplyDefaults:r(70548).supplyDefaults,calc:r(62624),colorbar:{min:\"cmin\",max:\"cmax\"},plot:r(31460).createIsosurfaceTrace,moduleType:\"trace\",name:\"isosurface\",basePlotModule:r(12536),categories:[\"gl3d\",\"showLegend\"],meta:{}}},52948:function(t,e,r){\"use strict\";var n=r(49084),i=r(29736).axisHoverFormat,a=r(21776).Ks,o=r(16716),s=r(45464),l=r(92880).extendFlat;t.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 0},n(\"\",{colorAttr:\"`intensity`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:o.opacity,flatshading:{valType:\"boolean\",dflt:!1,editType:\"calc\"},contour:{show:l({},o.contours.x.show,{}),color:o.contours.x.color,width:o.contours.x.width,editType:\"calc\"},lightposition:{x:l({},o.lightposition.x,{dflt:1e5}),y:l({},o.lightposition.y,{dflt:1e5}),z:l({},o.lightposition.z,{dflt:0}),editType:\"calc\"},lighting:l({vertexnormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-12,editType:\"calc\"},facenormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-6,editType:\"calc\"},editType:\"calc\"},o.lighting),hoverinfo:l({},s.hoverinfo,{editType:\"calc\"}),showlegend:l({},s.showlegend,{dflt:!1})})},1876:function(t,e,r){\"use strict\";var n=r(47128);t.exports=function(t,e){e.intensity&&n(t,e,{vals:e.intensity,containerStr:\"\",cLetter:\"c\"})}},576:function(t,e,r){\"use strict\";var n=r(67792).gl_mesh3d,i=r(67792).delaunay_triangulate,a=r(67792).alpha_shape,o=r(67792).convex_hull,s=r(33040).parseColorScale,l=r(3400).isArrayOrTypedArray,u=r(43080),c=r(8932).extractOpts,f=r(52094);function h(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var p=h.prototype;function d(t){for(var e=[],r=t.length,n=0;n=e-.5)return!1;return!0}p.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return l(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},p.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var n,l=t.x.length,h=f(v(r.xaxis,t.x,e.dataScale[0],t.xcalendar),v(r.yaxis,t.y,e.dataScale[1],t.ycalendar),v(r.zaxis,t.z,e.dataScale[2],t.zcalendar));if(t.i&&t.j&&t.k){if(t.i.length!==t.j.length||t.j.length!==t.k.length||!y(t.i,l)||!y(t.j,l)||!y(t.k,l))return;n=f(g(t.i),g(t.j),g(t.k))}else n=0===t.alphahull?o(h):t.alphahull>0?a(t.alphahull,h):function(t,e){for(var r=[\"x\",\"y\",\"z\"].indexOf(t),n=[],a=e.length,o=0;oy):g=A>w,y=A;var M=u(w,T,k,A);M.pos=_,M.yc=(w+A)/2,M.i=b,M.dir=g?\"increasing\":\"decreasing\",M.x=M.pos,M.y=[k,T],m&&(M.orig_p=r[b]),d&&(M.tx=e.text[b]),v&&(M.htx=e.hovertext[b]),x.push(M)}else x.push({pos:_,empty:!0})}return e._extremes[l._id]=a.findExtremes(l,n.concat(h,f),{padded:!0}),x.length&&(x[0].t={labels:{open:i(t,\"open:\")+\" \",high:i(t,\"high:\")+\" \",low:i(t,\"low:\")+\" \",close:i(t,\"close:\")+\" \"}}),x}t.exports={calc:function(t,e){var r=a.getFromId(t,e.xaxis),i=a.getFromId(t,e.yaxis),s=function(t,e,r){var i=r._minDiff;if(!i){var a,s=t._fullData,l=[];for(i=1/0,a=0;a\"+u.labels[x]+n.hoverLabelText(s,b,l.yhoverformat):((m=i.extendFlat({},h)).y0=m.y1=_,m.yLabelVal=b,m.yLabel=u.labels[x]+n.hoverLabelText(s,b,l.yhoverformat),m.name=\"\",f.push(m),g[b]=m)}return f}function h(t,e,r,i){var a=t.cd,o=t.ya,l=a[0].trace,f=a[0].t,h=c(t,e,r,i);if(!h)return[];var p=a[h.index],d=h.index=p.i,v=p.dir;function g(t){return f.labels[t]+n.hoverLabelText(o,l[t][d],l.yhoverformat)}var y=p.hi||l.hoverinfo,m=y.split(\"+\"),x=\"all\"===y,b=x||-1!==m.indexOf(\"y\"),_=x||-1!==m.indexOf(\"text\"),w=b?[g(\"open\"),g(\"high\"),g(\"low\"),g(\"close\")+\" \"+u[v]]:[];return _&&s(p,l,w),h.extraText=w.join(\"
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o=e.yaxis,s=e.xaxis,l=!!s.rangebreaks;i.makeTraceGroups(a,r,\"trace ohlc\").each((function(t){var e=n.select(this),r=t[0],a=r.t;if(!0!==r.trace.visible||a.empty)e.remove();else{var u=a.tickLen,c=e.selectAll(\"path\").data(i.identity);c.enter().append(\"path\"),c.exit().remove(),c.attr(\"d\",(function(t){if(t.empty)return\"M0,0Z\";var e=s.c2p(t.pos-u,!0),r=s.c2p(t.pos+u,!0),n=l?(e+r)/2:s.c2p(t.pos,!0);return\"M\"+e+\",\"+o.c2p(t.o,!0)+\"H\"+n+\"M\"+n+\",\"+o.c2p(t.h,!0)+\"V\"+o.c2p(t.l,!0)+\"M\"+r+\",\"+o.c2p(t.c,!0)+\"H\"+n}))}}))}},97384:function(t){\"use strict\";t.exports=function(t,e){var r,n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].t.bPos||0;if(!1===e)for(r=0;r=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(r))for(e=0;e0||c(s);u&&(o=\"array\");var f=r(\"categoryorder\",o);\"array\"===f?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),u||\"array\"!==f||(e.categoryorder=\"trace\")}}t.exports=function(t,e,r,c){function h(r,i){return 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r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.brush.svgBrush;a.wasDragged=!0,a._dragging=!0,a.grabbingBar?a.newExtent=[r-a.grabPoint,r+a.barLength-a.grabPoint].map(e.unitToPaddedPx.invert):a.newExtent=[a.startExtent,e.unitToPaddedPx.invert(r)].sort(s),e.brush.filterSpecified=!0,a.extent=a.stayingIntervals.concat([a.newExtent]),a.brushCallback(e),b(t.parentNode)}function T(t,e){var r=_(e,e.height-i.mouse(t)[1]-2*n.verticalPadding),a=\"crosshair\";r.clickableOrdinalRange?a=\"pointer\":r.region&&(a=r.region+\"-resize\"),i.select(document.body).style(\"cursor\",a)}function k(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||m()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,u=o.svgBrush;if(u.wasDragged=!1,u.grabbingBar=\"ns\"===s.region,u.grabbingBar){var c=l.map(e.unitToPaddedPx);u.grabPoint=r-c[0]-n.verticalPadding,u.barLength=c[1]-c[0]}u.clickableOrdinalRange=s.clickableOrdinalRange,u.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(u.stayingIntervals=u.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),u.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,u.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,m(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]a.newExtent[0];a.extent=a.stayingIntervals.concat(u?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),u?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function A(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}t.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e),n=r.slice();e.filter.set(n),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e,r){var i=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);i.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t,e,r){var i=r._context.staticPlot,a=t.selectAll(\".background\").data(o);a.enter().append(\"rect\").classed(\"background\",!0).call(d).call(v).style(\"pointer-events\",i?\"none\":\"auto\").attr(\"transform\",l(0,n.verticalPadding)),a.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var s=t.selectAll(\".highlight-shadow\").data(o);s.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",e).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),s.attr(\"y1\",(function(t){return t.height})).call(x);var u=t.selectAll(\".highlight\").data(o);u.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),u.attr(\"y1\",(function(t){return t.height})).call(x)}(i,e,r)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},61664:function(t,e,r){\"use strict\";t.exports={attributes:r(82296),supplyDefaults:r(60664),calc:r(95044),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:r(19976),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},19976:function(t,e,r){\"use strict\";var n=r(33428),i=r(84888)._M,a=r(24196),o=r(9616);e.name=\"parcoords\",e.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},e.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},e.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this,r=t.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":r,preserveAspectRatio:\"none\",x:0,y:0,width:t.style.width,height:t.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},95044:function(t,e,r){\"use strict\";var n=r(3400).isArrayOrTypedArray,i=r(8932),a=r(71688).wrap;t.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;rf&&(n.log(\"parcoords traces support up to \"+f+\" dimensions at the moment\"),d.splice(f));var v=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,c);o(e,l,c),Array.isArray(v)&&v.length||(e.visible=!1),h(e,v,\"values\",g);var y={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(c,\"labelfont\",y),n.coerceFont(c,\"tickfont\",y),n.coerceFont(c,\"rangefont\",y),c(\"labelangle\"),c(\"labelside\"),c(\"unselected.line.color\"),c(\"unselected.line.opacity\")}},95724:function(t,e,r){\"use strict\";var n=r(3400).isTypedArray;e.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},e.isOrdinal=function(t){return!!t.tickvals},e.isVisible=function(t){return t.visible||!(\"visible\"in t)}},29928:function(t,e,r){\"use strict\";var n=r(61664);n.plot=r(24196),t.exports=n},51352:function(t,e,r){\"use strict\";var n=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"attribute vec4 p01_04, p05_08, p09_12, p13_16,\",\" p17_20, p21_24, p25_28, p29_32,\",\" p33_36, p37_40, p41_44, p45_48,\",\" p49_52, p53_56, p57_60, colors;\",\"\",\"uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\",\" loA, hiA, loB, hiB, loC, hiC, loD, hiD;\",\"\",\"uniform vec2 resolution, viewBoxPos, viewBoxSize;\",\"uniform float maskHeight;\",\"uniform float drwLayer; // 0: context, 1: focus, 2: pick\",\"uniform vec4 contextColor;\",\"uniform sampler2D maskTexture, palette;\",\"\",\"bool isPick = (drwLayer > 1.5);\",\"bool isContext = (drwLayer < 0.5);\",\"\",\"const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\",\"const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\",\"\",\"float val(mat4 p, mat4 v) {\",\" return dot(matrixCompMult(p, v) * UNITS, UNITS);\",\"}\",\"\",\"float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\",\" float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\",\" float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\",\" return y1 * (1.0 - ratio) + y2 * ratio;\",\"}\",\"\",\"int iMod(int a, int b) {\",\" return a - b * (a / b);\",\"}\",\"\",\"bool fOutside(float p, float lo, float hi) {\",\" return (lo < hi) && (lo > p || p > hi);\",\"}\",\"\",\"bool vOutside(vec4 p, vec4 lo, vec4 hi) {\",\" return (\",\" fOutside(p[0], lo[0], hi[0]) ||\",\" fOutside(p[1], lo[1], hi[1]) ||\",\" fOutside(p[2], lo[2], hi[2]) ||\",\" fOutside(p[3], lo[3], hi[3])\",\" );\",\"}\",\"\",\"bool mOutside(mat4 p, mat4 lo, mat4 hi) {\",\" return (\",\" vOutside(p[0], lo[0], hi[0]) ||\",\" vOutside(p[1], lo[1], hi[1]) ||\",\" vOutside(p[2], lo[2], hi[2]) ||\",\" vOutside(p[3], lo[3], hi[3])\",\" );\",\"}\",\"\",\"bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\",\" return mOutside(A, loA, hiA) ||\",\" 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a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var u=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*u,a.offset=2*l*n,e(a),l*n+u>>8*e)%256/255}function v(t,e,r){for(var n=new Array(8*e),i=0,a=0;au&&(u=t[i].dim1.canvasX,o=i);0===s&&f(k,0,0,r.canvasWidth,r.canvasHeight);var c=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&ns._length&&(E=E.slice(0,s._length));var C,P=s.tickvals;function O(t,e){return{val:t,text:C[e]}}function I(t,e){return t.val-e.val}if(a(P)&&P.length){i.isTypedArray(P)&&(P=Array.from(P)),C=s.ticktext,a(C)&&C.length?C.length>P.length?C=C.slice(0,P.length):P.length>C.length&&(P=P.slice(0,C.length)):C=P.map(o(s.tickformat));for(var D=1;D=r||l>=i)return;var u=t.lineLayer.readPixel(s,i-1-l),c=0!==u[3],f=c?u[2]+256*(u[1]+256*u[0]):null,h={x:s,y:l,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:f};f!==N&&(c?a.hover(h):a.unhover&&a.unhover(h),N=f)}})),B.style(\"opacity\",(function(t){return t.pick?0:1})),p.style(\"background\",\"rgba(255, 255, 255, 0)\");var j=p.selectAll(\".\"+x.cn.parcoords).data(F,v);j.exit().remove(),j.enter().append(\"g\").classed(x.cn.parcoords,!0).style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"none\"),j.attr(\"transform\",(function(t){return c(t.model.translateX,t.model.translateY)}));var U=j.selectAll(\".\"+x.cn.parcoordsControlView).data(g,v);U.enter().append(\"g\").classed(x.cn.parcoordsControlView,!0),U.attr(\"transform\",(function(t){return c(t.model.pad.l,t.model.pad.t)}));var V=U.selectAll(\".\"+x.cn.yAxis).data((function(t){return t.dimensions}),v);V.enter().append(\"g\").classed(x.cn.yAxis,!0),U.each((function(t){D(V,t,w)})),B.each((function(t){if(t.viewModel){!t.lineLayer||a?t.lineLayer=_(this,t):t.lineLayer.update(t),(t.key||0===t.key)&&(t.viewModel[t.key]=t.lineLayer);var e=!t.context||a;t.lineLayer.render(t.viewModel.panels,e)}})),V.attr(\"transform\",(function(t){return c(t.xScale(t.xIndex),0)})),V.call(n.behavior.drag().origin((function(t){return t})).on(\"drag\",(function(t){var e=t.parent;E.linePickActive(!1),t.x=Math.max(-x.overdrag,Math.min(t.model.width+x.overdrag,n.event.x)),t.canvasX=t.x*t.model.canvasPixelRatio,V.sort((function(t,e){return t.x-e.x})).each((function(e,r){e.xIndex=r,e.x=t===e?e.x:e.xScale(e.xIndex),e.canvasX=e.x*e.model.canvasPixelRatio})),D(V,e,w),V.filter((function(e){return 0!==Math.abs(t.xIndex-e.xIndex)})).attr(\"transform\",(function(t){return 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n=r(45464),i=r(86968).u,a=r(25376),o=r(22548),s=r(21776).Ks,l=r(21776).Gw,u=r(92880).extendFlat,c=r(98192).c,f=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});t.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},pattern:c,editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:u({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:u({},f,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:u({},f,{}),outsidetextfont:u({},f,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:u({},f,{}),position:{valType:\"enumerated\",values:[\"top 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n=r(7316);e.name=\"pie\",e.plot=function(t,r,i,a){n.plotBasePlot(e.name,t,r,i,a)},e.clean=function(t,r,i,a){n.cleanBasePlot(e.name,t,r,i,a)}},45768:function(t,e,r){\"use strict\";var n=r(38248),i=r(49760),a=r(76308),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r=0})),(\"funnelarea\"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var u=0,c=0;c0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}function c(t,e,r,n,i){n(\"marker.line.width\")&&n(\"marker.line.color\",i?void 0:r.paper_bgcolor);var a=n(\"marker.colors\");l(n,\"marker.pattern\",a),t.marker&&!e.marker.pattern.fgcolor&&(e.marker.pattern.fgcolor=t.marker.colors),e.marker.pattern.bgcolor||(e.marker.pattern.bgcolor=r.paper_bgcolor)}t.exports={handleLabelsAndValues:u,handleMarkerDefaults:c,supplyDefaults:function(t,e,r,n){function l(r,n){return i.coerce(t,e,a,r,n)}var f=u(l(\"labels\"),l(\"values\")),h=f.len;if(e._hasLabels=f.hasLabels,e._hasValues=f.hasValues,!e._hasLabels&&e._hasValues&&(l(\"label0\"),l(\"dlabel\")),h){e._length=h,c(t,e,n,l,!0),l(\"scalegroup\");var p,d=l(\"text\"),v=l(\"texttemplate\");if(v||(p=l(\"textinfo\",i.isArrayOrTypedArray(d)?\"text+percent\":\"percent\")),l(\"hovertext\"),l(\"hovertemplate\"),v||p&&\"none\"!==p){var g=l(\"textposition\");s(t,e,n,l,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&l(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&l(\"insidetextorientation\")}else\"none\"===p&&l(\"textposition\",\"none\");o(e,n,l);var y=l(\"hole\");if(l(\"title.text\")){var m=l(\"title.position\",y?\"middle center\":\"top center\");y||\"middle center\"!==m||(e.title.position=\"top center\"),i.coerceFont(l,\"title.font\",n.font)}l(\"sort\"),l(\"direction\"),l(\"rotation\"),l(\"pull\")}else e.visible=!1}}},53644:function(t,e,r){\"use strict\";var n=r(10624).appendArrayMultiPointValues;t.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},21552:function(t,e,r){\"use strict\";var n=r(43616),i=r(76308);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},69656:function(t,e,r){\"use strict\";var n=r(3400);function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(n.isArrayOrTypedArray(t))for(var r=0;r\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],u._hasHoverLabel=!0}u._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[y(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[u.index],o=n.select(this).datum();u._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[y(o,i)],event:n.event}),u._hasHoverEvent=!1),u._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),u._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[u.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;ie&&e>n||r=-4;g-=2)y(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)y(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var m=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/m,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return 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C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=O(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,u=t.r/(void 0===(l=t.trace.aspectratio)?1:l),c=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(c+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?c*=2:-1!==a.title.position.indexOf(\"right\")&&(c+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=c/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function O(t){var e,r=t.pull;if(!r)return 0;if(l.isArrayOrTypedArray(r))for(r=0,e=0;er&&(r=t.pull[e]);return r}function I(t,e){for(var r=[],n=0;n1?c=(u=r.r)/i.aspectratio:u=(c=r.r)*i.aspectratio,l=(u*=(1+i.baseratio)/2)*c}o=Math.min(o,l/r.vTotal)}for(n=0;n\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(m(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,h=a._size;d(\"pie\",a),_(e,t),I(e,h);var v=l.makeTraceGroups(a._pielayer,e,\"trace\").each((function(e){var d=n.select(this),v=e[0],y=v.trace;!function(t){var 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n={type:e.type,id:this.layerIds[t],source:this.sourceIds[t],layout:e.layout,paint:e.paint};e.filter&&(n.filter=e.filter);for(var i,a=this.layerIds[t],o=this.subplot.getMapLayers(),s=0;s=0;r--){var i=e[r];n.removeLayer(c.layerIds[i])}t||n.removeSource(c.sourceIds.circle)}(t):function(t){for(var e=o.nonCluster,r=e.length-1;r>=0;r--){var i=e[r];n.removeLayer(c.layerIds[i]),t||n.removeSource(c.sourceIds[i])}}(t)}function h(t){l?function(t){t||c.addSource(\"circle\",a.circle,e.cluster);for(var r=o.cluster,n=0;n=0;r--){var n=e[r];t.removeLayer(this.layerIds[n]),t.removeSource(this.sourceIds[n])}},t.exports=function(t,e){var r,n,a,l=e[0].trace,u=l.cluster&&l.cluster.enabled,c=!0!==l.visible,f=new s(t,l.uid,u,c),h=i(t.gd,e),p=f.below=t.belowLookup[\"trace-\"+l.uid];if(u)for(f.addSource(\"circle\",h.circle,l.cluster),r=0;r\")}}t.exports={hoverPoints:function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index)return o;var 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i=r._fullLayout,a=r._fullData[d.index],s=n.select(this).datum();if(d._hasHoverEvent&&(e.originalEvent=n.event,r.emit(\"plotly_unhover\",{points:[f(s,a,h.eventDataKeys)],event:n.event}),d._hasHoverEvent=!1),d._hasHoverLabel&&(o.loneUnhover(i._hoverlayer.node()),d._hasHoverLabel=!1),y){var l=t.select(\"path.surface\");h.styleOne(l,s,a,r,{hovered:!1})}})),t.on(\"click\",(function(t){var e=r._fullLayout,a=r._fullData[d.index],s=g&&(u.isHierarchyRoot(t)||u.isLeaf(t)),c=u.getPtId(t),p=u.isEntry(t)?u.findEntryWithChild(v,c):u.findEntryWithLevel(v,c),y=u.getPtId(p),m={points:[f(t,a,h.eventDataKeys)],event:n.event};s||(m.nextLevel=y);var x=l.triggerHandler(r,\"plotly_\"+d.type+\"click\",m);if(!1!==x&&e.hovermode&&(r._hoverdata=[f(t,a,h.eventDataKeys)],o.click(r,n.event)),!s&&!1!==x&&!r._dragging&&!r._transitioning){i.call(\"_storeDirectGUIEdit\",a,e._tracePreGUI[a.uid],{level:a.level});var b={data:[{level:y}],traces:[d.index]},_={frame:{redraw:!1,duration:h.transitionTime},transition:{duration:h.transitionTime,easing:h.transitionEasing},mode:\"immediate\",fromcurrent:!0};o.loneUnhover(e._hoverlayer.node()),i.call(\"animate\",r,b,_)}}))}},78176:function(t,e,r){\"use strict\";var n=r(3400),i=r(76308),a=r(93972),o=r(69656);function s(t){return t.data.data.pid}e.findEntryWithLevel=function(t,r){var n;return r&&t.eachAfter((function(t){if(e.getPtId(t)===r)return n=t.copy()})),n||t},e.findEntryWithChild=function(t,r){var n;return t.eachAfter((function(t){for(var i=t.children||[],a=0;a0)},e.getMaxDepth=function(t){return t.maxdepth>=0?t.maxdepth:1/0},e.isHeader=function(t,r){return!(e.isLeaf(t)||t.depth===r._maxDepth-1)},e.getParent=function(t,r){return e.findEntryWithLevel(t,s(r))},e.listPath=function(t,r){var n=t.parent;if(!n)return[];var i=r?[n.data[r]]:[n];return e.listPath(n,r).concat(i)},e.getPath=function(t){return e.listPath(t,\"label\").join(\"/\")+\"/\"},e.formatValue=o.formatPieValue,e.formatPercent=function(t,e){var r=n.formatPercent(t,0);return\"0%\"===r&&(r=o.formatPiePercent(t,e)),r}},5621:function(t,e,r){\"use strict\";t.exports={moduleType:\"trace\",name:\"sunburst\",basePlotModule:r(54904),categories:[],animatable:!0,attributes:r(424),layoutAttributes:r(84920),supplyDefaults:r(25244),supplyLayoutDefaults:r(28732),calc:r(3776).calc,crossTraceCalc:r(3776).crossTraceCalc,plot:r(96488).plot,style:r(85676).style,colorbar:r(5528),meta:{}}},84920:function(t){\"use strict\";t.exports={sunburstcolorway:{valType:\"colorlist\",editType:\"calc\"},extendsunburstcolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},28732:function(t,e,r){\"use strict\";var n=r(3400),i=r(84920);t.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"sunburstcolorway\",e.colorway),r(\"extendsunburstcolors\")}},96488:function(t,e,r){\"use strict\";var n=r(33428),i=r(74148),a=r(67756).qy,o=r(43616),s=r(3400),l=r(72736),u=r(82744),c=u.recordMinTextSize,f=u.clearMinTextSize,h=r(37820),p=r(69656).getRotationAngle,d=h.computeTransform,v=h.transformInsideText,g=r(85676).styleOne,y=r(60100).resizeText,m=r(45716),x=r(27328),b=r(78176);function _(t,r,u,f){var h=t._context.staticPlot,y=t._fullLayout,_=!y.uniformtext.mode&&b.hasTransition(f),T=n.select(u).selectAll(\"g.slice\"),k=r[0],A=k.trace,M=k.hierarchy,S=b.findEntryWithLevel(M,A.level),E=b.getMaxDepth(A),L=y._size,C=A.domain,P=L.w*(C.x[1]-C.x[0]),O=L.h*(C.y[1]-C.y[0]),I=.5*Math.min(P,O),D=k.cx=L.l+L.w*(C.x[1]+C.x[0])/2,z=k.cy=L.t+L.h*(1-C.y[0])-O/2;if(!S)return T.remove();var R=null,F={};_&&T.each((function(t){F[b.getPtId(t)]={rpx0:t.rpx0,rpx1:t.rpx1,x0:t.x0,x1:t.x1,transform:t.transform},!R&&b.isEntry(t)&&(R=t)}));var B=function(t){return i.partition().size([2*Math.PI,t.height+1])(t)}(S).descendants(),N=S.height+1,j=0,U=E;k.hasMultipleRoots&&b.isHierarchyRoot(S)&&(B=B.slice(1),N-=1,j=1,U+=1),B=B.filter((function(t){return t.y1<=U}));var V=p(A.rotation);V&&B.forEach((function(t){t.x0+=V,t.x1+=V}));var q=Math.min(N,E),H=function(t){return(t-j)/q*I},G=function(t,e){return[t*Math.cos(e),-t*Math.sin(e)]},W=function(t){return s.pathAnnulus(t.rpx0,t.rpx1,t.x0,t.x1,D,z)},Y=function(t){return D+w(t)[0]*(t.transform.rCenter||0)+(t.transform.x||0)},X=function(t){return z+w(t)[1]*(t.transform.rCenter||0)+(t.transform.y||0)};(T=T.data(B,b.getPtId)).enter().append(\"g\").classed(\"slice\",!0),_?T.exit().transition().each((function(){var t=n.select(this);t.select(\"path.surface\").transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=b.getPtId(t),n=F[r],i=F[b.getPtId(S)];if(i){var o=(t.x1>i.x1?2*Math.PI:0)+V;e=t.rpx1Z?2*Math.PI:0)+V;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,$(t));else e={rpx0:0,rpx1:0};else e={x0:V,x1:V};return a(e,n)}(t);return function(t){return W(e(t))}})):f.attr(\"d\",W),u.call(m,S,t,r,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,A,t);var p=s.ensureSingle(u,\"g\",\"slicetext\"),w=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),T=s.ensureUniformFontSize(t,b.determineTextFont(A,i,y.font));w.text(e.formatSliceLabel(i,S,A,r,y)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,T).call(l.convertToTspans,t);var M=o.bBox(w.node());i.transform=v(M,i,k),i.transform.targetX=Y(i),i.transform.targetY=X(i);var E=function(t,e){var r=t.transform;return d(r,e),r.fontSize=T.size,c(A.type,r,y),s.getTextTransform(r)};_?w.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},R)if(t.parent)if(Z){var i=t.x1>Z?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,$(t));else e.x0=e.x1=V;else e.x0=e.x1=V;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),u=a(e.x0,t.x0),f=a(e.x1,t.x1),h=a(e.transform.scale,n.scale),p=a(e.transform.rotate,n.rotate),d=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,v=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=u(t),i=f(t),a=function(t){return v(Math.pow(t,d))}(t),s={pxmid:G(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return c(A.type,n,y),{transform:{targetX:Y(s),targetY:X(s),scale:h(t),rotate:p(t),rCenter:a}}}}(t);return function(t){return E(e(t),M)}})):w.attr(\"transform\",E(i,M))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}e.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,u=!r,c=!s.uniformtext.mode&&b.hasTransition(r);f(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return t[0].trace.uid}))).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),a.order(),c?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\")),u&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,u=n[0],c=t.data.data,f=u.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var v,g=o.split(\"+\"),y=function(t){return-1!==g.indexOf(t)},m=[];if(y(\"label\")&&c.label&&m.push(c.label),c.hasOwnProperty(\"v\")&&y(\"value\")&&m.push(b.formatValue(c.v,l)),!h){y(\"current path\")&&m.push(b.getPath(t.data));var x=0;y(\"percent parent\")&&x++,y(\"percent entry\")&&x++,y(\"percent root\")&&x++;var _=x>1;if(x){var w,T=function(t){v=b.formatPercent(w,l),_&&(v+=\" of \"+t),m.push(v)};y(\"percent parent\")&&!h&&(w=d/b.getValue(p),T(\"parent\")),y(\"percent entry\")&&(w=d/b.getValue(e),T(\"entry\")),y(\"percent root\")&&(w=d/b.getValue(f),T(\"root\"))}}return y(\"text\")&&(v=s.castOption(r,c.i,\"text\"),s.isValidTextValue(v)&&m.push(v)),m.join(\"
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o=e.data.data,l=!e.children,u=o.i,c=a.castOption(r,u,\"marker.line.color\")||i.defaultLine,f=a.castOption(r,u,\"marker.line.width\")||0;t.call(s,e,r,n).style(\"stroke-width\",f).call(i.stroke,c).style(\"opacity\",l?r.leaf.opacity:null)}t.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(e){var r=n.select(this),i=e[0].trace;r.style(\"opacity\",i.opacity),r.selectAll(\"path.surface\").each((function(e){n.select(this).call(l,e,i,t)}))}))},styleOne:l}},16716:function(t,e,r){\"use strict\";var n=r(76308),i=r(49084),a=r(29736).axisHoverFormat,o=r(21776).Ks,s=r(45464),l=r(92880).extendFlat,u=r(67824).overrideAll;function 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f=t.exports=u(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or 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n=r(47128);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},79164:function(t,e,r){\"use strict\";var n=r(67792).gl_surface3d,i=r(67792).ndarray,a=r(67792).ndarray_linear_interpolate.d2,o=r(70448),s=r(11240),l=r(3400).isArrayOrTypedArray,u=r(33040).parseColorScale,c=r(43080),f=r(8932).extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 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t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a>>26;for(;0!==i&&a>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;at.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o>26,this.words[o]=67108863&e;for(;0!==a&&o>26,this.words[o]=67108863&e;if(0===a&&o>>13,p=0|o[1],d=8191&p,v=p>>>13,g=0|o[2],y=8191&g,m=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],O=8191&P,I=P>>>13,D=0|o[8],z=8191&D,R=D>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,W=0|s[2],Y=8191&W,X=W>>>13,Z=0|s[3],K=8191&Z,J=Z>>>13,$=0|s[4],Q=8191&$,tt=$>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ut=st>>>13,ct=0|s[8],ft=8191&ct,ht=ct>>>13,pt=0|s[9],dt=8191&pt,vt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(u+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;u=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(v,U)|0,a=Math.imul(v,V);var yt=(u+(n=n+Math.imul(f,H)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,H)|0))<<13)|0;u=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(y,U),i=(i=Math.imul(y,V))+Math.imul(m,U)|0,a=Math.imul(m,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(v,H)|0,a=a+Math.imul(v,G)|0;var mt=(u+(n=n+Math.imul(f,Y)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,Y)|0))<<13)|0;u=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(y,H)|0,i=(i=i+Math.imul(y,G)|0)+Math.imul(m,H)|0,a=a+Math.imul(m,G)|0,n=n+Math.imul(d,Y)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(v,Y)|0,a=a+Math.imul(v,X)|0;var xt=(u+(n=n+Math.imul(f,K)|0)|0)+((8191&(i=(i=i+Math.imul(f,J)|0)+Math.imul(h,K)|0))<<13)|0;u=((a=a+Math.imul(h,J)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(y,Y)|0,i=(i=i+Math.imul(y,X)|0)+Math.imul(m,Y)|0,a=a+Math.imul(m,X)|0,n=n+Math.imul(d,K)|0,i=(i=i+Math.imul(d,J)|0)+Math.imul(v,K)|0,a=a+Math.imul(v,J)|0;var bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,Y)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,Y)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(y,K)|0,i=(i=i+Math.imul(y,J)|0)+Math.imul(m,K)|0,a=a+Math.imul(m,J)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0;var _t=(u+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;u=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,H)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,Y)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,K)|0,i=(i=i+Math.imul(b,J)|0)+Math.imul(_,K)|0,a=a+Math.imul(_,J)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0;var wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,U),i=(i=Math.imul(O,V))+Math.imul(I,U)|0,a=Math.imul(I,V),n=n+Math.imul(L,H)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,H)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,Y)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,K)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(k,K)|0,a=a+Math.imul(k,J)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0;var Tt=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(z,U),i=(i=Math.imul(z,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(O,H)|0,i=(i=i+Math.imul(O,G)|0)+Math.imul(I,H)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(L,Y)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,Y)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,K)|0,i=(i=i+Math.imul(M,J)|0)+Math.imul(S,K)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ut)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ut)|0;var kt=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(z,H)|0,i=(i=i+Math.imul(z,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(O,Y)|0,i=(i=i+Math.imul(O,X)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,X)|0,n=n+Math.imul(L,K)|0,i=(i=i+Math.imul(L,J)|0)+Math.imul(C,K)|0,a=a+Math.imul(C,J)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ut)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ut)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(v,ft)|0,a=a+Math.imul(v,ht)|0;var At=(u+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,vt)|0)+Math.imul(h,dt)|0))<<13)|0;u=((a=a+Math.imul(h,vt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(z,Y)|0,i=(i=i+Math.imul(z,X)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(O,K)|0,i=(i=i+Math.imul(O,J)|0)+Math.imul(I,K)|0,a=a+Math.imul(I,J)|0,n=n+Math.imul(L,Q)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(y,ft)|0,i=(i=i+Math.imul(y,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var Mt=(u+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,vt)|0)+Math.imul(v,dt)|0))<<13)|0;u=((a=a+Math.imul(v,vt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,Y),i=(i=Math.imul(B,X))+Math.imul(N,Y)|0,a=Math.imul(N,X),n=n+Math.imul(z,K)|0,i=(i=i+Math.imul(z,J)|0)+Math.imul(R,K)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(O,Q)|0,i=(i=i+Math.imul(O,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ut)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(u+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,vt)|0)+Math.imul(m,dt)|0))<<13)|0;u=((a=a+Math.imul(m,vt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,K),i=(i=Math.imul(B,J))+Math.imul(N,K)|0,a=Math.imul(N,J),n=n+Math.imul(z,Q)|0,i=(i=i+Math.imul(z,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,i=(i=i+Math.imul(O,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ut)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(u+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,vt)|0)+Math.imul(_,dt)|0))<<13)|0;u=((a=a+Math.imul(_,vt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(z,rt)|0,i=(i=i+Math.imul(z,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(O,at)|0,i=(i=i+Math.imul(O,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(u+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,vt)|0)+Math.imul(k,dt)|0))<<13)|0;u=((a=a+Math.imul(k,vt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(z,at)|0,i=(i=i+Math.imul(z,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,i=(i=i+Math.imul(O,ut)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ut)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,vt)|0)+Math.imul(S,dt)|0))<<13)|0;u=((a=a+Math.imul(S,vt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(z,lt)|0,i=(i=i+Math.imul(z,ut)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(O,ft)|0,i=(i=i+Math.imul(O,ht)|0)+Math.imul(I,ft)|0,a=a+Math.imul(I,ht)|0;var Pt=(u+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,vt)|0)+Math.imul(C,dt)|0))<<13)|0;u=((a=a+Math.imul(C,vt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ut))+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(z,ft)|0,i=(i=i+Math.imul(z,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Ot=(u+(n=n+Math.imul(O,dt)|0)|0)+((8191&(i=(i=i+Math.imul(O,vt)|0)+Math.imul(I,dt)|0))<<13)|0;u=((a=a+Math.imul(I,vt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var It=(u+(n=n+Math.imul(z,dt)|0)|0)+((8191&(i=(i=i+Math.imul(z,vt)|0)+Math.imul(R,dt)|0))<<13)|0;u=((a=a+Math.imul(R,vt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var Dt=(u+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,vt))+Math.imul(N,dt)|0))<<13)|0;return u=((a=Math.imul(N,vt))+(i>>>13)|0)+(Dt>>>26)|0,Dt&=67108863,l[0]=gt,l[1]=yt,l[2]=mt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=It,l[18]=Dt,0!==u&&(l[19]=u,r.length++),r};function v(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):v(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o>>=1)i++;return 1<>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<o)for(this.length-=o,u=0;u=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,v=1;0==(r.words[0]&v)&&d<26;++d,v<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:it.length)return 1;if(this.length=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){ni&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(x,m),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with 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e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return 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v=l(\"labels\");l(\"labelFont\")&&(v=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),u(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),u(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),u(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),u(\"gridColor\"),o(\"zeroEnable\"),u(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),u(\"backgroundColor\"),this._text?this._text&&(v||c)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,p=o,d=l;u&1<0?(p[c]=-1,d[c]=0):(p[c]=0,d[c]=1)}}var v=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var y=[0,0,0],m=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),f=c.cubeEdges,h=c.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E,L,C=v;for(A=0;A<3;++A)this.backgroundEnable[A]?C[A]=h[A]:C[A]=0;for(this._background.draw(r,n,i,a,C,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var P=[0,0,0];h[A]>0?P[A]=a[1][A]:P[A]=a[0][A];for(var O=0;O<2;++O){var I=(A+1+O)%3,D=(A+1+(1^O))%3;this.gridEnable[I]&&this._lines.drawGrid(I,D,this.bounds,P,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(O=0;O<2;++O)I=(A+1+O)%3,D=(A+1+(1^O))%3,this.zeroEnable[D]&&Math.min(a[0][D],a[1][D])<=0&&Math.max(a[0][D],a[1][D])>=0&&this._lines.drawZero(I,D,this.bounds,P,this.zeroLineColor[D],this.zeroLineWidth[D]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var z=u(y,M[A].primalMinor),R=u(m,M[A].mirrorMinor),F=this.lineTickLength;for(O=0;O<3;++O){var B=k/r[5*O];z[O]*=F[O]*B,R[O]*=F[O]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(L=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,q=u(x,M[A].primalOffset);for(O=0;O<3;++O)this.lineTickEnable[A]&&(q[O]+=k*U[O]*Math.max(this.lineTickLength[O],0)/r[5*O]);var H=[0,0,0];if(H[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,E=1,\"auto\"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),L=[0,0,0],j(A,U,V),O=0;O<3;++O)q[O]+=k*U[O]*this.tickPad[O]/r[5*O];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],q,this.tickColor[A],H,L,S)}if(this.labelEnable[A]){for(E=0,L=[0,0,0],this.labels[A].length>4&&(N(A),E=1),\"auto\"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),O=0;O<3;++O)q[O]+=k*U[O]*this.labelPad[O]/r[5*O];q[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],q,this.labelColor[A],[0,0,0],L,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},1011:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[u]=d;for(var v=-1;v<=1;v+=2)f[c]=v,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=u;u=c,c=g}var y=n(t,new Float32Array(e)),m=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],m),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,y,x,b)};var n=r(5827),i=r(2944),a=r(1943).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},2864:function(t,e,r){\"use strict\";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var m=0,x=0;x<2;++x){c[2]=a[x][2];for(var b=0;b<2;++b){c[1]=a[b][1];for(var _=0;_<2;++_)c[0]=a[_][0],h(l[m],c,s),m+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)u[x][k]=l[x][k]/T;p&&(u[x][2]*=-1),T<0&&(w<0||u[x][2]E&&(w|=1<E&&(w|=1<u[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x)(N=R^1<u[B][0]&&(B=N))}var j=v;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===z?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,q=w;for(A=0;A<3;++A)V[A]=q&1< HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n b - PI :\\n b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n // if ratio is set to 0.5 then it is 50%, 50%.\\n // when using a higher ratio e.g. 0.75 the result would\\n // likely be more horizontal than vertical.\\n\\n float b = positive_angle(a);\\n\\n return\\n (b < ( ratio) * HALF_PI) ? 0.0 :\\n (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n 0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n float b = positive_angle(a);\\n float div = TWO_PI / float(n);\\n float c = roundTo(b, div);\\n return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n return\\n (option > 2) ? look_round_n_directions(rawAngle + delta, option) : // option 3-n: round to n directions\\n (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n (option == 1) ? rawAngle + delta : // use free angle, and flip to align with one direction of the axis\\n (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n (option ==-1) ? 0.0 : // useful for backward compatibility, all texts remains horizontal\\n rawAngle; // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n (axis.y == 0.0) &&\\n (axis.z == 0.0);\\n\\nvoid main() {\\n //Compute world offset\\n float axisDistance = position.z;\\n vec3 dataPosition = axisDistance * axis + offset;\\n\\n float beta = angle; // i.e. user defined attributes for each tick\\n\\n float axisAngle;\\n float clipAngle;\\n float flip;\\n\\n if (enableAlign) {\\n axisAngle = (isAxisTitle) ? HALF_PI :\\n computeViewAngle(dataPosition, dataPosition + axis);\\n clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n beta += applyAlignOption(clipAngle, flip * PI);\\n }\\n\\n //Compute plane offset\\n vec2 planeCoord = position.xy * pixelScale;\\n\\n mat2 planeXform = scale * mat2(\\n cos(beta), sin(beta),\\n -sin(beta), cos(beta)\\n );\\n\\n vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n //Compute clip position\\n vec3 clipPosition = project(dataPosition);\\n\\n //Apply text offset in clip coordinates\\n clipPosition += vec3(viewOffset, 0.0);\\n\\n //Done\\n gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = color;\\n}\"]);e.f=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n vec3 realNormal = signAxis * normal;\\n\\n if(dot(realNormal, enable) > 0.0) {\\n vec3 minRange = min(bounds[0], bounds[1]);\\n vec3 maxRange = max(bounds[0], bounds[1]);\\n vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n } else {\\n gl_Position = vec4(0,0,0,0);\\n }\\n\\n colorChannel = abs(realNormal);\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n gl_FragColor = colorChannel.x * colors[0] +\\n colorChannel.y * colors[1] +\\n colorChannel.z * colors[2];\\n}\"]);e.bg=function(t){return i(t,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},9557:function(t,e,r){\"use strict\";t.exports=function(t,e,r,i,o,l){var u=n(t),f=a(t,[{buffer:u,size:3}]),h=s(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,i,o,l),p};var n=r(5827),a=r(2944),o=r(875),s=r(1943).f,l=window||i.global||{},u=l.__TEXT_CACHE||{};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}l.__TEXT_CACHE={};var f=c.prototype,h=[0,0];f.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},f.unbind=function(){this.vao.unbind()},f.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=u[r];l||(l=u[r]={});var c=l[e];c||(c=l[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=c.positions,p=c.cells,d=0,v=p.length;d=0;--y){var m=h[g[y]];a.push(f*m[0],-f*m[1],t)}}for(var l=[0,0,0],c=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=a.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(a.length/3|0)-f[d],l[d]=a.length/3|0;for(var v=0;v=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,u=o%a;o<0?(l=0|-Math.ceil(l),u=0|-u):(l=0|Math.floor(l),u|=0);var c=\"\"+l;if(o<0&&(c=\"-\"+c),i){for(var f=\"\"+u;f.length=t[0][i];--o)a.push({x:o*e[i],text:r(e[i],o)});n.push(a)}return n},e.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;nr)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,\"uint16\"):c(t,\"float32\"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=u(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},1140:function(t,e,r){\"use strict\";var n=r(2858);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,p=null,d=null,v=[],g=1/0,y=!1,m=0;mo&&(o=n.length(b)),m){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),y=!1):y=!0}y||(p=x,d=b),v.push(b)}var w=[s,u,f],T=[l,c,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A,m=0;for(var M=0;m=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=c({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,v=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],v=+t.vertexIntensityBounds[1];else for(var g=0;g0){var v=this.triShader;v.bind(),v.uniforms=u,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),c=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),f=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));f.generateMipmap(),f.minFilter=t.LINEAR_MIPMAP_LINEAR,f.magFilter=t.LINEAR;var p=i(t),d=i(t),v=i(t),g=i(t),y=i(t),m=new h(t,f,l,c,p,d,y,v,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return m.update(e),m}},7234:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * conePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(conePosition, 1.0);\\n vec4 t_position = view * conePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = conePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n gl_Position = projection * view * conePosition;\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},1950:function(t){t.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},6603:function(t,e,r){var 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i=c[n],a=0;a0&&((p=c.slice())[s]+=d[1][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)))}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},7667:function(t,e,r){\"use strict\";var n=r(6832),i=r(5158),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n vec4 worldPosition = model * vec4(position, 1.0);\\n worldPosition = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n gl_Position = projection * view * worldPosition;\\n fragColor = color;\\n fragPosition = position;\\n}\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) 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n=r(8931);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension(\"WEBGL_draw_buffers\");if(!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;ac||r<0||r>c)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var f=1;if(\"color\"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(f>1){if(!u)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(f>t.getParameter(u.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+f+\" draw buffers\")}}var h=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&f>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");h=t.FLOAT}else 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x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;yi||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var v=i.attributes;return this.positionBuffer.bind(),v.position.pointer(),this.weightBuffer.bind(),v.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),v.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var u,c,f,p,d=t.colorLevels||[0],v=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(u=y[0]=r[0],c=y[1]=o[0],f=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(u=y[0]=r[0]+(r[1]-r[0])/2,c=y[1]=o[0]+(o[1]-o[0])/2,f=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var m=1/(f-u),x=1/(p-c),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (\\n outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n fragColor.a * opacity == 0.\\n ) discard;\\n\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n float e = floor(log2(av));\\n float m = av * pow(2.0, -e) - 1.0;\\n\\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n\\n //Unpack exponent\\n float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0;\\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},6086:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),c=u(new Array(1024),[256,1,4]),p=0;p<1024;++p)c.data[p]=255;var d=a(e,c);d.wrap=e.REPEAT;var v=new y(e,r,o,s,l,d);return v.update(t),v};var n=r(5827),i=r(2944),a=r(8931),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(5070),u=r(5050),c=r(248),f=c.createShader,h=c.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function v(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=y.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:v(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:v(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,c=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],v=t.lineWidth||1,g=!1;t:for(e=1;e0){for(var w=0;w<24;++w)i.push(i[i.length-12]);c+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],m=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=m=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===m.length&&(m=[m[0],m[1],m[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(v)?v.length>e-1?v[e-1]:v.length>0?v[v.length-1]:[0,0,0,1]:v;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3]);c+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,m[0],m[1],m[2],m[3]),c+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=c,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e1.0001)return null;y+=g[f]}return Math.abs(y-1)>.001?null:[h,s(t,g),g]}},2056:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n , view\\n , projection\\n , inverseModel;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n gl_Position = project(position);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * vec4(position , 1.0);\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (f_color.a == 0.0 ||\\n outOfRange(clipBounds[0], clipBounds[1], f_data)\\n ) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_id = id;\\n f_position = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.pointShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},e.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},e.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},8116:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(2944),o=r(8931),s=r(115),l=r(104),u=r(7437),c=r(5050),f=r(9156),h=r(7212),p=r(5306),d=r(2056),v=r(4340),g=d.meshShader,y=d.wireShader,m=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=v,this.edgeVAO=m,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;ai[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t]&&T&&(c[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),c[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(c[1^t]+=M*p*v[t+2],ki[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t+2]&&T&&(c[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),v.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var c=0;c<2;++c)e[c]=2*(o[c]*u-a[c])/(a[2+c]-a[c])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),v.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,u=.5*(n[o+2]+n[o]),c=n[o+2]-n[o],f=a[o],v=a[o+2]-f,g=i[o],y=i[o+2]-g;p[o]=2*l/c*v/y,h[o]=2*(s-u)/c*v/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),v.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,u=t.bounds;for(o=0;o<2;++o){var c=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e=0){var v=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(v,e[1],v,e[3],p[d],h[d]):o.drawLine(e[0],v,e[2],v,p[d],h[d])}}for(d=0;d=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},u.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},u.removeObject=function(t){for(var e=this.objects,r=0;rMath.abs(e))u.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(8161),i=r(1152),a=r(6145),o=r(6475),s=r(2565),l=r(5233)},8245:function(t,e,r){var n=r(6832),i=r(5158),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n uv = position;\\n gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},1059:function(t,e,r){\"use strict\";var n=r(4296),i=r(7453),a=r(2771),o=r(6496),s=r(2611),l=r(4234),u=r(8126),c=r(6145),f=r(1120),h=r(5268),p=r(8245),d=r(2321)({tablet:!0,featureDetect:!0});function v(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return\"boolean\"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement(\"canvas\"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error(\"webgl not supported\");var m=t.bounds||[[-10,-10,-10],[10,10,10]],x=new v,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],O=!0,I=!0,D={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},z=(I=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:m,objects:E,shape:z,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:D,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,I=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",O=!0}}}function j(){for(var t=E.length,e=P.length,n=0;n0&&0===C[e-1];)C.pop(),P.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener(\"resize\",N),F.update=function(t){F._stopped||(t=t||{},O=!0,I=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),L.push(-1),O=!0,I=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),O=!0,I=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener(\"resize\",N),e.removeEventListener(\"webglcontextlost\",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;tx.distance)continue;for(var u=0;u 1.0) {\\n discard;\\n }\\n baseColor = mix(borderColor, color, step(radius, centerFraction));\\n gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n }\\n}\\n\"]),e.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\"]),e.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"])},8271:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(5306),o=r(8023);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,u,c=s.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),u=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e>>1;for(r=0;r=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),c=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=c<5,r.uniforms.pointSize=c,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(u[0]=255&t,u[1]=t>>8&255,u[2]=t>>16&255,u[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=u,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},6093:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],p=r[0],d=r[1],v=r[2],g=r[3];return(a=u*p+c*d+f*v+h*g)<0&&(a=-a,p=-p,d=-d,v=-v,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*p,t[1]=s*c+l*d,t[2]=s*f+l*v,t[3]=s*h+l*g,t}},8240:function(t){\"use strict\";t.exports=function(t){return t||0===t?t.toString():\"\"}},4123:function(t,e,r){\"use strict\";var n=r(875);t.exports=function(t,e,r){var a=i[e];if(a||(a=i[e]={}),t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,u,c=n(t,o);if(r&&1!==r){for(l=0;l max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale *= highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n\\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale *= highlightScale;\\n }\\n\\n vec4 clipCenter = projection * view * model * vec4(position, 1);\\n vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (\\n outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n interpColor.a * opacity == 0.\\n ) discard;\\n gl_FragColor = interpColor * opacity;\\n}\\n\"]),u=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},p={vertex:s,fragment:l,attributes:c},d={vertex:a,fragment:u,attributes:c},v={vertex:o,fragment:u,attributes:c},g={vertex:s,fragment:u,attributes:c};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,f)},e.createOrtho=function(t){return y(t,h)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,v)},e.createPickProject=function(t){return y(t,g)}},2182:function(t,e,r){\"use strict\";var n=r(3596),i=r(5827),a=r(2944),o=r(5306),s=r(104),l=r(9282),u=r(4123),c=r(8240),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),u=l.createPickOrtho(e),c=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=new g(e,r,n,o,f,h,p,d,a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,u,c);return v.update(t),v};var y=g.prototype;y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},y.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var m=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function C(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,u=r.model||f,c=r.view||f,h=r.projection||f,d=e.axesBounds,v=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],m[0]=2/o.drawingBufferWidth,m[1]=2/o.drawingBufferHeight,t.bind(),l.view=c,l.projection=h,l.screenSize=m,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=v,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var y=T,L=0;L<16;++L)y[L]=0;for(L=0;L<4;++L)y[5*L]=1;y[5*g]=0,i[g]<0?y[12+g]=d[0][g]:y[12+g]=d[1][g],s(y,u,y),l.model=y;var C=(g+1)%3,P=(g+2)%3,O=M(x),I=M(b);O[C]=1,I[P]=1;var D=p(0,0,0,S(_,O)),z=p(0,0,0,S(w,I));if(Math.abs(D[1])>Math.abs(z[1])){var R=D;D=z,z=R,R=O,O=I,I=R;var F=C;C=P,P=F}D[0]<0&&(O[C]=-1),z[1]>0&&(I[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(u[4*C+L],2),N+=Math.pow(u[4*P+L],2);O[C]/=Math.sqrt(B),I[P]/=Math.sqrt(N),l.axes[0]=O,l.axes[1]=I,l.fragClipBounds[0]=E(k,v[0],g,-1e8),l.fragClipBounds[1]=E(k,v[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var u=t.uniforms;u.model=n.model||f,u.view=n.view||f,u.projection=n.projection||f,m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,u.screenSize=m,u.highlightId=r.highlightId,u.highlightScale=r.highlightScale,u.fragClipBounds=L,u.clipBounds=r.axes.bounds,u.opacity=r.opacity,u.pickGroup=r.pickId/255,u.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},y.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},y.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){Array.isArray(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",u=t.alignment||[0,0];if(2===u.length)i=u[0],a=u[1];else for(i=[],a=[],n=0;n0){var I=0,D=x,z=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(y)&&Array.isArray(y[0]);t:for(n=0;n<_;++n){for(m+=1,w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T])}k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n0?1-M[0][0]:W<0?1+M[1][0]:1,Y*=Y>0?1-M[0][1]:Y<0?1+M[1][1]:1],Z=k.cells||[],K=k.positions||[];for(T=0;T0){var y=r*c;o.drawBox(f-y,h-y,p+y,h+y,a),o.drawBox(f-y,d-y,p+y,d+y,a),o.drawBox(f-y,h-y,f+y,d+y,a),o.drawBox(p-y,h-y,p+y,d+y,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},2611:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=e[0],a=e[1];return new l(t,n(t,r,a,{}),i.mallocUint8(r*a*4))};var n=r(4234),i=r(5306),a=r(5050),o=r(2288).nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var 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a||(a[s[0]]=[]),a=a[s[0]];for(var l=1;l1)for(var l=0;l 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * tubePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(tubePosition, 1.0);\\n vec4 t_position = view * tubePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = tubePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 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dataCoordinate;\\n vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n vec4 clipPosition = projection * view * worldPosition;\\n clipPosition.z += zOffset;\\n\\n gl_Position = clipPosition;\\n value = f + objectOffset.z;\\n kill = -1.0;\\n planeCoordinate = uv.zw;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Don't do lighting for contours\\n surfaceNormal = vec3(1,0,0);\\n eyeDirection = vec3(0,1,0);\\n lightDirection = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n float vh = 255.0 * v;\\n float upper = floor(vh);\\n float lower = fract(vh);\\n return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n if ((kill > 0.0) ||\\n (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},e.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},3754:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=m(e),n=b(e),s=x(e),l=_(e),u=i(e),c=a(e,[{buffer:u,size:4,stride:w,offset:0},{buffer:u,size:3,stride:w,offset:16},{buffer:u,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),v=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);v.minFilter=e.LINEAR,v.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,u,c,v,s,l,f,h,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||\"jet\",g.update(y),g};var n=r(2288),i=r(5827),a=r(2944),o=r(8931),s=r(5306),l=r(9156),u=r(7498),c=r(7382),f=r(5050),h=r(4162),p=r(104),d=r(7437),v=r(5070),g=r(9144),y=r(9054),m=y.createShader,x=y.createContourShader,b=y.createPickShader,_=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,u,c,h,p,d,v,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=v,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var L=E.prototype;L.genColormap=function(t,e){var r=!1,n=c([l({colormap:t,nshades:S,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return u.divseq(n,255),this.hasAlphaScale=r,n},L.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},L.isOpaque=function(){return!this.isTransparent()},L.pickSlots=1,L.setPickBase=function(t){this.pickId=t};var C=[0,0,0],P={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function O(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||C,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=P.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var u=P.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return P.showSurface=o,P.showContour=s,P}var I={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},D=T.slice(),z=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=z,n.vertexColor=this.vertexColor;var s=D;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=O(n,this);if(c.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var p=i+c,d=s+h,g=f*(h?l:1-l),y=0;y<3;++y)u[y]+=this._field[y].get(p,d)*g;for(var m=this._pickResult.level,x=0;x<3;++x)if(m[x]=v.le(this.contourLevels[x],u[x]),m[x]<0)this.contourLevels[x].length>0&&(m[x]=0);else if(m[x]Math.abs(_-u[x])&&(m[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},L.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},L.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=B(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=B(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=B(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=j(t.contourColor)),\"contourProject\"in t&&(this.contourProject=B(t.contourProject,(function(t){return B(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=j(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var u=l[o];for(y=0;y<2;++y)if(u.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],u)}}else if(t.ticks){var c=t.ticks;if(!Array.isArray(c)||2!==c.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=c[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y0){for(var xt=0;xt<5;++xt)$.pop();U-=1}continue t}$.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var bt=s.mallocFloat($.length);for(o=0;o<$.length;++o)bt[o]=$[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},L.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},L.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],p=this._field[u],d=h(c,r[o]),v=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;es||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=d(o,e.stride.slice()),u=0;\"float32\"===r?u=t.FLOAT:\"float64\"===r?(u=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var f,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}}u!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(u=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)f=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var m=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,m,0);\"float32\"!==r&&\"float64\"!==r||u!==t.UNSIGNED_BYTE?i.assign(x,e):c(x,e),f=p.subarray(0,y)}var b=v(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,u,f),l||a.free(p),new h(t,b,o[0],o[1],g,u)}(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var c=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=h.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function v(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function g(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return 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e>t==t>0?o===a?(r+=1,o=0):o+=1:0===o?(o=a,r-=1):o-=1,n.pack(o,r)}},115:function(t,e){e.vertexNormals=function(t,e,r){for(var n=e.length,i=new Array(n),a=void 0===r?1e-6:r,o=0;oa){var b=i[u],_=1/Math.sqrt(g*m);for(x=0;x<3;++x){var w=(x+1)%3,T=(x+2)%3;b[x]+=_*(y[w]*v[T]-y[T]*v[w])}}}for(o=0;oa)for(_=1/Math.sqrt(k),x=0;x<3;++x)b[x]*=_;else for(x=0;x<3;++x)b[x]=0}return i},e.faceNormals=function(t,e,r){for(var n=t.length,i=new Array(n),a=void 0===r?1e-6:r,o=0;oa?1/Math.sqrt(p):0,u=0;u<3;++u)h[u]*=p;i[o]=h}return i}},567:function(t){\"use strict\";t.exports=function(t,e,r,n,i,a,o,s,l,u){var c=e+a+u;if(f>0){var f=Math.sqrt(c+1);t[0]=.5*(o-l)/f,t[1]=.5*(s-n)/f,t[2]=.5*(r-a)/f,t[3]=.5*f}else{var h=Math.max(e,a,u);f=Math.sqrt(2*h-c+1),e>=h?(t[0]=.5*f,t[1]=.5*(i+r)/f,t[2]=.5*(s+n)/f,t[3]=.5*(o-l)/f):a>=h?(t[0]=.5*(r+i)/f,t[1]=.5*f,t[2]=.5*(l+o)/f,t[3]=.5*(s-n)/f):(t[0]=.5*(n+s)/f,t[1]=.5*(o+l)/f,t[2]=.5*f,t[3]=.5*(r-i)/f)}return t}},7774:function(t,e,r){\"use strict\";t.exports=function(t){var e=(t=t||{}).center||[0,0,0],r=t.rotation||[0,0,0,1],n=t.radius||1;e=[].slice.call(e,0,3),c(r=[].slice.call(r,0,4),r);var i=new f(r,e,Math.log(n));return i.setDistanceLimits(t.zoomMin,t.zoomMax),(\"eye\"in t||\"up\"in t)&&i.lookAt(0,t.eye,t.center,t.up),i};var n=r(8444),i=r(3012),a=r(5950),o=r(7437),s=r(567);function l(t,e,r){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2))}function u(t,e,r,n){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2)+Math.pow(n,2))}function c(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=u(r,n,i,a);o>1e-6?(t[0]=r/o,t[1]=n/o,t[2]=i/o,t[3]=a/o):(t[0]=t[1]=t[2]=0,t[3]=1)}function f(t,e,r){this.radius=n([r]),this.center=n(e),this.rotation=n(t),this.computedRadius=this.radius.curve(0),this.computedCenter=this.center.curve(0),this.computedRotation=this.rotation.curve(0),this.computedUp=[.1,0,0],this.computedEye=[.1,0,0],this.computedMatrix=[.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],this.recalcMatrix(0)}var h=f.prototype;h.lastT=function(){return Math.max(this.radius.lastT(),this.center.lastT(),this.rotation.lastT())},h.recalcMatrix=function(t){this.radius.curve(t),this.center.curve(t),this.rotation.curve(t);var e=this.computedRotation;c(e,e);var r=this.computedMatrix;a(r,e);var n=this.computedCenter,i=this.computedEye,o=this.computedUp,s=Math.exp(this.computedRadius[0]);i[0]=n[0]+s*r[2],i[1]=n[1]+s*r[6],i[2]=n[2]+s*r[10],o[0]=r[1],o[1]=r[5],o[2]=r[9];for(var l=0;l<3;++l){for(var u=0,f=0;f<3;++f)u+=r[l+4*f]*i[f];r[12+l]=-u}},h.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r},h.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},h.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},h.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=i[1],o=i[5],s=i[9],u=l(a,o,s);a/=u,o/=u,s/=u;var 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r,n,o,s,l,u,c,f,h,p=1,d=t.length,v=\"\";for(n=0;n=0),s.type){case\"b\":r=parseInt(r,10).toString(2);break;case\"c\":r=String.fromCharCode(parseInt(r,10));break;case\"d\":case\"i\":r=parseInt(r,10);break;case\"j\":r=JSON.stringify(r,null,s.width?parseInt(s.width):0);break;case\"e\":r=s.precision?parseFloat(r).toExponential(s.precision):parseFloat(r).toExponential();break;case\"f\":r=s.precision?parseFloat(r).toFixed(s.precision):parseFloat(r);break;case\"g\":r=s.precision?String(Number(r.toPrecision(s.precision))):parseFloat(r);break;case\"o\":r=(parseInt(r,10)>>>0).toString(8);break;case\"s\":r=String(r),r=s.precision?r.substring(0,s.precision):r;break;case\"t\":r=String(!!r),r=s.precision?r.substring(0,s.precision):r;break;case\"T\":r=Object.prototype.toString.call(r).slice(8,-1).toLowerCase(),r=s.precision?r.substring(0,s.precision):r;break;case\"u\":r=parseInt(r,10)>>>0;break;case\"v\":r=r.valueOf(),r=s.precision?r.substring(0,s.precision):r;break;case\"x\":r=(parseInt(r,10)>>>0).toString(16);break;case\"X\":r=(parseInt(r,10)>>>0).toString(16).toUpperCase()}i.json.test(s.type)?v+=r:(!i.number.test(s.type)||f&&!s.sign?h=\"\":(h=f?\"+\":\"-\",r=r.toString().replace(i.sign,\"\")),u=s.pad_char?\"0\"===s.pad_char?\"0\":s.pad_char.charAt(1):\" 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s[t]=n}(t),arguments)}function o(t,e){return a.apply(null,[t].concat(e||[]))}var s=Object.create(null);e.sprintf=a,e.vsprintf=o,\"undefined\"!=typeof window&&(window.sprintf=a,window.vsprintf=o,void 0===(n=function(){return{sprintf:a,vsprintf:o}}.call(e,r,e,t))||(t.exports=n))}()},4162:function(t,e,r){\"use strict\";t.exports=function(t,e){if(t.dimension<=0)return{positions:[],cells:[]};if(1===t.dimension)return function(t,e){for(var r=i(t,e),n=r.length,a=new Array(n),o=new Array(n),s=0;sn|0},vertex:function(t,e,r,n,i,a,o,s,l,u,c,f,h){var p=(o<<0)+(s<<1)+(l<<2)+(u<<3)|0;if(0!==p&&15!==p)switch(p){case 0:case 15:c.push([t-.5,e-.5]);break;case 1:c.push([t-.25-.25*(n+r-2*h)/(r-n),e-.25-.25*(i+r-2*h)/(r-i)]);break;case 2:c.push([t-.75-.25*(-n-r+2*h)/(n-r),e-.25-.25*(a+n-2*h)/(n-a)]);break;case 3:c.push([t-.5,e-.5-.5*(i+r+a+n-4*h)/(r-i+n-a)]);break;case 4:c.push([t-.25-.25*(a+i-2*h)/(i-a),e-.75-.25*(-i-r+2*h)/(i-r)]);break;case 5:c.push([t-.5-.5*(n+r+a+i-4*h)/(r-n+i-a),e-.5]);break;case 6:c.push([t-.5-.25*(-n-r+a+i)/(n-r+i-a),e-.5-.25*(-i-r+a+n)/(i-r+n-a)]);break;case 7:c.push([t-.75-.25*(a+i-2*h)/(i-a),e-.75-.25*(a+n-2*h)/(n-a)]);break;case 8:c.push([t-.75-.25*(-a-i+2*h)/(a-i),e-.75-.25*(-a-n+2*h)/(a-n)]);break;case 9:c.push([t-.5-.25*(n+r+-a-i)/(r-n+a-i),e-.5-.25*(i+r+-a-n)/(r-i+a-n)]);break;case 10:c.push([t-.5-.5*(-n-r-a-i+4*h)/(n-r+a-i),e-.5]);break;case 11:c.push([t-.25-.25*(-a-i+2*h)/(a-i),e-.75-.25*(i+r-2*h)/(r-i)]);break;case 12:c.push([t-.5,e-.5-.5*(-i-r-a-n+4*h)/(i-r+a-n)]);break;case 13:c.push([t-.75-.25*(n+r-2*h)/(r-n),e-.25-.25*(-a-n+2*h)/(a-n)]);break;case 14:c.push([t-.25-.25*(-n-r+2*h)/(n-r),e-.25-.25*(-i-r+2*h)/(i-r)])}},cell:function(t,e,r,n,i,a,o,s,l){i?s.push([t,e]):s.push([e,t])}});return function(t,e){var r=[],i=[];return n(t,r,i,e),{positions:r,cells:i}}}},o={}},6946:function(t,e,r){\"use strict\";t.exports=function t(e,r,i){i=i||{};var a=o[e];a||(a=o[e]={\" \":{data:new Float32Array(0),shape:.2}});var s=a[r];if(!s)if(r.length<=1||!/\\d/.test(r))s=a[r]=function(t){for(var e=t.cells,r=t.positions,n=new Float32Array(6*e.length),i=0,a=0,o=0;o0&&(f+=.02);var p=new Float32Array(c),d=0,v=-.5*f;for(h=0;hMath.max(r,n)?i[2]=1:r>Math.max(e,n)?i[0]=1:i[1]=1;for(var a=0,o=0,l=0;l<3;++l)a+=t[l]*t[l],o+=i[l]*t[l];for(l=0;l<3;++l)i[l]-=o/a*t[l];return s(i,i),i}function h(t,e,r,i,a,o,s,l){this.center=n(r),this.up=n(i),this.right=n(a),this.radius=n([o]),this.angle=n([s,l]),this.angle.bounds=[[-1/0,-Math.PI/2],[1/0,Math.PI/2]],this.setDistanceLimits(t,e),this.computedCenter=this.center.curve(0),this.computedUp=this.up.curve(0),this.computedRight=this.right.curve(0),this.computedRadius=this.radius.curve(0),this.computedAngle=this.angle.curve(0),this.computedToward=[0,0,0],this.computedEye=[0,0,0],this.computedMatrix=new Array(16);for(var u=0;u<16;++u)this.computedMatrix[u]=.5;this.recalcMatrix(0)}var p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),c=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,c+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(c);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],v=this.computedAngle[1],g=Math.cos(d),y=Math.sin(d),m=Math.cos(v),x=Math.sin(v),b=this.computedCenter,_=g*m,w=y*m,T=x,k=-g*x,A=-y*x,M=m,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],O=E[9],I=E[2],D=E[6],z=E[10],R=P*z-O*D,F=O*I-C*z,B=C*D-P*I,N=u(R,F,B);for(R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B,a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){c=0;for(var j=0;j<3;++j)c+=E[a+4*j]*S[j];E[12+a]=-c}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,u=0;u<3;++u)i[4*u]=o[u],i[4*u+1]=s[u],i[4*u+2]=l[u];for(a(i,i,n,d),u=0;u<3;++u)o[u]=i[4*u],s[u]=i[4*u+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=u(a,o,s);a/=l,o/=l,s/=l;var c=i[0],f=i[4],h=i[8],p=c*a+f*o+h*s,d=u(c-=a*p,f-=o*p,h-=s*p),v=(c/=d)*e+a*r,g=(f/=d)*e+o*r,y=(h/=d)*e+s*r;this.center.move(t,v,g,y);var m=Math.exp(this.computedRadius[0]);m=Math.max(1e-4,m+n),this.radius.set(t,Math.log(m))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;\"number\"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),v=Math.max(h,p,d);h===v?(s=s<0?-1:1,l=f=0):d===v?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=u(s,l,f);s/=g,l/=g,f/=g}var y,m,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=u(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=u(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,H,G,W),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,O=E*k+L*A+C*M;y=R<0?-Math.PI/2:Math.PI/2,m=Math.atan2(O,P)}else{var I=e[2],D=e[6],z=e[10],R=I*s+D*l+z*f,F=I*x+D*b+z*_,B=I*k+D*A+z*M;y=Math.asin(c(R)),m=Math.atan2(B,F)}this.angle.jump(t,m,y),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var q=V[15],H=V[12]/q,G=V[13]/q,W=V[14]/q,Y=Math.exp(this.computedRadius[0]);this.center.jump(t,H-N*Y,G-j*Y,W-U*Y)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=u(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=u(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,v=d[0],g=d[1],y=d[2],m=i*v+a*g+o*y,x=u(v-=m*i,g-=m*a,y-=m*o);if(!(x<.01&&(x=u(v=a*h-o*f,g=o*l-i*h,y=i*f-a*l))<1e-6)){v/=x,g/=x,y/=x,this.up.set(t,i,a,o),this.right.set(t,v,g,y),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*y-o*g,_=o*v-i*y,w=i*g-a*v,T=u(b,_,w),k=i*l+a*f+o*h,A=v*l+g*f+y*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(c(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var O=Math.abs(C+2*Math.PI-E),I=Math.abs(C-E),D=Math.abs(C-2*Math.PI-E);O0?r.pop():new ArrayBuffer(t)}function d(t){return new Uint8Array(p(t),0,t)}function v(t){return new Uint16Array(p(2*t),0,t)}function g(t){return new Uint32Array(p(4*t),0,t)}function y(t){return new Int8Array(p(t),0,t)}function m(t){return new Int16Array(p(2*t),0,t)}function x(t){return new 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Bt(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function bn(t){var e,r,n,i=t.valueSpec,a=Ut(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,u=\"array\"===Qr(t.value.stops)&&\"array\"===Qr(t.value.stops[0])&&\"object\"===Qr(t.value.stops[0][0]),c=yn({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Bt(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;return e=e.concat(mn({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Qr(r)&&0===r.length&&e.push(new Bt(t.key,r,\"array must have at least one stop\")),e},default:function(t){return Hn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&c.push(new Bt(t.key,t.value,'missing required property 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expected, \"+s+\" found\";return Kr(i)&&void 0===a&&(c+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Bt(t.key,u,c)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l=2&&\"$id\"!==t[1]&&\"$type\"!==t[1];case\"in\":return t.length>=3&&(\"string\"!=typeof t[1]||Array.isArray(t[2]));case\"!in\":case\"!has\":case\"none\":return!1;case\"==\":case\"!=\":case\">\":case\">=\":case\"<\":case\"<=\":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case\"any\":case\"all\":for(var e=0,r=t.slice(1);ee?1:0}function Sn(t){if(!Array.isArray(t))return!1;if(\"within\"===t[0])return!0;for(var e=1;e\"===r||\"<=\"===r||\">=\"===r?Ln(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(En))):\"all\"===r?[\"all\"].concat(t.slice(1).map(En)):\"none\"===r?[\"all\"].concat(t.slice(1).map(En).map(On)):\"in\"===r?Cn(t[1],t.slice(2)):\"!in\"===r?On(Cn(t[1],t.slice(2))):\"has\"===r?Pn(t[1]):\"!has\"===r?On(Pn(t[1])):\"within\"!==r||t}function Ln(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function Cn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(Mn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function Pn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function On(t){return[\"!\",t]}function In(t){return Tn(Vt(t.value))?_n(jt({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):Dn(t)}function Dn(t){var e=t.value,r=t.key;if(\"array\"!==Qr(e))return[new Bt(r,e,\"array expected, \"+Qr(e)+\" found\")];var n,i=t.styleSpec,a=[];if(e.length<1)return[new Bt(r,e,\"filter array must have at least 1 element\")];switch(a=a.concat(wn({key:r+\"[0]\",value:e[0],valueSpec:i.filter_operator,style:t.style,styleSpec:t.styleSpec})),Ut(e[0])){case\"<\":case\"<=\":case\">\":case\">=\":e.length>=2&&\"$type\"===Ut(e[1])&&a.push(new Bt(r,e,'\"$type\" cannot be use with operator \"'+e[0]+'\"'));case\"==\":case\"!=\":3!==e.length&&a.push(new Bt(r,e,'filter array for operator \"'+e[0]+'\" must have 3 elements'));case\"in\":case\"!in\":e.length>=2&&\"string\"!==(n=Qr(e[1]))&&a.push(new Bt(r+\"[1]\",e[1],\"string expected, \"+n+\" found\"));for(var o=2;o=c[p+0]&&n>=c[p+1])?(o[h]=!0,a.push(u[h])):o[h]=!1}}},ri.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),u=this._convertToCellCoord(e),c=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},ri.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},ri.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},ri.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=ei+this.cells.length+1+1,r=0,n=0;n=0)){var f=t[c];u[c]=ai[l].shallow.indexOf(c)>=0?f:ci(f,e)}t instanceof Error&&(u.message=t.message)}if(u.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==l&&(u.$name=l),u}throw new Error(\"can't serialize object of type \"+typeof t)}function fi(t){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||li(t)||ui(t)||ArrayBuffer.isView(t)||t instanceof ni)return t;if(Array.isArray(t))return t.map(fi);if(\"object\"==typeof t){var e=t.$name||\"Object\",r=ai[e].klass;if(!r)throw new Error(\"can't deserialize unregistered class \"+e);if(r.deserialize)return r.deserialize(t);for(var n=Object.create(r.prototype),i=0,a=Object.keys(t);i=0?s:fi(s)}}return n}throw new Error(\"can't deserialize object of type \"+typeof t)}var hi=function(){this.first=!0};hi.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},\"Arabic 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t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description Characters\":function(t){return t>=12272&&t<=12287},\"CJK Symbols and Punctuation\":function(t){return t>=12288&&t<=12351},Hiragana:function(t){return t>=12352&&t<=12447},Katakana:function(t){return t>=12448&&t<=12543},Bopomofo:function(t){return t>=12544&&t<=12591},\"Hangul Compatibility Jamo\":function(t){return t>=12592&&t<=12687},Kanbun:function(t){return t>=12688&&t<=12703},\"Bopomofo Extended\":function(t){return t>=12704&&t<=12735},\"CJK Strokes\":function(t){return t>=12736&&t<=12783},\"Katakana Phonetic Extensions\":function(t){return t>=12784&&t<=12799},\"Enclosed CJK Letters and Months\":function(t){return t>=12800&&t<=13055},\"CJK Compatibility\":function(t){return t>=13056&&t<=13311},\"CJK Unified Ideographs Extension A\":function(t){return t>=13312&&t<=19903},\"Yijing Hexagram 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e=0,r=t;e=65097&&t<=65103)||pi[\"CJK Compatibility Ideographs\"](t)||pi[\"CJK Compatibility\"](t)||pi[\"CJK Radicals Supplement\"](t)||pi[\"CJK Strokes\"](t)||!(!pi[\"CJK Symbols and Punctuation\"](t)||t>=12296&&t<=12305||t>=12308&&t<=12319||12336===t)||pi[\"CJK Unified Ideographs Extension A\"](t)||pi[\"CJK Unified Ideographs\"](t)||pi[\"Enclosed CJK Letters and Months\"](t)||pi[\"Hangul Compatibility Jamo\"](t)||pi[\"Hangul Jamo Extended-A\"](t)||pi[\"Hangul Jamo Extended-B\"](t)||pi[\"Hangul Jamo\"](t)||pi[\"Hangul Syllables\"](t)||pi.Hiragana(t)||pi[\"Ideographic Description Characters\"](t)||pi.Kanbun(t)||pi[\"Kangxi Radicals\"](t)||pi[\"Katakana Phonetic Extensions\"](t)||pi.Katakana(t)&&12540!==t||!(!pi[\"Halfwidth and Fullwidth Forms\"](t)||65288===t||65289===t||65293===t||t>=65306&&t<=65310||65339===t||65341===t||65343===t||t>=65371&&t<=65503||65507===t||t>=65512&&t<=65519)||!(!pi[\"Small Form Variants\"](t)||t>=65112&&t<=65118||t>=65123&&t<=65126)||pi[\"Unified Canadian 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zi=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(tn(t))return new vn(t,e);if(cn(t)){var r=dn(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=ue.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};zi.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},zi.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var Ri=function(t){this.property=t,this.value=new zi(t,void 0)};Ri.prototype.transitioned=function(t,e){return new Bi(this.property,this.value,e,p({},t.transition,this.transition),t.now)},Ri.prototype.untransitioned=function(){return new Bi(this.property,this.value,null,{},0)};var 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Ni=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Ni.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);in.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Hi),Wi=function(t){this.specification=t};Wi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new Di(Math.floor(e.zoom-1),e)),t.expression.evaluate(new Di(Math.floor(e.zoom),e)),t.expression.evaluate(new Di(Math.floor(e.zoom+1),e)),e)}},Wi.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Wi.prototype.interpolate=function(t){return t};var Yi=function(t){this.specification=t};Yi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Yi.prototype.interpolate=function(){return!1};var Xi=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new zi(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new Ri(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};oi(\"DataDrivenProperty\",Hi),oi(\"DataConstantProperty\",qi),oi(\"CrossFadedDataDrivenProperty\",Gi),oi(\"CrossFadedProperty\",Wi),oi(\"ColorRampProperty\",Yi);var Zi=\"-transition\",Ki=function(t){function 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this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate($n,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return x(t,Zi)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Jn,n,t,e,r))return!1}if(x(t,Zi))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),_(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 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i,a=(i=t.type,Ji[i].BYTES_PER_ELEMENT),o=r=ea(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:ea(r,Math.max(n,e)),alignment:e}}function ea(t,e){return Math.ceil(t/e)*e}Qi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Qi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Qi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Qi.prototype.clear=function(){this.length=0},Qi.prototype.resize=function(t){this.reserve(t),this.length=t},Qi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Qi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var ra=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Qi);ra.prototype.bytesPerElement=4,oi(\"StructArrayLayout2i4\",ra);var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new 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this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Qi);ia.prototype.bytesPerElement=12,oi(\"StructArrayLayout2i4i12\",ia);var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Qi);aa.prototype.bytesPerElement=8,oi(\"StructArrayLayout2i4ub8\",aa);var oa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Qi);oa.prototype.bytesPerElement=8,oi(\"StructArrayLayout2f8\",oa);var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l,u)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c){var f=10*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint16[f+8]=u,this.uint16[f+9]=c,t},e}(Qi);sa.prototype.bytesPerElement=20,oi(\"StructArrayLayout10ui20\",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u,c,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,u,c,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=u,this.int16[p+9]=c,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Qi);la.prototype.bytesPerElement=24,oi(\"StructArrayLayout4i4ui4i24\",la);var ua=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Qi);ua.prototype.bytesPerElement=12,oi(\"StructArrayLayout3f12\",ua);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Qi);ca.prototype.bytesPerElement=4,oi(\"StructArrayLayout1ul4\",ca);var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u){var c=10*t,f=5*t;return this.int16[c+0]=e,this.int16[c+1]=r,this.int16[c+2]=n,this.int16[c+3]=i,this.int16[c+4]=a,this.int16[c+5]=o,this.uint32[f+3]=s,this.uint16[c+8]=l,this.uint16[c+9]=u,t},e}(Qi);fa.prototype.bytesPerElement=20,oi(\"StructArrayLayout6i1ul2ui20\",fa);var ha=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Qi);ha.prototype.bytesPerElement=12,oi(\"StructArrayLayout2i2i2i12\",ha);var pa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Qi);pa.prototype.bytesPerElement=16,oi(\"StructArrayLayout2f1f2i16\",pa);var da=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Qi);da.prototype.bytesPerElement=12,oi(\"StructArrayLayout2ub2f12\",da);var va=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return 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r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return 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f=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,f),o.primitiveLength++),i?this.e2=f:this.e1=f},vl.prototype.updateScaledDistance=function(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance},vl.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},oi(\"LineBucket\",vl,{omit:[\"layers\",\"patternFeatures\"]});var gl=new Xi({\"line-cap\":new qi(Ft.layout_line[\"line-cap\"]),\"line-join\":new Hi(Ft.layout_line[\"line-join\"]),\"line-miter-limit\":new qi(Ft.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new qi(Ft.layout_line[\"line-round-limit\"]),\"line-sort-key\":new Hi(Ft.layout_line[\"line-sort-key\"])}),yl={paint:new Xi({\"line-opacity\":new Hi(Ft.paint_line[\"line-opacity\"]),\"line-color\":new Hi(Ft.paint_line[\"line-color\"]),\"line-translate\":new 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r=p({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Hi),xl=new ml(yl.paint.properties[\"line-width\"].specification);xl.useIntegerZoom=!0;var bl=function(t){function e(e){t.call(this,e,yl),this.gradientVersion=0}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){if(\"line-gradient\"===t){var e=this._transitionablePaint._values[\"line-gradient\"].value.expression;this.stepInterpolant=e._styleExpression.expression instanceof tr,this.gradientVersion=(this.gradientVersion+1)%l}},e.prototype.gradientExpression=function(){return this._transitionablePaint._values[\"line-gradient\"].value.expression},e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r),this.paint._values[\"line-floorwidth\"]=xl.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,e)},e.prototype.createBucket=function(t){return new 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Ll=24,Cl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<>1,c=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-c)-1,p>>=-c,c+=s;c>0;a=256*a+t[e+f],f+=h,c-=8);for(o=a&(1<<-c)-1,a>>=-c,c+=n;c>0;o=256*o+t[e+f],f+=h,c-=8);if(0===a)a=1-u;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=u}return(p?-1:1)*o*Math.pow(2,a-n)},Pl=function(t,e,r,n,i,a){var o,s,l,u=8*a-i-1,c=(1<>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,v=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=c):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=c?(s=0,o=c):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<0;t[r+p]=255&o,p+=d,o/=256,u-=8);t[r+p-d]|=128*v},Ol=Il;function Il(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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n=\"\",i=e;i239?4:l>223?3:l>191?2:1;if(i+c>r)break;1===c?l<128&&(u=l):2===c?128==(192&(a=t[i+1]))&&(u=(31&l)<<6|63&a)<=127&&(u=null):3===c?(a=t[i+1],o=t[i+2],128==(192&a)&&128==(192&o)&&((u=(15&l)<<12|(63&a)<<6|63&o)<=2047||u>=55296&&u<=57343)&&(u=null)):4===c&&(a=t[i+1],o=t[i+2],s=t[i+3],128==(192&a)&&128==(192&o)&&128==(192&s)&&((u=(15&l)<<18|(63&a)<<12|(63&o)<<6|63&s)<=65535||u>=1114112)&&(u=null)),null===u?(u=65533,c=1):u>65535&&(u-=65536,n+=String.fromCharCode(u>>>10&1023|55296),u=56320|1023&u),n+=String.fromCharCode(u),i+=c}return n}(this.buf,e,t)},readBytes:function(){var t=this.readVarint()+this.pos,e=this.buf.subarray(this.pos,t);return this.pos=t,e},readPackedVarint:function(t,e){if(this.type!==Il.Bytes)return t.push(this.readVarint(e));var r=Fl(this);for(t=t||[];this.pos127;);else if(e===Il.Bytes)this.pos=this.readVarint()+this.pos;else if(e===Il.Fixed32)this.pos+=4;else{if(e!==Il.Fixed64)throw new Error(\"Unimplemented type: 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qi(Ft.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new qi(Ft.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new qi(Ft.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Hi(Ft.layout_symbol[\"text-field\"]),\"text-font\":new Hi(Ft.layout_symbol[\"text-font\"]),\"text-size\":new Hi(Ft.layout_symbol[\"text-size\"]),\"text-max-width\":new Hi(Ft.layout_symbol[\"text-max-width\"]),\"text-line-height\":new qi(Ft.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Hi(Ft.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Hi(Ft.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Hi(Ft.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new qi(Ft.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Hi(Ft.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new qi(Ft.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new qi(Ft.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Hi(Ft.layout_symbol[\"text-rotate\"]),\"text-padding\":new qi(Ft.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new qi(Ft.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Hi(Ft.layout_symbol[\"text-transform\"]),\"text-offset\":new Hi(Ft.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new qi(Ft.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new qi(Ft.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new qi(Ft.layout_symbol[\"text-optional\"])}),dc={paint:new Xi({\"icon-opacity\":new Hi(Ft.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Hi(Ft.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Hi(Ft.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Hi(Ft.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Hi(Ft.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new qi(Ft.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new qi(Ft.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Hi(Ft.paint_symbol[\"text-opacity\"]),\"text-color\":new Hi(Ft.paint_symbol[\"text-color\"],{runtimeType:Zt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Hi(Ft.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Hi(Ft.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Hi(Ft.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new qi(Ft.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new qi(Ft.paint_symbol[\"text-translate-anchor\"])}),layout:pc},vc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Gt,this.defaultValue=t};vc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},vc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},vc.prototype.outputDefined=function(){return!1},vc.prototype.serialize=function(){return null},oi(\"FormatSectionOverride\",vc,{omit:[\"defaultValue\"]});var gc=function(t){function e(e){t.call(this,e,dc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a\",targetMapId:n,sourceMapId:a.mapId})}}},Cc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else S()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Cc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Cc.prototype.processTask=function(t,e){var r=this;if(\"\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(fi(e.error)):n(null,fi(e.data)))}else{var i=!1,a=C(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"\",sourceMapId:r.mapId,error:e?ci(e):null,data:ci(n,a)},a)}:function(t){i=!0},s=null,l=fi(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var u=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,u[0],l.source)[u[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Oc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Oc.prototype.setNorthEast=function(t){return this._ne=t instanceof Dc?new Dc(t.lng,t.lat):Dc.convert(t),this},Oc.prototype.setSouthWest=function(t){return this._sw=t instanceof Dc?new Dc(t.lng,t.lat):Dc.convert(t),this},Oc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Dc)e=t,r=t;else{if(!(t instanceof Oc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Oc.convert(a))}var o=t;return this.extend(Dc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Dc(e.lng,e.lat),this._ne=new Dc(r.lng,r.lat)),this},Oc.prototype.getCenter=function(){return new Dc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Oc.prototype.getSouthWest=function(){return this._sw},Oc.prototype.getNorthEast=function(){return this._ne},Oc.prototype.getNorthWest=function(){return new Dc(this.getWest(),this.getNorth())},Oc.prototype.getSouthEast=function(){return new Dc(this.getEast(),this.getSouth())},Oc.prototype.getWest=function(){return this._sw.lng},Oc.prototype.getSouth=function(){return this._sw.lat},Oc.prototype.getEast=function(){return this._ne.lng},Oc.prototype.getNorth=function(){return this._ne.lat},Oc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Oc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Oc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Oc.prototype.contains=function(t){var e=Dc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Oc.convert=function(t){return!t||t instanceof Oc?t:new Oc(t)};var Ic=6371008.8,Dc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Dc.prototype.wrap=function(){return new Dc(h(this.lng,-180,180),this.lat)},Dc.prototype.toArray=function(){return[this.lng,this.lat]},Dc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Dc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Ic*Math.acos(Math.min(i,1))},Dc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Oc(new Dc(this.lng-r,this.lat-e),new Dc(this.lng+r,this.lat+e))},Dc.convert=function(t){if(t instanceof Dc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Dc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Dc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: , lat: }, an object {lon: , lat: }, or an array of [, ]\")};var zc=2*Math.PI*Ic;function Rc(t){return zc*Math.cos(t*Math.PI/180)}function Fc(t){return(180+t)/360}function Bc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Nc(t,e){return t/Rc(e)}function jc(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Uc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Uc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Dc.convert(t);return new Uc(Fc(r.lng),Bc(r.lat),Nc(e,r.lat))},Uc.prototype.toLngLat=function(){return new Dc(360*this.x-180,jc(this.y))},Uc.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Rc(jc(e));var t,e},Uc.prototype.meterInMercatorCoordinateUnits=function(){return 1/zc*(t=jc(this.y),1/Math.cos(t*Math.PI/180));var t};var Vc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Gc(0,t,t,e,r)};Vc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Vc.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Pc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Pc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<this.canonical.z?new Hc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Hc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Hc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?Gc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):Gc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Hc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ>e&&t.canonical.y===this.canonical.y>>e},Hc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Hc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Hc(e,this.wrap,e,r,n),new Hc(e,this.wrap,e,r+1,n),new Hc(e,this.wrap,e,r,n+1),new Hc(e,this.wrap,e,r+1,n+1)]},Hc.prototype.isLessThan=function(t){return this.wrapt.wrap)&&(this.overscaledZt.overscaledZ)&&(this.canonical.xt.canonical.x)&&this.canonical.y=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Wc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Wc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Wc.prototype.getPixels=function(){return new $o({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Wc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,u=a;u=0&&c[3]>=0&&s.insert(o,c[0],c[1],c[2],c[3])}},Jc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new tl.VectorTile(new Ol(this.rawTileData)).layers,this.sourceLayerCoder=new Yc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Jc.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=po/t.tileSize/t.scale,l=An(o.filter),u=t.queryGeometry,c=t.queryPadding*s,f=Qc(u),h=this.grid.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c),p=Qc(t.cameraQueryGeometry),d=0,v=this.grid3D.query(p.minX-c,p.minY-c,p.maxX+c,p.maxY+c,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o=l.x&&i>=l.y)return!0}var u=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var c=0,f=u;c=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),d=this.vtLayers[h].feature(n);if(i.needGeometry){var v=mo(d,!0);if(!i.filter(new Di(this.tileID.overscaledZ),v,this.tileID.canonical))return}else if(!i.filter(new Di(this.tileID.overscaledZ),d))return;for(var g=this.getId(d,h),y=0;yn)i=!1;else if(e)if(this.expirationTimeht&&(t.getActor().send(\"enforceCacheSizeLimit\",ft),xt=0)},t.clamp=f,t.clearTileCache=function(t){var e=s.caches.delete(ct);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Fu,t.clone=function(t){var e=new Fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=w,t.clone$2=function(t){var e=new Fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ml,t.config=j,t.create=function(){var t=new Fo(16);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new Fo(9);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new Fo(4);return Fo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=fn,t.createLayout=ta,t.createStyleLayer=function(t){return\"custom\"===t.type?new _c(t):new wc[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=er,t.offscreenCanvasSupported=bt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t},t.parseGlyphPBF=function(t){return new Ol(t).readFields(Ql,[])},t.pbf=Ol,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=po/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,u=t.layers[0]._unevaluatedLayout._values,c={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;c.compositeTextSizes=[u[\"text-size\"].possiblyEvaluate(new Di(h),o),u[\"text-size\"].possiblyEvaluate(new Di(p),o)]}if(\"composite\"===t.iconSizeData.kind){var d=t.iconSizeData,v=d.minZoom,g=d.maxZoom;c.compositeIconSizes=[u[\"icon-size\"].possiblyEvaluate(new Di(v),o),u[\"icon-size\"].possiblyEvaluate(new Di(g),o)]}c.layoutTextSize=u[\"text-size\"].possiblyEvaluate(new Di(t.zoom+1),o),c.layoutIconSize=u[\"icon-size\"].possiblyEvaluate(new Di(t.zoom+1),o),c.textMaxSize=u[\"text-size\"].possiblyEvaluate(new Di(18));for(var y=l.get(\"text-line-height\")*Ll,m=\"map\"===l.get(\"text-rotation-alignment\")&&\"point\"!==l.get(\"symbol-placement\"),x=l.get(\"text-keep-upright\"),b=l.get(\"text-size\"),_=function(){var a=T[w],s=l.get(\"text-font\").evaluate(a,{},o).join(\",\"),u=b.evaluate(a,{},o),f=c.layoutTextSize.evaluate(a,{},o),h=c.layoutIconSize.evaluate(a,{},o),p={horizontal:{},vertical:void 0},d=a.text,v=[0,0];if(d){var g=d.toString(),_=l.get(\"text-letter-spacing\").evaluate(a,{},o)*Ll,A=function(t){for(var e=0,r=t;e=po||f.y<0||f.y>=po||function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,A){var M,S,E,L,C,P=t.addToLineVertexArray(e,r),O=0,I=0,D=0,z=0,R=-1,F=-1,B={},N=ja(\"\"),j=0,U=0;if(void 0===s._unevaluatedLayout.getValue(\"text-radial-offset\")?(j=(M=s.layout.get(\"text-offset\").evaluate(b,{},T).map((function(t){return t*Ll})))[0],U=M[1]):(j=s.layout.get(\"text-radial-offset\").evaluate(b,{},T)*Ll,U=Ju),t.allowVerticalPlacement&&n.vertical){var V=s.layout.get(\"text-rotate\").evaluate(b,{},T)+90,q=n.vertical;L=new Hu(l,e,u,c,f,q,h,p,d,V),o&&(C=new Hu(l,e,u,c,f,o,g,y,d,V))}if(i){var H=s.layout.get(\"icon-rotate\").evaluate(b,{}),G=\"none\"!==s.layout.get(\"icon-text-fit\"),W=Nu(i,H,w,G),Y=o?Nu(o,H,w,G):void 0;E=new Hu(l,e,u,c,f,i,g,y,!1,H),O=4*W.length;var X=t.iconSizeData,Z=null;\"source\"===X.kind?(Z=[Au*s.layout.get(\"icon-size\").evaluate(b,{})])[0]>ec&&k(t.layerIds[0]+': Value for \"icon-size\" is >= '+tc+'. Reduce your \"icon-size\".'):\"composite\"===X.kind&&((Z=[Au*_.compositeIconSizes[0].evaluate(b,{},T),Au*_.compositeIconSizes[1].evaluate(b,{},T)])[0]>ec||Z[1]>ec)&&k(t.layerIds[0]+': Value for \"icon-size\" is >= '+tc+'. 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r=this.loading,n=t.uid;r&&r[n]&&r[n].abort&&(r[n].abort(),delete r[n]),e()},l.prototype.removeTile=function(t,e){var r=this.loaded,n=t.uid;r&&r[n]&&delete r[n],e()};var u=t.window.ImageBitmap,c=function(){this.loaded={}};c.prototype.loadTile=function(e,r){var n=e.uid,i=e.encoding,a=e.rawImageData,o=u&&a instanceof u?this.getImageData(a):a,s=new t.DEMData(n,o,i);this.loaded=this.loaded||{},this.loaded[n]=s,r(null,s)},c.prototype.getImageData=function(e){this.offscreenCanvas&&this.offscreenCanvasContext||(this.offscreenCanvas=new OffscreenCanvas(e.width,e.height),this.offscreenCanvasContext=this.offscreenCanvas.getContext(\"2d\")),this.offscreenCanvas.width=e.width,this.offscreenCanvas.height=e.height,this.offscreenCanvasContext.drawImage(e,0,0,e.width,e.height);var r=this.offscreenCanvasContext.getImageData(-1,-1,e.width+2,e.height+2);return this.offscreenCanvasContext.clearRect(0,0,this.offscreenCanvas.width,this.offscreenCanvas.height),new 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v,g,y,m,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;v=g=y=m=null,x=lt(t,c,r-_,r+T,0,h.minX,h.maxX,l),b=lt(t,c,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(v=lt(x,c,n-_,n+T,1,h.minY,h.maxY,l),g=lt(x,c,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(y=lt(b,c,n-_,n+T,1,h.minY,h.maxY,l),m=lt(b,c,n+w,n+k,1,h.minY,h.maxY,l),b=null),u>1&&console.timeEnd(\"clipping\"),s.push(v||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(m||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,u=t,c=e,f=r;!l&&u>0;)u--,c=Math.floor(c/2),f=Math.floor(f/2),l=this.tiles[kt(u,c,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",u,c,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,u,c,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?mt(this.tiles[s],i):null):null};var Mt=function(e){function r(t,r,n,i){e.call(this,t,r,n,At),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{if(n.filter){var s=t.createExpression(n.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(\"error\"===s.result)throw new Error(s.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));var l=o.features.filter((function(t){return s.value.evaluate({zoom:0},t)}));o={type:\"FeatureCollection\",features:l}}e._geoJSONIndex=n.cluster?new V(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),u=0,c=l;u=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e=0?1.2:1))}function y(t,e,r,n,i,a,o){for(var s=0;s65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=void 0,d=i/r*(n+1);if(l.isDash){var v=n-Math.abs(d);p=Math.sqrt(h*h+v*v)}else p=n-Math.sqrt(h*h+d*d);this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],u=0;u1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=l.isDash?h:-h;this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o=n&&e.x=i&&e.y0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+10&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),O=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;or.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];tthis.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},j.prototype.has=function(t){return t.wrapped().key in this.data},j.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},j.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},j.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},j.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},j.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},j.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},j.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Dt(this._source.type)){for(var u={},c={},f=0,h=Object.keys(l);fthis._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var m=d.children(this._source.maxzoom);if(n[m[0].key]&&n[m[1].key]&&n[m[2].key]&&n[m[3].key])continue}for(var x=v.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(v=this.getTile(_))&&x&&(v=this._addTile(_)),v&&(n[_.key]=_,x=v.wasRequested(),v.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),u=s.map((function(t){return o.pointCoordinate(t)})),c=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,v=0,g=u;v=0&&y[1].y+g>=0){var m=l.map((function(t){return s.getTilePoint(t)})),x=u.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:m,cameraQueryGeometry:x,scale:v})}}},x=0;x=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Dt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(oa.workerUrl)}Ot.maxOverzooming=10,Ot.maxUnderzooming=3;var Rt=\"mapboxgl_preloaded_worker_pool\",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var u=i?e.textSizeData:e.iconSizeData,c=t.evaluateSizeForZoom(u,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,v=n.transform.width/n.transform.height,g=!1,y=0;yMath.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.yn.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=r/24,y=e.lineOffsetX*g,m=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=oe(g,l,y,m,n,f,h,e,u,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}v=[w.first];for(var M=e.glyphStartIndex+1;M0?C.point:ue(h,L,S,1,a),O=se(e.writingMode,S,P,d);if(O)return O}var I=ce(g*l.getoffsetX(e.glyphStartIndex),y,m,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,u,o,p);if(!I)return{notEnoughRoom:!0};v=[I]}for(var D=0,z=v;D0?1:-1,v=0;i&&(d*=-1,v=Math.PI),d<0&&(v+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,m=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)=u)return null;if(m=y,w.push(y),void 0===(y=h[g])){var T=new t.Point(c.getx(g),c.gety(g)),k=re(T,f);if(k.signedDistanceFromCamera>0)y=h[g]=k.point;else{var A=g-d;y=ue(0===x?o:new t.Point(c.getx(A),c.gety(A)),T,m,_-x+1,f)}}x+=b,b=m.dist(y)}var M=(_-x)/b,S=y.sub(m),E=S.mult(M)._add(m);E._add(S._unit()._perp()._mult(n*d));var L=v+Math.atan2(y.y-m.y,y.x-m.x);return w.push(E),{point:E,angle:L,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var u=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,u,c,i),n?u.length>0:u},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,u=this.boxCells[i];if(null!==u)for(var c=this.bboxes,f=0,h=u;f=c[d+0]&&n>=c[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:c[d],y1:c[d+1],x2:c[d+2],y2:c[d+3]})}}}var v=this.circleCells[i];if(null!==v)for(var g=this.circles,y=0,m=v;yo*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var u=(o-i)/2,c=Math.abs(e-(i+u));if(c>u+r)return!1;if(l<=s||c<=u)return!0;var f=l-s,h=c-u;return f*f+h*h<=r*r};var fe=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function he(t,e){for(var r=0;r=1;P--)C.push(E.path[P]);for(var O=1;O0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.xM.x||F.yM.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=z;N=this.screenRightBoundary||nthis.screenBottomBoundary},ve.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t=0&&e0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(v=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:v},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:y,placedGlyphBoxes:m}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,u=a.textLabelPlaneMatrix,c=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,v=a.collisionGroup,g=s.get(\"text-optional\"),y=s.get(\"icon-optional\"),m=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=m&&(x||!o.hasIconData()||y),A=x&&(m||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,O=null,I=0,D=0,z=0;a.textFeatureIndex?I=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(I=e.featureIndex),a.verticalTextFeatureIndex&&(D=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,u=e.textBoxScale,c=w&&!x?r:null,h={box:[],offscreen:!1},p=m?2*N.length:N.length,d=0;d=N.length,k=i.attemptAnchorPlacement(g,t,a,s,u,b,_,f,l,v,y,e,o,n,c);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,m,f,l,v.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),W=t.evaluateSizeForFeature(o.textSizeData,d,G),Y=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(m,G,o.lineVertexArray,o.glyphOffsetArray,W,l,u,c,n,_,v.predicate,X,Y),T=m||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(z=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?ke(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,v.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(O=Z(a.verticalIconBox)).box.length>0:(O=Z(a.iconBox)).box.length>0,S=S&&O.offscreen}var K=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=y||0===e.numIconVertices;if(K||J?J?K||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&D?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,D,v.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID)),M&&O&&i.collisionIndex.insertCollisionBox(O.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,z,v.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID),n)){var $=o.bucketInstanceId,Q=i.collisionCircleArrays[$];void 0===Q&&(Q=i.collisionCircleArrays[$]=new be);for(var tt=0;tt=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C=0&&(e.text.placedSymbolArray.get(u).crossTileID=a>=0&&u!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(m){var T=De(y.text),k=_?ze:T;d(e.text,s,k);var A=w?ze:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=De(y.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:ze;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var O=C?ze:L;d(e.icon,a.numVerticalIconVertices,O),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var I=e.collisionArrays[n];if(I){var D=new t.Point(0,0);if(I.textBox||I.verticalTextBox){var z=!0;if(u){var R=i.variableOffsets[v];R?(D=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),c&&D._rotate(f?i.transform.angle:-i.transform.angle)):z=!1}I.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||_,D.x,D.y),I.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||w,D.x,D.y)}var F=Boolean(!w&&I.verticalIconBox);I.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,h?D.x:0,h?D.y:0),I.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,h?D.x:0,h?D.y:0)}}},g=0;gt},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Le=Math.pow(2,17),Ce=Math.pow(2,16),Pe=Math.pow(2,9),Oe=Math.pow(2,8),Ie=Math.pow(2,1);function De(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Le+e*Ce+r*Pe+e*Oe+r*Ie+e}var ze=0,Re=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;nt.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var u=o[t.scaledTo(Number(a)).key];u&&u.findMatches(e.symbolInstances,t,i)}}for(var c=0;c1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,c())})),u=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){u=null,o||(o=t,a=e,c())}));function c(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],u=l.width,c=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,v=l.stretchX,g=l.stretchY,y=l.content,m=new t.RGBAImage({width:u,height:c});t.RGBAImage.copy(e,m,{x:f,y:h},{x:0,y:0},{width:u,height:c}),r[s]={data:m,pixelRatio:d,sdf:p,stretchX:v,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),u&&(u.cancel(),u=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._afterImageUpdated(e)},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._afterImageUpdated(e)},r.prototype._afterImageUpdated=function(e){this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ot(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(qe(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is required to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s=0;d--){var v=this._order[d];if(r(v))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id] 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),nr=_r(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),ir=_r(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),ar=_r(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),or=_r(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),sr=_r(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),lr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ur=_r(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),cr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),fr=_r(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hr=_r(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),pr=_r(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),dr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),vr=_r(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),gr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),yr=_r(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=_r(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),xr=_r(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),br=_r(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function _r(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n=e.match(/attribute ([\\w]+) ([\\w]+)/g),i=t.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),a=e.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,(function(t,e,r,n,i){return s[i]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+i+\"\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,n,i){var a=\"float\"===n?\"vec2\":\"vec4\",o=i.match(/color/)?\"color\":a;return s[i]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+i+\" = a_\"+i+\";\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+r+\" \"+n+\" \"+i+\" = a_\"+i+\";\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+r+\" \"+n+\" \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),staticAttributes:n,staticUniforms:o}}var wr=Object.freeze({__proto__:null,prelude:Ze,background:Ke,backgroundPattern:Je,circle:$e,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:ur,fillExtrusionPattern:cr,hillshadePrepare:fr,hillshade:hr,line:pr,lineGradient:dr,linePattern:vr,lineSDF:gr,raster:yr,symbolIcon:mr,symbolSDF:xr,symbolTextAndIcon:br}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};function kr(t){for(var e=[],r=0;r>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}Ar.prototype.draw=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=t.gl;if(!this.failedToCreate){for(var m in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[m].set(o[m]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],b=0,_=c.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new Tr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],u,w.vertexOffset,d,v),y.drawElements(e,w.primitiveLength*x,y.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Sr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var u=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[u.r,u.g,u.b],u_vertical_gradient:+n,u_opacity:i}},Er=function(e,r,n,i,a,o,s){return t.extend(Sr(e,r,n,i),Mr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Lr=function(t){return{u_matrix:t}},Cr=function(e,r,n,i){return t.extend(Lr(e),Mr(n,r,i))},Pr=function(t,e){return{u_matrix:t,u_world:e}},Or=function(e,r,n,i,a){return t.extend(Cr(e,r,n,i),{u_world:a})},Ir=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=ge(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Dr=function(t,e,r){var n=ge(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},zr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Rr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Fr=function(t){return{u_matrix:t}},Br=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ge(e,1,r),u_intensity:n}},Nr=function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}},jr=function(e,r,n){var i=n.paint.get(\"hillshade-shadow-color\"),a=n.paint.get(\"hillshade-highlight-color\"),o=n.paint.get(\"hillshade-accent-color\"),s=n.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===n.paint.get(\"hillshade-illumination-anchor\")&&(s-=e.transform.angle);var l,u,c,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,u=Math.pow(2,l.canonical.z),c=l.canonical.y,[new t.MercatorCoordinate(0,c/u).toLngLat().lat,new t.MercatorCoordinate(0,(c+1)/u).toLngLat().lat]),u_light:[n.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}},Ur=function(e,r){var n=r.stride,i=t.create();return t.ortho(i,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(i,i,[0,-t.EXTENT,0]),{u_matrix:i,u_image:1,u_dimension:[n,n],u_zoom:e.overscaledZ,u_unpack:r.getUnpackVector()}};var Vr=function(e,r,n){var i=e.transform;return{u_matrix:Yr(e,r,n),u_ratio:1/ge(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},qr=function(e,r,n,i){return t.extend(Vr(e,r,n),{u_image:0,u_image_height:i})},Hr=function(e,r,n,i){var a=e.transform,o=Wr(r,a);return{u_matrix:Yr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ge(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Gr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Wr(r,o),u=\"round\"===n.layout.get(\"line-cap\"),c=s.getDash(i.from,u),f=s.getDash(i.to,u),h=c.width*a.fromScale,p=f.width*a.toScale;return t.extend(Vr(e,r,n),{u_patternscale_a:[l/h,-c.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:f.y,u_mix:a.t})};function Wr(t,e){return 1/ge(t,1,e.tileZoom)}function Yr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Xr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Zr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Zr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Kr,Jr=function(t,e,r,n,i,a,o,s,l,u){var c=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:c.cameraToCenterDistance,u_pitch:c.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:c.width/c.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:u,u_texture:0}},$r=function(e,r,n,i,a,o,s,l,u,c,f){var h=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,u,c),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Qr=function(e,r,n,i,a,o,s,l,u,c){return t.extend($r(e,r,n,i,a,o,s,l,!0,u,!0),{u_texsize_icon:c,u_texture_icon:1})},tn=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},en=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,u=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/u,f=c*(n.tileID.canonical.x+n.tileID.wrap*u),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},rn={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image),u_image_height:new t.Uniform1f(e,r.u_image_height)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function nn(e,r,n,i,a,o,s){for(var l=e.context,u=l.gl,c=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d0){var _=t.create(),w=m;t.mul(_,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,y.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&c.draw(l,u.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Dr(m,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M=0&&(v[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else he(y.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P0){var s=t.browser.now(),l=(s-e.timeAdded)/o,u=r?(s-r.timeAdded)/o:-1,c=n.getSource(),f=a.coveringZoomLevel({tileSize:c.tileSize,roundZoom:c.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-u,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var gn=new t.Color(1,0,0,1),yn=new t.Color(0,1,0,1),mn=new t.Color(0,0,1,1),xn=new t.Color(1,0,1,1),bn=new t.Color(0,1,1,1);function _n(t){var e=t.transform.padding;wn(t,t.transform.height-(e.top||0),3,gn),wn(t,e.bottom||0,3,yn),Tn(t,e.left||0,3,mn),Tn(t,t.transform.width-(e.right||0),3,xn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;kn(t,e-a/2,r-i/2,a,i,n),kn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,bn)}function wn(t,e,r,n){kn(t,0,e+r/2,t.transform.width,r,n)}function Tn(t,e,r,n){kn(t,e-r/2,0,r,t.transform.height,n)}function kn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function An(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,u=Et.disabled,c=e.colorModeForRenderPass(),f=\"$debug\";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,u,c,Ct.disabled,Rr(o,t.Color.red),f,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var h=r.getTileByID(n.key).latestRawTileData,p=h&&h.byteLength||0,d=Math.floor(p/1024),v=r.getTile(n).tileSize,g=512/Math.min(v,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+\" \"+d+\"kb\"),s.draw(i,a.TRIANGLES,l,u,Lt.alphaBlended,Ct.disabled,Rr(o,t.Color.transparent,g),f,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var Mn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,u=\"map\"===a,c=\"map\"===o,f=0,h=e;f256&&this.clearStencil(),r.setColorMode(Lt.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=c[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer0?e.pop():null},Sn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Sn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new Ar(this.context,t,wr[t],e,rn[t],this._showOverdrawInspector)),this.cache[r]},Sn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Sn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Sn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Sn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var En=function(t,e){this.points=t,this.planes=e};En.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new En(a,o)};var Ln=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Ln.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var u=Number.MAX_VALUE,c=-Number.MAX_VALUE,f=0;fthis.max[l]-this.min[l])return 0}return 1};var Cn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};Cn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Cn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Cn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Cn.prototype.clone=function(){return new Cn(this.top,this.bottom,this.left,this.right)},Cn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Pn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Cn,this._posMatrixCache={},this._alignedPosMatrixCache={}},On={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};Pn.prototype.clone=function(){var t=new Pn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},On.minZoom.get=function(){return this._minZoom},On.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},On.maxZoom.get=function(){return this._maxZoom},On.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},On.minPitch.get=function(){return this._minPitch},On.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},On.maxPitch.get=function(){return this._maxPitch},On.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},On.renderWorldCopies.get=function(){return this._renderWorldCopies},On.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},On.worldSize.get=function(){return this.tileSize*this.scale},On.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},On.size.get=function(){return new t.Point(this.width,this.height)},On.bearing.get=function(){return-this.angle/Math.PI*180},On.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},On.pitch.get=function(){return this._pitch/Math.PI*180},On.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},On.fov.get=function(){return this._fov/Math.PI*180},On.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},On.zoom.get=function(){return this._zoom},On.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},On.center.get=function(){return this._center},On.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},On.padding.get=function(){return this._edgeInsets.toJSON()},On.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},On.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Pn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},Pn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},Pn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},Pn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),u=s-1;u<=l+1;u++)0!==u&&r.push(new t.UnwrappedTileID(u,e));return r},Pn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&re.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=En.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var u=function(t){return{aabb:new Ln([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},c=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)c.push(u(-d)),c.push(u(d));for(c.push(u(0));c.length>0;){var v=c.pop(),g=v.x,y=v.y,m=v.fullyVisible;if(!m){var x=v.aabb.intersects(s);if(0===x)continue;m=2===x}var b=v.aabb.distanceX(o),_=v.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<T&&v.zoom>=l)f.push({tileID:new t.OverscaledTileID(v.zoom===h?p:v.zoom,v.wrap,v.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);c.push({aabb:v.aabb.quadrant(k),zoom:v.zoom+1,x:A,y:M,wrap:v.wrap,fullyVisible:m})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},Pn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},On.unmodified.get=function(){return this._unmodified},Pn.prototype.zoomScale=function(t){return Math.pow(2,t)},Pn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Pn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},Pn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},On.point.get=function(){return this.project(this.center)},Pn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},Pn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Pn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Pn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},Pn.prototype.coordinateLocation=function(t){return t.toLngLat()},Pn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,u=n[1]/a,c=r[2]/i,f=n[2]/a,h=c===f?0:(0-c)/(f-c);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,u,h)/this.worldSize)},Pn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},Pn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},Pn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},Pn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},Pn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Pn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},Pn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,u=this.size,c=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-ao&&(i=o-g)}if(this.lngRange){var y=p.x,m=u.x/2;y-ml&&(n=l-m)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=c,this._constraining=!1}},Pn.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,u=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),c=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,c,u),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),v=Math.sin(this.angle),g=s-Math.round(s)+d*h+v*p,y=l-Math.round(l)+d*p+v*h,m=new Float64Array(f);if(t.translate(m,m,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=m,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Pn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Pn.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},Pn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},In.prototype._updateHashUnthrottled=function(){var e=t.window.location.href.replace(/(#.+)?$/,this.getHashString());try{t.window.history.replaceState(t.window.history.state,null,e)}catch(t){}};var Dn={linearity:.3,easing:t.bezier(0,0,.3,1)},zn=t.extend({deceleration:2500,maxSpeed:1400},Dn),Rn=t.extend({deceleration:20,maxSpeed:1400},Dn),Fn=t.extend({deceleration:1e3,maxSpeed:360},Dn),Bn=t.extend({deceleration:1e3,maxSpeed:90},Dn),Nn=function(t){this._map=t,this.clear()};function jn(t,e){(!t.duration||t.duration0&&r-e[0].time>160;)e.shift()},Nn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n=this._clickTolerance||this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.dblclick=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseover=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.mouseout=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.touchstart=function(t){return this._firePreventable(new qn(t.type,this._map,t))},Gn.prototype.touchmove=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchend=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchcancel=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Wn=function(t){this._map=t};Wn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Wn.prototype.mousemove=function(t){this._map.fire(new Vn(t.type,this._map,t))},Wn.prototype.mousedown=function(){this._delayContextMenu=!0},Wn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Vn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Wn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Vn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Wn.prototype.isEnabled=function(){return!0},Wn.prototype.isActive=function(){return!1},Wn.prototype.enable=function(){},Wn.prototype.disable=function(){};var Yn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Xn(t,e){for(var r={},n=0;nthis.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n30)&&(this.aborted=!0)}}},Zn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Kn=function(t){this.singleTap=new Zn(t),this.numTaps=t.numTaps,this.reset()};Kn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Kn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Kn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Kn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new Kn({numTouches:1,numTaps:2}),this._zoomOut=new Kn({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var $n={};$n[0]=1,$n[2]=2;var Qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Qn.prototype._correctButton=function(t,e){return!1},Qn.prototype._move=function(t,e){return{}},Qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r)if(t.preventDefault(),function(t,e){var r=$n[e];return void 0===t.buttons||(t.buttons&r)!==r}(t,this._eventButton))this.reset();else if(this._moved||!(e.dist(r)0&&(this._active=!0);var i=Xn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var u=i[l],c=this._touches[l];c&&(a._add(u),o._add(u.sub(c)),s++,i[l]=u)}if(this._touches=i,!(sMath.abs(t.x)}var fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ci(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ci(t)&&ci(e)&&a}},e}(ii),hi={panStep:100,bearingStep:15,pitchStep:10},pi=function(){var t=hi;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep,this._rotationDisabled=!1};function di(t){return t*(2-t)}pi.prototype.reset=function(){this._active=!1},pi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return this._rotationDisabled&&(n=0,i=0),{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:di,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},pi.prototype.enable=function(){this._enabled=!0},pi.prototype.disable=function(){this._enabled=!1,this.reset()},pi.prototype.isEnabled=function(){return this._enabled},pi.prototype.isActive=function(){return this._active},pi.prototype.disableRotation=function(){this._rotationDisabled=!0},pi.prototype.enableRotation=function(){this._rotationDisabled=!1};var vi=4.000244140625,gi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll([\"_onTimeout\"],this)};gi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},gi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},gi.prototype.isEnabled=function(){return!!this._enabled},gi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},gi.prototype.isZooming=function(){return!!this._zooming},gi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},gi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},gi.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%vi==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},gi.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},gi.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},gi.prototype.renderFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>vi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,u=this._easing,c=!1;if(\"wheel\"===this._type&&l&&u){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=u(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):c=!0}else o=s,c=!0;return this._active=!0,c&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!c,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},gi.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},gi.prototype.reset=function(){this._active=!1};var yi=function(t,e){this._clickZoom=t,this._tapZoom=e};yi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},yi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},yi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},yi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var mi=function(){this.reset()};mi.prototype.reset=function(){this._active=!1},mi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},mi.prototype.enable=function(){this._enabled=!0},mi.prototype.disable=function(){this._enabled=!1,this.reset()},mi.prototype.isEnabled=function(){return this._enabled},mi.prototype.isActive=function(){return this._active};var xi=function(){this._tap=new Kn({numTouches:1,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},xi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},xi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},xi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var bi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};bi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},bi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},bi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},bi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var _i=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};_i.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},_i.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},_i.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},_i.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var wi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};wi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},wi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},wi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},wi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var Ti=function(t){return t.zoom||t.drag||t.pitch||t.rotate},ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ai(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var Mi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Nn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!0}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;aa?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(v));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),u=this.getPadding(),c=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(c-o),v=t.Point.convert(e.offset),g=a.centerPoint.add(v),y=a.pointLocation(g),m=t.LngLat.convert(e.center||y);this._normalizeCenter(m);var x=a.project(y),b=a.project(m).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,c),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),O=function(t){return C(P)/C(P+_*t)},I=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},D=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(D)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var z=Te.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*D,d=1/O(i);a.zoom=1===e?c:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(u,p,e),g=a.centerPoint.add(v));var y=1===e?m:a.unproject(x.add(b.mult(I(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop(!1)}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)180?-360:r<-180?360:0}},r}(t.Evented),Ei=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_toggleAttribution\",\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Ei.prototype.getDefaultPosition=function(){return\"bottom-right\"},Ei.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._compactButton=r.create(\"button\",\"mapboxgl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),this._innerContainer.setAttribute(\"role\",\"list\"),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Ei.prototype._setElementTitle=function(t,e){var r=this._map._getUIString(\"AttributionControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)},Ei.prototype._toggleAttribution=function(){this._container.classList.contains(\"mapboxgl-compact-show\")?(this._container.classList.remove(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"false\")):(this._container.classList.add(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"true\"))},Ei.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Ei.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\",\"mapboxgl-compact-show\")};var Li=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};Li.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},Li.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Li.prototype.getDefaultPosition=function(){return\"bottom-left\"},Li.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Li.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},Li.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var Ci=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Ci.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Ci.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;re.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>zi)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new Pn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Ci,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Pi,e.locale),this._clickTolerance=e.clickTolerance,this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ii))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_onMapScroll\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1),t.window.addEventListener(\"orientationchange\",this._onWindowResize,!1)),this.handlers=new Mi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new In(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Ei({customAttribution:e.customAttribution})),this.addControl(new Li,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&(r=e.getDefaultPosition?e.getDefaultPosition():\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.hasControl=function(t){return this._controls.indexOf(t)>-1},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()zi)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Vn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Vn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Vn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ui.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ui.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ui.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ui.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ui.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ui.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ui.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ui.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)=r}this._isDragging&&(this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\")))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Yi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Xi=0,Zi=!1,Ki=function(e){function n(r){e.call(this),this.options=t.extend({},Yi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Gi?n(Gi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Gi=\"denied\"!==t.state,n(Gi)})):(Gi=!!t.window.navigator.geolocation,n(Gi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Xi=0,Zi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitudee.getEast()||r.latitudee.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Zi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Wi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Wi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Xi--,Zi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Xi>1?(e={maximumAge:6e5,timeout:0},Zi=!0):(e=this.options.positionOptions,Zi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ji={maxWidth:100,unit:\"metric\"},$i=function(e){this.options=t.extend({},Ji,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?ta(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):ta(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?ta(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?ta(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):ta(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function ta(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\" \"+n}$i.prototype.getDefaultPosition=function(){return\"bottom-left\"},$i.prototype._onMove=function(){Qi(this._map,this._container,this.options)},$i.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},$i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},$i.prototype.setUnit=function(t){this.options.unit=t,Qi(this._map,this._container,this.options)};var ea=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};ea.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},ea.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},ea.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},ea.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},ea.prototype._isFullscreen=function(){return this._fullscreen},ea.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},ea.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var ra={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\"},na=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \"),ia=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(ra),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container);return this._content.appendChild(t),this._createCloseButton(),this._update(),this._focusFirstElement(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.setOffset=function(t){return this.options.offset=t,this._update(),this},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createCloseButton=function(){this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(t){var e=this,n=this._lngLat||this._trackPointer;if(this._map&&n&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return e._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||t)){var i=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat),a=this.options.anchor,o=aa(this.options.offset);if(!a){var s,l=this._container.offsetWidth,u=this._container.offsetHeight;s=i.y+o.bottom.ythis._map.transform.height-u?[\"bottom\"]:[],i.xthis._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var c=i.add(o[a]).round();r.setTransform(this._container,qi[a]+\" translate(\"+c.x+\"px,\"+c.y+\"px)\"),Hi(this._container,a,\"popup\")}},n.prototype._focusFirstElement=function(){if(this.options.focusAfterOpen&&this._container){var t=this._container.querySelector(na);t&&t.focus()}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);function aa(e){if(e){if(\"number\"==typeof e){var r=Math.round(Math.sqrt(.5*Math.pow(e,2)));return{center:new t.Point(0,0),top:new t.Point(0,e),\"top-left\":new t.Point(r,r),\"top-right\":new t.Point(-r,r),bottom:new t.Point(0,-e),\"bottom-left\":new t.Point(r,-r),\"bottom-right\":new t.Point(-r,-r),left:new t.Point(e,0),right:new t.Point(-e,0)}}if(e instanceof t.Point||Array.isArray(e)){var n=t.Point.convert(e);return{center:n,top:n,\"top-left\":n,\"top-right\":n,bottom:n,\"bottom-left\":n,\"bottom-right\":n,left:n,right:n}}return{center:t.Point.convert(e.center||[0,0]),top:t.Point.convert(e.top||[0,0]),\"top-left\":t.Point.convert(e[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(e[\"top-right\"]||[0,0]),bottom:t.Point.convert(e.bottom||[0,0]),\"bottom-left\":t.Point.convert(e[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(e[\"bottom-right\"]||[0,0]),left:t.Point.convert(e.left||[0,0]),right:t.Point.convert(e.right||[0,0])}}return aa(new t.Point(0,0))}var oa={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Fi,NavigationControl:ji,GeolocateControl:Ki,AttributionControl:Ei,ScaleControl:$i,FullscreenControl:ea,Popup:ia,Marker:Wi,Style:Ye,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){jt().acquire(Rt)},clearPrewarmedResources:function(){var t=Bt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Rt),Bt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. 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0===e&&(e=\"kilometers\"),void 0===r&&(r=\"kilometers\"),!(t>=0))throw new Error(\"length must be a positive number\");return c(f(t,e),r)},e.convertArea=function(t,r,n){if(void 0===r&&(r=\"meters\"),void 0===n&&(n=\"kilometers\"),!(t>=0))throw new Error(\"area must be a positive number\");var i=e.areaFactors[r];if(!i)throw new Error(\"invalid original units\");var a=e.areaFactors[n];if(!a)throw new Error(\"invalid final units\");return t/i*a},e.isNumber=p,e.isObject=function(t){return!!t&&t.constructor===Object},e.validateBBox=function(t){if(!t)throw new Error(\"bbox is required\");if(!Array.isArray(t))throw new Error(\"bbox must be an Array\");if(4!==t.length&&6!==t.length)throw new Error(\"bbox must be an Array of 4 or 6 numbers\");t.forEach((function(t){if(!p(t))throw new Error(\"bbox must only contain numbers\")}))},e.validateId=function(t){if(!t)throw new Error(\"id is required\");if(-1===[\"string\",\"number\"].indexOf(typeof t))throw new Error(\"id must be a number or a 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Math.round(t*r)/r},e.radiansToLength=c,e.lengthToRadians=f,e.lengthToDegrees=function(t,e){return h(f(t,e))},e.bearingToAzimuth=function(t){var e=t%360;return e<0&&(e+=360),e},e.radiansToDegrees=h,e.degreesToRadians=function(t){return t%360*Math.PI/180},e.convertLength=function(t,e,r){if(void 0===e&&(e=\"kilometers\"),void 0===r&&(r=\"kilometers\"),!(t>=0))throw new Error(\"length must be a positive number\");return c(f(t,e),r)},e.convertArea=function(t,r,n){if(void 0===r&&(r=\"meters\"),void 0===n&&(n=\"kilometers\"),!(t>=0))throw new Error(\"area must be a positive number\");var i=e.areaFactors[r];if(!i)throw new Error(\"invalid original units\");var a=e.areaFactors[n];if(!a)throw new Error(\"invalid final units\");return t/i*a},e.isNumber=p,e.isObject=function(t){return!!t&&t.constructor===Object},e.validateBBox=function(t){if(!t)throw new Error(\"bbox is required\");if(!Array.isArray(t))throw new Error(\"bbox must be an Array\");if(4!==t.length&&6!==t.length)throw new Error(\"bbox 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t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst 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return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign 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u=t.family.join(\", \"),c=[t.style];t.style!=t.variant&&c.push(t.variant),t.variant!=t.weight&&c.push(t.weight),_&&t.weight!=t.stretch&&c.push(t.stretch),e.font[r]={baseString:i,family:u,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:y(u,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:c.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h2){for(var 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K=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(K);for(var J=0;J1?this.counts[J]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[J]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*J,4*J+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[J]:this.opacity,baseline:null!=this.baselineOffset[J]?this.baselineOffset[J]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[J]?this.alignOffset[J]:this.alignOffset[0]:0,atlas:this.fontAtlas[J]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*J,2*J+2):this.positionOffset}}else 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i=t.seg,a=n.seg,o=i.start,s=i.end,u=a.start,c=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,u,c);if(!1===f){if(!e.pointsCollinear(o,s,u))return!1;if(e.pointsSame(o,c)||e.pointsSame(s,u))return!1;var h=e.pointsSame(o,u),p=e.pointsSame(s,c);if(h&&p)return n;var d=!h&&e.pointBetween(o,u,c),v=!p&&e.pointBetween(s,u,c);if(h)return v?l(n,s):l(t,c),n;d&&(p||(v?l(n,s):l(t,c)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,u):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,c)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=u(h),d=p.before?p.before.ev:null,v=p.after?p.after.ev:null;function g(){if(d){var t=c(h,d);if(t)return t}return!!v&&c(h,v)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!v&&v.seg);var y,m,x=g();if(x)t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(x.seg.myFill.above=!x.seg.myFill.above):x.seg.otherFill=h.seg.myFill,r&&r.segmentUpdate(x.seg),h.other.remove(),h.remove();if(a.getHead()!==h){r&&r.rewind(h.seg);continue}t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=v?v.seg.myFill.above:i,h.seg.myFill.above=m?!h.seg.myFill.below:h.seg.myFill.below):null===h.seg.otherFill&&(y=v?h.primary===v.primary?v.seg.otherFill.above:v.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:y,below:y}),r&&r.status(h.seg,!!d&&d.seg,!!v&&v.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&c(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var 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normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\",frag:\"\\nprecision highp float;\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=tthis.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},26368:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},24747:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},65616:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o(8+(t-=this.jdEpoch)+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s(20+(t-=this.jdEpoch),20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},30632:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},73040:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var u=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(u)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(u,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var u=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,u)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 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n=r(50896);function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var 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" ] }, "metadata": {}, @@ -1684,7 +551,7 @@ }, { "cell_type": "markdown", - "id": "7d265967-e6e5-440c-badf-156a43943c88", + "id": "30", "metadata": {}, "source": [ "### Convergence and Parameter Trajectories\n", @@ -1694,21 +561,38 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "f66e0b0f-4861-42dd-bb7f-8734fcca3328", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:02.971034Z", - "iopub.status.busy": "2024-04-14T18:58:02.970869Z", - "iopub.status.idle": "2024-04-14T18:58:17.121157Z", - "shell.execute_reply": "2024-04-14T18:58:17.120837Z" - } - }, + "execution_count": null, + "id": "31", + "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": [ - "10020030040050060070000.10.20.30.40.5ConvergenceIterationCost" + "text/html": [ + "
" ] }, "metadata": {}, @@ -1716,8 +600,32 @@ }, { "data": { - "image/svg+xml": [ - "0100020003000400000.050.10.150.20100020003000400000.20.40.60.80100020003000400000.510100020003000400005k10k0100020003000400002000400060008000R0 [Ohm]R1 [Ohm]R2 [Ohm]C1 [F]C2 [F]Parameter ConvergenceFunction CallFunction CallFunction CallFunction CallFunction CallR0 [Ohm]R1 [Ohm]R2 [Ohm]C1 [F]C2 [F]" + "text/html": [ + "
" ] }, "metadata": {}, @@ -1731,7 +639,7 @@ }, { "cell_type": "markdown", - "id": "d483bbe5", + "id": "32", "metadata": {}, "source": [ "## Validating the Fit\n", @@ -1741,16 +649,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "10b36ce3", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:17.123003Z", - "iopub.status.busy": "2024-04-14T18:58:17.122887Z", - "iopub.status.idle": "2024-04-14T18:58:17.127095Z", - "shell.execute_reply": "2024-04-14T18:58:17.126840Z" - } - }, + "execution_count": null, + "id": "33", + "metadata": {}, "outputs": [], "source": [ "df_pulse_two = pd.DataFrame(\n", @@ -1762,7 +663,7 @@ }, { "cell_type": "markdown", - "id": "0035483a", + "id": "34", "metadata": {}, "source": [ "Next, we construct a new `pybop.Dataset` from the second pulse data," @@ -1770,16 +671,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "f19eb048", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:17.128509Z", - "iopub.status.busy": "2024-04-14T18:58:17.128434Z", - "iopub.status.idle": "2024-04-14T18:58:17.130194Z", - "shell.execute_reply": "2024-04-14T18:58:17.129970Z" - } - }, + "execution_count": null, + "id": "35", + "metadata": {}, "outputs": [], "source": [ "dataset_two_pulse = pybop.Dataset(\n", @@ -1793,7 +687,7 @@ }, { "cell_type": "markdown", - "id": "db5360da", + "id": "36", "metadata": {}, "source": [ "Now that we have a new dataset, we update the target within the problem class as well as the `Initial SoC` value. Once that has been completed, we rebuild the model." @@ -1801,26 +695,18 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "0aa12385", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:17.131389Z", - "iopub.status.busy": "2024-04-14T18:58:17.131319Z", - "iopub.status.idle": "2024-04-14T18:58:17.140453Z", - "shell.execute_reply": "2024-04-14T18:58:17.140222Z" - } - }, + "execution_count": null, + "id": "37", + "metadata": {}, "outputs": [], "source": [ "problem.set_target(dataset_two_pulse)\n", - "model.parameter_set[\"Initial SoC\"] = 0.8 - 0.0075\n", - "model.rebuild(dataset_two_pulse)" + "model.build(dataset=dataset_two_pulse, initial_state={\"Initial SoC\": 0.8 - 0.0075})" ] }, { "cell_type": "markdown", - "id": "22a1a39d", + "id": "38", "metadata": {}, "source": [ "Let's plot the parameterised forward model against the new pulse data:" @@ -1828,21 +714,38 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "11fc77a6", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:17.141599Z", - "iopub.status.busy": "2024-04-14T18:58:17.141526Z", - "iopub.status.idle": "2024-04-14T18:58:17.173731Z", - "shell.execute_reply": "2024-04-14T18:58:17.173364Z" - } - }, + "execution_count": null, + "id": "39", + "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": [ - "051015203.853.93.954ReferenceModelParameter ExtrapolationTime / sVoltage / V" + "text/html": [ + "
" ] }, "metadata": {}, @@ -1855,7 +758,7 @@ }, { "cell_type": "markdown", - "id": "f7056d7d", + "id": "40", "metadata": {}, "source": [ "As expected, when identifying parameters from a single pulse, extrapolation to different operating conditions is challenging. To solve this issue, parameter identification with various pulse datasets is recommended." @@ -1863,7 +766,7 @@ }, { "cell_type": "markdown", - "id": "c544a81c-1215-4794-b7db-c57c46125c77", + "id": "41", "metadata": {}, "source": [ "### Conclusion\n", @@ -1888,7 +791,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/examples/notebooks/comparing_cost_functions.ipynb b/examples/notebooks/comparing_cost_functions.ipynb new file mode 100644 index 000000000..ce54750cf --- /dev/null +++ b/examples/notebooks/comparing_cost_functions.ipynb @@ -0,0 +1,739 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigating different cost functions\n", + "\n", + "In this notebook, we take a look at the different fitting cost functions offered in PyBOP. Cost functions for fitting problems conventionally describe the distance between two points (the target and the prediction) which is to be minimised via PyBOP's optimisation algorithms. \n", + "\n", + "First, we install and import the required packages below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "go = pybop.PlotlyManager().go\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this notebook, we need to construct parameters, a model and a problem class before we can compare differing cost functions. We start with two parameters, but this is an arbitrary selection and can be expanded given the model and data in question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Positive electrode thickness [m]\",\n", + " prior=pybop.Gaussian(7.56e-05, 0.5e-05),\n", + " bounds=[65e-06, 10e-05],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive particle radius [m]\",\n", + " prior=pybop.Gaussian(5.22e-06, 0.5e-06),\n", + " bounds=[2e-06, 9e-06],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will construct the Single Particle Model (SPM) with the Chen2020 parameter set, but like the above, this is an arbitrary selection and can be replaced with any PyBOP model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, as we will need reference data to compare our model predictions to (via the cost function), we will create synthetic data from the model constructed above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t_eval = np.arange(0, 900, 10)\n", + "values = model.predict(t_eval=t_eval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then construct the PyBOP dataset class with the synthetic data as," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": values[\"Voltage [V]\"].data,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can put this all together and construct the problem class. In this situation, we are going to compare differing fitting cost functions, so we construct the `FittingProblem`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sum of Squared Errors and Root Mean Squared Error\n", + "\n", + "First, let's start with two commonly-used cost functions: the sum of squared errors (SSE) and the root mean squared error (RMSE). Constructing these classes is very concise in PyBOP, and only requires the problem class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost_SSE = pybop.SumSquaredError(problem)\n", + "cost_RMSE = pybop.RootMeanSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can investigate how these functions differ when fitting the parameters. To acquire the cost value for each of these, we can simply use the call method of the constructed class, such as:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.1753460077019054e-09" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost_SSE([7.56e-05, 5.22e-06])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use the `Parameters` class for this," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7.56e-05 5.22e-06]\n" + ] + }, + { + "data": { + "text/plain": [ + "1.1753460077019054e-09" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(parameters.current_value())\n", + "cost_SSE(parameters.current_value())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to generate a random sample of candidate solutions from the parameter class prior, we can also do that as:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7.60957550e-05 5.48691392e-06]\n" + ] + }, + { + "data": { + "text/plain": [ + "0.014466627355628724" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample = parameters.rvs()\n", + "print(sample)\n", + "cost_SSE(sample)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparing RMSE and SSE\n", + "\n", + "Now, let's vary one of the parameters, and keep a fixed value for the other, to create a scatter plot comparing the cost values for the RMSE and SSE functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_range = np.linspace(4.72e-06, 5.72e-06, 75)\n", + "y_SSE = []\n", + "y_RMSE = []\n", + "for i in x_range:\n", + " y_SSE.append(cost_SSE([7.56e-05, i]))\n", + " y_RMSE.append(cost_RMSE([7.56e-05, i]))\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter(x=x_range, y=y_SSE, mode=\"lines\", name=\"SSE\"))\n", + "fig.add_trace(go.Scatter(x=x_range, y=y_RMSE, mode=\"lines\", name=\"RMSE\"))\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this situation, it's clear that the curvature of the SSE cost is greater than that of the RMSE. This can improve the rate of convergence for certain optimisation algorithms. However, with incorrect hyperparameter values, larger gradients can also result in the algorithm not converging due to sampling locations outside of the \"cost valley\", e.g. infeasible parameter values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Minkowski distance\n", + "\n", + "Next, let's investigate the Minkowski distance. The Minkowski cost takes a general form, which allows for hyperparameter calibration on the cost function itself, given by\n", + "\n", + "$\\mathcal{L_p} = \\displaystyle \\Big(\\sum_i |\\hat{y_i}-y_i|^p\\Big)^{1/p}$\n", + "\n", + "where $p ≥ 0$ is the order of the Minkowski distance.\n", + "\n", + "For $p = 1$, it is the Manhattan distance. \n", + "For $p = 2$, it is the Euclidean distance. \n", + "For $p ≥ 1$, the Minkowski distance is a metric, but for $0
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_minkowski = []\n", + "for i in x_range:\n", + " y_minkowski.append(cost_minkowski([7.56e-05, i]))\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=np.asarray(y_RMSE) * np.sqrt(len(t_eval)),\n", + " mode=\"lines\",\n", + " name=\"RMSE*N\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=np.sqrt(y_SSE),\n", + " mode=\"lines\",\n", + " line=dict(dash=\"dash\"),\n", + " name=\"sqrt(SSE)\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range, y=y_minkowski, mode=\"lines\", line=dict(dash=\"dot\"), name=\"Minkowski\"\n", + " )\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, these lines lie on top of one another. Now, let's take a look at how the Minkowski cost changes for different orders, `p`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p_orders = np.append(0.75, np.linspace(1, 3, 5))\n", + "y_minkowski = tuple(\n", + " [pybop.Minkowski(problem, p=j)([7.56e-05, i]) for i in x_range] for j in p_orders\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "for k, _ in enumerate(p_orders):\n", + " fig.add_trace(\n", + " go.Scatter(x=x_range, y=y_minkowski[k], mode=\"lines\", name=f\"Minkowski {_}\")\n", + " )\n", + "fig.update_yaxes(range=[0, np.max(y_minkowski[2])])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As seen above, the Minkowski cost allows for a range of different cost functions to be created. This provides users with another hyperparameter to calibrate for optimisation algorithm convergence. This addition does expand the global search space, and should be carefully considered before deciding upon.\n", + "\n", + "### Sum of Power\n", + "Next, we introduce a similar cost function, the `SumofPower` implementation. This cost function is the $p$-th power of the Minkowski distance of order $p$. It provides a generalised formulation for the Sum of Squared Errors (SSE) cost function, and is given by,\n", + "\n", + "$\\mathcal{L_p} = \\displaystyle \\sum_i |\\hat{y_i}-y_i|^p$\n", + "\n", + "where $p ≥ 0$ is the power order. A few special cases include,\n", + "\n", + "$p = 1$: Sum of Absolute Differences\n", + "$p = 2$: Sum of Squared Differences\n", + "\n", + "Next we repeat the above examples with the addition of the `SumofPower` class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost_sumofpower = pybop.SumofPower(problem, p=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_sumofpower = []\n", + "for i in x_range:\n", + " y_sumofpower.append(cost_sumofpower([7.56e-05, i]))\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=np.asarray(y_RMSE) * np.sqrt(len(t_eval)),\n", + " mode=\"lines\",\n", + " name=\"RMSE*N\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=y_SSE,\n", + " mode=\"lines\",\n", + " line=dict(dash=\"dash\"),\n", + " name=\"SSE\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=y_sumofpower,\n", + " mode=\"lines\",\n", + " line=dict(dash=\"dot\"),\n", + " name=\"Sum of Power\",\n", + " )\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the `SumofPower` with order `p=2` equates to the `SSE` implementation. Next, we compare the `Minkowski` to the `SumofPower`," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p_orders = np.append(0.75, np.linspace(1, 2, 2))\n", + "\n", + "y_minkowski = tuple(\n", + " [pybop.Minkowski(problem, p=j)([7.56e-05, i]) for i in x_range] for j in p_orders\n", + ")\n", + "\n", + "y_sumofpower = tuple(\n", + " [pybop.SumofPower(problem, p=j)([7.56e-05, i]) for i in x_range] for j in p_orders\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "for k, _ in enumerate(p_orders):\n", + " fig.add_trace(\n", + " go.Scatter(x=x_range, y=y_minkowski[k], mode=\"lines\", name=f\"Minkowski {_}\")\n", + " )\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=y_sumofpower[k],\n", + " mode=\"lines\",\n", + " line=dict(dash=\"dash\"),\n", + " name=f\"Sum of Power {_}\",\n", + " )\n", + " )\n", + "fig.update_yaxes(range=[0, 2.5])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure demonstrates the distinct behaviour of the `Minkowski` distance and the `SumofPower` function. One notable difference is the effect of the `1/p` exponent in the `Minkowski` distance, which has a linearising impact on the response. This linearisation can enhance the robustness of certain optimisation algorithms, potentially making them less sensitive to outliers or extreme values. However, this increased robustness may come at the cost of a slower convergence rate, as the linearised response might require more iterations to reach the optimal solution. In contrast, the `SumofPower` function does not exhibit this linearising effect, which can lead to faster convergence in some cases but may be more susceptible to the influence of outliers or extreme values.\n", + "\n", + "In this notebook, we've shown the different fitting cost functions offered in PyBOP. Selection between these functions can affect the optimisation result in the case that the optimiser hyperparameter values are not properly calibrated. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pybop-3.12", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebooks/cost-compute-methods.ipynb b/examples/notebooks/cost-compute-methods.ipynb new file mode 100644 index 000000000..32d8099f1 --- /dev/null +++ b/examples/notebooks/cost-compute-methods.ipynb @@ -0,0 +1,372 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Using the Cost/Likelihood classes\n", + "This example will introduce the cost function methods used for both evaluating the output of and predicting the forward model. This example will use a cost class (`pybop.SumofPower`) as an example, but the methods discussed here are transferable to the other cost classes as well as the likelihood classes.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Importing Libraries\n", + "\n", + "With the environment set up, we can now import PyBOP alongside other libraries we will need:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "First, to construct a `pybop.Cost` class, we need the following objects:\n", + "- Model\n", + "- Dataset\n", + "- Parameters to identify\n", + "- Problem\n", + "\n", + "Given the above, we will first construct the model, then the parameters and corresponding dataset. Once that is complete, the problem will be created. With the cost class created, we will showcase the different interactions users can have with the class. A small example with evaluation as well as computation is presented." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "Now that we have the model constructed, let's define the parameters for identification." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative electrode active material volume fraction\",\n", + " initial_value=0.6,\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive electrode active material volume fraction\",\n", + " initial_value=0.6,\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "Next, we generate some synthetic data from the model using the `model.predict` method. This then gets corrupted with Gaussian noise and used to create the Dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "t_eval = np.linspace(0, 10, 100)\n", + "values = model.predict(t_eval=t_eval)\n", + "\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": values[\"Voltage [V]\"].data,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "Now that we have the model, parameters, and dataset, we can combine them and construct the problem class. This class forms the basis for evaluating the forward model for the defined fitting process (parameters and operating conditions)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "Perfect, let's now construct the cost class and move onto the main point of this example," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "cost = pybop.SumofPower(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "The conventional way to use the cost class is through the `cost.__call__` method, which is completed below," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.08963993888559865" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.5, 0.5])" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "This does two things, it first evaluates the forward model at the given parameter values of `[0.5,0.5]`, then it computes the cost for the forward models prediction compared to the problem target values, which are provided from the dataset we constructed above. \n", + "\n", + "However, there is an alternative method to achieve this which provides the user with more flexibility in their assessment of the cost function, this is done through the `cost.compute` method, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.08963993888559865" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out = problem.evaluate([0.5, 0.5])\n", + "cost.compute(out)" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": {}, + "source": [ + "This splits the evaluation of the forward model and the computation of the cost function into two separate calls, allowing for the model evaluation to be decoupled from the cost computation. This decoupling can be helpful in the case where you want to assess the problem across multiple costs (see pybop.WeightedCost for a PyBOP implementation of this), or want to modify the problem output before assessing a cost.\n", + "\n", + "Next, let's present a few of these use-cases. In the first use-case, the problem is evaluated once, with random noise added and the cost computed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "def my_cost(inputs):\n", + " y = problem.evaluate(inputs)\n", + " y[\"Voltage [V]\"] += np.random.normal(0, 0.003, len(t_eval))\n", + " return cost.compute(y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.08910088339381227" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_cost([0.5, 0.5])" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": {}, + "source": [ + "The above method showcases how the `cost.__call__` method can be constructed at the user level. Furthermore, the above example can be reimplemented with gradient calculations as well via the `calculate_gradient` argument within the `cost.compute` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [], + "source": [ + "def my_cost_gradient(inputs):\n", + " y, dy = problem.evaluateS1(inputs)\n", + " y[\"Voltage [V]\"] += np.random.normal(0, 0.003, len(t_eval))\n", + " return cost.compute(y, dy=dy, calculate_grad=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.08917807157201464, array([-0.57688969, -0.48453944]))" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_cost_gradient([0.5, 0.5])" + ] + }, + { + "cell_type": "markdown", + "id": "24", + "metadata": {}, + "source": [ + "This provides the computed cost for the parameter values, alongside the gradient with respect to those parameters. This is the exact structure that is used within PyBOP's gradient-based optimisers. Finally, the above can be easily reproduced via the `cost.__call__` method with the corresponding `calculate_gradient=True` argument." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.08963668887423992, array([-0.58045629, -0.48653053]))" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.5, 0.5], calculate_grad=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/creating_a_model.ipynb b/examples/notebooks/creating_a_model.ipynb new file mode 100644 index 000000000..2dfe66af7 --- /dev/null +++ b/examples/notebooks/creating_a_model.ipynb @@ -0,0 +1,142 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Creating a Model\n", + "\n", + "In this notebook, we create and solve a single particle model (SPM). This is achieved using a predefined parameter set introduced in Marquis et al. [[1]](https://doi.org/10.1149/1945-7111/abbce4) \n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "## Creating a Model\n", + "\n", + "PyBOP offers the both forward emperical and physics-based forward models. These are provided by PyBaMM, with PyBOP adding wrappers on the underlying classes to reduce complexity and improve stability with parameter inference and design optimisation. Likewise, PyBOP provides a light wrapper on the PyBaMM parameter sets, with user-defined parameters available through the same `pybop.ParameterSet` class.\n", + "\n", + "Let's construct the parameter set and then the single-particle model (SPM):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Marquis2019\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the model is constructed with the Maquis parameter set, we can use the `model.predict` method as a light wrapper on the `PyBaMM.Simulation` class. This is the recommended way to generate synthetic data, but not for parameter inference as the performance cost of constructing the `Simulation` class is high. For parameter inference, `model.simulate` and `model.simulateS1` offer a performant way to solve the forward model with and without sensitivities respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing the Model\n", + "Having constructed the model, we can now have a look at its voltage discharge curve to verify that it is working. The discharge curve is evaluated on the time interval specified by `t_eval`. `model.predict` returns the `PyBaMM.solution` object with all of its functionality. As we are only working with the forward model, PyBaMM plotting methods will be used; however, when performing parameter inference or design optimisation, PyBOP plotting methods are recommended." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_eval = [0, 3700]\n", + "solution = model.predict([], t_eval) # No inputs i.e []\n", + "\n", + "# Plot with PyBaMM\n", + "solution.plot_voltage_components()" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/ecm_trust-constr.ipynb b/examples/notebooks/ecm_trust-constr.ipynb new file mode 100644 index 000000000..222e4ebfc --- /dev/null +++ b/examples/notebooks/ecm_trust-constr.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Empirical models with scipy trust-constr:\n", + "## Identifying equivalent circuit parameters using bounds, nonlinear constraints, and gradient-based optimisers\n", + "\n", + "Here, we provide a short example of how to identify equivalent-circuit parameters using the [scipy trust-constr](https://docs.scipy.org/doc/scipy-1.14.0/reference/generated/scipy.optimize.minimize.html) method -- a trust-region based optimiser that can handle parameter bounds, and both linear and nonlinear constraints. As shown here, these turn out to be useful tools for fitting empirical battery models.\n", + "\n", + "### Importing libraries\n", + "\n", + "If you don't already have PyBOP installed, check out the [installation guide](https://pybop-docs.readthedocs.io/en/latest/installation.html) first.\n", + "\n", + "We begin by importing the necessary libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy.optimize\n", + "\n", + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Initialising the model and parameters\n", + "\n", + "PyBOP needs to know some model parameters in order to run. Where these are unknown, and to be fitted, any sensible initial guess can be provided.\n", + "\n", + "Model parameters can either be defined using a PyBOP ```ParameterSet``` object, or by importing from a JSON file. For clarity, we will define the initial parameters from a ```ParameterSet```.\n", + "\n", + "Here, we'll fit a Thevenin model with two RC pairs. The initial parameter set must contain all the parameters that the PyBaMM model will use, and therefore it will need definitions for each circuit component, and each RC pair overpotential." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = {\n", + " \"chemistry\": \"ecm\",\n", + " \"Initial SoC\": 0.5,\n", + " \"Initial temperature [K]\": 25 + 273.15,\n", + " \"Cell capacity [A.h]\": 5,\n", + " \"Nominal cell capacity [A.h]\": 5,\n", + " \"Ambient temperature [K]\": 25 + 273.15,\n", + " \"Current function [A]\": 5,\n", + " \"Upper voltage cut-off [V]\": 4.2,\n", + " \"Lower voltage cut-off [V]\": 3.0,\n", + " \"Cell thermal mass [J/K]\": 1000,\n", + " \"Cell-jig heat transfer coefficient [W/K]\": 10,\n", + " \"Jig thermal mass [J/K]\": 500,\n", + " \"Jig-air heat transfer coefficient [W/K]\": 10,\n", + " \"Open-circuit voltage [V]\": pybop.empirical.Thevenin().default_parameter_values[\n", + " \"Open-circuit voltage [V]\"\n", + " ],\n", + " \"R0 [Ohm]\": 0.01,\n", + " \"Element-1 initial overpotential [V]\": 0,\n", + " \"Element-2 initial overpotential [V]\": 0,\n", + " \"R1 [Ohm]\": 0.005,\n", + " \"R2 [Ohm]\": 0.0003,\n", + " \"C1 [F]\": 10000,\n", + " \"C2 [F]\": 5000,\n", + " \"Entropic change [V/K]\": 0.0004,\n", + "}\n", + "\n", + "model = pybop.empirical.Thevenin(\n", + " parameter_set=parameter_set, options={\"number of rc elements\": 2}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "### Generating synthetic data\n", + "\n", + "Ordinarily, we would want to fit models to experimental data. For simplicity we'll use our model to generate some synthetic data, by simulating a discharge and corrupting the results with random noise. We will then use this synthetic data to form a PyBOP ```Dataset```, from which we can demonstrate the model fitting procedure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 0.001\n", + "t_eval = np.linspace(0, 500, 500)\n", + "values = model.predict(t_eval=t_eval)\n", + "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(0, sigma, len(t_eval))\n", + "\n", + "# Form dataset\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "### Identifying parameters\n", + "\n", + "Now for the fun part! We begin by defining the parameters that we want to fit. In this case, it's the series resistance $R_0$, and the RC parameters $R_1$ and $C_1$. Since we don't want to make things too easy, we've set up our parameters to have quite different values to those used in simulating the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"R0 [Ohm]\",\n", + " prior=pybop.Gaussian(2e-3, 1e-4),\n", + " bounds=[1e-4, 1e-1],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R1 [Ohm]\",\n", + " prior=pybop.Gaussian(1e-3, 1e-4),\n", + " bounds=[1e-5, 1e-2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C1 [F]\",\n", + " prior=pybop.Gaussian(5000, 300),\n", + " bounds=[2500, 5e4],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "We can now set up a problem and cost for identifying these parameters from our synthetic dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "The voltage over any given RC branch will evolve over a timescale $\\tau = R \\times C$. The data we work with will place some limits on which parameters can realistically be identified. We can't expect to fit fast timescales with low sample-rate data; similarly, we can't get good parameters for a long timescale if we only have short amounts of data. \n", + "\n", + "To illustrate, imagine trying to fit a timescale of $\\tau=0.1$ s from data with a sample rate of 1 Hz. Virtually all the interesting dynamics will have happened over the course of a single sample, so there's not enough information to work from for parameter identification. Similarly, it would be unreasonable to fit a timescale of $\\tau=1000$ s from only one minute of data; across the range of data that we have, the dynamics will have barely changed, giving us very little to work from for fitting $R$ and $C$.\n", + "\n", + "In general therefore, we need to be careful to make sure our parameter values are sensible. To make sure that the optimiser doesn't propose excessively long or short timescales, we can use nonlinear constraints. The ```scipy.optimize.NonlinearConstraint``` function handles this for us." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "tau_constraint = scipy.optimize.NonlinearConstraint(lambda x: x[1] * x[2], 5, 750)" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "Let's dig into what's going on here.\n", + "\n", + "The nonlinear constraint will receive a parameter vector from PyBOP. We know from our parameters list that parameter 0 will be $R_0$, \n", + "parameter 1 will be $R_1$, and parameter 2 will be $C_1$. We can therefore compute the RC timescale $R_1 \\times C_1$ using ```lambda x: x[1] * x[2]```. Next, we tell scipy that we want this to be constrained to the range $5\\leq R_1 \\times C_1 \\leq 750$. There's a lot of flexibility in our choice of lower and upper bounds, however these are reasonable numbers given our sampling rate (1 Hz), and data time-span (500 s).\n", + "\n", + "Now all we need to do is set up an optimiser to use this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "optim = pybop.SciPyMinimize(\n", + " cost,\n", + " method=\"trust-constr\",\n", + " constraints=tau_constraint,\n", + " max_iterations=100,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "Note that ```COBYLA```, ```COBYQA```, and ```SLSQP``` can also be used as the method; these are all different Scipy optimisers with constraint capabilities.\n", + "\n", + "Finally, we run the parameteriser, and plot some results. Don't worry if the solver sometimes terminates early -- this happens when the model receives a bad set of parameters, but PyBOP will catch and handle these cases automatically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated parameters: [8.41410880e-03 6.45916310e-03 4.57841684e+03]\n" + ] + }, + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, final_cost = optim.run()\n", + "print(\"Estimated parameters:\", x)\n", + "\n", + "# Plot the time series\n", + "pybop.plot_dataset(dataset)\n", + "\n", + "# Plot the timeseries output\n", + "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\")\n", + "\n", + "# Plot convergence\n", + "pybop.plot_convergence(optim)\n", + "# Plot the parameter traces\n", + "pybop.plot_parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "Here, we have considered parameter estimation for an equivalent circuit model. We have introduced some issues of practical identifiability, whereby particularly fast or slow timescales can't be parameterised very well. To work around this challenge, nonlinear constraints have been implemented.\n", + "\n", + "A difficulty here is that fitting RC parameters is a [sloppy problem](https://sethna.lassp.cornell.edu/Sloppy/FittingExponentials.html) -- there's a range of timescales that will all produce reasonable looking results, and an optimiser will struggle to choose any one timescale over another. Gradient-free methods such as ```pybop.CMAES``` may do a better job of fitting timescales. Nevertheless, the resistance values can be fitted very accurately with gradient-based methods, whereas gradient-free methods don't always come up with such a good solution.\n", + "\n", + "The ideal approach would be to fit resistances with gradient-based or ordinary least-squares methods, and timescales using gradient-free optimisers. In the absence of this approach, general recommendations are as follows...\n", + "\n", + "+ If good initial guesses are available for the RC timescales, use a gradient-based method in the way we've done here, as it'll typically give the most accurate fit;\n", + "+ Otherwise, use a gradient-free method to estimate timescales, then maybe consider moving back to a gradient-based method!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/electrode_balancing.ipynb b/examples/notebooks/electrode_balancing.ipynb new file mode 100644 index 000000000..16e0f6e45 --- /dev/null +++ b/examples/notebooks/electrode_balancing.ipynb @@ -0,0 +1,429 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Electrode balancing\n", + "\n", + "In this notebook we provide an example on how to perform electrode balancing for a half cell. The goal is to find the conversion from capacity to stoichiometry for a given measured electrode, by using a reference dataset for which voltage is known as a function of the stoichiometry.\n", + "\n", + "## Set the environment\n", + "We start by installing and importing the necessary libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "# Import the necessary libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pybamm\n", + "\n", + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the data\n", + "We start by loading the data, which is available in the [pybamm-param repository](https://www.github.com/paramm-team/pybamm-param). We load half cell data (which is the versus stoichiometry and thus the reference one) and the three-electrode data (which is the one we want to balance). The measurements are for an LGM50 cell, with a graphite and SiOx negative electrode and an NMC811 positive electrode." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# .csv files are uploaded for Anode\n", + "base_url = \"https://raw.githubusercontent.com/paramm-team/pybamm-param/develop/pbparam/input/data/\"\n", + "reference_data = pd.read_csv(\n", + " base_url + \"anode_OCP_2_lit.csv\"\n", + ") # half cell lithiation data\n", + "measured_data = pd.read_csv(\n", + " base_url + \"anode_OCP_3_lit.csv\"\n", + ") # three-electrode full cell lithiation data\n", + "\n", + "# Drop negative capacity values\n", + "measured_data = measured_data.drop(\n", + " measured_data[measured_data[\"Capacity [A.h]\"] < 0].index\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup model, parameters and data\n", + "\n", + "To perform the electrode balancing, we will use an ECM model consisting only of the OCV component. To achieve that, we will set the resistance to zero. We will also change the upper and lower voltage limits to ensure we do not hit them during the optimisation. For the OCV, we will use the reference data we just loaded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ocv(sto):\n", + " return pybamm.Interpolant(\n", + " reference_data[\"Stoichiometry\"].to_numpy(),\n", + " reference_data[\"Voltage [V]\"].to_numpy(),\n", + " sto,\n", + " \"reference OCV\",\n", + " )\n", + "\n", + "\n", + "parameter_set = pybop.empirical.Thevenin().default_parameter_values\n", + "parameter_set.update(\n", + " {\n", + " \"Initial SoC\": 0,\n", + " \"Entropic change [V/K]\": 0,\n", + " \"R0 [Ohm]\": 0,\n", + " \"Lower voltage cut-off [V]\": 0,\n", + " \"Upper voltage cut-off [V]\": 5,\n", + " \"Open-circuit voltage [V]\": ocv,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can just assemble the model. We use the `Thevenin` model with no RC elements:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybop.empirical.Thevenin(\n", + " parameter_set=parameter_set, options={\"number of rc elements\": 0}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define the parameters we want to optimise. In this case, we need to optimise the initial SoC and the cell capacity, which will be needed to convert the capacity to stoichiometry." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Initial SoC\",\n", + " prior=pybop.Uniform(0, 0.5),\n", + " bounds=[0, 0.5],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Cell capacity [A.h]\",\n", + " prior=pybop.Uniform(0.01, 50),\n", + " bounds=[0.01, 50],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to assemble the dataset. This is a bit tricky, as we are doing an electrode balancing but in theory we are solving a discharge problem. However, we can use that if we impose a 1 A discharge, the time (in hours) will be the same as the capacity in (in Ah). Therefore, we can treat time as capacity, we just need to scale it to the correct units (as PyBaMM models take time in seconds). Note that in this case current is negative as we are lithiating the electrode." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Form dataset\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": measured_data[\"Capacity [A.h]\"].to_numpy() * 3600,\n", + " \"Current function [A]\": -np.ones(len(measured_data[\"Capacity [A.h]\"])),\n", + " \"Voltage [V]\": measured_data[\"Voltage [V]\"].to_numpy(),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Identifying the parameters\n", + "\n", + "Once we have defined the model, parameters and dataset, we can just proceed to the optimisation. We define the `FittingProblem` and the cost, for which we choose the sum squared error." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We choose the `SciPyMinimize` optimiser and we solve the optimisation problem. We can then print and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/lib/python3.12/site-packages/pybamm/solvers/base_solver.py:762: SolverWarning:\n", + "\n", + "Explicit interpolation times not implemented for CasADi solver with 'safe' mode\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial parameters: [2.31587786e-02 4.62292192e+01]\n", + "Estimated parameters: [0.00923318 4.9721263 ]\n" + ] + } + ], + "source": [ + "optim = pybop.SciPyMinimize(cost, max_iterations=300)\n", + "x, final_cost = optim.run()\n", + "print(\"Initial parameters:\", optim.x0)\n", + "print(\"Estimated parameters:\", x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Converting capacity to stoichiometry\n", + "\n", + "However, the whole goal of the electrode balancing was to convert capacity to stoichiometry, so how can we do that? To convert capacity $Q$ to stoichiometry $x$, we can simply use the following equation:\n", + "\n", + "$$\n", + "x = \\pm \\frac{Q}{Q_{\\text{cell}}} + x_0.\n", + "$$\n", + "Here, the choice of plus and minus depends on whether we are lithiating or delithiating the electrode (it is related to whether the current in the data is positive or negative). $Q_{\\text{cell}}$ is the cell capacity and $x_0$ is the initial stoichiometry, which are the two parameters we fitted. We can now convert the measured data and plot it against the reference data to check that the electrode balancing was correct." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from plotly import graph_objects as go\n", + "\n", + "fig = go.Figure(\n", + " layout=go.Layout(title=\"OCP Balance\", width=800, height=600),\n", + ")\n", + "\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=reference_data[\"Stoichiometry\"],\n", + " y=reference_data[\"Voltage [V]\"],\n", + " mode=\"lines\",\n", + " name=\"reference\",\n", + " ),\n", + ")\n", + "\n", + "Q = x[1]\n", + "sto_0 = x[0]\n", + "\n", + "sto = measured_data[\"Capacity [A.h]\"].to_numpy() / Q + sto_0\n", + "\n", + "fig.add_trace(\n", + " go.Scatter(x=sto, y=measured_data[\"Voltage [V]\"], mode=\"lines\", name=\"fitted\"),\n", + ")\n", + "\n", + "# Update axes labels\n", + "fig.update_xaxes(title_text=\"Stoichiometry\")\n", + "fig.update_yaxes(title_text=\"Voltage [V]\")\n", + "\n", + "# Show figure\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebooks/equivalent_circuit_identification.ipynb b/examples/notebooks/equivalent_circuit_identification.ipynb index 6184c191a..b610ceff3 100644 --- a/examples/notebooks/equivalent_circuit_identification.ipynb +++ b/examples/notebooks/equivalent_circuit_identification.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "00940c64-4748-4b08-9a35-ea98ce311e71", + "id": "0", "metadata": {}, "source": [ "# Equivalent Circuit Parameter Identification\n", @@ -11,72 +11,34 @@ "\n", "### Setting up the Environment\n", "\n", - "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "dd0e1a20-1ba3-4ff5-8f6a-f9c6f25c2a4a", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:35.622147Z", - "iopub.status.busy": "2024-04-14T18:57:35.621660Z", - "iopub.status.idle": "2024-04-14T18:57:40.849137Z", - "shell.execute_reply": "2024-04-14T18:57:40.848620Z" - } - }, + "execution_count": null, + "id": "1", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (24.0)\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (8.1.2)\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: comm>=0.1.3 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from ipywidgets) (0.2.2)\r\n", - 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"Note: you may need to restart the kernel to use updated packages.\n" + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" ] }, { @@ -88,13 +50,13 @@ } ], "source": [ - "%pip install --upgrade pip ipywidgets\n", + "%pip install --upgrade pip ipywidgets -q\n", "%pip install pybop -q" ] }, { "cell_type": "markdown", - "id": "90efc3d3-bf00-423d-ba81-246e4763b499", + "id": "2", "metadata": {}, "source": [ "### Importing Libraries\n", @@ -104,26 +66,21 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "d6afb8f9-3872-4a7e-a76d-0b50855fe089", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:40.859077Z", - "iopub.status.busy": "2024-04-14T18:57:40.857904Z", - "iopub.status.idle": "2024-04-14T18:57:46.230603Z", - "shell.execute_reply": "2024-04-14T18:57:46.229895Z" - } - }, + "execution_count": null, + "id": "3", + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", - "import pybop" + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" ] }, { "cell_type": "markdown", - "id": "a976f817-f0b3-421e-8cd3-a49be9128068", + "id": "4", "metadata": {}, "source": [ "## Importing Parameters\n", @@ -133,16 +90,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "734d6d86-61e3-4125-bcea-e83b3235814b", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.247269Z", - "iopub.status.busy": "2024-04-14T18:57:46.246744Z", - "iopub.status.idle": "2024-04-14T18:57:46.249217Z", - "shell.execute_reply": "2024-04-14T18:57:46.248814Z" - } - }, + "execution_count": null, + "id": "5", + "metadata": {}, "outputs": [], "source": [ "# parameter_set = pybop.ParameterSet(\n", @@ -153,7 +103,7 @@ }, { "cell_type": "markdown", - "id": "11f17daf-4a04-4ccd-8175-8e5a37f79f7f", + "id": "6", "metadata": {}, "source": [ "Alternatively, define the initial parameter set with a dictionary. Ensure you have definitions for all R's, C's, and initial overpotentials for any additional RC elements.\n", @@ -163,16 +113,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "8d4a0635-51da-4998-8b48-deda13a49e39", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.251312Z", - "iopub.status.busy": "2024-04-14T18:57:46.251183Z", - "iopub.status.idle": "2024-04-14T18:57:46.424946Z", - "shell.execute_reply": "2024-04-14T18:57:46.424250Z" - } - }, + "execution_count": null, + "id": "7", + "metadata": {}, "outputs": [], "source": [ "parameter_set = pybop.ParameterSet(\n", @@ -207,7 +150,7 @@ }, { "cell_type": "markdown", - "id": "017695fd-ee78-4113-af18-2fea04cf6126", + "id": "8", "metadata": {}, "source": [ "## Identifying the Parameters\n", @@ -217,16 +160,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "e84b6dd0-8f9e-4b68-b7cb-f3bcb9988802", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.427992Z", - "iopub.status.busy": "2024-04-14T18:57:46.427831Z", - "iopub.status.idle": "2024-04-14T18:57:46.432028Z", - "shell.execute_reply": "2024-04-14T18:57:46.431542Z" - } - }, + "execution_count": null, + "id": "9", + "metadata": {}, "outputs": [], "source": [ "model = pybop.empirical.Thevenin(\n", @@ -236,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "bf63b4f9-de38-4e70-9472-1de4973a0954", + "id": "10", "metadata": {}, "source": [ "In this example, we are going to try to fit all five parameters at once. This isn't recommend for real-life application as identifiablity is challenging to guarantee with this large a parameter space. To do this, we define the `pybop.Parameters` as," @@ -244,16 +180,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "e75da7e3-8815-4159-a5ad-600a235b028c", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.434256Z", - "iopub.status.busy": "2024-04-14T18:57:46.434089Z", - "iopub.status.idle": "2024-04-14T18:57:46.436984Z", - "shell.execute_reply": "2024-04-14T18:57:46.436537Z" - } - }, + "execution_count": null, + "id": "11", + "metadata": {}, "outputs": [], "source": [ "parameters = pybop.Parameters(\n", @@ -287,7 +216,7 @@ }, { "cell_type": "markdown", - "id": "3ab5afb4-5007-4cef-9802-c25dc077e466", + "id": "12", "metadata": {}, "source": [ "Let's create some synthetic data to identify the parameters. This data is then corrupted with a small amount of Gaussian noise to represent some additional uncertainty in the measured values. We can then form the `pybop.Dataset` from this data." @@ -295,16 +224,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "c346b106-99a9-46bc-8b5d-d330ed911660", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.438835Z", - "iopub.status.busy": "2024-04-14T18:57:46.438684Z", - "iopub.status.idle": "2024-04-14T18:57:46.478613Z", - "shell.execute_reply": "2024-04-14T18:57:46.478339Z" - } - }, + "execution_count": null, + "id": "13", + "metadata": {}, "outputs": [], "source": [ "sigma = 0.001\n", @@ -324,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "8ce6c438-a402-4b1b-ad8a-598ceee74f2f", + "id": "14", "metadata": {}, "source": [ "The `FittingProblem` class provides us with a single class that holds all of the objects we need to evaluate our selected `SumSquaredError` cost function. " @@ -332,16 +254,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "62369a4d-96e5-49d2-8951-4468b3fc5831", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.480234Z", - "iopub.status.busy": "2024-04-14T18:57:46.480123Z", - "iopub.status.idle": "2024-04-14T18:57:46.488949Z", - "shell.execute_reply": "2024-04-14T18:57:46.488688Z" - } - }, + "execution_count": null, + "id": "15", + "metadata": {}, "outputs": [], "source": [ "problem = pybop.FittingProblem(model, parameters, dataset)\n", @@ -350,7 +265,7 @@ }, { "cell_type": "markdown", - "id": "ab62ee34-85ee-4b5a-ab25-3bd7dd47f312", + "id": "16", "metadata": {}, "source": [ "The cost function can be interrogated manually via the `cost([params])` API. In this example, that would look like the following," @@ -358,24 +273,17 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "f69b34f5-0b46-4646-acbe-991046997b98", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.490406Z", - "iopub.status.busy": "2024-04-14T18:57:46.490322Z", - "iopub.status.idle": "2024-04-14T18:57:46.510798Z", - "shell.execute_reply": "2024-04-14T18:57:46.510375Z" - } - }, + "execution_count": null, + "id": "17", + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.024944621550803514" + "0.017319071604517344" ] }, - "execution_count": 9, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -386,7 +294,7 @@ }, { "cell_type": "markdown", - "id": "3ef5b0da-f755-43c6-8904-79d7ee0f218c", + "id": "18", "metadata": {}, "source": [ "Next, we construct the optimisation class with our algorithm of choice and run it. In this case, we select the CMA-ES method as it provides global optimisation capability. For the sake of reducing the runtime of this example, we limit the maximum iterations to 100; however, feel free to update this value." @@ -394,25 +302,18 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "6244882e-11ad-4bfe-a512-f1c687a06a08", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.512725Z", - "iopub.status.busy": "2024-04-14T18:57:46.512597Z", - "iopub.status.idle": "2024-04-14T18:57:49.259154Z", - "shell.execute_reply": "2024-04-14T18:57:49.257712Z" - } - }, + "execution_count": null, + "id": "19", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Initial parameters: [2.29696565e-04 3.53341865e-05 1.63145688e-05 1.07259649e+04\n", - " 9.73352990e+03]\n", - "Estimated parameters: [1.00367942e-03 3.80907013e-04 1.00242178e-04 1.07259636e+04\n", - " 9.73353073e+03]\n" + "Initial parameters: [2.38024513e-04 1.33033378e-04 2.63603390e-05 1.15341178e+04\n", + " 1.55762679e+04]\n", + "Estimated parameters: [7.72266444e-04 5.85341699e-04 1.25813056e-04 1.15341189e+04\n", + " 1.55762670e+04]\n" ] } ], @@ -425,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "93ee37a3-67f6-4c6a-a05d-507700cfa9da", + "id": "20", "metadata": {}, "source": [ "## Plotting and Visualisation\n", @@ -435,21 +336,62 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "2cec5659-31fa-4164-82f0-4467a4894729", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:49.273422Z", - "iopub.status.busy": "2024-04-14T18:57:49.272340Z", - "iopub.status.idle": "2024-04-14T18:57:50.177989Z", - "shell.execute_reply": "2024-04-14T18:57:50.173807Z" - } - }, + "execution_count": null, + "id": "21", + "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": [ - "02004006008003.63.623.643.663.68ReferenceModelOptimised ComparisonTime / sVoltage / V" + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" ] }, "metadata": {}, @@ -462,7 +404,7 @@ }, { "cell_type": "markdown", - "id": "7d265967-e6e5-440c-badf-156a43943c88", + "id": "22", "metadata": {}, "source": [ "### Convergence and Parameter Trajectories\n", @@ -472,21 +414,38 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "f66e0b0f-4861-42dd-bb7f-8734fcca3328", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:50.189616Z", - "iopub.status.busy": "2024-04-14T18:57:50.188971Z", - "iopub.status.idle": "2024-04-14T18:57:52.898771Z", - "shell.execute_reply": "2024-04-14T18:57:52.897811Z" - } - }, + "execution_count": null, + "id": "23", + "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": [ - "51015202500.0020.0040.0060.0080.010.0120.0140.016ConvergenceIterationCost" + "text/html": [ + "
" ] }, "metadata": {}, @@ -494,8 +453,32 @@ }, { "data": { - "image/svg+xml": [ - "0501001502000.00050.0010.001505010015020000.00050.0010501001502000200μ400μ600μ800μ05010015020010.725962k10.725963k10.725964k10.725965k0501001502009,733.5299,733.539,733.5319,733.5329,733.533R0 [Ohm]R1 [Ohm]R2 [Ohm]C1 [F]C1 [F]Parameter ConvergenceFunction CallFunction CallFunction CallFunction CallFunction CallR0 [Ohm]R1 [Ohm]R2 [Ohm]C1 [F]C1 [F]" + "text/html": [ + "
" ] }, "metadata": {}, @@ -509,7 +492,7 @@ }, { "cell_type": "markdown", - "id": "c544a81c-1215-4794-b7db-c57c46125c77", + "id": "24", "metadata": {}, "source": [ "### Conclusion\n", @@ -534,7 +517,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/examples/notebooks/maximum_a_posteriori.ipynb b/examples/notebooks/maximum_a_posteriori.ipynb new file mode 100644 index 000000000..299ec9a79 --- /dev/null +++ b/examples/notebooks/maximum_a_posteriori.ipynb @@ -0,0 +1,878 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Maximum a Posteriori Parameter Inference\n", + "\n", + "In this notebook, we introduce the Maximum a Posteriori (MAP), which extends Maximum Likelihood Estimation (MLE) by inclusion of a prior $p(\\theta)$ into the cost function. To include this prior information, we construct a Bayesian Posterior with Bayesian's Theorem given as,\n", + "\n", + "$$\n", + "P(\\theta|D) = \\frac{P(D|\\theta)P(\\theta)}{P(D)}\n", + "$$\n", + "\n", + "where, \n", + "$~$ \n", + "$P(\\theta|D)$ represents the posterior and can be read as \"the probability of the parameters $(\\theta)$ given the data $(D)$\", \n", + "$P(D|\\theta)$ is the probability of the data given the parameters, commonly called the likelihood, \n", + "$P(\\theta)$ represents the probability of the parameters commonly called the prior, \n", + "$P(D)$ is the probability of the data and is commonly called the marginal probability. \n", + "\n", + "However, as the marginal probability is commonly difficult to compute and represents a normalisation constant, in the case of MAP this term is forgone and the proportional posterior is optmised instead. This is given as,\n", + "\n", + "$$\n", + "P(\\theta|D) \\propto P(D|\\theta)P(\\theta)\n", + "$$\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install and import PyBOP as well as upgrade dependencies. We also fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "import time\n", + "\n", + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "### Creating the model\n", + "\n", + "To demonstrate the MAP process, we will first need a forward model and data to run parameter inference on. As we are introducing this as a simple example, we will use the PyBOP forward model with white noise as the reference. This requires defining a parameter set and the model itself." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating Forward Model\n", + "\n", + "We can now simulate the model using the `model.predict` method. This method is a light wrapper on the `Pybamm.Simulation` class and be used as such. For this example, we use the default current function for the `Chen2020` parameter set (5A) to generate the voltage data. As the goal is to investigate the MAP method, we will generate a range of observations from the forward model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sBasxv8U04p3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations in each trajectory: [2, 4, 8, 16, 32, 64, 128, 256]\n" + ] + } + ], + "source": [ + "n = 8 # Number of time-series trajectories\n", + "observations = [\n", + " 2**j for j in range(1, n + 1)\n", + "] # Number of observations in each trajectory\n", + "values = []\n", + "for i in observations:\n", + " t_eval = np.linspace(0, 10, i)\n", + " values.append(model.predict(t_eval=t_eval)) # predict and store\n", + "\n", + "print(f\"Number of observations in each trajectory: {observations}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding noise to synthetic voltage data\n", + "\n", + "To make the parameter inference more realistic, we add gaussian noise with zero mean to the data. While this doesn't truly represent the challenge of parameter inference with experimental data, this does ensure the cost landscape curvature isn't perfect. For a more realistic representation of experimental data, a different noise function could be used. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 0.005\n", + "corrupt_values = values[1][\"Voltage [V]\"].data + np.random.normal(\n", + " 0, sigma, len(values[1][\"Voltage [V]\"].data)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating the PyBOP dataset\n", + "\n", + "The reference trajectory needs to be included in the optimisation task, which is handed within the `Dataset` class. In this situation, this class is composed of the time, current, and the noisy voltage data; however, if we were performing parameter inference from a different measured signal, such as 'Cell Temperature', that would need to be included." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zuvGHWID04p_" + }, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": values[1][\"Time [s]\"].data,\n", + " \"Current function [A]\": values[1][\"Current [A]\"].data,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffS3CF_704qA" + }, + "source": [ + "### Constructing Parameters Class\n", + "Next, we need to select the forward model parameters for inference. The PyBOP parameters class composes as many individual PyBOP parameters as the user wants (whether these parameters can be identified is left out of this example). This class requires the parameter name, which must resolve to a parameter within the `ParameterSet` defined above. Additionally, this class can accept an `initial_value` which will be used by the optimiser, as well as bounds. For this example, we will provide a `prior` to the parameter class, which will be used later by the MAP process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WPCybXIJ04qA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default bounds applied based on prior distribution.\n", + "Default bounds applied based on prior distribution.\n" + ] + } + ], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative particle radius [m]\",\n", + " prior=pybop.Gaussian(4e-6, 1e-6),\n", + " true_value=parameter_set[\"Negative particle radius [m]\"],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive particle radius [m]\",\n", + " prior=pybop.Gaussian(5e-6, 1e-6),\n", + " true_value=parameter_set[\"Positive particle radius [m]\"],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "### Setting up the Fitting Problem, Likelihood, and Posterior\n", + "\n", + "With the datasets and parameters defined, we can now construct the `FittingProblem` which composes the model, parameters, and dataset providing a single class with the required information for simulating and assessing the forward model. \n", + "\n", + "As described in the introduction to this notebook, the MAP method uses the non-normalised posterior for optimisation. This is defined in PyBOP as the `LogPosterior` class, and has arguments for the likelihood and prior functions. If a prior is not provided, the parameter priors are used as default. In this example, we will use a `GaussianLogLikelihoodKnownSigma` likelihood function, and the default priors set above. For numerical reasons, we optimise the log of the posterior; however this doesn't affect the final results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "etMzRtx404qA" + }, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=sigma)\n", + "posterior = pybop.LogPosterior(likelihood)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "caprp-bV04qB" + }, + "source": [ + "### Plotting the Posterior components\n", + "\n", + "Next, to investigate the individual components of the Posterior. The `LogPosterior` class provides attributes of the prior and likelihood. To investigate the contributions of each to the Posterior we plot the landscapes across a selected parameter range." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-9OVt0EQ04qB" + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "steps = 10 # Number of discretisation points\n", + "bounds = np.asarray([[1e-6, 9e-6], [1e-6, 9e-6]])\n", + "pybop.plot2d(posterior.prior, bounds=bounds, steps=steps, title=\"Log Prior\")\n", + "pybop.plot2d(posterior.likelihood, bounds=bounds, steps=steps, title=\"Log Likelihood\")\n", + "pybop.plot2d(posterior, bounds=bounds, steps=steps, title=\"Log Posterior\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the prior represents a two-dimensional gaussian distribution with a mode at $[4e-6,5e-6]$. The likelihood appears to have a banded shape with ridge of optimal points traversing the higher parameter values. Finally, the Posterior forms the combination of the two. This is the benefit of the MAP process, as it allows for previous information to be included in the parameter inference task. Previous knowledge is encapsulated within the prior function and influences the Posterior, depending on the magnitude of the likelihood function.\n", + "\n", + "To show how this is used within a PyBOP optimisation task, we select the Covariance Matrix Adaptation Evolution Strategy optimiser and run the optimisation. We can then plot the parameter trajectories to investigate how the optimiser performed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inferred Parameters: [5.35425483e-06 5.58415228e-06] in 8.549633979797363 seconds\n", + "True Parameters: [5.86e-06 5.22e-06]\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "optim = pybop.CMAES(\n", + " posterior,\n", + " min_iterations=20,\n", + " max_iterations=100,\n", + ")\n", + "start_time = time.time()\n", + "x, final_cost = optim.run()\n", + "print(f\"Inferred Parameters: {x} in {time.time() - start_time} seconds\")\n", + "print(f\"True Parameters: {parameters.true_value()}\")\n", + "pybop.plot_parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the optimisation process returns values close to the true optimal. In this case, as the synthetic data is drawn from the forward model (with additive noise), the underlying structure is close to identical. This gives the optimisation process a very well posed landscape, and as such it finds the correct parameter values. \n", + "\n", + "This is not always the case, especially in inference tasks with low-quality data, sloppy parameters, or poor excitation. In these cases, the prior influence can help 'steer' the optimisation process towards the combination of the likelihood and the user's prior knowledge of the parameters." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-4pZsDmS04qC" + }, + "source": [ + "## Investigating how the number of observations effects the Posterior\n", + "\n", + "We've seen above that the proportional posterior can be represented from its components, the log-likelihood and log-prior. Next, to better understand when each of these terms can become dominating within the parameter inference problem we vary the number of measurement observations (i.e. the number of samples in the dataset) and inspect the construct posterior.\n", + "\n", + "This is completed below with an increasing series of $2^n$, where $n$ is set above in our original creation of the trajectories. This will give us a visual representation of how the posterior changes with increasing observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hgz8SV4i04qC", + "outputId": "e1e42ae7-5075-4c47-dd68-1b22ecc170f6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fixed Sigma for output 1: 0.005\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fixed Sigma for output 1: 0.005\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fixed Sigma for output 1: 0.005\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fixed Sigma for output 1: 0.005\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fixed Sigma for output 1: 0.005\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i, val in enumerate(values): # Loop through trajectories\n", + " dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": val[\"Time [s]\"].data,\n", + " \"Current function [A]\": val[\"Current [A]\"].data,\n", + " \"Voltage [V]\": val[\"Voltage [V]\"].data\n", + " + np.random.normal(0, sigma, len(val[\"Voltage [V]\"].data)),\n", + " }\n", + " )\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", + " likelihood = pybop.GaussianLogLikelihood(problem, sigma0=sigma)\n", + " posterior = pybop.LogPosterior(likelihood)\n", + " pybop.plot2d(\n", + " posterior,\n", + " bounds=bounds,\n", + " steps=steps,\n", + " title=f\"Posterior with {observations[i]} observations\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above contour plots showcase the influence decay of the prior on the posterior as observations are increased. This is expected as prior knowledge on the posterior should be reduced as additional high fidelity observations are obtained. In the case of lower quality, or noisy data, the prior influence can maintain influence.\n", + "\n", + "The influence of the prior and likelihood on the posterior should be investigated during a parameter inference process, as presented above. This provides insight into how much influence prior knowledge has on the optimisation task, whether the likelihood function is well posed with smooth curvature, and finally the overall scale of the posterior." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Concluding Thoughts\n", + "\n", + "This notebook illustrates the process of parameter inference with the Maximum a Posteriori method. This process enables encapsulation of prior knowledge into the optimisation process with influence decay as observations of the system increase. This influence decay has been presented above across observations obtained from the set $({2^n \\mid n \\in \\mathbb{N}})$." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/multi_model_identification.ipynb b/examples/notebooks/multi_model_identification.ipynb index a66a78f2b..cb838e321 100644 --- a/examples/notebooks/multi_model_identification.ipynb +++ b/examples/notebooks/multi_model_identification.ipynb @@ -12,35 +12,20 @@ "\n", "### Setting up the Environment\n", "\n", - "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "execution": { - "iopub.execute_input": "2024-04-14T18:57:35.623854Z", - "iopub.status.busy": "2024-04-14T18:57:35.623132Z", - "iopub.status.idle": "2024-04-14T18:57:41.586245Z", - "shell.execute_reply": "2024-04-14T18:57:41.585767Z" - }, "id": "X87NUGPW04py", "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "%pip install --upgrade pip ipywidgets pybamm -q\n", "%pip install pybop -q" @@ -57,23 +42,35 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:41.588855Z", - "iopub.status.busy": "2024-04-14T18:57:41.588623Z", - "iopub.status.idle": "2024-04-14T18:57:46.230426Z", - "shell.execute_reply": "2024-04-14T18:57:46.229830Z" - }, "id": "SQdt4brD04p1" }, "outputs": [], "source": [ "import numpy as np\n", - "import plotly.graph_objects as go\n", "import pybamm\n", "\n", - "import pybop" + "import pybop\n", + "\n", + "go = pybop.PlotlyManager().go\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" ] }, { @@ -96,14 +93,8 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.244362Z", - "iopub.status.busy": "2024-04-14T18:57:46.243275Z", - "iopub.status.idle": "2024-04-14T18:57:46.344434Z", - "shell.execute_reply": "2024-04-14T18:57:46.344182Z" - }, "id": "zuvGHWID04p_" }, "outputs": [], @@ -126,36 +117,22 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.346150Z", - "iopub.status.busy": "2024-04-14T18:57:46.346043Z", - "iopub.status.idle": "2024-04-14T18:57:46.348252Z", - "shell.execute_reply": "2024-04-14T18:57:46.348022Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "n_points = 650\n", + "n_points = 450\n", "t_eval = np.linspace(0, 1600 + 1000, n_points)\n", "current = np.concatenate(\n", - " [np.ones(400) * parameter_set[\"Nominal cell capacity [A.h]\"], np.zeros(250)]\n", + " [np.ones(200) * parameter_set[\"Nominal cell capacity [A.h]\"], np.zeros(250)]\n", ")\n", - "init_soc = 0.5" + "initial_state = {\"Initial SoC\": 0.5}" ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.349698Z", - "iopub.status.busy": "2024-04-14T18:57:46.349591Z", - "iopub.status.idle": "2024-04-14T18:57:46.351528Z", - "shell.execute_reply": "2024-04-14T18:57:46.351100Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "dataset = pybop.Dataset(\n", @@ -168,18 +145,11 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.353062Z", - "iopub.status.busy": "2024-04-14T18:57:46.352968Z", - "iopub.status.idle": "2024-04-14T18:58:01.367368Z", - "shell.execute_reply": "2024-04-14T18:58:01.366627Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "synth_model.build(dataset, init_soc=init_soc)\n", + "synth_model.build(dataset=dataset, initial_state=initial_state)\n", "synth_model.signal = [\"Voltage [V]\"]\n", "values = synth_model.simulate(t_eval=t_eval, inputs={})" ] @@ -195,3456 +165,62 @@ }, { "cell_type": "code", - 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" + ] }, "metadata": {}, "output_type": "display_data" @@ -3686,15 +262,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:01.681988Z", - "iopub.status.busy": "2024-04-14T18:58:01.681850Z", - "iopub.status.idle": "2024-04-14T18:58:01.683744Z", - "shell.execute_reply": "2024-04-14T18:58:01.683506Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "dataset = pybop.Dataset(\n", @@ -3717,14 +286,8 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:01.685149Z", - "iopub.status.busy": "2024-04-14T18:58:01.685052Z", - "iopub.status.idle": "2024-04-14T18:58:01.687180Z", - "shell.execute_reply": "2024-04-14T18:58:01.686972Z" - }, "id": "WPCybXIJ04qA" }, "outputs": [], @@ -3733,13 +296,13 @@ " pybop.Parameter(\n", " \"Positive electrode thickness [m]\",\n", " prior=pybop.Gaussian(7.56e-05, 0.05e-05),\n", - " bounds=[60e-06, 80e-06],\n", + " bounds=[65e-06, 85e-06],\n", " true_value=parameter_set[\"Positive electrode thickness [m]\"],\n", " ),\n", " pybop.Parameter(\n", " \"Negative electrode thickness [m]\",\n", - " prior=pybop.Gaussian(8e-05, 0.05e-05),\n", - " bounds=[65e-06, 90e-06],\n", + " prior=pybop.Gaussian(8.52e-05, 0.05e-05),\n", + " bounds=[75e-06, 95e-06],\n", " true_value=parameter_set[\"Negative electrode thickness [m]\"],\n", " ),\n", ")" @@ -3756,15 +319,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:01.688581Z", - "iopub.status.busy": "2024-04-14T18:58:01.688496Z", - "iopub.status.idle": "2024-04-14T18:58:01.775886Z", - "shell.execute_reply": "2024-04-14T18:58:01.775510Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "models = [\n", @@ -3784,14 +340,8 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:01.777342Z", - "iopub.status.busy": "2024-04-14T18:58:01.777250Z", - "iopub.status.idle": "2024-04-14T19:02:11.103750Z", - "shell.execute_reply": "2024-04-14T19:02:11.103457Z" - }, "id": "etMzRtx404qA" }, "outputs": [ @@ -3799,10 +349,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running Single Particle Model\n", - "Halt: Maximum number of iterations (60) reached.\n", - "Running Single Particle Model with Electrolyte\n", - "Halt: Maximum number of iterations (60) reached.\n" + "Running Single Particle Model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "Running Single Particle Model with Electrolyte\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n" ] } ], @@ -3811,7 +373,8 @@ "xs = []\n", "for model in models:\n", " print(f\"Running {model.name}\")\n", - " problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)\n", + " model.set_initial_state(initial_state)\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", " cost = pybop.SumSquaredError(problem)\n", " optim = pybop.XNES(\n", " cost, verbose=True, max_iterations=60, max_unchanged_iterations=15\n", @@ -3823,14 +386,8 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T19:02:11.106047Z", - "iopub.status.busy": "2024-04-14T19:02:11.105928Z", - "iopub.status.idle": "2024-04-14T19:02:11.107948Z", - "shell.execute_reply": "2024-04-14T19:02:11.107722Z" - }, "id": "N3FtAhrT04qB" }, "outputs": [ @@ -3838,8 +395,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "| Model: Single Particle Model | Results: [6.00001046e-05 8.53959364e-05] |\n", - "| Model: Single Particle Model with Electrolyte | Results: [6.56023366e-05 8.49563105e-05] |\n" + "| Model: Single Particle Model | Results: [6.50355739e-05 8.10453665e-05] |\n", + "| Model: Single Particle Model with Electrolyte | Results: [7.08088980e-05 8.53871336e-05] |\n" ] } ], @@ -3872,21 +429,39 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T19:02:11.109183Z", - "iopub.status.busy": "2024-04-14T19:02:11.109086Z", - "iopub.status.idle": "2024-04-14T19:02:12.193561Z", - "shell.execute_reply": "2024-04-14T19:02:12.193263Z" - }, "id": "ZVfozY0A04qC" }, "outputs": [ { "data": { - "image/svg+xml": [ - "050010001500200025002.833.23.43.6ReferenceModelSingle Particle ModelTime / sVoltage / V" + "text/html": [ + "
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"Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "execution": { - "iopub.execute_input": "2024-04-14T18:57:35.623566Z", - "iopub.status.busy": "2024-04-14T18:57:35.621718Z", - "iopub.status.idle": "2024-04-14T18:57:40.837085Z", - "shell.execute_reply": "2024-04-14T18:57:40.836603Z" - }, "id": "X87NUGPW04py", "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" }, @@ -36,49 +30,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (24.0)\n", - "Collecting pip\n", - " Using cached pip-24.1.1-py3-none-any.whl.metadata (3.6 kB)\n", - "Collecting ipywidgets\n", - " Using cached ipywidgets-8.1.3-py3-none-any.whl.metadata (2.4 kB)\n", - "Requirement already satisfied: comm>=0.1.3 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipywidgets) (0.2.2)\n", - "Requirement already satisfied: ipython>=6.1.0 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipywidgets) (8.26.0)\n", - "Requirement already satisfied: traitlets>=4.3.1 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipywidgets) (5.14.3)\n", - "Collecting widgetsnbextension~=4.0.11 (from ipywidgets)\n", - " Using cached widgetsnbextension-4.0.11-py3-none-any.whl.metadata (1.6 kB)\n", - "Collecting jupyterlab-widgets~=3.0.11 (from ipywidgets)\n", - " Using cached jupyterlab_widgets-3.0.11-py3-none-any.whl.metadata (4.1 kB)\n", - "Requirement already satisfied: decorator in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", - "Requirement already satisfied: jedi>=0.16 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", - "Requirement already satisfied: matplotlib-inline in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.7)\n", - "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.47)\n", - "Requirement already satisfied: pygments>=2.4.0 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets) (2.18.0)\n", - "Requirement already satisfied: stack-data in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", - "Requirement already satisfied: pexpect>4.3 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", - "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.4)\n", - "Requirement already satisfied: ptyprocess>=0.5 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", - "Requirement already satisfied: wcwidth in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n", - "Requirement already satisfied: executing>=1.2.0 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", - "Requirement already satisfied: asttokens>=2.1.0 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", - "Requirement already satisfied: pure-eval in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", - "Requirement already satisfied: six>=1.12.0 in /home/engs2510/.pyenv/versions/3.12.2/envs/pybop/lib/python3.12/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", - "Using cached pip-24.1.1-py3-none-any.whl (1.8 MB)\n", - "Using cached ipywidgets-8.1.3-py3-none-any.whl (139 kB)\n", - "Using cached jupyterlab_widgets-3.0.11-py3-none-any.whl (214 kB)\n", - "Using cached widgetsnbextension-4.0.11-py3-none-any.whl (2.3 MB)\n", - "Installing collected packages: widgetsnbextension, pip, jupyterlab-widgets, ipywidgets\n", - " Attempting uninstall: pip\n", - " Found existing installation: pip 24.0\n", - " Uninstalling pip-24.0:\n", - " Successfully uninstalled pip-24.0\n", - "Successfully installed ipywidgets-8.1.3 jupyterlab-widgets-3.0.11 pip-24.1.1 widgetsnbextension-4.0.11\n", - "Note: you may need to restart the kernel to use updated packages.\n", + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ - "%pip install --upgrade pip ipywidgets\n", + "%pip install --upgrade pip ipywidgets -q\n", "%pip install pybop -q" ] }, @@ -95,21 +73,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:40.839580Z", - "iopub.status.busy": "2024-04-14T18:57:40.839331Z", - "iopub.status.idle": "2024-04-14T18:57:46.229867Z", - "shell.execute_reply": "2024-04-14T18:57:46.229451Z" - }, "id": "SQdt4brD04p1" }, "outputs": [], "source": [ "import numpy as np\n", "\n", - "import pybop" + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" ] }, { @@ -129,15 +119,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.240177Z", - "iopub.status.busy": "2024-04-14T18:57:46.239573Z", - "iopub.status.idle": "2024-04-14T18:57:46.367163Z", - "shell.execute_reply": "2024-04-14T18:57:46.366832Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", @@ -156,21 +139,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.370360Z", - "iopub.status.busy": "2024-04-14T18:57:46.370226Z", - "iopub.status.idle": "2024-04-14T18:57:46.658845Z", - "shell.execute_reply": "2024-04-14T18:57:46.658537Z" - }, "id": "sBasxv8U04p3" }, "outputs": [], "source": [ "t_eval = np.arange(0, 2000, 10)\n", - "init_soc = 1.0\n", - "values = synth_model.predict(t_eval=t_eval, init_soc=init_soc)" + "initial_state = {\"Initial SoC\": 1.0}\n", + "values = synth_model.predict(t_eval=t_eval, initial_state=initial_state)" ] }, { @@ -184,15 +161,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.660483Z", - "iopub.status.busy": "2024-04-14T18:57:46.660397Z", - "iopub.status.idle": "2024-04-14T18:57:46.675801Z", - "shell.execute_reply": "2024-04-14T18:57:46.675337Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "sigma = 0.002\n", @@ -228,14 +198,8 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.677466Z", - "iopub.status.busy": "2024-04-14T18:57:46.677354Z", - "iopub.status.idle": "2024-04-14T18:57:46.690633Z", - "shell.execute_reply": "2024-04-14T18:57:46.690395Z" - }, "id": "zuvGHWID04p_" }, "outputs": [], @@ -262,14 +226,8 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.691889Z", - "iopub.status.busy": "2024-04-14T18:57:46.691798Z", - "iopub.status.idle": "2024-04-14T18:57:46.693610Z", - "shell.execute_reply": "2024-04-14T18:57:46.693361Z" - }, "id": "WPCybXIJ04qA" }, "outputs": [], @@ -299,15 +257,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.695076Z", - "iopub.status.busy": "2024-04-14T18:57:46.694955Z", - "iopub.status.idle": "2024-04-14T18:57:46.696878Z", - "shell.execute_reply": "2024-04-14T18:57:46.696576Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "gradient_optimisers = [\n", @@ -344,14 +295,8 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.698400Z", - "iopub.status.busy": "2024-04-14T18:57:46.698293Z", - "iopub.status.idle": "2024-04-14T18:59:08.049333Z", - "shell.execute_reply": "2024-04-14T18:59:08.048882Z" - }, "id": "etMzRtx404qA" }, "outputs": [ @@ -360,149 +305,21 @@ "output_type": "stream", "text": [ "Running AdamW\n", - "NOTE: Boundaries ignored by AdamW\n", + "NOTE: Boundaries ignored by AdamW\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Running GradientDescent\n", - "NOTE: Boundaries ignored by \n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", - "Error: Error in Function::call for 'event_0' [MXFunction] at .../casadi/core/function.cpp:361:\n", - ".../casadi/core/function_internal.hpp:1649: Input 1 (i1) has mismatching shape. Got 100-by-1. Allowed dimensions, in general, are:\n", - " - The input dimension N-by-M (here 300-by-1)\n", - " - A scalar, i.e. 1-by-1\n", - " - M-by-N if N=1 or M=1 (i.e. a transposed vector)\n", - " - N-by-M1 if K*M1=M for some K (argument repeated horizontally)\n", - " - N-by-P*M, indicating evaluation with multiple arguments (P must be a multiple of 1 for consistency with previous inputs)\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Running IRPropMin\n" ] } @@ -510,11 +327,15 @@ "source": [ "optims = []\n", "xs = []\n", - "problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)\n", + "model.set_initial_state(initial_state)\n", + "problem = pybop.FittingProblem(model, parameters, dataset)\n", "cost = pybop.SumSquaredError(problem)\n", "for optimiser in gradient_optimisers:\n", " print(f\"Running {optimiser.__name__}\")\n", - " optim = optimiser(cost, max_unchanged_iterations=20, max_iterations=60)\n", + " sigma0 = 0.01 if optimiser is pybop.GradientDescent else None\n", + " optim = optimiser(\n", + " cost, sigma0=sigma0, max_unchanged_iterations=20, max_iterations=60\n", + " )\n", " x, _ = optim.run()\n", " optims.append(optim)\n", " xs.append(x)" @@ -522,19 +343,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Running CMAES\n", - "Running SNES\n", - "Running PSO\n", - "Running XNES\n", + "Running CMAES\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running SNES\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running PSO\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running XNES\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Running NelderMead\n", - "NOTE: Boundaries ignored by \n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Running CuckooSearch\n" ] } @@ -550,14 +401,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Running SciPyMinimize\n", + "Running SciPyMinimize\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Running SciPyDifferentialEvolution\n", "Ignoring x0. Initial conditions are not used for differential_evolution.\n" ] @@ -584,31 +441,24 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:59:08.051986Z", - "iopub.status.busy": "2024-04-14T18:59:08.051532Z", - "iopub.status.idle": "2024-04-14T18:59:08.054819Z", - "shell.execute_reply": "2024-04-14T18:59:08.054559Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "| Optimiser: AdamW | Results: [0.79283046 0.66146761] |\n", - "| Optimiser: Gradient descent | Results: [0.54971799 0.92691691] |\n", - "| Optimiser: iRprop- | Results: [0.72245096 0.67281911] |\n", - "| Optimiser: Covariance Matrix Adaptation Evolution Strategy (CMA-ES) | Results: [0.72099365 0.67312846] |\n", - "| Optimiser: Seperable Natural Evolution Strategy (SNES) | Results: [0.72092695 0.67313321] |\n", - "| Optimiser: Particle Swarm Optimisation (PSO) | Results: [0.71681934 0.67366943] |\n", - "| Optimiser: Exponential Natural Evolution Strategy (xNES) | Results: [0.71352763 0.67470134] |\n", + "| Optimiser: AdamW | Results: [0.70901607 0.676607 ] |\n", + "| Optimiser: Gradient descent | Results: [0.67939892 0.6815674 ] |\n", + "| Optimiser: iRprop- | Results: [0.72167189 0.67300586] |\n", + "| Optimiser: Covariance Matrix Adaptation Evolution Strategy (CMA-ES) | Results: [0.72099642 0.673128 ] |\n", + "| Optimiser: Seperable Natural Evolution Strategy (SNES) | Results: [0.72125026 0.67307644] |\n", + "| Optimiser: Particle Swarm Optimisation (PSO) | Results: [0.7335151 0.67158104] |\n", + "| Optimiser: Exponential Natural Evolution Strategy (xNES) | Results: [0.67677389 0.68423077] |\n", "| Optimiser: Nelder-Mead | Results: [0.72127038 0.67308243] |\n", - "| Optimiser: Cuckoo Search | Results: [0.70772893 0.67571981] |\n", + "| Optimiser: Cuckoo Search | Results: [0.71228325 0.67482605] |\n", "| Optimiser: SciPyMinimize | Results: [0.62747952 0.7 ] |\n", - "| Optimiser: SciPyDifferentialEvolution | Results: [0.72100138 0.67312735] |\n" + "| Optimiser: SciPyDifferentialEvolution | Results: [0.72099808 0.67312761] |\n" ] } ], @@ -648,26 +498,35 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 467 }, - "execution": { - "iopub.execute_input": "2024-04-14T18:59:08.056146Z", - "iopub.status.busy": "2024-04-14T18:59:08.056059Z", - "iopub.status.idle": "2024-04-14T18:59:09.176513Z", - "shell.execute_reply": "2024-04-14T18:59:09.176211Z" - }, "id": "tJUJ80Ve04qD", "outputId": "855fbaa2-1e09-4935-eb1a-8caf7f99eb75" }, "outputs": [ { "data": { - "image/svg+xml": [ - "05001000150020003.53.63.73.83.94ReferenceModelAdamWTime / sVoltage / V" + "text/html": [ + " \n", + " " ] }, "metadata": {}, @@ -675,8 +534,32 @@ }, { "data": { - "image/svg+xml": [ - "05001000150020003.53.63.73.83.944.1ReferenceModelGradient descentTime / sVoltage / V" + "text/html": [ + "
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"_model_name": "FloatSliderModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "FloatSliderView", - "behavior": "drag-tap", - "continuous_update": true, - "description": "t", - "description_allow_html": false, - "disabled": false, - "layout": "IPY_MODEL_06f2374f91c8455bb63252092512f2ed", - "max": 1.1333333333333333, - "min": 0, - "orientation": "horizontal", - "readout": true, - "readout_format": ".2f", - "step": 0.011333333333333332, - "style": "IPY_MODEL_56ff19291e464d63b23e63b8e2ac9ea3", - "tabbable": null, - "tooltip": null, - "value": 0 - } - } - } + "version": "3.12.4" } }, "nbformat": 4, diff --git a/examples/notebooks/optimiser_calibration.ipynb b/examples/notebooks/optimiser_calibration.ipynb index 3b09cd377..3ffba2753 100644 --- a/examples/notebooks/optimiser_calibration.ipynb +++ b/examples/notebooks/optimiser_calibration.ipynb @@ -12,22 +12,16 @@ "\n", "### Setting up the Environment\n", "\n", - "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "execution": { - "iopub.execute_input": "2024-04-14T18:57:35.622071Z", - "iopub.status.busy": "2024-04-14T18:57:35.621492Z", - "iopub.status.idle": "2024-04-14T18:57:40.830883Z", - "shell.execute_reply": "2024-04-14T18:57:40.830233Z" - }, "id": "X87NUGPW04py", "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" }, @@ -36,34 +30,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (24.0)\n", - "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (8.1.2)\n", - "Requirement already satisfied: comm>=0.1.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (0.2.1)\n", - "Requirement already satisfied: ipython>=6.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (8.22.1)\n", - "Requirement already satisfied: traitlets>=4.3.1 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (5.14.1)\n", - "Requirement already satisfied: widgetsnbextension~=4.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (4.0.10)\n", - "Requirement already satisfied: jupyterlab-widgets~=3.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (3.0.10)\n", - "Requirement already satisfied: decorator in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", - "Requirement already satisfied: jedi>=0.16 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", - "Requirement already satisfied: matplotlib-inline in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", - "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.43)\n", - "Requirement already satisfied: pygments>=2.4.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", - "Requirement already satisfied: stack-data in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", - "Requirement already satisfied: pexpect>4.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", - "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", - "Requirement already satisfied: ptyprocess>=0.5 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", - "Requirement already satisfied: wcwidth in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n", - "Requirement already satisfied: executing>=1.2.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", - "Requirement already satisfied: asttokens>=2.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", - "Requirement already satisfied: pure-eval in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", - "Requirement already satisfied: six>=1.12.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n", + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ - "%pip install --upgrade pip ipywidgets\n", + "%pip install --upgrade pip ipywidgets -q\n", "%pip install pybop -q" ] }, @@ -80,21 +73,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:40.833454Z", - "iopub.status.busy": "2024-04-14T18:57:40.833211Z", - "iopub.status.idle": "2024-04-14T18:57:46.230918Z", - "shell.execute_reply": "2024-04-14T18:57:46.230420Z" - }, "id": "SQdt4brD04p1" }, "outputs": [], "source": [ "import numpy as np\n", "\n", - "import pybop" + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" ] }, { @@ -114,15 +119,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.247936Z", - "iopub.status.busy": "2024-04-14T18:57:46.247644Z", - "iopub.status.idle": "2024-04-14T18:57:46.446493Z", - "shell.execute_reply": "2024-04-14T18:57:46.446198Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", @@ -133,7 +131,7 @@ " }\n", ")\n", "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)\n", - "init_soc = 0.4\n", + "initial_state = {\"Initial SoC\": 0.4}\n", "experiment = pybop.Experiment(\n", " [\n", " (\n", @@ -143,7 +141,7 @@ " ]\n", " * 2\n", ")\n", - "values = model.predict(init_soc=init_soc, experiment=experiment)" + "values = model.predict(initial_state=initial_state, experiment=experiment)" ] }, { @@ -157,15 +155,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.448424Z", - "iopub.status.busy": "2024-04-14T18:57:46.448310Z", - "iopub.status.idle": "2024-04-14T18:57:46.463231Z", - "shell.execute_reply": "2024-04-14T18:57:46.462472Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "sigma = 0.002\n", @@ -203,14 +194,8 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.466648Z", - "iopub.status.busy": "2024-04-14T18:57:46.466165Z", - "iopub.status.idle": "2024-04-14T18:57:46.479234Z", - "shell.execute_reply": "2024-04-14T18:57:46.478887Z" - }, "id": "zuvGHWID04p_" }, "outputs": [], @@ -237,14 +222,8 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.480892Z", - "iopub.status.busy": "2024-04-14T18:57:46.480800Z", - "iopub.status.idle": "2024-04-14T18:57:46.482933Z", - "shell.execute_reply": "2024-04-14T18:57:46.482614Z" - }, "id": "WPCybXIJ04qA" }, "outputs": [], @@ -278,14 +257,8 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.484362Z", - "iopub.status.busy": "2024-04-14T18:57:46.484269Z", - "iopub.status.idle": "2024-04-14T18:57:46.549050Z", - "shell.execute_reply": "2024-04-14T18:57:46.548811Z" - }, "id": "etMzRtx404qA" }, "outputs": [ @@ -293,12 +266,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "NOTE: Boundaries ignored by Gradient Descent\n" + "NOTE: Boundaries ignored by \n" ] } ], "source": [ - "problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)\n", + "problem = pybop.FittingProblem(model, parameters, dataset)\n", "cost = pybop.SumSquaredError(problem)\n", "optim = pybop.GradientDescent(cost, sigma0=0.2, max_iterations=100)" ] @@ -316,14 +289,8 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.550494Z", - "iopub.status.busy": "2024-04-14T18:57:46.550406Z", - "iopub.status.idle": "2024-04-14T18:57:47.825440Z", - "shell.execute_reply": "2024-04-14T18:57:47.824752Z" - }, "id": "-9OVt0EQ04qB" }, "outputs": [], @@ -344,17 +311,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "execution": { - "iopub.execute_input": "2024-04-14T18:57:47.828249Z", - "iopub.status.busy": "2024-04-14T18:57:47.827941Z", - "iopub.status.idle": "2024-04-14T18:57:47.839261Z", - "shell.execute_reply": "2024-04-14T18:57:47.838577Z" - }, "id": "Hgz8SV4i04qC", "outputId": "e1e42ae7-5075-4c47-dd68-1b22ecc170f6" }, @@ -362,10 +323,10 @@ { "data": { "text/plain": [ - "array([0.64609807, 0.51472958])" + "array([0.64605501, 0.51469905])" ] }, - "execution_count": 9, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -383,20 +344,61 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:47.841607Z", - "iopub.status.busy": "2024-04-14T18:57:47.841388Z", - "iopub.status.idle": "2024-04-14T18:57:50.112707Z", - "shell.execute_reply": "2024-04-14T18:57:50.111902Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": [ - "0500100015003.53.553.63.653.7ReferenceModelOptimised ComparisonTime / sVoltage / V" + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" ] }, "metadata": {}, @@ -418,36 +420,71 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:50.118807Z", - "iopub.status.busy": "2024-04-14T18:57:50.118279Z", - "iopub.status.idle": "2024-04-14T18:59:24.213183Z", - "shell.execute_reply": "2024-04-14T18:59:24.212776Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.001\n", - "NOTE: Boundaries ignored by Gradient Descent\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "0.012285714285714285\n", - "NOTE: Boundaries ignored by Gradient Descent\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "0.023571428571428573\n", - "NOTE: Boundaries ignored by Gradient Descent\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "0.03485714285714286\n", - "NOTE: Boundaries ignored by Gradient Descent\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "0.046142857142857145\n", - "NOTE: Boundaries ignored by Gradient Descent\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "0.05742857142857143\n", - "NOTE: Boundaries ignored by Gradient Descent\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "0.06871428571428571\n", - "NOTE: Boundaries ignored by Gradient Descent\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "0.08\n", - "NOTE: Boundaries ignored by Gradient Descent\n" + "NOTE: Boundaries ignored by \n" ] } ], @@ -457,7 +494,7 @@ "optims = []\n", "for sigma in sigmas:\n", " print(sigma)\n", - " problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", " cost = pybop.SumSquaredError(problem)\n", " optim = pybop.GradientDescent(cost, sigma0=sigma, max_iterations=100)\n", " x, final_cost = optim.run()\n", @@ -467,35 +504,28 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:59:24.215261Z", - "iopub.status.busy": "2024-04-14T18:59:24.215124Z", - "iopub.status.idle": "2024-04-14T18:59:24.217691Z", - "shell.execute_reply": "2024-04-14T18:59:24.217462Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "| Sigma: 0.001 | Num Iterations: 100 | Best Cost: 0.008590687346571011 | Results: [0.58273999 0.64430015] |\n", - "| Sigma: 0.012285714285714285 | Num Iterations: 100 | Best Cost: 0.0017482878947612424 | Results: [0.62229759 0.5406604 ] |\n", - "| Sigma: 0.023571428571428573 | Num Iterations: 100 | Best Cost: 0.0013871420979637958 | Results: [0.63941964 0.52140605] |\n", - "| Sigma: 0.03485714285714286 | Num Iterations: 100 | Best Cost: 0.001571369568098984 | Results: [0.62907481 0.53267599] |\n", - "| Sigma: 0.046142857142857145 | Num Iterations: 28 | Best Cost: 0.0013533853388748253 | Results: [0.64673791 0.51409832] |\n", - "| Sigma: 0.05742857142857143 | Num Iterations: 25 | Best Cost: 0.0013584031053821507 | Results: [0.64390064 0.51673076] |\n", - "| Sigma: 0.06871428571428571 | Num Iterations: 74 | Best Cost: 0.0013568172573032275 | Results: [0.64444354 0.51631924] |\n", - "| Sigma: 0.08 | Num Iterations: 73 | Best Cost: 0.0013551215844470215 | Results: [0.64505654 0.51551585] |\n" + "| Sigma: 0.001 | Num Iterations: 100 | Best Cost: 0.014783332157851634 | Results: [0.53090948 0.59339862] |\n", + "| Sigma: 0.012285714285714285 | Num Iterations: 100 | Best Cost: 0.002790217328868725 | Results: [0.60345692 0.56598814] |\n", + "| Sigma: 0.023571428571428573 | Num Iterations: 100 | Best Cost: 0.0017177540172860569 | Results: [0.62409599 0.5385165 ] |\n", + "| Sigma: 0.03485714285714286 | Num Iterations: 100 | Best Cost: 0.0014363025957628353 | Results: [0.63571019 0.52533286] |\n", + "| Sigma: 0.046142857142857145 | Num Iterations: 100 | Best Cost: 0.0013701538891978493 | Results: [0.641494 0.51930683] |\n", + "| Sigma: 0.05742857142857143 | Num Iterations: 100 | Best Cost: 0.0013574904717929912 | Results: [0.64414678 0.5165881 ] |\n", + "| Sigma: 0.06871428571428571 | Num Iterations: 87 | Best Cost: 0.0013563752974500437 | Results: [0.64460623 0.51615892] |\n", + "| Sigma: 0.08 | Num Iterations: 75 | Best Cost: 0.001356018556480287 | Results: [0.64463942 0.51613016] |\n" ] } ], "source": [ "for optim, sigma in zip(optims, sigmas):\n", " print(\n", - " f\"| Sigma: {sigma} | Num Iterations: {optim._iterations} | Best Cost: {optim.pints_optimiser.f_best()} | Results: {optim.pints_optimiser.x_best()} |\"\n", + " f\"| Sigma: {sigma} | Num Iterations: {optim.result.n_iterations} | Best Cost: {optim.pints_optimiser.f_best()} | Results: {optim.pints_optimiser.x_best()} |\"\n", " )" ] }, @@ -508,20 +538,37 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:59:24.219013Z", - "iopub.status.busy": "2024-04-14T18:59:24.218873Z", - "iopub.status.idle": "2024-04-14T18:59:34.711291Z", - "shell.execute_reply": "2024-04-14T18:59:34.710975Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": [ - "204060801000.00850.0090.00950.010.01050.0110.01150.012Sigma: 0.001IterationCost" + "text/html": [ + "
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"_model_name": "FloatSliderModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "FloatSliderView", - "behavior": "drag-tap", - "continuous_update": true, - "description": "t", - "description_allow_html": false, - "disabled": false, - "layout": "IPY_MODEL_06f2374f91c8455bb63252092512f2ed", - "max": 1.1333333333333333, - "min": 0, - "orientation": "horizontal", - "readout": true, - "readout_format": ".2f", - "step": 0.011333333333333332, - "style": "IPY_MODEL_56ff19291e464d63b23e63b8e2ac9ea3", - "tabbable": null, - "tooltip": null, - "value": 0 - } - } - } + "version": "3.12.4" } }, "nbformat": 4, diff --git a/examples/notebooks/optimiser_interface.ipynb b/examples/notebooks/optimiser_interface.ipynb index efe4b71ff..f860fb4b7 100644 --- a/examples/notebooks/optimiser_interface.ipynb +++ b/examples/notebooks/optimiser_interface.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "00940c64-4748-4b08-9a35-ea98ce311e71", + "id": "0", "metadata": {}, "source": [ "# Interacting with PyBOP optimisers\n", @@ -14,60 +14,72 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "dd0e1a20-1ba3-4ff5-8f6a-f9c6f25c2a4a", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:35.622147Z", - "iopub.status.busy": "2024-04-14T18:57:35.621660Z", - "iopub.status.idle": "2024-04-14T18:57:40.849137Z", - "shell.execute_reply": "2024-04-14T18:57:40.848620Z" - } - }, + "execution_count": null, + "id": "1", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/3.12.2/envs/pybop-3.12/lib/python3.12/site-packages (24.0)\n", - "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/3.12.2/envs/pybop-3.12/lib/python3.12/site-packages (8.1.2)\n", - "Requirement already satisfied: comm>=0.1.3 in /Users/engs2510/.pyenv/versions/3.12.2/envs/pybop-3.12/lib/python3.12/site-packages (from ipywidgets) (0.2.2)\n", - "Requirement already satisfied: ipython>=6.1.0 in /Users/engs2510/.pyenv/versions/3.12.2/envs/pybop-3.12/lib/python3.12/site-packages (from ipywidgets) (8.23.0)\n", - 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"%pip install --upgrade pip ipywidgets\n", + "%pip install --upgrade pip ipywidgets -q\n", "%pip install pybop -q\n", "\n", "# Import the necessary libraries\n", "import numpy as np\n", "\n", - "import pybop" + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" ] }, { "cell_type": "markdown", - "id": "017695fd-ee78-4113-af18-2fea04cf6126", + "id": "4", "metadata": {}, "source": [ "## Setup the model, problem, and cost\n", @@ -77,17 +89,18 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "c346b106-99a9-46bc-8b5d-d330ed911660", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.438835Z", - "iopub.status.busy": "2024-04-14T18:57:46.438684Z", - "iopub.status.idle": "2024-04-14T18:57:46.478613Z", - "shell.execute_reply": "2024-04-14T18:57:46.478339Z" + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting open-circuit voltage to default function\n" + ] } - }, - "outputs": [], + ], "source": [ "# Load the parameters\n", "parameter_set = pybop.ParameterSet(\n", @@ -126,7 +139,7 @@ }, { "cell_type": "markdown", - "id": "3ef5b0da-f755-43c6-8904-79d7ee0f218c", + "id": "6", "metadata": {}, "source": [ "## Interacting with the Optimisers\n", @@ -143,16 +156,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "6244882e-11ad-4bfe-a512-f1c687a06a08", - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.512725Z", - "iopub.status.busy": "2024-04-14T18:57:46.512597Z", - "iopub.status.idle": "2024-04-14T18:57:49.259154Z", - "shell.execute_reply": "2024-04-14T18:57:49.257712Z" - } - }, + "execution_count": null, + "id": "7", + "metadata": {}, "outputs": [], "source": [ "optim_one = pybop.XNES(\n", @@ -166,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "c62e23f7", + "id": "8", "metadata": {}, "source": [ "Next, the `Optimisation` interface is less direct than the previous one, but provides a single class to work with across PyBOP workflows. The options are passed the same way as the above method, through kwargs or get() / set() methods." @@ -174,8 +180,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "479fc846", + "execution_count": null, + "id": "9", "metadata": {}, "outputs": [], "source": [ @@ -190,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "5c6ea9fd", + "id": "10", "metadata": {}, "source": [ "We can show the equivalence of these two methods by comparing the optimiser objects:" @@ -198,8 +204,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "de56587e", + "execution_count": null, + "id": "11", "metadata": {}, "outputs": [ { @@ -208,7 +214,7 @@ "True" ] }, - "execution_count": 5, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -219,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "9f6634c0", + "id": "12", "metadata": {}, "source": [ "For completeness, we can show the optimiser solutions:" @@ -227,8 +233,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "66b74f3e", + "execution_count": null, + "id": "13", "metadata": {}, "outputs": [ { @@ -247,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "94653584", + "id": "14", "metadata": {}, "source": [ "## Closing Comments\n", @@ -275,7 +281,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/examples/notebooks/pouch_cell_identification.ipynb b/examples/notebooks/pouch_cell_identification.ipynb index d952e22c7..fb42463fb 100644 --- a/examples/notebooks/pouch_cell_identification.ipynb +++ b/examples/notebooks/pouch_cell_identification.ipynb @@ -8,26 +8,20 @@ "source": [ "## Pouch Cell Model Parameter Identification\n", "\n", - "In this notebook, we present the single particle model with a two dimensional current collector. This is achieved via the potential-pair models introduced in [[1]](10.1149/1945-7111/abbce4) as implemented in PyBaMM. At a high-level this is accomplished as a potential-pair model which is resolved across the discretised spatial locations.\n", + "In this notebook, we present the single particle model with a two dimensional current collector. This is achieved via the potential-pair models introduced in Marquis et al. [[1]](https://doi.org/10.1149/1945-7111/abbce4) as implemented in PyBaMM. At a high-level this is accomplished as a potential-pair model which is resolved across the discretised spatial locations.\n", "\n", "### Setting up the Environment\n", "\n", - "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "execution": { - "iopub.execute_input": "2024-04-14T18:57:35.624055Z", - "iopub.status.busy": "2024-04-14T18:57:35.623198Z", - "iopub.status.idle": "2024-04-14T18:57:40.867986Z", - "shell.execute_reply": "2024-04-14T18:57:40.864663Z" - }, "id": "X87NUGPW04py", "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" }, @@ -36,52 +30,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (24.0)\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (8.1.2)\r\n" + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - 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"Requirement already satisfied: six>=1.12.0 in /Users/engs2510/.pyenv/versions/pybop-3.12/lib/python3.12/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\r\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" ] }, { @@ -93,7 +63,7 @@ } ], "source": [ - "%pip install --upgrade pip ipywidgets\n", + "%pip install --upgrade pip ipywidgets -q\n", "%pip install pybop -q" ] }, @@ -110,22 +80,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:40.873481Z", - "iopub.status.busy": "2024-04-14T18:57:40.872975Z", - "iopub.status.idle": "2024-04-14T18:57:46.229776Z", - "shell.execute_reply": "2024-04-14T18:57:46.229372Z" - }, "id": "SQdt4brD04p1" }, "outputs": [], "source": [ "import numpy as np\n", - "import plotly.graph_objects as go\n", "\n", - "import pybop" + "import pybop\n", + "\n", + "go = pybop.PlotlyManager().go\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" ] }, { @@ -145,15 +111,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.237533Z", - "iopub.status.busy": "2024-04-14T18:57:46.237264Z", - "iopub.status.idle": "2024-04-14T18:57:46.342790Z", - "shell.execute_reply": "2024-04-14T18:57:46.342522Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "parameter_set = pybop.ParameterSet.pybamm(\"Marquis2019\")\n", @@ -180,15 +139,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.344521Z", - "iopub.status.busy": "2024-04-14T18:57:46.344361Z", - "iopub.status.idle": "2024-04-14T18:57:46.346351Z", - "shell.execute_reply": "2024-04-14T18:57:46.346066Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "model.var_pts[\"y\"] = 5\n", @@ -206,14 +158,8 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.347876Z", - "iopub.status.busy": "2024-04-14T18:57:46.347770Z", - "iopub.status.idle": "2024-04-14T18:57:47.494091Z", - "shell.execute_reply": "2024-04-14T18:57:47.493276Z" - }, "id": "sBasxv8U04p3" }, "outputs": [], @@ -233,15 +179,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:47.500309Z", - "iopub.status.busy": "2024-04-14T18:57:47.499942Z", - "iopub.status.idle": "2024-04-14T18:57:47.601699Z", - "shell.execute_reply": "2024-04-14T18:57:47.601228Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "sigma = 0.001\n", @@ -277,14 +216,8 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { - 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For further reading on different solvers, see the PyBaMM solver documentation:\n", + "\n", + "[[1]: PyBaMM Solvers](https://docs.pybamm.org/en/stable/source/api/solvers/index.html#)\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n", + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "import time\n", + "\n", + "import numpy as np\n", + "import pybamm\n", + "\n", + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "### Setting up the model, and problem\n", + "\n", + "We start by constructing a pybop model, and a synthetic dataset needed for the pybop problem we will be using for the solver benchmarking " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Model\n", + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)\n", + "\n", + "# Synthetic data\n", + "t_eval = np.arange(0, 900, 2)\n", + "values = model.predict(t_eval=t_eval)\n", + "\n", + "# Dataset\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": values[\"Voltage [V]\"].data,\n", + " }\n", + ")\n", + "\n", + "# Parameters\n", + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.6, 0.02),\n", + " bounds=[0.5, 0.8],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.48, 0.02),\n", + " bounds=[0.4, 0.7],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "### Defining the solvers for benchmarking\n", + "\n", + "Now that we have set up the majority of the pybop objects, we construct the solvers we want to benchmark on the given model, and applied current." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "solvers = [\n", + " pybamm.IDAKLUSolver(atol=1e-6, rtol=1e-6),\n", + " pybamm.CasadiSolver(atol=1e-6, rtol=1e-6, mode=\"safe\"),\n", + " pybamm.CasadiSolver(atol=1e-6, rtol=1e-6, mode=\"fast\"),\n", + " pybamm.CasadiSolver(atol=1e-6, rtol=1e-6, mode=\"fast with events\"),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we construct a range of inputs for the parameters defined above, and select the number of instances in that range to benchmark on. For more statistically repeatable results, increase the variable `n` below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n = 50 # Number of solves\n", + "inputs = list(zip(np.linspace(0.45, 0.6, n), np.linspace(0.45, 0.6, n)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's benchmark the solvers without sensitivities. This provides a reference for the non-gradient based pybop optimisers and samplers. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Evaluate IDA KLU solver: 0.303\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Evaluate CasADi solver with 'safe' mode: 1.076\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Evaluate CasADi solver with 'fast' mode: 1.003\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Evaluate CasADi solver with 'fast with events' mode: 1.008\n" + ] + } + ], + "source": [ + "for solver in solvers:\n", + " model.solver = solver\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", + "\n", + " start_time = time.time()\n", + " for input_values in inputs:\n", + " problem.evaluate(inputs=input_values)\n", + " print(f\"Time Evaluate {solver.name}: {time.time() - start_time:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Excellent, given the above results, we know which solver we should select for optimisation on your machine, i.e. the one with the smallest time. \n", + "\n", + "Next, let's repeat the same toy problem, but for the gradient-based cost evaluation," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time EvaluateS1 IDA KLU solver: 0.671\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time EvaluateS1 CasADi solver with 'safe' mode: 4.479\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time EvaluateS1 CasADi solver with 'fast' mode: 3.440\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time EvaluateS1 CasADi solver with 'fast with events' mode: 3.287\n" + ] + } + ], + "source": [ + "for solver in solvers:\n", + " model.solver = solver\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", + "\n", + " start_time = time.time()\n", + " for input_values in inputs:\n", + " problem.evaluateS1(inputs=input_values)\n", + " print(f\"Time EvaluateS1 {solver.name}: {time.time() - start_time:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have the relevant information for the gradient-based optimisers. Likewise to the above results, we should select the solver with the smallest time." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/spm_AdamW.ipynb b/examples/notebooks/spm_AdamW.ipynb index ec9a961a5..7a55313e0 100644 --- a/examples/notebooks/spm_AdamW.ipynb +++ b/examples/notebooks/spm_AdamW.ipynb @@ -16,22 +16,16 @@ "\n", "### Setting up the Environment\n", "\n", - "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "execution": { - "iopub.execute_input": "2024-04-04T13:51:40.337833Z", - "iopub.status.busy": "2024-04-04T13:51:40.337689Z", - "iopub.status.idle": "2024-04-04T13:51:41.935008Z", - "shell.execute_reply": "2024-04-04T13:51:41.934618Z" - }, "id": "X87NUGPW04py", "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" }, @@ -40,34 +34,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (24.0)\n", - "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (8.1.2)\n", - "Requirement already satisfied: comm>=0.1.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (0.2.1)\n", - 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"Requirement already satisfied: ptyprocess>=0.5 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", - "Requirement already satisfied: wcwidth in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n", - "Requirement already satisfied: executing>=1.2.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", - "Requirement already satisfied: asttokens>=2.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", - "Requirement already satisfied: pure-eval in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", - "Requirement already satisfied: six>=1.12.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n", + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ - "%pip install --upgrade pip ipywidgets\n", + "%pip install --upgrade pip ipywidgets -q\n", "%pip install pybop -q" ] }, @@ -84,21 +77,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-04T13:51:41.936561Z", - "iopub.status.busy": "2024-04-04T13:51:41.936439Z", - "iopub.status.idle": "2024-04-04T13:51:42.508083Z", - "shell.execute_reply": "2024-04-04T13:51:42.507654Z" - }, "id": "SQdt4brD04p1" }, "outputs": [], "source": [ "import numpy as np\n", "\n", - "import pybop" + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" ] }, { @@ -118,15 +123,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-04T13:51:42.509591Z", - "iopub.status.busy": "2024-04-04T13:51:42.509437Z", - "iopub.status.idle": "2024-04-04T13:51:42.534794Z", - "shell.execute_reply": "2024-04-04T13:51:42.534452Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", @@ -144,14 +142,8 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-04T13:51:42.536154Z", - "iopub.status.busy": "2024-04-04T13:51:42.536069Z", - "iopub.status.idle": "2024-04-04T13:51:42.610305Z", - "shell.execute_reply": "2024-04-04T13:51:42.609892Z" - }, "id": "sBasxv8U04p3" }, "outputs": [], @@ -171,15 +163,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-04T13:51:42.611946Z", - "iopub.status.busy": "2024-04-04T13:51:42.611728Z", - "iopub.status.idle": "2024-04-04T13:51:42.621525Z", - "shell.execute_reply": "2024-04-04T13:51:42.621156Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "sigma = 0.001\n", @@ -215,14 +200,8 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-04T13:51:42.622671Z", - "iopub.status.busy": "2024-04-04T13:51:42.622478Z", - 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"NOTE: This is a brittle example, the classes and methods below will be integrated into PyBOP in a future release.\n", - "\n", - "A design optimisation example loosely based on work by L.D. Couto available at https://doi.org/10.1016/j.energy.2022.125966.\n", + "A design optimisation example loosely based on work by L.D. Couto available at [[1]](https://doi.org/10.1016/j.energy.2022.125966).\n", "\n", "The target is to maximise the gravimetric energy density over a range of possible design parameter values, including for example:\n", "\n", @@ -18,22 +16,16 @@ "\n", "### Setting up the Environment\n", "\n", - "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "execution": { - "iopub.execute_input": "2024-04-14T18:57:35.623967Z", - "iopub.status.busy": "2024-04-14T18:57:35.623399Z", - "iopub.status.idle": "2024-04-14T18:57:41.585471Z", - "shell.execute_reply": "2024-04-14T18:57:41.584895Z" - }, "id": "X87NUGPW04py", "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" }, @@ -42,19 +34,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Note: you may need to restart the kernel to use updated packages.\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ - "%pip install --upgrade pip ipywidgets pybamm -q\n", + "%pip install --upgrade pip ipywidgets -q\n", "%pip install pybop -q" ] }, @@ -69,19 +55,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:41.587953Z", - "iopub.status.busy": "2024-04-14T18:57:41.587606Z", - "iopub.status.idle": "2024-04-14T18:57:46.230723Z", - "shell.execute_reply": "2024-04-14T18:57:46.230142Z" - }, "id": "SQdt4brD04p1" }, "outputs": [], "source": [ - "import pybop" + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" ] }, { @@ -104,14 +104,8 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.244423Z", - "iopub.status.busy": "2024-04-14T18:57:46.243274Z", - "iopub.status.idle": "2024-04-14T18:57:46.344865Z", - "shell.execute_reply": "2024-04-14T18:57:46.344504Z" - }, "id": "zuvGHWID04p_" }, "outputs": [], @@ -131,14 +125,8 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.346645Z", - "iopub.status.busy": "2024-04-14T18:57:46.346525Z", - "iopub.status.idle": "2024-04-14T18:57:46.348677Z", - "shell.execute_reply": "2024-04-14T18:57:46.348288Z" - }, "id": "WPCybXIJ04qA" }, "outputs": [], @@ -166,15 +154,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.349880Z", - "iopub.status.busy": "2024-04-14T18:57:46.349789Z", - "iopub.status.idle": "2024-04-14T18:57:46.351626Z", - "shell.execute_reply": "2024-04-14T18:57:46.351281Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "experiment = pybop.Experiment(\n", @@ -193,19 +174,26 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.353083Z", - "iopub.status.busy": "2024-04-14T18:57:46.352995Z", - "iopub.status.idle": "2024-04-14T18:57:46.892665Z", - "shell.execute_reply": "2024-04-14T18:57:46.892318Z" - }, "id": "etMzRtx404qA" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/2s/pn1xwy191_7bv761mlf_cgzh0000gq/T/ipykernel_61167/3053274142.py:1: UserWarning:\n", + "\n", + "The nominal capacity is fixed at the initial model value.\n", + "\n" + ] + } + ], "source": [ - "problem = pybop.DesignProblem(model, parameters, experiment, init_soc=1.0)\n", + "problem = pybop.DesignProblem(\n", + " model, parameters, experiment, initial_state={\"Initial SoC\": 0.7}\n", + ")\n", "cost = pybop.GravimetricEnergyDensity(problem)" ] }, @@ -220,14 +208,8 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.894578Z", - "iopub.status.busy": "2024-04-14T18:57:46.894454Z", - "iopub.status.idle": "2024-04-14T18:57:46.896416Z", - "shell.execute_reply": "2024-04-14T18:57:46.896162Z" - }, "id": "N3FtAhrT04qB" }, "outputs": [], @@ -246,14 +228,8 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:57:46.897807Z", - "iopub.status.busy": "2024-04-14T18:57:46.897703Z", - "iopub.status.idle": "2024-04-14T18:58:05.954495Z", - "shell.execute_reply": "2024-04-14T18:58:05.953904Z" - }, "id": "-9OVt0EQ04qB" }, "outputs": [ @@ -262,23 +238,17 @@ "output_type": "stream", "text": [ "Halt: Maximum number of iterations (15) reached.\n", - "Estimated parameters: [6.50259261e-05 2.18540132e-06]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial gravimetric energy density: 386.97 Wh.kg-1\n", - "Optimised gravimetric energy density: 411.23 Wh.kg-1\n" + "Estimated parameters: [8.27568156e-05 2.07975193e-06]\n", + "Initial gravimetric energy density: 256.31 Wh.kg-1\n", + "Optimised gravimetric energy density: 279.37 Wh.kg-1\n" ] } ], "source": [ "x, final_cost = optim.run()\n", "print(\"Estimated parameters:\", x)\n", - "print(f\"Initial gravimetric energy density: {-cost(optim.x0):.2f} Wh.kg-1\")\n", - "print(f\"Optimised gravimetric energy density: {-final_cost:.2f} Wh.kg-1\")" + "print(f\"Initial gravimetric energy density: {cost(optim.x0):.2f} Wh.kg-1\")\n", + "print(f\"Optimised gravimetric energy density: {final_cost:.2f} Wh.kg-1\")" ] }, { @@ -305,21 +275,63 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { - "execution": { - "iopub.execute_input": "2024-04-14T18:58:05.957518Z", - "iopub.status.busy": "2024-04-14T18:58:05.957344Z", - "iopub.status.idle": "2024-04-14T18:58:06.874737Z", - "shell.execute_reply": "2024-04-14T18:58:06.874105Z" - }, "id": "ZVfozY0A04qC" }, "outputs": [ { "data": { - "image/svg+xml": [ - "01000200030002.42.62.833.23.43.63.84ReferenceOptimisedOptimised ComparisonTime / sVoltage / V" + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" ] }, "metadata": {}, @@ -327,8 +339,6 @@ } ], "source": [ - "if cost.update_capacity:\n", - " problem._model.approximate_capacity(x)\n", "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");" ] }, @@ -345,26 +355,44 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 467 }, - "execution": { - "iopub.execute_input": "2024-04-14T18:58:06.877016Z", - "iopub.status.busy": "2024-04-14T18:58:06.876676Z", - "iopub.status.idle": "2024-04-14T18:58:16.428669Z", - "shell.execute_reply": "2024-04-14T18:58:16.428218Z" - }, "id": "tJUJ80Ve04qD", "outputId": "855fbaa2-1e09-4935-eb1a-8caf7f99eb75" }, "outputs": [ { "data": { - "image/svg+xml": [ - "70μ80μ90μ100μ2μ3μ4μ5μ6μ7μ8μ9μ−400−380−360−340−320Cost LandscapePositive electrode thickness [m]Positive particle radius [m]" + "text/html": [ + "
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true_value=0.7, ), pybop.Parameter( "Positive electrode active material volume fraction", prior=pybop.Gaussian(0.48, 0.05), bounds=[0.4, 0.75], initial_value=0.41, - true_value=0.67, ), ) -init_soc = 0.7 experiment = pybop.Experiment( [ ( @@ -32,9 +35,7 @@ ), ] ) -values = model.predict( - init_soc=init_soc, experiment=experiment, inputs=parameters.as_dict("true") -) +values = model.predict(initial_state={"Initial SoC": 0.7}, experiment=experiment) sigma = 0.002 corrupt_values = values["Voltage [V]"].data + np.random.normal( @@ -51,7 +52,7 @@ ) # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc) +problem = pybop.FittingProblem(model, parameters, dataset) cost = pybop.GaussianLogLikelihood(problem, sigma0=sigma * 4) optim = pybop.Optimisation( cost, diff --git a/examples/scripts/ecm_tau_constraints.py b/examples/scripts/ecm_tau_constraints.py new file mode 100644 index 000000000..48c0ddc4d --- /dev/null +++ b/examples/scripts/ecm_tau_constraints.py @@ -0,0 +1,195 @@ +from typing import Union + +import numpy as np + +import pybop + +""" +When fitting empirical models, the parameters we are able to identify +will be constrained from the data that's available. For example, it's +no good trying to fit an RC timescale of 0.1 s from data sampled at +1 Hz! Likewise, an RC timescale of 100 s cannot be meaningfully fitted +to just 10 s of data. To ensure the optimiser doesn't propose +excessively long or short timescales - beyond what can reasonably be +inferred from the data - it is common to apply nonlinear constraints +on the parameter space. This script fits an RC pair with the +constraint 0.5 <= R1 * C1 <= 1, to highlight a possible method for +applying constraints on the timescales. + +An alternative approach is given i the ecm_trust-constr notebook, +which can lead to better results and higher optimisation efficiency +when good timescale guesses are available. +""" + +# Import the ECM parameter set from JSON +# parameter_set = pybop.ParameterSet( +# json_path="examples/scripts/parameters/initial_ecm_parameters.json" +# ) +# parameter_set.import_parameters() + + +# Alternatively, define the initial parameter set with a dictionary +# Add definitions for R's, C's, and initial overpotentials for any additional RC elements +parameter_set = { + "chemistry": "ecm", + "Initial SoC": 0.75, + "Initial temperature [K]": 25 + 273.15, + "Cell capacity [A.h]": 5, + "Nominal cell capacity [A.h]": 5, + "Ambient temperature [K]": 25 + 273.15, + "Current function [A]": 5, + "Upper voltage cut-off [V]": 4.2, + "Lower voltage cut-off [V]": 3.0, + "Cell thermal mass [J/K]": 1000, + "Cell-jig heat transfer coefficient [W/K]": 10, + "Jig thermal mass [J/K]": 500, + "Jig-air heat transfer coefficient [W/K]": 10, + "Open-circuit voltage [V]": pybop.empirical.Thevenin().default_parameter_values[ + "Open-circuit voltage [V]" + ], + "R0 [Ohm]": 0.001, + "Element-1 initial overpotential [V]": 0, + "Element-2 initial overpotential [V]": 0, + "R1 [Ohm]": 0.0002, + "R2 [Ohm]": 0.0003, + "C1 [F]": 10000, + "C2 [F]": 5000, + "Entropic change [V/K]": 0.0004, +} + + +def get_parameter_checker( + tau_mins: Union[float, list[float]], + tau_maxs: Union[float, list[float]], + fitted_rc_pair_indices: Union[int, list[int]], +): + """Returns a function to check parameters against given tau bounds. + The resulting check_params function will be sent off to PyBOP; the + rest of the code does some light checking of the constraints. + + Parameters + ---------- + tau_mins: float or list[float] + Lower bounds on timescale tau_i = Ri * Ci + tau_maxs: float or list[float] + Upper bounds on timescale tau_i = Ri * Ci + fitted_rc_pair_indices: int or list[float] + The index of each RC pair whose parameters are to be fitted. + Eg. [1, 2] means fitting R1, R2, C1, C2. The timescale of RC + pair fitted_rc_pair_indices[j] is constrained to be in the + range tau_mins[j] <= R * C <= tau_maxs[j] + + Returns + ------- + check_params + Function to check the proposed parameter values match the + requested constraints + + """ + + # Ensure inputs are lists + tau_mins = [tau_mins] if not isinstance(tau_mins, list) else tau_mins + tau_maxs = [tau_maxs] if not isinstance(tau_maxs, list) else tau_maxs + fitted_rc_pair_indices = ( + [fitted_rc_pair_indices] + if not isinstance(fitted_rc_pair_indices, list) + else fitted_rc_pair_indices + ) + + # Validate input lengths + if len(tau_mins) != len(fitted_rc_pair_indices) or len(tau_maxs) != len( + fitted_rc_pair_indices + ): + raise ValueError( + "tau_mins and tau_maxs must have the same length as fitted_rc_pair_indices" + ) + + def check_params( + inputs: dict[str, float] = None, + parameter_set=None, + allow_infeasible_solutions: bool = False, + ) -> bool: + """Checks if the given inputs are within the tau bounds.""" + # Allow simulation to run if inputs are None + if inputs is None or inputs == {}: + return True + + # Check every respective R*C against tau bounds + for i, tau_min, tau_max in zip(fitted_rc_pair_indices, tau_mins, tau_maxs): + tau = inputs[f"R{i} [Ohm]"] * inputs[f"C{i} [F]"] + if not tau_min <= tau <= tau_max: + return False + return True + + return check_params + + +# Define the model +params = pybop.ParameterSet(params_dict=parameter_set) +model = pybop.empirical.Thevenin( + parameter_set=params, + check_params=get_parameter_checker( + 0, 1.0, 1 + ), # Set the model up to automatically check parameters + options={"number of rc elements": 2}, +) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "R0 [Ohm]", + prior=pybop.Gaussian(0.0002, 0.0001), + bounds=[1e-4, 1e-2], + ), + pybop.Parameter( + "R1 [Ohm]", + prior=pybop.Gaussian(0.0001, 0.0001), + bounds=[1e-5, 1e-2], + ), + pybop.Parameter( + "C1 [F]", + prior=pybop.Gaussian(10000, 2500), + bounds=[2500, 5e4], + ), +) + +sigma = 0.001 +t_eval = np.arange(0, 600, 3) +values = model.predict(t_eval=t_eval) +corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": t_eval, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": corrupt_values, + } +) + +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset) +cost = pybop.RootMeanSquaredError(problem) +optim = pybop.XNES( + cost, + sigma0=[1e-4, 1e-4, 100], # Set parameter specific step size + allow_infeasible_solutions=False, + max_unchanged_iterations=30, + max_iterations=125, +) + +x, final_cost = optim.run() +print("Estimated parameters:", x) + + +# Plot the time series +pybop.plot_dataset(dataset) + +# Plot the timeseries output +pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison") + +# Plot convergence +pybop.plot_convergence(optim) + +# Plot the parameter traces +pybop.plot_parameters(optim) diff --git a/examples/scripts/eis_fitting.py b/examples/scripts/eis_fitting.py new file mode 100644 index 000000000..f86e7707a --- /dev/null +++ b/examples/scripts/eis_fitting.py @@ -0,0 +1,82 @@ +import numpy as np + +import pybop + +# Define model +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +parameter_set["Contact resistance [Ohm]"] = 0.0 +initial_state = {"Initial SoC": 0.5} +n_frequency = 20 +sigma0 = 1e-4 +f_eval = np.logspace(-4, 5, n_frequency) +model = pybop.lithium_ion.SPM( + parameter_set=parameter_set, + eis=True, + options={"surface form": "differential", "contact resistance": "true"}, +) + +# Create synthetic data for parameter inference +sim = model.simulateEIS( + inputs={ + "Negative electrode active material volume fraction": 0.531, + "Positive electrode active material volume fraction": 0.732, + }, + f_eval=f_eval, + initial_state=initial_state, +) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.75), + bounds=[0.375, 0.75], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.75), + bounds=[0.375, 0.75], + ), +) + + +def noise(sigma, values): + # Generate real part noise + real_noise = np.random.normal(0, sigma, values) + + # Generate imaginary part noise + imag_noise = np.random.normal(0, sigma, values) + + # Combine them into a complex noise + return real_noise + 1j * imag_noise + + +# Form dataset +dataset = pybop.Dataset( + { + "Frequency [Hz]": f_eval, + "Current function [A]": np.ones(n_frequency) * 0.0, + "Impedance": sim["Impedance"] + noise(sigma0, len(sim["Impedance"])), + } +) + +signal = ["Impedance"] +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +cost = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=sigma0) +optim = pybop.CMAES(cost, max_iterations=100, sigma0=0.25, max_unchanged_iterations=30) + +x, final_cost = optim.run() +print("Estimated parameters:", x) + +# Plot the nyquist +pybop.nyquist(problem, problem_inputs=x, title="Optimised Comparison") + +# Plot convergence +pybop.plot_convergence(optim) + +# Plot the parameter traces +pybop.plot_parameters(optim) + +# Plot 2d landscape +pybop.plot2d(optim, steps=10) diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py index 622a68e50..3845a9679 100644 --- a/examples/scripts/exp_UKF.py +++ b/examples/scripts/exp_UKF.py @@ -4,7 +4,7 @@ from examples.standalone.model import ExponentialDecay # Parameter set and model definition -parameter_set = {"k": "[input]", "y0": "[input]"} +parameter_set = {"k": 0.1, "y0": 1.0} model = ExponentialDecay(parameter_set=parameter_set, n_states=1) # Fitting parameters @@ -13,22 +13,21 @@ "k", prior=pybop.Gaussian(0.1, 0.05), bounds=[0, 1], - true_value=0.1, + true_value=parameter_set["k"], ), pybop.Parameter( "y0", prior=pybop.Gaussian(1, 0.05), bounds=[0, 3], - true_value=1.0, + true_value=parameter_set["y0"], ), ) # Make a prediction with measurement noise sigma = 1e-2 t_eval = np.linspace(0, 20, 10) -model.parameters = parameters true_inputs = parameters.as_dict("true") -values = model.predict(t_eval=t_eval, inputs=true_inputs) +values = model.predict(t_eval=t_eval) values = values["2y"].data corrupt_values = values + np.random.normal(0, sigma, len(t_eval)) @@ -40,7 +39,7 @@ # Verification step: make another prediction using the Observer class model.build(parameters=parameters) simulator = pybop.Observer(parameters, model, signal=["2y"]) -simulator._time_data = t_eval +simulator.domain_data = t_eval measurements = simulator.evaluate(true_inputs) # Verification step: Compare by plotting diff --git a/examples/scripts/functional_parameters.py b/examples/scripts/functional_parameters.py new file mode 100644 index 000000000..39b179c0f --- /dev/null +++ b/examples/scripts/functional_parameters.py @@ -0,0 +1,97 @@ +import numpy as np +import pybamm + +import pybop + +# This example demonstrates how to use a pybamm.FunctionalParameter to +# optimise functional parameters using PyBOP. + +# Method: Define a new scalar parameter for use in a functional parameter +# that already exists in the model, for example an exchange current density. + + +# Load parameter set +parameter_set = pybop.ParameterSet.pybamm("Chen2020") + + +# Define a new function using pybamm parameters +def positive_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): + # New parameters + j0_ref = pybamm.Parameter( + "Positive electrode reference exchange-current density [A.m-2]" + ) + alpha = pybamm.Parameter("Positive electrode charge transfer coefficient") + + # Existing parameters + c_e_init = pybamm.Parameter("Initial concentration in electrolyte [mol.m-3]") + + return ( + j0_ref + * ((c_e / c_e_init) * (c_s_surf / c_s_max) * (1 - c_s_surf / c_s_max)) ** alpha + ) + + +# Give default values to the new scalar parameters and pass the new function +parameter_set.update( + { + "Positive electrode reference exchange-current density [A.m-2]": 1, + "Positive electrode charge transfer coefficient": 0.5, + }, + check_already_exists=False, +) +parameter_set["Positive electrode exchange-current density [A.m-2]"] = ( + positive_electrode_exchange_current_density +) + +# Model definition +model = pybop.lithium_ion.SPM( + parameter_set=parameter_set, options={"contact resistance": "true"} +) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode reference exchange-current density [A.m-2]", + prior=pybop.Gaussian(1, 0.1), + ), + pybop.Parameter( + "Positive electrode charge transfer coefficient", + prior=pybop.Gaussian(0.5, 0.1), + ), +) + +# Generate data +sigma = 0.001 +t_eval = np.arange(0, 900, 3) +values = model.predict(t_eval=t_eval) +corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": t_eval, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": corrupt_values, + } +) + +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset) +cost = pybop.RootMeanSquaredError(problem) +optim = pybop.SciPyMinimize(cost, max_iterations=125) + +# Run optimisation +x, final_cost = optim.run() +print("Estimated parameters:", x) + +# Plot the timeseries output +pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison") + +# Plot convergence +pybop.plot_convergence(optim) + +# Plot the parameter traces +pybop.plot_parameters(optim) + +# Plot the cost landscape with optimisation path +pybop.plot2d(optim, steps=15) diff --git a/examples/scripts/mcmc_example.py b/examples/scripts/mcmc_example.py new file mode 100644 index 000000000..436886d00 --- /dev/null +++ b/examples/scripts/mcmc_example.py @@ -0,0 +1,94 @@ +import numpy as np +import plotly.graph_objects as go +import pybamm + +import pybop + +# Parameter set and model definition +solver = pybamm.IDAKLUSolver() +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +parameter_set.update( + { + "Negative electrode active material volume fraction": 0.63, + "Positive electrode active material volume fraction": 0.71, + } +) +synth_model = pybop.lithium_ion.DFN(parameter_set=parameter_set, solver=solver) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.02), + transformation=pybop.LogTransformation(), + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.65, 0.02), + transformation=pybop.LogTransformation(), + ), +) + +# Generate data +init_soc = 0.5 +sigma = 0.002 +experiment = pybop.Experiment( + [ + ("Discharge at 0.5C for 6 minutes (5 second period)",), + ] +) +values = synth_model.predict( + initial_state={"Initial SoC": init_soc}, experiment=experiment +) + + +def noise(sigma): + return np.random.normal(0, sigma, len(values["Voltage [V]"].data)) + + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data + noise(sigma), + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data + + noise(sigma), + } +) + +model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=pybamm.IDAKLUSolver()) +model.build(initial_state={"Initial SoC": init_soc}) +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] + +# Generate problem, likelihood, and sampler +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.002) +posterior = pybop.LogPosterior(likelihood) + +optim = pybop.DifferentialEvolutionMCMC( + posterior, + chains=3, + max_iterations=300, + warm_up=100, + verbose=True, + # parallel=True, # uncomment to enable parallelisation (MacOS/WSL/Linux only) +) +result = optim.run() + +# Create a histogram +fig = go.Figure() +for _i, data in enumerate(result): + fig.add_trace(go.Histogram(x=data[:, 0], name="Neg", opacity=0.75)) + fig.add_trace(go.Histogram(x=data[:, 1], name="Pos", opacity=0.75)) + +# Update layout for better visualization +fig.update_layout( + title="Posterior distribution of volume fractions", + xaxis_title="Value", + yaxis_title="Count", + barmode="overlay", +) + +# Show the plot +fig.show() diff --git a/examples/scripts/multi_fitting.py b/examples/scripts/multi_fitting.py new file mode 100644 index 000000000..186575cc5 --- /dev/null +++ b/examples/scripts/multi_fitting.py @@ -0,0 +1,79 @@ +import numpy as np + +import pybop + +# Parameter set and model definition +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +model = pybop.lithium_ion.SPM(parameter_set=parameter_set) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + true_value=parameter_set["Negative electrode active material volume fraction"], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.58, 0.05), + true_value=parameter_set["Positive electrode active material volume fraction"], + ), +) + +# Generate a dataset and a fitting problem +sigma = 0.001 +experiment = pybop.Experiment([("Discharge at 0.5C for 2 minutes (4 second period)")]) +values = model.predict(initial_state={"Initial SoC": 0.8}, experiment=experiment) +dataset_1 = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data + + np.random.normal(0, sigma, len(values["Voltage [V]"].data)), + } +) +problem_1 = pybop.FittingProblem(model, parameters, dataset_1) + +# Generate a second dataset and problem +model = model.new_copy() +experiment = pybop.Experiment([("Discharge at 1C for 1 minutes (4 second period)")]) +values = model.predict(initial_state={"Initial SoC": 0.8}, experiment=experiment) +dataset_2 = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data + + np.random.normal(0, sigma, len(values["Voltage [V]"].data)), + } +) +problem_2 = pybop.FittingProblem(model, parameters, dataset_2) + +# Combine the problems into one +problem = pybop.MultiFittingProblem(problem_1, problem_2) + +# Generate the cost function and optimisation class +cost = pybop.SumSquaredError(problem) +optim = pybop.IRPropMin( + cost, + verbose=True, + max_iterations=100, +) + +# Run optimisation +x, final_cost = optim.run() +print("True parameters:", parameters.true_value()) +print("Estimated parameters:", x) + +# Plot the timeseries output +pybop.quick_plot(problem_1, problem_inputs=x, title="Optimised Comparison") +pybop.quick_plot(problem_2, problem_inputs=x, title="Optimised Comparison") + +# Plot convergence +pybop.plot_convergence(optim) + +# Plot the parameter traces +pybop.plot_parameters(optim) + +# Plot the cost landscape with optimisation path +bounds = np.array([[0.5, 0.8], [0.4, 0.7]]) +pybop.plot2d(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/selecting_a_solver.py b/examples/scripts/selecting_a_solver.py new file mode 100644 index 000000000..248d943f5 --- /dev/null +++ b/examples/scripts/selecting_a_solver.py @@ -0,0 +1,60 @@ +import time + +import numpy as np +import pybamm + +import pybop + +# Parameter set and model definition +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +model = pybop.lithium_ion.SPM(parameter_set=parameter_set) + +solvers = [ + pybamm.IDAKLUSolver(atol=1e-6, rtol=1e-6), + pybamm.CasadiSolver(mode="safe", atol=1e-6, rtol=1e-6), + pybamm.CasadiSolver(mode="fast with events", atol=1e-6, rtol=1e-6), +] + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", initial_value=0.55 + ), + pybop.Parameter( + "Positive electrode active material volume fraction", initial_value=0.55 + ), +) + +# Define test protocol and generate data +experiment = pybop.Experiment([("Discharge at 0.5C for 10 minutes (3 second period)")]) +values = model.predict( + initial_state={"Initial open-circuit voltage [V]": 4.2}, experiment=experiment +) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data, + } +) + +# Create the list of input dicts +n = 150 # Number of solves +inputs = list(zip(np.linspace(0.45, 0.6, n), np.linspace(0.45, 0.6, n))) + +# Iterate over the solvers and print benchmarks +for solver in solvers: + model.solver = solver + problem = pybop.FittingProblem(model, parameters, dataset) + + start_time = time.time() + for input_values in inputs: + problem.evaluate(inputs=input_values) + print(f"Time Evaluate {solver.name}: {time.time() - start_time:.3f}") + + start_time = time.time() + for input_values in inputs: + problem.evaluateS1(inputs=input_values) + print(f"Time EvaluateS1 {solver.name}: {time.time() - start_time:.3f}") diff --git a/examples/scripts/spm_AdamW.py b/examples/scripts/spm_AdamW.py index 796849bee..c19aa25ff 100644 --- a/examples/scripts/spm_AdamW.py +++ b/examples/scripts/spm_AdamW.py @@ -1,10 +1,12 @@ import numpy as np +import pybamm import pybop -# Parameter set and model definition +# Define model and use high-performant solver for sensitivities +solver = pybamm.IDAKLUSolver() parameter_set = pybop.ParameterSet.pybamm("Chen2020") -model = pybop.lithium_ion.SPM(parameter_set=parameter_set) +model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) # Fitting parameters parameters = pybop.Parameters( @@ -19,7 +21,6 @@ ) # Generate data -init_soc = 0.5 sigma = 0.003 experiment = pybop.Experiment( [ @@ -30,7 +31,7 @@ ] * 2 ) -values = model.predict(init_soc=init_soc, experiment=experiment) +values = model.predict(initial_state={"Initial SoC": 0.5}, experiment=experiment) def noise(sigma): @@ -50,10 +51,8 @@ def noise(sigma): signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=init_soc -) -cost = pybop.RootMeanSquaredError(problem) +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +cost = pybop.Minkowski(problem, p=2) optim = pybop.AdamW( cost, verbose=True, diff --git a/examples/scripts/spm_CMAES.py b/examples/scripts/spm_CMAES.py index ed38144a9..7836d72a5 100644 --- a/examples/scripts/spm_CMAES.py +++ b/examples/scripts/spm_CMAES.py @@ -13,18 +13,20 @@ prior=pybop.Gaussian(6e-06, 0.1e-6), bounds=[1e-6, 9e-6], true_value=parameter_set["Negative particle radius [m]"], + transformation=pybop.LogTransformation(), ), pybop.Parameter( "Positive particle radius [m]", prior=pybop.Gaussian(4.5e-06, 0.1e-6), bounds=[1e-6, 9e-6], true_value=parameter_set["Positive particle radius [m]"], + transformation=pybop.LogTransformation(), ), ) # Generate data sigma = 0.001 -t_eval = np.arange(0, 900, 3) +t_eval = np.arange(0, 900, 5) values = model.predict(t_eval=t_eval) corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) @@ -42,7 +44,7 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.SumSquaredError(problem) -optim = pybop.CMAES(cost, max_iterations=100) +optim = pybop.CMAES(cost, sigma0=0.25, max_unchanged_iterations=10, max_iterations=40) # Run the optimisation x, final_cost = optim.run() @@ -62,7 +64,7 @@ pybop.plot_parameters(optim) # Plot the cost landscape -pybop.plot2d(cost, steps=15) +pybop.plot2d(cost, steps=5) # Plot the cost landscape with optimisation path -pybop.plot2d(optim, steps=15) +pybop.plot2d(optim, steps=5) diff --git a/examples/scripts/spm_IRPropMin.py b/examples/scripts/spm_IRPropMin.py index 1969f6f9d..738c6a6b4 100644 --- a/examples/scripts/spm_IRPropMin.py +++ b/examples/scripts/spm_IRPropMin.py @@ -1,10 +1,12 @@ import numpy as np +import pybamm import pybop -# Define model +# Define model and use high-performant solver for sensitivities +solver = pybamm.IDAKLUSolver() parameter_set = pybop.ParameterSet.pybamm("Chen2020") -model = pybop.lithium_ion.SPM(parameter_set=parameter_set) +model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) # Fitting parameters parameters = pybop.Parameters( @@ -35,7 +37,7 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.SumSquaredError(problem) +cost = pybop.Minkowski(problem, p=2) optim = pybop.IRPropMin(cost, max_iterations=100) x, final_cost = optim.run() @@ -52,4 +54,4 @@ # Plot the cost landscape with optimisation path bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]]) -pybop.plot2d(optim, bounds=bounds, steps=15) +pybop.plot2d(optim, gradient=True, bounds=bounds, steps=15) diff --git a/examples/scripts/spm_MAP.py b/examples/scripts/spm_MAP.py index f613eccba..dd8216674 100644 --- a/examples/scripts/spm_MAP.py +++ b/examples/scripts/spm_MAP.py @@ -2,12 +2,15 @@ import pybop +# Set variables +sigma = 0.002 + # Construct and update initial parameter values parameter_set = pybop.ParameterSet.pybamm("Chen2020") parameter_set.update( { - "Negative electrode active material volume fraction": 0.63, - "Positive electrode active material volume fraction": 0.51, + "Negative electrode active material volume fraction": 0.43, + "Positive electrode active material volume fraction": 0.56, } ) @@ -18,28 +21,38 @@ parameters = pybop.Parameters( pybop.Parameter( "Negative electrode active material volume fraction", - prior=pybop.Gaussian(0.6, 0.05), - bounds=[0.5, 0.8], + prior=pybop.Uniform(0.3, 0.8), + bounds=[0.3, 0.8], + initial_value=0.653, true_value=parameter_set["Negative electrode active material volume fraction"], ), pybop.Parameter( "Positive electrode active material volume fraction", - prior=pybop.Gaussian(0.48, 0.05), + prior=pybop.Uniform(0.3, 0.8), bounds=[0.4, 0.7], + initial_value=0.657, true_value=parameter_set["Positive electrode active material volume fraction"], ), ) -# Generate data -sigma = 0.005 -t_eval = np.arange(0, 900, 3) -values = model.predict(t_eval=t_eval) -corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) +# Generate data and corrupt it with noise +experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (4 second period)", + "Charge at 0.5C for 3 minutes (4 second period)", + ), + ] +) +values = model.predict(initial_state={"Initial SoC": 0.7}, experiment=experiment) +corrupt_values = values["Voltage [V]"].data + np.random.normal( + 0, sigma, len(values["Voltage [V]"].data) +) # Form dataset dataset = pybop.Dataset( { - "Time [s]": t_eval, + "Time [s]": values["Time [s]"].data, "Current function [A]": values["Current [A]"].data, "Voltage [V]": corrupt_values, } @@ -47,9 +60,10 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.MAP(problem, pybop.GaussianLogLikelihoodKnownSigma, sigma0=sigma) -optim = pybop.AdamW( +cost = pybop.LogPosterior(pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=sigma)) +optim = pybop.IRPropMin( cost, + sigma0=0.05, max_unchanged_iterations=20, min_iterations=20, max_iterations=100, @@ -61,7 +75,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(problem, problem_inputs=x[0:2], title="Optimised Comparison") +pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -73,5 +87,5 @@ pybop.plot2d(cost, steps=15) # Plot the cost landscape with optimisation path -bounds = np.asarray([[0.55, 0.77], [0.48, 0.68]]) +bounds = np.asarray([[0.35, 0.7], [0.45, 0.625]]) pybop.plot2d(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/spm_MLE.py b/examples/scripts/spm_MLE.py index 9c9b3f368..20b6b1632 100644 --- a/examples/scripts/spm_MLE.py +++ b/examples/scripts/spm_MLE.py @@ -2,8 +2,14 @@ import pybop -# Define model +# Define model and set initial parameter values parameter_set = pybop.ParameterSet.pybamm("Chen2020") +parameter_set.update( + { + "Negative electrode active material volume fraction": 0.63, + "Positive electrode active material volume fraction": 0.51, + } +) model = pybop.lithium_ion.SPM(parameter_set=parameter_set) # Fitting parameters @@ -19,31 +25,39 @@ ), ) -# Set initial parameter values -parameter_set.update( - { - "Negative electrode active material volume fraction": 0.63, - "Positive electrode active material volume fraction": 0.51, - } -) # Generate data -sigma = 0.005 -t_eval = np.arange(0, 900, 3) -values = model.predict(t_eval=t_eval) -corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) +sigma = 0.002 +experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (3 second period)", + "Charge at 0.5C for 3 minutes (3 second period)", + ), + ] +) +values = model.predict(initial_state={"Initial SoC": 0.5}, experiment=experiment) + + +def noise(sigma): + return np.random.normal(0, sigma, len(values["Voltage [V]"].data)) + # Form dataset dataset = pybop.Dataset( { - "Time [s]": t_eval, + "Time [s]": values["Time [s]"].data, "Current function [A]": values["Current [A]"].data, - "Voltage [V]": corrupt_values, + "Voltage [V]": values["Voltage [V]"].data + noise(sigma), + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data + + noise(sigma), } ) + +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem(model, parameters, dataset) -likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=sigma) +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +likelihood = pybop.GaussianLogLikelihood(problem, sigma0=sigma * 4) optim = pybop.IRPropMin( likelihood, max_unchanged_iterations=20, diff --git a/examples/scripts/spm_NelderMead.py b/examples/scripts/spm_NelderMead.py index e07801e04..09a06f666 100644 --- a/examples/scripts/spm_NelderMead.py +++ b/examples/scripts/spm_NelderMead.py @@ -19,7 +19,6 @@ ) # Generate data -init_soc = 0.5 sigma = 0.003 experiment = pybop.Experiment( [ @@ -30,7 +29,7 @@ ] * 2 ) -values = model.predict(init_soc=init_soc, experiment=experiment) +values = model.predict(initial_state={"Initial SoC": 0.5}, experiment=experiment) def noise(sigma): @@ -48,11 +47,9 @@ def noise(sigma): } ) -signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=init_soc -) +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.RootMeanSquaredError(problem) optim = pybop.NelderMead( cost, diff --git a/examples/scripts/spm_SNES.py b/examples/scripts/spm_SNES.py index 93046d63a..7cddfad91 100644 --- a/examples/scripts/spm_SNES.py +++ b/examples/scripts/spm_SNES.py @@ -35,7 +35,7 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.SumSquaredError(problem) +cost = pybop.SumofPower(problem, p=2) optim = pybop.SNES(cost, max_iterations=100) x, final_cost = optim.run() diff --git a/examples/scripts/spm_UKF.py b/examples/scripts/spm_UKF.py index c69883b23..bc7b8e77e 100644 --- a/examples/scripts/spm_UKF.py +++ b/examples/scripts/spm_UKF.py @@ -22,7 +22,7 @@ # Make a prediction with measurement noise sigma = 0.001 -t_eval = np.arange(0, 900, 0.5) +t_eval = np.arange(0, 250, 0.5) values = model.predict(t_eval=t_eval) corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) @@ -36,12 +36,9 @@ ) # Build the model to get the number of states -model.build(dataset=dataset.data, parameters=parameters) +model.build(dataset=dataset, parameters=parameters) # Define the UKF observer, setting the particle boundaries as uncertain states -signal = ["Voltage [V]"] -n_states = model.n_states -n_signals = len(signal) covariance = np.diag([0] * 20 + [sigma**2] + [0] * 20 + [sigma**2]) process_noise = np.diag([0] * 20 + [1e-6] + [0] * 20 + [1e-6]) measurement_noise = np.diag([sigma**2]) @@ -52,12 +49,11 @@ process_noise, measurement_noise, dataset, - signal=signal, ) # Generate problem, cost function, and optimisation class cost = pybop.ObserverCost(observer) -optim = pybop.PSO(cost, verbose=True) +optim = pybop.XNES(cost, verbose=True) # Parameter identification using the current observer implementation is very slow # so let's restrict the number of iterations and reduce the number of plots diff --git a/examples/scripts/spm_descent.py b/examples/scripts/spm_descent.py index 94573f0c0..37c4a3c1e 100644 --- a/examples/scripts/spm_descent.py +++ b/examples/scripts/spm_descent.py @@ -1,10 +1,12 @@ import numpy as np +import pybamm import pybop -# Parameter set and model definition +# Define model and use high-performant solver for sensitivities +solver = pybamm.IDAKLUSolver() parameter_set = pybop.ParameterSet.pybamm("Chen2020") -model = pybop.lithium_ion.SPM(parameter_set=parameter_set) +model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) # Fitting parameters parameters = pybop.Parameters( @@ -35,10 +37,10 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.SumSquaredError(problem) +cost = pybop.RootMeanSquaredError(problem) optim = pybop.GradientDescent( cost, - sigma0=0.011, + sigma0=0.05, verbose=True, max_iterations=125, ) diff --git a/examples/scripts/spm_scipymin.py b/examples/scripts/spm_scipymin.py index b6cec3f08..fdf208df7 100644 --- a/examples/scripts/spm_scipymin.py +++ b/examples/scripts/spm_scipymin.py @@ -34,7 +34,7 @@ # Define the cost to optimise signal = ["Voltage [V]"] -problem = pybop.FittingProblem(model, parameters, dataset, signal=signal, init_soc=0.98) +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.RootMeanSquaredError(problem) # Build the optimisation problem diff --git a/examples/scripts/spm_weighted_cost.py b/examples/scripts/spm_weighted_cost.py new file mode 100644 index 000000000..d34d2a3c6 --- /dev/null +++ b/examples/scripts/spm_weighted_cost.py @@ -0,0 +1,73 @@ +import numpy as np + +import pybop + +# Parameter set and model definition +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +model = pybop.lithium_ion.SPM(parameter_set=parameter_set) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + bounds=[0.5, 0.8], + true_value=parameter_set["Negative electrode active material volume fraction"], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.58, 0.05), + bounds=[0.4, 0.7], + true_value=parameter_set["Positive electrode active material volume fraction"], + ), +) + +# Generate data +sigma = 0.001 +experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (3 second period)", + "Charge at 0.5C for 3 minutes (3 second period)", + ), + ] + * 2 +) +values = model.predict(experiment=experiment, initial_state={"Initial SoC": 0.5}) + + +def noise(sigma): + return np.random.normal(0, sigma, len(values["Voltage [V]"].data)) + + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data + noise(sigma), + } +) + +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset) +cost1 = pybop.SumSquaredError(problem) +cost2 = pybop.RootMeanSquaredError(problem) +weighted_cost = pybop.WeightedCost(cost1, cost2, weights=[0.1, 1]) + +for cost in [weighted_cost, cost1, cost2]: + optim = pybop.IRPropMin(cost, max_iterations=60) + + # Run the optimisation + x, final_cost = optim.run() + print("True parameters:", parameters.true_value()) + print("Estimated parameters:", x) + + # Plot the timeseries output + pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison") + + # Plot convergence + pybop.plot_convergence(optim) + + # Plot the cost landscape with optimisation path + pybop.plot2d(optim, steps=15) diff --git a/examples/scripts/spme_max_energy.py b/examples/scripts/spme_max_energy.py index f5b7c827c..51e5bf535 100644 --- a/examples/scripts/spme_max_energy.py +++ b/examples/scripts/spme_max_energy.py @@ -9,12 +9,6 @@ # electrode widths, particle radii, volume fractions and # separator width. -# NOTE: This script can be easily adjusted to consider the volumetric -# (instead of gravimetric) energy density by changing the line which -# defines the cost and changing the output to: -# print(f"Initial volumetric energy density: {cost(optim.x0):.2f} Wh.m-3") -# print(f"Optimised volumetric energy density: {final_cost:.2f} Wh.m-3") - # Define parameter set and model parameter_set = pybop.ParameterSet.pybamm("Chen2020", formation_concentrations=True) model = pybop.lithium_ion.SPMe(parameter_set=parameter_set) @@ -37,31 +31,36 @@ experiment = pybop.Experiment( ["Discharge at 1C until 2.5 V (5 seconds period)"], ) -init_soc = 1 # start from full charge signal = ["Voltage [V]", "Current [A]"] # Generate problem problem = pybop.DesignProblem( - model, parameters, experiment, signal=signal, init_soc=init_soc + model, + parameters, + experiment, + signal=signal, + initial_state={"Initial SoC": 1.0}, + update_capacity=True, ) -# Generate cost function and optimisation class: -cost = pybop.GravimetricEnergyDensity(problem) -optim = pybop.PSO( - cost, verbose=True, allow_infeasible_solutions=False, max_iterations=15 -) +# Generate multiple cost functions and combine them +cost1 = pybop.GravimetricEnergyDensity(problem) +cost2 = pybop.VolumetricEnergyDensity(problem) +cost = pybop.WeightedCost(cost1, cost2, weights=[1, 1e-3]) # Run optimisation +optim = pybop.PSO( + cost, verbose=True, allow_infeasible_solutions=False, max_iterations=10 +) x, final_cost = optim.run() print("Estimated parameters:", x) -print(f"Initial gravimetric energy density: {cost(optim.x0):.2f} Wh.kg-1") -print(f"Optimised gravimetric energy density: {final_cost:.2f} Wh.kg-1") +print(f"Initial gravimetric energy density: {cost1(optim.x0):.2f} Wh.kg-1") +print(f"Optimised gravimetric energy density: {cost1(x):.2f} Wh.kg-1") +print(f"Initial volumetric energy density: {cost2(optim.x0):.2f} Wh.m-3") +print(f"Optimised volumetric energy density: {cost2(x):.2f} Wh.m-3") # Plot the timeseries output -if cost.update_capacity: - problem._model.approximate_capacity(x) pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison") # Plot the cost landscape with optimisation path -if len(x) == 2: - pybop.plot2d(optim, steps=3) +pybop.plot2d(optim, steps=5) diff --git a/examples/standalone/cost.py b/examples/standalone/cost.py index 99917f3fd..1d76c88f8 100644 --- a/examples/standalone/cost.py +++ b/examples/standalone/cost.py @@ -1,3 +1,5 @@ +import numpy as np + import pybop @@ -21,7 +23,7 @@ class StandaloneCost(pybop.BaseCost): Methods ------- - __call__(x, grad=None) + __call__(x) Calculate the cost for a given parameter value. """ @@ -43,25 +45,18 @@ def __init__(self, problem=None): ) self.x0 = self.parameters.initial_value() - def _evaluate(self, inputs, grad=None): + def compute( + self, y: dict = None, dy: np.ndarray = None, calculate_grad: bool = False + ): """ - Calculate the cost for a given parameter value. + Compute the cost for a given parameter value. The cost function is defined as cost(x) = x^2 + 42, where x is the parameter value. - Parameters - ---------- - inputs : Dict - The parameters for which to evaluate the cost. - grad : array-like, optional - Unused parameter, present for compatibility with gradient-based - optimizers. - Returns ------- float The calculated cost value for the given parameter. """ - - return inputs["x"] ** 2 + 42 + return self.parameters["x"].value ** 2 + 42 diff --git a/examples/standalone/model.py b/examples/standalone/model.py index f5d5a7ab2..82a9f2d87 100644 --- a/examples/standalone/model.py +++ b/examples/standalone/model.py @@ -48,11 +48,11 @@ def __init__( self._parameter_set = self.default_parameter_values self._unprocessed_parameter_set = self._parameter_set - self.geometry = self.pybamm_model.default_geometry - self.submesh_types = self.pybamm_model.default_submesh_types - self.var_pts = self.pybamm_model.default_var_pts - self.spatial_methods = self.pybamm_model.default_spatial_methods - self.solver = pybamm.CasadiSolver(mode="fast") + self._geometry = self.pybamm_model.default_geometry + self._submesh_types = self.pybamm_model.default_submesh_types + self._var_pts = self.pybamm_model.default_var_pts + self._spatial_methods = self.pybamm_model.default_spatial_methods + self._solver = pybamm.CasadiSolver(mode="fast") self._model_with_set_params = None self._built_model = None self._built_initial_soc = None diff --git a/examples/standalone/problem.py b/examples/standalone/problem.py index d76f9dca5..ecdfd0644 100644 --- a/examples/standalone/problem.py +++ b/examples/standalone/problem.py @@ -16,27 +16,25 @@ def __init__( check_model=True, signal=None, additional_variables=None, - init_soc=None, + initial_state=None, ): - super().__init__( - parameters, model, check_model, signal, additional_variables, init_soc - ) + super().__init__(parameters, model, check_model, signal, additional_variables) self._dataset = dataset.data # Check that the dataset contains time and current - for name in ["Time [s]"] + self.signal: + for name in ["Time [s]", *self.signal]: if name not in self._dataset: raise ValueError(f"expected {name} in list of dataset") - self._time_data = self._dataset["Time [s]"] - self.n_time_data = len(self._time_data) - if np.any(self._time_data < 0): + self._domain_data = self._dataset[self.domain] + self.n_data = len(self._domain_data) + if np.any(self._domain_data < 0): raise ValueError("Times can not be negative.") - if np.any(self._time_data[:-1] >= self._time_data[1:]): + if np.any(self._domain_data[:-1] >= self._domain_data[1:]): raise ValueError("Times must be increasing.") for signal in self.signal: - if len(self._dataset[signal]) != self.n_time_data: + if len(self._dataset[signal]) != self.n_data: raise ValueError( f"Time data and {signal} data must be the same length." ) @@ -58,7 +56,7 @@ def evaluate(self, inputs): """ return { - signal: inputs["Gradient"] * self._time_data + inputs["Intercept"] + signal: inputs["Gradient"] * self._domain_data + inputs["Intercept"] for signal in self.signal } @@ -80,7 +78,7 @@ def evaluateS1(self, inputs): y = self.evaluate(inputs) - dy = np.zeros((self.n_time_data, self.n_outputs, self.n_parameters)) - dy[:, 0, 0] = self._time_data + dy = np.zeros((self.n_data, self.n_outputs, self.n_parameters)) + dy[:, 0, 0] = self._domain_data return (y, dy) diff --git a/noxfile.py b/noxfile.py index 1cbbd08f9..d2e8adadb 100644 --- a/noxfile.py +++ b/noxfile.py @@ -83,9 +83,27 @@ def notebooks(session): ) +@nox.session(name="notebooks-overwrite") +def notebooks_overwrite(session): + """Run the Jupyter notebooks.""" + session.install("setuptools", "wheel") + session.install("-e", ".[all,dev]", silent=False) + if PYBOP_SCHEDULED: + session.run("pip", "install", f"pybamm=={PYBAMM_VERSION}", silent=False) + session.run( + "pytest", + "--notebooks", + "--nbmake", + "--overwrite", + "--nbmake-timeout=1000", + "examples/", + ) + + @nox.session(name="tests") def run_tests(session): """Run all the tests.""" + session.install("setuptools", "wheel") session.install("-e", ".[all,dev]", silent=False) if PYBOP_SCHEDULED: session.run("pip", "install", f"pybamm=={PYBAMM_VERSION}", silent=False) diff --git a/pybop/__init__.py b/pybop/__init__.py index 92c869f48..6863ccf8d 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -43,7 +43,7 @@ # # Utilities # -from ._utils import is_numeric +from ._utils import is_numeric, SymbolReplacer # # Experiment class @@ -55,12 +55,23 @@ # from ._dataset import Dataset +# +# Transformation classes +# +from .transformation.base_transformation import Transformation +from .transformation.transformations import ( + IdentityTransformation, + ScaledTransformation, + LogTransformation, + ComposedTransformation, +) + # # Parameter classes # from .parameters.parameter import Parameter, Parameters from .parameters.parameter_set import ParameterSet -from .parameters.priors import BasePrior, Gaussian, Uniform, Exponential +from .parameters.priors import BasePrior, Gaussian, Uniform, Exponential, JointLogPrior # # Model classes @@ -72,19 +83,22 @@ from .models.base_model import Inputs # -# Problem class +# Problem classes # from .problems.base_problem import BaseProblem from .problems.fitting_problem import FittingProblem +from .problems.multi_fitting_problem import MultiFittingProblem from .problems.design_problem import DesignProblem # -# Cost function class +# Cost classes # from .costs.base_cost import BaseCost from .costs.fitting_costs import ( RootMeanSquaredError, SumSquaredError, + Minkowski, + SumofPower, ObserverCost, ) from .costs.design_costs import ( @@ -96,11 +110,12 @@ BaseLikelihood, GaussianLogLikelihood, GaussianLogLikelihoodKnownSigma, - MAP, + LogPosterior, ) +from .costs._weighted_cost import WeightedCost # -# Optimiser class +# Optimiser classes # from .optimisers._cuckoo import CuckooSearchImpl @@ -126,6 +141,24 @@ ) from .optimisers.optimisation import Optimisation +# +# Monte Carlo classes +# +from .samplers.base_sampler import BaseSampler +from .samplers.base_pints_sampler import BasePintsSampler +from .samplers.pints_samplers import ( + NUTS, DREAM, AdaptiveCovarianceMCMC, + DifferentialEvolutionMCMC, DramACMC, + EmceeHammerMCMC, + HaarioACMC, HaarioBardenetACMC, + HamiltonianMCMC, MALAMCMC, + MetropolisRandomWalkMCMC, MonomialGammaHamiltonianMCMC, + PopulationMCMC, RaoBlackwellACMC, + RelativisticMCMC, SliceDoublingMCMC, + SliceRankShrinkingMCMC, SliceStepoutMCMC, +) +from .samplers.mcmc_sampler import MCMCSampler + # # Observer classes # @@ -133,15 +166,16 @@ from .observers.observer import Observer # -# Plotting class +# Plotting classes # from .plotting.plotly_manager import PlotlyManager -from .plotting.quick_plot import StandardPlot, StandardSubplot, plot_trajectories +from .plotting.standard_plots import StandardPlot, StandardSubplot, plot_trajectories from .plotting.plot2d import plot2d from .plotting.plot_dataset import plot_dataset from .plotting.plot_convergence import plot_convergence from .plotting.plot_parameters import plot_parameters from .plotting.plot_problem import quick_plot +from .plotting.nyquist import nyquist # # Remove any imported modules, so we don't expose them as part of pybop diff --git a/pybop/_dataset.py b/pybop/_dataset.py index 0da8be4be..120fcb61d 100644 --- a/pybop/_dataset.py +++ b/pybop/_dataset.py @@ -1,6 +1,7 @@ +from typing import Union + import numpy as np -from pybamm import Interpolant, solvers -from pybamm import t as pybamm_t +from pybamm import solvers class Dataset: @@ -77,58 +78,68 @@ def __getitem__(self, key): return self.data[key] - def Interpolant(self): - """ - Create an interpolation function of the dataset based on the independent variable. - - Currently, only time-based interpolation is supported. This method modifies - the instance's Interpolant attribute to be an interpolation function that - can be evaluated at different points in time. - - Raises - ------ - NotImplementedError - If the independent variable for interpolation is not supported. - """ - - if self.variable == "time": - self.Interpolant = Interpolant(self.x, self.y, pybamm_t) - else: - NotImplementedError("Only time interpolation is supported") - - def check(self, signal=["Voltage [V]"]): + def check(self, domain: str = None, signal: Union[str, list[str]] = None) -> bool: """ Check the consistency of a PyBOP Dataset against the expected format. + Parameters + ---------- + domain : str, optional + The domain of the dataset. Defaults to "Time [s]". + signal : str or List[str], optional + The signal(s) to check. Defaults to ["Voltage [V]"]. + Returns ------- bool - If True, the dataset has the expected attributes. + True if the dataset has the expected attributes. Raises ------ ValueError If the time series and the data series are not consistent. """ - if isinstance(signal, str): - signal = [signal] + self.domain = domain or "Time [s]" + signals = [signal] if isinstance(signal, str) else (signal or ["Voltage [V]"]) + + # Check that the dataset contains domain and chosen signals + missing_attributes = set([self.domain, *signals]) - set(self.names) + if missing_attributes: + raise ValueError( + f"Expected {', '.join(missing_attributes)} in list of dataset" + ) + + domain_data = self.data[self.domain] + + # Check domain-specific constraints + if self.domain == "Time [s]": + self._check_time_constraints(domain_data) + elif self.domain == "Frequency [Hz]": + self._check_frequency_constraints(domain_data) - # Check that the dataset contains time and chosen signal - for name in ["Time [s]"] + signal: - if name not in self.names: - raise ValueError(f"expected {name} in list of dataset") + # Check for consistent data length + self._check_data_consistency(domain_data, signals) - # Check for increasing times - time_data = self.data["Time [s]"] + return True + + @staticmethod + def _check_time_constraints(time_data: np.ndarray) -> None: if np.any(time_data < 0): - raise ValueError("Times can not be negative.") + raise ValueError("Times cannot be negative.") if np.any(time_data[:-1] >= time_data[1:]): raise ValueError("Times must be increasing.") - # Check for consistent data - n_time_data = len(time_data) - for s in signal: - if len(self.data[s]) != n_time_data: - raise ValueError(f"Time data and {s} data must be the same length.") - - return True + @staticmethod + def _check_frequency_constraints(freq_data: np.ndarray) -> None: + if np.any(freq_data < 0): + raise ValueError("Frequencies cannot be negative.") + + def _check_data_consistency( + self, domain_data: np.ndarray, signals: list[str] + ) -> None: + n_domain_data = len(domain_data) + for s in signals: + if len(self.data[s]) != n_domain_data: + raise ValueError( + f"{self.domain} data and {s} data must be the same length." + ) diff --git a/pybop/_experiment.py b/pybop/_experiment.py index a651dffc2..b674c14dc 100644 --- a/pybop/_experiment.py +++ b/pybop/_experiment.py @@ -3,7 +3,7 @@ class Experiment(Experiment): """ - Wraps the Experiment class for generating experiment conditions for PyBaMM models. + Light wrapper of the PyBaMM Experiment class for generating experiment conditions for PyBaMM models. Credit: PyBaMM Base class for experimental conditions under which to run the model. In general, a @@ -38,11 +38,9 @@ class Experiment(Experiment): def __init__( self, operating_conditions, - period="1 minute", + period=None, temperature=None, termination=None, - drive_cycles=None, - cccv_handling=None, ): super().__init__( operating_conditions, diff --git a/pybop/_utils.py b/pybop/_utils.py index 6fbfeaab5..426b0d277 100644 --- a/pybop/_utils.py +++ b/pybop/_utils.py @@ -1,4 +1,7 @@ +from typing import Optional + import numpy as np +import pybamm def is_numeric(x): @@ -6,3 +9,168 @@ def is_numeric(x): Check if a variable is numeric. """ return isinstance(x, (int, float, np.number)) + + +class SymbolReplacer: + """ + Helper class to replace all instances of one or more symbols in an expression tree + with another symbol, as defined by the dictionary `symbol_replacement_map` + Originally developed by pybamm: https://github.com/pybamm-team/pybamm + + Parameters + ---------- + symbol_replacement_map : dict {:class:`pybamm.Symbol` -> :class:`pybamm.Symbol`} + Map of which symbols should be replaced by which. + processed_symbols: dict {:class:`pybamm.Symbol` -> :class:`pybamm.Symbol`}, optional + cached replaced symbols + process_initial_conditions: bool, optional + Whether to process initial conditions, default is True + """ + + def __init__( + self, + symbol_replacement_map: dict[pybamm.Symbol, pybamm.Symbol], + processed_symbols: Optional[dict[pybamm.Symbol, pybamm.Symbol]] = None, + process_initial_conditions: bool = True, + ): + self._symbol_replacement_map = symbol_replacement_map + self._processed_symbols = processed_symbols or {} + self._process_initial_conditions = process_initial_conditions + + def process_model(self, unprocessed_model, inplace=True): + """ + Replace all instances of a symbol in a PyBaMM model class. + + Parameters + ---------- + unprocessed_model : :class:`pybamm.BaseModel` + Model class to assign parameter values to + inplace: bool, optional + If True, replace the parameters in the model in place. Otherwise, return a + new model with parameter values set. Default is True. + """ + + model = unprocessed_model if inplace else unprocessed_model.new_copy() + + for variable, equation in unprocessed_model.rhs.items(): + pybamm.logger.verbose(f"Replacing symbols in {variable!r} (rhs)") + model.rhs[self.process_symbol(variable)] = self.process_symbol(equation) + + for variable, equation in unprocessed_model.algebraic.items(): + pybamm.logger.verbose(f"Replacing symbols in {variable!r} (algebraic)") + model.algebraic[self.process_symbol(variable)] = self.process_symbol( + equation + ) + + for variable, equation in unprocessed_model.initial_conditions.items(): + pybamm.logger.verbose( + f"Replacing symbols in {variable!r} (initial conditions)" + ) + if self._process_initial_conditions: + model.initial_conditions[self.process_symbol(variable)] = ( + self.process_symbol(equation) + ) + else: + model.initial_conditions[self.process_symbol(variable)] = equation + + model.boundary_conditions = self.process_boundary_conditions(unprocessed_model) + + for variable, equation in unprocessed_model.variables.items(): + pybamm.logger.verbose(f"Replacing symbols in {variable!r} (variables)") + model.variables[variable] = self.process_symbol(equation) + + model.events = self._process_events(unprocessed_model.events) + pybamm.logger.info(f"Finish replacing symbols in {model.name}") + + return model + + def _process_events(self, events: list) -> list: + new_events = [] + for event in events: + pybamm.logger.verbose(f"Replacing symbols in event '{event.name}'") + new_events.append( + pybamm.Event( + event.name, self.process_symbol(event.expression), event.event_type + ) + ) + return new_events + + def process_boundary_conditions(self, model): + """ + Process boundary conditions for a PybaMM model class + Boundary conditions are dictionaries {"left": left bc, "right": right bc} + in general, but may be imposed on the tabs (or *not* on the tab) for a + small number of variables, e.g. {"negative tab": neg. tab bc, + "positive tab": pos. tab bc "no tab": no tab bc}. + """ + boundary_conditions = {} + sides = ["left", "right", "negative tab", "positive tab", "no tab"] + for variable, bcs in model.boundary_conditions.items(): + processed_variable = self.process_symbol(variable) + boundary_conditions[processed_variable] = {} + + for side in sides: + try: + bc, typ = bcs[side] + pybamm.logger.verbose( + f"Replacing symbols in {variable!r} ({side} bc)" + ) + processed_bc = (self.process_symbol(bc), typ) + boundary_conditions[processed_variable][side] = processed_bc + except KeyError as err: + # Don't raise if side is not in the boundary conditions + if err.args[0] in side: + pass + # Raise otherwise + else: # pragma: no cover + raise KeyError(err) from err + + return boundary_conditions + + def process_symbol(self, symbol): + """ + This function recurses down the tree, replacing any symbols in + self._symbol_replacement_map.keys() with their corresponding value + + Parameters + ---------- + symbol : :class:`pybamm.Symbol` + The symbol to replace + + Returns + ------- + :class:`pybamm.Symbol` + Symbol with all replacements performed + """ + if symbol in self._processed_symbols: + return self._processed_symbols[symbol] + + processed_symbol = self._process_symbol(symbol) + self._processed_symbols[symbol] = processed_symbol + return processed_symbol + + def _process_symbol(self, symbol: pybamm.Symbol) -> pybamm.Symbol: + if symbol in self._symbol_replacement_map: + return self._symbol_replacement_map[symbol] + + if isinstance(symbol, pybamm.BinaryOperator): + # process children + new_left = self.process_symbol(symbol.left) + new_right = self.process_symbol(symbol.right) + return symbol._binary_new_copy(new_left, new_right) # noqa: SLF001 + + if isinstance(symbol, pybamm.UnaryOperator): + new_child = self.process_symbol(symbol.child) + return symbol._unary_new_copy(new_child) # noqa: SLF001 + + if isinstance(symbol, pybamm.Function): + new_children = [self.process_symbol(child) for child in symbol.children] + # Return a new copy with the replaced symbols + return symbol._function_new_copy(new_children) # noqa: SLF001 + + if isinstance(symbol, pybamm.Concatenation): + new_children = [self.process_symbol(child) for child in symbol.children] + return symbol._concatenation_new_copy(new_children) # noqa: SLF001 + + # Return leaf + return symbol diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index c0f580a2a..153e27f15 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -1,10 +1,12 @@ -from typing import List, Tuple, Union +from typing import Optional, Union import numpy as np +import scipy.stats as stats +import pybop from pybop.costs.base_cost import BaseCost -from pybop.parameters.parameter import Inputs, Parameter, Parameters -from pybop.parameters.priors import Uniform +from pybop.parameters.parameter import Parameter, Parameters +from pybop.parameters.priors import BasePrior, JointLogPrior, Uniform from pybop.problems.base_problem import BaseProblem @@ -14,8 +16,8 @@ class BaseLikelihood(BaseCost): """ def __init__(self, problem: BaseProblem): - super(BaseLikelihood, self).__init__(problem) - self.n_time_data = problem.n_time_data + super().__init__(problem) + self.n_data = problem.n_data class GaussianLogLikelihoodKnownSigma(BaseLikelihood): @@ -32,53 +34,40 @@ class GaussianLogLikelihoodKnownSigma(BaseLikelihood): per dimension. """ - def __init__(self, problem: BaseProblem, sigma0: Union[List[float], float]): - super(GaussianLogLikelihoodKnownSigma, self).__init__(problem) + def __init__(self, problem: BaseProblem, sigma0: Union[list[float], float]): + super().__init__(problem) sigma0 = self.check_sigma0(sigma0) self.sigma2 = sigma0**2.0 - self._offset = -0.5 * self.n_time_data * np.log(2 * np.pi * self.sigma2) + self._offset = -0.5 * self.n_data * np.log(2 * np.pi * self.sigma2) self._multip = -1 / (2.0 * self.sigma2) - self._dl = np.ones(self.n_parameters) - def _evaluate(self, inputs: Inputs, grad: Union[None, np.ndarray] = None) -> float: - """ - Evaluates the Gaussian log-likelihood for the given parameters with known sigma. + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - y = self.problem.evaluate(inputs) - if any( - len(y.get(key, [])) != len(self._target.get(key, [])) for key in self.signal - ): - return -np.inf # prediction length doesn't match target - - e = np.sum( - [ - np.sum( - self._offset - + self._multip * np.sum((self._target[signal] - y[signal]) ** 2.0) - ) - for signal in self.signal - ] - ) + Compute the Gaussian log-likelihood for the given parameters with known sigma. - return e if self.n_outputs != 1 else e.item() - - def _evaluateS1(self, inputs: Inputs) -> Tuple[float, np.ndarray]: - """ - Calls the problem.evaluateS1 method and calculates the log-likelihood and gradient. + This method only computes the likelihood, without calling the problem.evaluateS1. """ - y, dy = self.problem.evaluateS1(inputs) + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) - if any( - len(y.get(key, [])) != len(self._target.get(key, [])) for key in self.signal - ): - return -np.inf, -self._dl - - likelihood = self._evaluate(inputs) + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (-np.inf, -self.grad_fail) if calculate_grad else -np.inf + # Calculate residuals and error r = np.asarray([self._target[signal] - y[signal] for signal in self.signal]) - dl = np.sum((np.sum((r * dy.T), axis=2) / self.sigma2), axis=1) + e = np.sum(self._offset + self._multip * np.sum(np.real(r * np.conj(r)))) + + if calculate_grad: + dl = np.sum((np.sum((r * dy.T), axis=2) / self.sigma2), axis=1) + return e, dl - return likelihood, dl + return e def check_sigma0(self, sigma0: Union[np.ndarray, float]): """ @@ -90,7 +79,7 @@ def check_sigma0(self, sigma0: Union[np.ndarray, float]): if np.shape(sigma0) not in [(), (1,), (self.n_outputs,)]: raise ValueError( "sigma0 must be either a scalar value (one standard deviation for " - + "all coordinates) or an array with one entry per dimension." + "all coordinates) or an array with one entry per dimension." ) return sigma0 @@ -115,20 +104,19 @@ class GaussianLogLikelihood(BaseLikelihood): def __init__( self, problem: BaseProblem, - sigma0: Union[float, List[float], List[Parameter]] = 0.002, + sigma0: Union[float, list[float], list[Parameter]] = 1e-2, dsigma_scale: float = 1.0, ): - super(GaussianLogLikelihood, self).__init__(problem) + super().__init__(problem) self._dsigma_scale = dsigma_scale - self._logpi = -0.5 * self.n_time_data * np.log(2 * np.pi) + self._logpi = -0.5 * self.n_data * np.log(2 * np.pi) self.sigma = Parameters() self._add_sigma_parameters(sigma0) self.parameters.join(self.sigma) - self._dl = np.ones(self.n_parameters) def _add_sigma_parameters(self, sigma0): - sigma0 = [sigma0] if not isinstance(sigma0, List) else sigma0 + sigma0 = [sigma0] if not isinstance(sigma0, list) else sigma0 sigma0 = self._pad_sigma0(sigma0) for i, value in enumerate(sigma0): @@ -152,6 +140,7 @@ def _add_single_sigma(self, index, value): f"Sigma for output {index+1}", initial_value=value, prior=Uniform(0.5 * value, 1.5 * value), + bounds=[1e-8, 3 * value], ) ) else: @@ -173,185 +162,160 @@ def dsigma_scale(self, new_value): raise ValueError("dsigma_scale must be non-negative") self._dsigma_scale = new_value - def _evaluate(self, inputs: Inputs, grad: Union[None, np.ndarray] = None) -> float: + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Evaluates the Gaussian log-likelihood for the given parameters. + Compute the Gaussian log-likelihood for the given parameters. - Parameters - ---------- - inputs : Inputs - The parameters for which to evaluate the log-likelihood, including the `n_outputs` - standard deviations of the Gaussian distributions. + This method only computes the likelihood, without calling problem.evaluate(). Returns ------- float The log-likelihood value, or -inf if the standard deviations are non-positive. """ - self.parameters.update(values=list(inputs.values())) - + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) sigma = self.sigma.current_value() - if np.any(sigma <= 0): - return -np.inf - y = self.problem.evaluate(self.problem.parameters.as_dict()) - if any( - len(y.get(key, [])) != len(self._target.get(key, [])) for key in self.signal - ): - return -np.inf # prediction length doesn't match target + if not self.verify_prediction(y): + return (-np.inf, -self.grad_fail) if calculate_grad else -np.inf + # Calculate residuals and error + r = np.asarray([self._target[signal] - y[signal] for signal in self.signal]) e = np.sum( - [ - np.sum( - self._logpi - - self.n_time_data * np.log(sigma) - - np.sum((self._target[signal] - y[signal]) ** 2.0) - / (2.0 * sigma**2.0) - ) - for signal in self.signal - ] + self._logpi + - self.n_data * np.log(sigma) + - np.sum(np.real(r * np.conj(r)), axis=1) / (2.0 * sigma**2.0) ) - return e if self.n_outputs != 1 else e.item() - - def _evaluateS1(self, inputs: Inputs) -> Tuple[float, np.ndarray]: - """ - Calls the problem.evaluateS1 method and calculates the log-likelihood. - - Parameters - ---------- - inputs : Inputs - The parameters for which to evaluate the log-likelihood. - - Returns - ------- - Tuple[float, np.ndarray] - The log-likelihood and its gradient. - """ - self.parameters.update(values=list(inputs.values())) - - sigma = self.sigma.current_value() - if np.any(sigma <= 0): - return -np.inf, -self._dl - - y, dy = self.problem.evaluateS1(self.problem.parameters.as_dict()) - if any( - len(y.get(key, [])) != len(self._target.get(key, [])) for key in self.signal - ): - return -np.inf, -self._dl - - likelihood = self._evaluate(inputs) - - r = np.asarray([self._target[signal] - y[signal] for signal in self.signal]) - dl = np.sum((np.sum((r * dy.T), axis=2) / (sigma**2.0)), axis=1) - dsigma = ( - -self.n_time_data / sigma + np.sum(r**2.0, axis=1) / (sigma**3.0) - ) / self._dsigma_scale - dl = np.concatenate((dl.flatten(), dsigma)) + if calculate_grad: + dl = np.sum((np.sum((r * dy.T), axis=2) / (sigma**2.0)), axis=1) + dsigma = ( + -self.n_data / sigma + np.sum(r**2.0, axis=1) / (sigma**3.0) + ) / self._dsigma_scale + dl = np.concatenate((dl.flatten(), dsigma)) + return e, dl - return likelihood, dl + return e -class MAP(BaseLikelihood): +class LogPosterior(BaseLikelihood): """ - Maximum a posteriori cost function. + The Log Posterior for a given problem. - Computes the maximum a posteriori cost function, which is the sum of the - log likelihood and the log prior. The goal of maximising is achieved by - setting minimising = False in the optimiser settings. - - Inherits all parameters and attributes from ``BaseLikelihood``. + Computes the log posterior which is proportional to the sum of the log + likelihood and the log prior. + Parameters + ---------- + log_likelihood : BaseLikelihood + The likelihood class of type ``BaseLikelihood``. + log_prior : Optional, Union[pybop.BasePrior, stats.rv_continuous] + The prior class of type ``BasePrior`` or ``stats.rv_continuous``. + If not provided, the prior class will be taken from the parameter priors + constructed in the `pybop.Parameters` class. + gradient_step : float, default: 1e-3 + The step size for the finite-difference gradient calculation + if the ``log_prior`` is not of type ``BasePrior``. """ - def __init__(self, problem, likelihood, sigma0=None, gradient_step=1e-3): - super(MAP, self).__init__(problem) - self.sigma0 = sigma0 + def __init__( + self, + log_likelihood: BaseLikelihood, + log_prior: Optional[Union[pybop.BasePrior, stats.rv_continuous]] = None, + gradient_step: float = 1e-3, + ): + super().__init__(problem=log_likelihood.problem) self.gradient_step = gradient_step - if self.sigma0 is None: - self.sigma0 = [] - for param in self.problem.parameters: - self.sigma0.append(param.prior.sigma) - - try: - self.likelihood = likelihood(problem=self.problem, sigma0=self.sigma0) - except Exception as e: - raise ValueError( - f"An error occurred when constructing the Likelihood class: {e}" - ) - if hasattr(self, "likelihood") and not isinstance( - self.likelihood, BaseLikelihood - ): - raise ValueError(f"{self.likelihood} must be a subclass of BaseLikelihood") + # Store the likelihood and prior + self._log_likelihood = log_likelihood + self.parameters = self._log_likelihood.parameters + self._has_separable_problem = self._log_likelihood.has_separable_problem - def _evaluate(self, inputs: Inputs, grad=None) -> float: - """ - Calculate the maximum a posteriori cost for a given set of parameters. - - Parameters - ---------- - inputs : Inputs - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. - - Returns - ------- - float - The maximum a posteriori cost. - """ - log_likelihood = self.likelihood._evaluate(inputs) - log_prior = sum( - self.parameters[key].prior.logpdf(value) for key, value in inputs.items() - ) - - posterior = log_likelihood + log_prior - return posterior + if log_prior is None: + self._prior = JointLogPrior(*self.parameters.priors()) + else: + self._prior = log_prior - def _evaluateS1(self, inputs: Inputs) -> Tuple[float, np.ndarray]: + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Compute the maximum a posteriori with respect to the parameters. - The method passes the likelihood gradient to the optimiser without modification. + Calculate the posterior cost for a given forward model prediction. Parameters ---------- - inputs : Inputs - The parameters for which to compute the cost and gradient. + y : dict + The data for which to evaluate the cost. + dy : np.ndarray, optional + The correspond sensitivities in the data. + calculate_grad : bool, optional + Whether to calculate the gradient of the cost function. Returns ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. + Union[float, Tuple[float, np.ndarray]] + The posterior cost, and optionally the gradient. """ - log_likelihood, dl = self.likelihood._evaluateS1(inputs) - log_prior = sum( - self.parameters[key].prior.logpdf(value) for key, value in inputs.items() - ) + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) + + if calculate_grad: + if isinstance(self._prior, BasePrior): + log_prior, dp = self._prior.logpdfS1(self.parameters.current_value()) + else: + # Compute log prior first + log_prior = self._prior.logpdf(self.parameters.current_value()) + + # Compute a finite difference approximation of the gradient of the log prior + delta = self.parameters.initial_value() * self.gradient_step + dp = [] + + for parameter, step_size in zip(self.parameters, delta): + param_value = parameter.value + upper_value = param_value * (1 + step_size) + lower_value = param_value * (1 - step_size) + + log_prior_upper = parameter.prior.logpdf(upper_value) + log_prior_lower = parameter.prior.logpdf(lower_value) + + gradient = (log_prior_upper - log_prior_lower) / ( + 2 * step_size * param_value + np.finfo(float).eps + ) + dp.append(gradient) + else: + log_prior = self._prior.logpdf(self.parameters.current_value()) - # Compute a finite difference approximation of the gradient of the log prior - delta = self.parameters.initial_value() * self.gradient_step - prior_gradient = [] + if not np.isfinite(log_prior).any(): + return (-np.inf, -self.grad_fail) if calculate_grad else -np.inf - for parameter, step_size in zip(self.problem.parameters, delta): - param_value = inputs[parameter.name] + if calculate_grad: + log_likelihood, dl = self._log_likelihood.compute( + y, dy, calculate_grad=True + ) - log_prior_upper = parameter.prior.logpdf(param_value * (1 + step_size)) - log_prior_lower = parameter.prior.logpdf(param_value * (1 - step_size)) + posterior = log_likelihood + log_prior + total_gradient = dl + dp - gradient = (log_prior_upper - log_prior_lower) / ( - 2 * step_size * param_value + np.finfo(float).eps - ) - prior_gradient.append(gradient) + return posterior, total_gradient + log_likelihood = self._log_likelihood.compute(y) posterior = log_likelihood + log_prior - total_gradient = dl + prior_gradient + return posterior - return posterior, total_gradient + @property + def prior(self) -> BasePrior: + return self._prior + + @property + def likelihood(self) -> BaseLikelihood: + return self._log_likelihood diff --git a/pybop/costs/_weighted_cost.py b/pybop/costs/_weighted_cost.py new file mode 100644 index 000000000..10d7d5440 --- /dev/null +++ b/pybop/costs/_weighted_cost.py @@ -0,0 +1,127 @@ +from typing import Optional, Union + +import numpy as np + +from pybop import BaseCost, BaseLikelihood, DesignCost + + +class WeightedCost(BaseCost): + """ + A subclass for constructing a linear combination of cost functions as + a single weighted cost function. + + Inherits all parameters and attributes from ``BaseCost``. + + Attributes + --------------------- + costs : pybop.BaseCost + The individual PyBOP cost objects. + weights : list[float] + A list of values with which to weight the cost values. + has_identical_problems : bool + If True, the shared problem will be evaluated once and saved before the + self.compute() method of each cost is called (default: False). + has_separable_problem: bool + This attribute must be set to False for WeightedCost objects. If the + corresponding attribute of an individual cost is True, the problem is + separable from the cost function and will be evaluated before the + individual cost evaluation is called. + """ + + def __init__(self, *costs, weights: Optional[list[float]] = None): + if not all(isinstance(cost, BaseCost) for cost in costs): + raise TypeError("All costs must be instances of BaseCost.") + self.costs = [cost for cost in costs] + if len(set(type(cost.problem) for cost in self.costs)) > 1: + raise TypeError("All problems must be of the same class type.") + self.minimising = not any( + isinstance(cost, (BaseLikelihood, DesignCost)) for cost in self.costs + ) + + # Check if weights are provided + if weights is not None: + try: + self.weights = np.asarray(weights, dtype=float) + except ValueError: + raise ValueError("Weights must be numeric values.") from None + + if self.weights.size != len(self.costs): + raise ValueError("Number of weights must match number of costs.") + else: + self.weights = np.ones(len(self.costs)) + + # Check if all costs depend on the same problem + self._has_identical_problems = all( + cost.has_separable_problem and cost.problem is self.costs[0].problem + for cost in self.costs + ) + + if self._has_identical_problems: + super().__init__(self.costs[0].problem) + else: + super().__init__() + + for cost in self.costs: + self.parameters.join(cost.parameters) + + # Weighted costs do not use this functionality + self._has_separable_problem = False + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: + """ + Computes the cost function for the given predictions. + + Parameters + ---------- + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. + + Returns + ------- + float + The weighted cost value. + """ + if self._has_identical_problems: + inputs = self.problem.parameters.as_dict() + if calculate_grad: + y, dy = self.problem.evaluateS1(inputs) + else: + y = self.problem.evaluate(inputs) + + e = np.empty_like(self.costs) + de = np.empty((len(self.parameters), len(self.costs))) + + for i, cost in enumerate(self.costs): + inputs = cost.parameters.as_dict() + if self._has_identical_problems: + y, dy = (y, dy) + elif cost.has_separable_problem: + if calculate_grad: + y, dy = cost.problem.evaluateS1(inputs) + else: + y = cost.problem.evaluate(inputs) + + if calculate_grad: + e[i], de[:, i] = cost.compute(y, dy=dy, calculate_grad=True) + else: + e[i] = cost.compute(y) + + e = np.dot(e, self.weights) + if calculate_grad: + de = np.dot(de, self.weights) + return e, de + + return e + + @property + def has_identical_problems(self): + return self._has_identical_problems diff --git a/pybop/costs/base_cost.py b/pybop/costs/base_cost.py index b5ad603c3..71de3f35e 100644 --- a/pybop/costs/base_cost.py +++ b/pybop/costs/base_cost.py @@ -1,3 +1,8 @@ +from typing import Optional, Union + +import numpy as np +from numpy import ndarray + from pybop import BaseProblem from pybop.parameters.parameter import Inputs, Parameters @@ -16,42 +21,65 @@ class BaseCost: problem : object A problem instance containing the data and functions necessary for evaluating the cost function. - _target : array-like + target : array-like An array containing the target data to fit. n_outputs : int The number of outputs in the model. + has_separable_problem : bool + If True, the problem is separable from the cost function and will be + evaluated in advance of the call to self.compute() (default: False). + _de : float + The gradient of the cost function to use if an error occurs during + evaluation. Defaults to 1.0. """ - def __init__(self, problem=None): + def __init__(self, problem: Optional[BaseProblem] = None): self.parameters = Parameters() + self.transformation = None self.problem = problem + self.verbose = False + self._has_separable_problem = False + self.y = None + self.dy = None + self._de = 1.0 if isinstance(self.problem, BaseProblem): - self._target = self.problem._target + self._target = self.problem.target self.parameters.join(self.problem.parameters) self.n_outputs = self.problem.n_outputs self.signal = self.problem.signal + self.transformation = self.parameters.construct_transformation() + self._has_separable_problem = True + self.grad_fail = None + self.set_fail_gradient() @property def n_parameters(self): return len(self.parameters) - def __call__(self, x, grad=None): - """ - Call the evaluate function for a given set of parameters. - """ - return self.evaluate(x, grad) + @property + def has_separable_problem(self): + return self._has_separable_problem - def evaluate(self, x, grad=None): + @property + def target(self): + return self._target + + def __call__( + self, + inputs: Union[Inputs, list], + calculate_grad: bool = False, + apply_transform: bool = False, + ): """ - Call the evaluate function for a given set of parameters. + This method calls the forward model via problem.evaluate(inputs), + and computes the cost for the given output by calling self.compute(). Parameters ---------- - x : array-like - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + inputs : Inputs or array-like + The parameters for which to compute the cost and gradient. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- @@ -63,30 +91,42 @@ def evaluate(self, x, grad=None): ValueError If an error occurs during the calculation of the cost. """ - inputs = self.parameters.verify(x) - - try: - return self._evaluate(inputs, grad) - - except NotImplementedError as e: - raise e - - except Exception as e: - raise ValueError(f"Error in cost calculation: {e}") - - def _evaluate(self, inputs: Inputs, grad=None): + # Apply transformation if needed + self.has_transform = self.transformation is not None and apply_transform + if self.has_transform: + inputs = self.transformation.to_model(inputs) + inputs = self.parameters.verify(inputs) + self.parameters.update(values=list(inputs.values())) + + y, dy = None, None + if self._has_separable_problem: + if calculate_grad: + y, dy = self.problem.evaluateS1(self.problem.parameters.as_dict()) + cost, grad = self.compute(y, dy=dy, calculate_grad=calculate_grad) + if self.has_transform and np.isfinite(cost): + jac = self.transformation.jacobian(inputs) + grad = np.matmul(grad, jac) + return cost, grad + + y = self.problem.evaluate(self.problem.parameters.as_dict()) + return self.compute(y, dy=dy, calculate_grad=calculate_grad) + + def compute(self, y: dict, dy: ndarray, calculate_grad: bool = False): """ - Calculate the cost function value for a given set of parameters. + Compute the cost and if `calculate_grad` is True, its gradient with + respect to the predictions. + This method only computes the cost, without calling the `problem.evaluate()`. This method must be implemented by subclasses. Parameters ---------- - inputs : Inputs - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- @@ -100,55 +140,46 @@ def _evaluate(self, inputs: Inputs, grad=None): """ raise NotImplementedError - def evaluateS1(self, x): + def set_fail_gradient(self, de: float = 1.0): """ - Call _evaluateS1 for a given set of parameters. + Set the fail gradient to a specified value. + + The fail gradient is used if an error occurs during the calculation + of the gradient. This method allows updating the default gradient value. Parameters ---------- - x : array-like - The parameters for which to compute the cost and gradient. - - Returns - ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. + de : float + The new fail gradient value to be used. """ - inputs = self.parameters.verify(x) - - try: - return self._evaluateS1(inputs) - - except NotImplementedError as e: - raise e - - except Exception as e: - raise ValueError(f"Error in cost calculation: {e}") + if not isinstance(de, float): + de = float(de) + self._de = de + self.grad_fail = self._de * np.ones(self.n_parameters) - def _evaluateS1(self, inputs: Inputs): + def verify_prediction(self, y): """ - Compute the cost and its gradient with respect to the parameters. + Verify that the prediction matches the target data. Parameters ---------- - inputs : Inputs - The parameters for which to compute the cost and gradient. + y : dict + The model predictions. Returns ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - NotImplementedError - If the method has not been implemented by the subclass. + bool + True if the prediction matches the target data, otherwise False. """ - raise NotImplementedError + if any( + len(y.get(key, [])) != len(self._target.get(key, [])) for key in self.signal + ): + return False + + return True + + def verify_args(self, dy: ndarray, calculate_grad: bool): + if calculate_grad and dy is None: + raise ValueError( + "Forward model sensitivities need to be provided alongside `calculate_grad=True` for `cost.compute`." + ) diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index 85f3dee40..78d513f68 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -1,9 +1,6 @@ -import warnings - import numpy as np from pybop.costs.base_cost import BaseCost -from pybop.parameters.parameter import Inputs class DesignCost(BaseCost): @@ -16,13 +13,9 @@ class DesignCost(BaseCost): --------------------- problem : object The associated problem containing model and evaluation methods. - parameter_set : object) - The set of parameters from the problem's model. - dt : float - The time step size used in the simulation. """ - def __init__(self, problem, update_capacity=False): + def __init__(self, problem): """ Initialises the gravimetric energy density calculator with a problem. @@ -31,59 +24,8 @@ def __init__(self, problem, update_capacity=False): problem : object The problem instance containing the model and data. """ - super(DesignCost, self).__init__(problem) + super().__init__(problem) self.problem = problem - if update_capacity is True: - nominal_capacity_warning = ( - "The nominal capacity is approximated for each iteration." - ) - else: - nominal_capacity_warning = ( - "The nominal capacity is fixed at the initial model value." - ) - warnings.warn(nominal_capacity_warning, UserWarning) - self.update_capacity = update_capacity - self.parameter_set = problem.model.parameter_set - self.update_simulation_data(self.parameters.as_dict("initial")) - - def update_simulation_data(self, inputs: Inputs): - """ - Updates the simulation data based on the initial parameter values. - - Parameters - ---------- - inputs : Inputs - The initial parameter values for the simulation. - """ - if self.update_capacity: - self.problem.model.approximate_capacity(inputs) - solution = self.problem.evaluate(inputs) - - if "Time [s]" not in solution: - raise ValueError("The solution does not contain time data.") - self.problem._time_data = solution["Time [s]"] - self.problem._target = {key: solution[key] for key in self.problem.signal} - self.dt = solution["Time [s]"][1] - solution["Time [s]"][0] - - def _evaluate(self, inputs: Inputs, grad=None): - """ - Computes the value of the cost function. - - This method must be implemented by subclasses. - - Parameters - ---------- - inputs : Inputs - The parameters for which to compute the cost. - grad : array, optional - Gradient information, not used in this method. - - Raises - ------ - NotImplementedError - If the method has not been implemented by the subclass. - """ - raise NotImplementedError class GravimetricEnergyDensity(DesignCost): @@ -96,50 +38,43 @@ class GravimetricEnergyDensity(DesignCost): Inherits all parameters and attributes from ``DesignCost``. """ - def __init__(self, problem, update_capacity=False): - super(GravimetricEnergyDensity, self).__init__(problem, update_capacity) + def __init__(self, problem): + super().__init__(problem) - def _evaluate(self, inputs: Inputs, grad=None): + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> float: """ - Computes the cost function for the energy density. + Computes the cost function for the given predictions. Parameters ---------- - inputs : Inputs - The parameters for which to compute the cost. - grad : array, optional - Gradient information, not used in this method. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + Note: not used in design optimisation classes. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float The gravimetric energy density or -infinity in case of infeasible parameters. """ - try: - with warnings.catch_warnings(): - # Convert UserWarning to an exception - warnings.filterwarnings("error", category=UserWarning) - - if self.update_capacity: - self.problem.model.approximate_capacity(inputs) - solution = self.problem.evaluate(inputs) - - voltage, current = solution["Voltage [V]"], solution["Current [A]"] - energy_density = np.trapz(voltage * current, dx=self.dt) / ( - 3600 * self.problem.model.cell_mass(self.parameter_set) - ) - - return energy_density - - # Catch infeasible solutions and return infinity - except UserWarning as e: - print(f"Ignoring this sample due to: {e}") + if not any(np.isfinite(y[signal][0]) for signal in self.signal): return -np.inf - # Catch any other exception and return infinity - except Exception as e: - print(f"An error occurred during the evaluation: {e}") - return -np.inf + voltage, current = y["Voltage [V]"], y["Current [A]"] + dt = y["Time [s]"][1] - y["Time [s]"][0] + energy_density = np.trapz(voltage * current, dx=dt) / ( + 3600 * self.problem.model.cell_mass() + ) + + return energy_density class VolumetricEnergyDensity(DesignCost): @@ -152,47 +87,40 @@ class VolumetricEnergyDensity(DesignCost): Inherits all parameters and attributes from ``DesignCost``. """ - def __init__(self, problem, update_capacity=False): - super(VolumetricEnergyDensity, self).__init__(problem, update_capacity) + def __init__(self, problem): + super().__init__(problem) - def _evaluate(self, inputs: Inputs, grad=None): + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> float: """ - Computes the cost function for the energy density. + Computes the cost function for the given predictions. Parameters ---------- - inputs : Inputs - The parameters for which to compute the cost. - grad : array, optional - Gradient information, not used in this method. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + Note: not used in design optimisation classes. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float The volumetric energy density or -infinity in case of infeasible parameters. """ - try: - with warnings.catch_warnings(): - # Convert UserWarning to an exception - warnings.filterwarnings("error", category=UserWarning) - - if self.update_capacity: - self.problem.model.approximate_capacity(inputs) - solution = self.problem.evaluate(inputs) - - voltage, current = solution["Voltage [V]"], solution["Current [A]"] - energy_density = np.trapz(voltage * current, dx=self.dt) / ( - 3600 * self.problem.model.cell_volume(self.parameter_set) - ) - - return energy_density - - # Catch infeasible solutions and return infinity - except UserWarning as e: - print(f"Ignoring this sample due to: {e}") + if not any(np.isfinite(y[signal][0]) for signal in self.signal): return -np.inf - # Catch any other exception and return infinity - except Exception as e: - print(f"An error occurred during the evaluation: {e}") - return -np.inf + voltage, current = y["Voltage [V]"], y["Current [A]"] + dt = y["Time [s]"][1] - y["Time [s]"][0] + energy_density = np.trapz(voltage * current, dx=dt) / ( + 3600 * self.problem.model.cell_volume() + ) + + return energy_density diff --git a/pybop/costs/fitting_costs.py b/pybop/costs/fitting_costs.py index e474fd110..9e8c83d37 100644 --- a/pybop/costs/fitting_costs.py +++ b/pybop/costs/fitting_costs.py @@ -1,8 +1,9 @@ +from typing import Union + import numpy as np from pybop.costs.base_cost import BaseCost from pybop.observers.observer import Observer -from pybop.parameters.parameter import Inputs class RootMeanSquaredError(BaseCost): @@ -18,205 +19,275 @@ class RootMeanSquaredError(BaseCost): """ def __init__(self, problem): - super(RootMeanSquaredError, self).__init__(problem) - - # Default fail gradient - self._de = 1.0 - - def _evaluate(self, inputs: Inputs, grad=None): + super().__init__(problem) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Calculate the root mean square error for a given set of parameters. + Computes the cost function for the given predictions. Parameters ---------- - inputs : Inputs - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float The root mean square error. - """ - prediction = self.problem.evaluate(inputs) + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) - for key in self.signal: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): - return np.float64(np.inf) # prediction doesn't match target + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (np.inf, self.grad_fail) if calculate_grad else np.inf - e = np.asarray( - [ - np.sqrt(np.mean((prediction[signal] - self._target[signal]) ** 2)) - for signal in self.signal - ] - ) + # Calculate residuals and error + r = np.asarray([y[signal] - self._target[signal] for signal in self.signal]) + e = np.sqrt(np.mean(np.abs(r) ** 2, axis=1)) + + if calculate_grad: + de = np.mean((r * dy.T), axis=2) / (e + np.finfo(float).eps) + return ( + (e.item(), de.flatten()) + if self.n_outputs == 1 + else (e.sum(), de.sum(1)) + ) + + return e.item() if self.n_outputs == 1 else np.sum(e) + + +class SumSquaredError(BaseCost): + """ + Sum of squared errors cost function. + + Computes the sum of the squares of the differences between model predictions + and target data, which serves as a measure of the total error between the + predicted and observed values. + + Inherits all parameters and attributes from ``BaseCost``. - if self.n_outputs == 1: - return e.item() - else: - return np.sum(e) + """ - def _evaluateS1(self, inputs: Inputs): + def __init__(self, problem): + super().__init__(problem) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Compute the cost and its gradient with respect to the parameters. + Computes the cost function for the given predictions. Parameters ---------- - inputs : Inputs - The parameters for which to compute the cost and gradient. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. + float + The Sum of Squared Error. """ - y, dy = self.problem.evaluateS1(inputs) + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) - for key in self.signal: - if len(y.get(key, [])) != len(self._target.get(key, [])): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - return e, de + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (np.inf, self.grad_fail) if calculate_grad else np.inf + # Calculate residuals and error r = np.asarray([y[signal] - self._target[signal] for signal in self.signal]) - e = np.sqrt(np.mean(r**2, axis=1)) - de = np.mean((r * dy.T), axis=2) / (e + np.finfo(float).eps) + e = np.sum(np.sum(np.abs(r) ** 2, axis=0), axis=0) - if self.n_outputs == 1: - return e.item(), de.flatten() - else: - return np.sum(e), np.sum(de, axis=1) + if calculate_grad: + de = 2 * np.sum((r * dy.T), axis=(1, 2)) + return e, de - def set_fail_gradient(self, de): - """ - Set the fail gradient to a specified value. + return e - The fail gradient is used if an error occurs during the calculation - of the gradient. This method allows updating the default gradient value. - Parameters - ---------- - de : float - The new fail gradient value to be used. - """ - de = float(de) - self._de = de +class Minkowski(BaseCost): + """ + The Minkowski distance is a generalisation of several distance metrics, + including the Euclidean and Manhattan distances. It is defined as: + .. math:: + L_p(x, y) = ( \\sum_i |x_i - y_i|^p )^(1/p) -class SumSquaredError(BaseCost): - """ - Sum of squared errors cost function. + where p > 0 is the order of the Minkowski distance. For p ≥ 1, the + Minkowski distance is a metric. For 0 < p < 1, it is not a metric, as it + does not satisfy the triangle inequality, although a metric can be + obtained by removing the (1/p) exponent. - Computes the sum of the squares of the differences between model predictions - and target data, which serves as a measure of the total error between the - predicted and observed values. + Special cases: - Inherits all parameters and attributes from ``BaseCost``. + * p = 1: Manhattan distance + * p = 2: Euclidean distance + * p → ∞: Chebyshev distance (not implemented as yet) + + This class implements the Minkowski distance as a cost function for + optimisation problems, allowing for flexible distance-based optimisation + across various problem domains. Additional Attributes --------------------- - _de : float - The gradient of the cost function to use if an error occurs during - evaluation. Defaults to 1.0. - + p : float, optional + The order of the Minkowski distance. """ - def __init__(self, problem): - super(SumSquaredError, self).__init__(problem) - - # Default fail gradient - self._de = 1.0 - - def _evaluate(self, inputs: Inputs, grad=None): + def __init__(self, problem, p: float = 2.0): + super().__init__(problem) + if p < 0: + raise ValueError( + "The order of the Minkowski distance must be greater than 0." + ) + elif not np.isfinite(p): + raise ValueError( + "For p = infinity, an implementation of the Chebyshev distance is required." + ) + self.p = float(p) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Calculate the sum of squared errors for a given set of parameters. + Computes the cost function for the given predictions. Parameters ---------- - inputs : Inputs - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float - The sum of squared errors. + The Minkowski cost. """ - prediction = self.problem.evaluate(inputs) + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) - for key in self.signal: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): - return np.float64(np.inf) # prediction doesn't match target + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (np.inf, self.grad_fail) if calculate_grad else np.inf - e = np.asarray( - [ - np.sum((prediction[signal] - self._target[signal]) ** 2) - for signal in self.signal - ] - ) - if self.n_outputs == 1: - return e.item() - else: - return np.sum(e) + # Calculate residuals and error + r = np.asarray([y[signal] - self._target[signal] for signal in self.signal]) + e = np.sum(np.abs(r) ** self.p) ** (1 / self.p) + + if calculate_grad: + de = np.sum( + np.sum(np.sign(r) * np.abs(r) ** (self.p - 1) * dy.T, axis=2) + / (e ** (self.p - 1) + np.finfo(float).eps), + axis=1, + ) + return e, de + + return e + + +class SumofPower(BaseCost): + """ + The Sum of Power [1] is a generalised cost function based on the p-th power + of absolute differences between two vectors. It is defined as: + + .. math:: + C_p(x, y) = \\sum_i |x_i - y_i|^p + + where p ≥ 0 is the power order. + + This class implements the Sum of Power as a cost function for + optimisation problems, allowing for flexible power-based optimisation + across various problem domains. + + Special cases: + + * p = 1: Sum of Absolute Differences + * p = 2: Sum of Squared Differences + * p → ∞: Maximum Absolute Difference + + Note that this is not normalised, unlike distance metrics. To get a + distance metric, you would need to take the p-th root of the result. + + [1]: https://mathworld.wolfram.com/PowerSum.html + + Additional Attributes + --------------------- + p : float, optional + The power order for Sum of Power. + """ - def _evaluateS1(self, inputs: Inputs): + def __init__(self, problem, p: float = 2.0): + super().__init__(problem) + if p < 0: + raise ValueError("The order of 'p' must be greater than 0.") + elif not np.isfinite(p): + raise ValueError("p = np.inf is not yet supported.") + self.p = float(p) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Compute the cost and its gradient with respect to the parameters. + Computes the cost function for the given predictions. Parameters ---------- - inputs : Inputs - The parameters for which to compute the cost and gradient. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. + float + The Sum of Power cost. """ - y, dy = self.problem.evaluateS1(inputs) - for key in self.signal: - if len(y.get(key, [])) != len(self._target.get(key, [])): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - return e, de + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) + + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (np.inf, self.grad_fail) if calculate_grad else np.inf + # Calculate residuals and error r = np.asarray([y[signal] - self._target[signal] for signal in self.signal]) - e = np.sum(np.sum(r**2, axis=0), axis=0) - de = 2 * np.sum(np.sum((r * dy.T), axis=2), axis=1) + e = np.sum(np.abs(r) ** self.p) - return e, de + if calculate_grad: + de = self.p * np.sum( + np.sign(r) * np.abs(r) ** (self.p - 1) * dy.T, axis=(1, 2) + ) + return e, de - def set_fail_gradient(self, de): - """ - Set the fail gradient to a specified value. - - The fail gradient is used if an error occurs during the calculation - of the gradient. This method allows updating the default gradient value. - - Parameters - ---------- - de : float - The new fail gradient value to be used. - """ - de = float(de) - self._de = de + return e class ObserverCost(BaseCost): @@ -233,47 +304,33 @@ class ObserverCost(BaseCost): def __init__(self, observer: Observer): super().__init__(problem=observer) self._observer = observer - - def _evaluate(self, inputs: Inputs, grad=None): + self._has_separable_problem = False + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> float: """ - Calculate the observer cost for a given set of parameters. + Computes the cost function for the given predictions. Parameters ---------- - inputs : Inputs - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float The observer cost (negative of the log likelihood). """ + inputs = self.parameters.as_dict() log_likelihood = self._observer.log_likelihood( - self._target, self._observer.time_data(), inputs + self._target, self._observer.domain_data, inputs ) return -log_likelihood - - def evaluateS1(self, inputs: Inputs): - """ - Compute the cost and its gradient with respect to the parameters. - - Parameters - ---------- - inputs : Inputs - The parameters for which to compute the cost and gradient. - - Returns - ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. - """ - raise NotImplementedError diff --git a/pybop/models/base_model.py b/pybop/models/base_model.py index eba7ed88e..ad51010dd 100644 --- a/pybop/models/base_model.py +++ b/pybop/models/base_model.py @@ -1,12 +1,15 @@ import copy from dataclasses import dataclass -from typing import Any, Dict, Optional, Union +from typing import Callable, Optional, Union import casadi import numpy as np import pybamm +from pybamm import IDAKLUSolver as IDAKLUSolver +from scipy.sparse import csc_matrix +from scipy.sparse.linalg import spsolve -from pybop import Dataset, Experiment, Parameters, ParameterSet +from pybop import Dataset, Experiment, Parameters, ParameterSet, SymbolReplacer from pybop.parameters.parameter import Inputs @@ -38,50 +41,98 @@ class BaseModel: """ A base class for constructing and simulating models using PyBaMM. - This class serves as a foundation for building specific models in PyBaMM. - It provides methods to set up the model, define parameters, and perform - simulations. The class is designed to be subclassed for creating models - with custom behaviour. - + This class serves as a foundation for constructing models based on PyBaMM models. It + provides methods to set up the model, define parameters, and perform simulations. The + class is designed to be subclassed for creating models with custom behaviour. + + This class is based on PyBaMM's Simulation class. A PyBOP model is set up via a + similar 3-step process. The `pybamm_model` attributes echoes the `model` attribute of + a simulation, which tracks the model through the build process. Firstly, note that a + PyBaMM `model` must first be built via `build_model` before a simulation or PyBOP + model can be built. The 3-step process is then as follows. + + The `pybamm_model` attribute is first defined as an instance of the imported PyBaMM + model, using any given model options. This initial version of the model is saved as + the `_unprocessed_model` for future reference. Next, the type of each parameter in + the parameter set as well as the geometry of the model is set. Parameters may be set + as an input, interpolant, functional or just a standard PyBaMM parameter. This + version of the model is referred to as the `model_with_set_params`. After its + creation, the `pybamm_model` attribute is updated to point at this version of the + model. Finally, the model required for simulations is built by defining the mesh and + processing the discretisation. The complete model is referred to as the `built_model` + and this version is used to run simulations. + + In order to rebuild a model with a different initial state or geometry, the + `built_model` and the `model_with_set_params` must be cleared and the `pybamm_model` + reset to the `unprocessed_model` in order to start the build process again. """ - def __init__(self, name="Base Model", parameter_set=None): + def __init__( + self, + name: str = "Base Model", + parameter_set: Optional[ParameterSet] = None, + check_params: Callable = None, + eis=False, + ): """ - Initialize the BaseModel with an optional name. + Initialise the BaseModel with an optional name and a parameter set. Parameters ---------- name : str, optional The name given to the model instance. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. + check_params : Callable, optional + A compatibility check for the model parameters. Function, with + signature + check_params( + inputs: dict, + allow_infeasible_solutions: bool, optional + ) + Returns true if parameters are valid, False otherwise. Can be + used to impose constraints on valid parameters. + + Additional Attributes + --------------------- + pybamm_model : pybamm.BaseModel + An instance of a PyBaMM model. + parameters : pybop.Parameters + The input parameters. + param_check_counter : int + A counter for the number of parameter checks (default: 0). + allow_infeasible_solutions : bool, optional + If True, parameter values will be simulated whether or not they are feasible + (default: True). """ self.name = name + self.eis = eis if parameter_set is None: self._parameter_set = None elif isinstance(parameter_set, dict): - self._parameter_set = pybamm.ParameterValues(parameter_set) + self._parameter_set = pybamm.ParameterValues(parameter_set).copy() elif isinstance(parameter_set, pybamm.ParameterValues): - self._parameter_set = parameter_set + self._parameter_set = parameter_set.copy() else: # a pybop parameter set - self._parameter_set = pybamm.ParameterValues(parameter_set.params) + self._parameter_set = pybamm.ParameterValues(parameter_set.params).copy() + self.param_checker = check_params + self.pybamm_model = None self.parameters = Parameters() - self.dataset = None - self.signal = None - self.additional_variables = [] - self.rebuild_parameters = {} - self.standard_parameters = {} self.param_check_counter = 0 self.allow_infeasible_solutions = True def build( self, - dataset: Dataset = None, - parameters: Union[Parameters, Dict] = None, + parameters: Union[Parameters, dict] = None, + inputs: Optional[Inputs] = None, + initial_state: Optional[dict] = None, + dataset: Optional[Dataset] = None, check_model: bool = True, - init_soc: float = None, ) -> None: """ - Construct the PyBaMM model if not already built, and set parameters. + Construct the PyBaMM model, if not already built or if there are changes to any + `rebuild_parameters` or the initial state. This method initializes the model components, applies the given parameters, sets up the mesh and discretisation if needed, and prepares the model @@ -89,35 +140,48 @@ def build( Parameters ---------- - dataset : pybamm.Dataset, optional - The dataset to be used in the model construction. parameters : pybop.Parameters or Dict, optional A pybop Parameters class or dictionary containing parameter values to apply to the model. + inputs : Inputs + The input parameters to be used when building the model. + initial_state : dict, optional + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + Defaults to None, indicating that the existing initial state of charge (for an ECM) + or initial concentrations (for an EChem model) will be used. + dataset : pybop.Dataset or dict, optional + The dataset to be used in the model construction. check_model : bool, optional If True, the model will be checked for correctness after construction. - init_soc : float, optional - The initial state of charge to be used in simulations. """ - self.dataset = dataset - if parameters is not None: - self.parameters = parameters - self.classify_and_update_parameters(self.parameters) + if parameters is not None or inputs is not None: + # Classify parameters and clear the model if rebuild required + inputs = self.classify_parameters(parameters, inputs=inputs) + + if initial_state is not None: + # Clear the model if rebuild required (currently if any initial state) + self.set_initial_state(initial_state, inputs=inputs) + + if not self.pybamm_model._built: # noqa: SLF001 + self.pybamm_model.build_model() - if init_soc is not None: - self.set_init_soc(init_soc) + if self.eis: + self.set_up_for_eis(self.pybamm_model) + self._parameter_set["Current function [A]"] = 0 + + V_scale = getattr(self.pybamm_model.variables["Voltage [V]"], "scale", 1) + I_scale = getattr(self.pybamm_model.variables["Current [A]"], "scale", 1) + self.z_scale = self._parameter_set.evaluate(V_scale / I_scale) + + if dataset is not None and not self.eis: + self.set_current_function(dataset) if self._built_model: return - elif self.pybamm_model.is_discretised: self._model_with_set_params = self.pybamm_model self._built_model = self.pybamm_model - else: - if not self.pybamm_model._built: - self.pybamm_model.build_model() - self.set_params() - + self.set_parameters() self._mesh = pybamm.Mesh(self.geometry, self.submesh_types, self.var_pts) self._disc = pybamm.Discretisation( mesh=self.mesh, @@ -129,140 +193,197 @@ def build( ) # Clear solver and setup model - self._solver._model_set_up = {} + self._solver._model_set_up = {} # noqa: SLF001 self.n_states = self._built_model.len_rhs_and_alg # len_rhs + len_alg - def set_init_soc(self, init_soc: float): + def convert_to_pybamm_initial_state(self, initial_state: dict): """ - Set the initial state of charge for the battery model. + Convert an initial state of charge into a float and an initial open-circuit + voltage into a string ending in "V". Parameters ---------- - init_soc : float - The initial state of charge to be used in the model. - """ - if self._built_initial_soc != init_soc: - # reset - self._model_with_set_params = None - self._built_model = None - self.op_conds_to_built_models = None - self.op_conds_to_built_solvers = None - - param = self.pybamm_model.param - self._parameter_set = ( - self._unprocessed_parameter_set.set_initial_stoichiometries( - init_soc, param=param, inplace=False - ) - ) - # Save solved initial SOC in case we need to rebuild the model - self._built_initial_soc = init_soc + initial_state : dict + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + + Returns + ------- + float or str + If float, this value is used as the initial state of charge (as a decimal between 0 + and 1). If str ending in "V", this value is used as the initial open-circuit voltage. - def set_params(self, rebuild=False): + Raises + ------ + ValueError + If the input is not a dictionary with a single, valid key. + """ + if len(initial_state) > 1: + raise ValueError("Expecting only one initial state.") + elif "Initial SoC" in initial_state.keys(): + return initial_state["Initial SoC"] + elif "Initial open-circuit voltage [V]" in initial_state.keys(): + return str(initial_state["Initial open-circuit voltage [V]"]) + "V" + else: + raise ValueError(f'Unrecognised initial state: "{list(initial_state)[0]}"') + + def set_initial_state(self, initial_state: dict, inputs: Optional[Inputs] = None): """ - Assign the parameters to the model. + Set the initial state of charge or concentrations for the battery model. - This method processes the model with the given parameters, sets up - the geometry, and updates the model instance. + Parameters + ---------- + initial_state : dict + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + inputs : Inputs + The input parameters to be used when building the model. """ - if self.model_with_set_params and not rebuild: - return + self.clear() - # Mark any simulation inputs in the parameter set - for key in self.standard_parameters.keys(): - self._parameter_set[key] = "[input]" + initial_state = self.convert_to_pybamm_initial_state(initial_state) + + if isinstance(self.pybamm_model, pybamm.equivalent_circuit.Thevenin): + initial_state = self.get_initial_state(initial_state, inputs=inputs) + self._unprocessed_parameter_set.update({"Initial SoC": initial_state}) - if self.dataset is not None and (not self.rebuild_parameters or not rebuild): - if ( - self.parameters is None - or "Current function [A]" not in self.parameters.keys() - ): - self._parameter_set["Current function [A]"] = pybamm.Interpolant( - self.dataset["Time [s]"], - self.dataset["Current function [A]"], + else: + if not self.pybamm_model._built: # noqa: SLF001 + self.pybamm_model.build_model() + + # Temporary construction of attributes for PyBaMM + self.model = self._model = self.pybamm_model + self._unprocessed_parameter_values = self._unprocessed_parameter_set + + # Set initial state via PyBaMM's Simulation class + pybamm.Simulation.set_initial_soc(self, initial_state, inputs=inputs) + + # Update the default parameter set for consistency + self._unprocessed_parameter_set = self._parameter_values + + # Clear the pybamm objects + del self.model # can be removed after PyBaMM's next release, fixed with pybamm-team/PyBaMM#4319 + del self._model + del self._unprocessed_parameter_values + del self._parameter_values + + # Use a copy of the updated default parameter set + self._parameter_set = self._unprocessed_parameter_set.copy() + + def set_current_function(self, dataset: Union[Dataset, dict]): + """ + Update the input current function according to the data. + + Parameters + ---------- + dataset : pybop.Dataset or dict, optional + The dataset to be used in the model construction. + """ + if "Current function [A]" in self._parameter_set.keys(): + if "Current function [A]" not in self.parameters.keys(): + current = pybamm.Interpolant( + dataset["Time [s]"], + dataset["Current function [A]"], pybamm.t, ) - # Set t_eval - self.time_data = self._parameter_set["Current function [A]"].x[0] + # Update both the active and unprocessed parameter sets for consistency + self._parameter_set["Current function [A]"] = current + self._unprocessed_parameter_set["Current function [A]"] = current + + def set_parameters(self): + """ + Assign the parameters to the model. + + This method processes the model with the given parameters, sets up + the geometry, and updates the model instance. + """ + if self._model_with_set_params: + return self._model_with_set_params = self._parameter_set.process_model( self._unprocessed_model, inplace=False ) - if self.geometry is not None: - self._parameter_set.process_geometry(self.geometry) + self._parameter_set.process_geometry(self._geometry) self.pybamm_model = self._model_with_set_params - def rebuild( - self, - dataset: Dataset = None, - parameters: Union[Parameters, Dict] = None, - parameter_set: ParameterSet = None, - check_model: bool = True, - init_soc: float = None, - ) -> None: + def set_up_for_eis(self, model): """ - Rebuild the PyBaMM model for a given parameter set. + Set up the model for electrochemical impedance spectroscopy (EIS) simulations. - This method requires the self.build() method to be called first, and - then rebuilds the model for a given parameter set. Specifically, - this method applies the given parameters, sets up the mesh and - discretisation if needed, and prepares the model for simulations. + This method sets up the model for EIS simulations by adding the necessary + algebraic equations and variables to the model. + Originally developed by pybamm-eis: https://github.com/pybamm-team/pybamm-eis Parameters ---------- - dataset : pybamm.Dataset, optional - The dataset to be used in the model construction. - parameters : pybop.Parameters or Dict, optional - A pybop Parameters class or dictionary containing parameter values to apply to the model. - parameter_set : pybop.parameter_set, optional - A PyBOP parameter set object or a dictionary containing the parameter values - check_model : bool, optional - If True, the model will be checked for correctness after construction. - init_soc : float, optional - The initial state of charge to be used in simulations. - """ - self.dataset = dataset - - if parameters is not None: - self.classify_and_update_parameters(parameters) - - if init_soc is not None: - self.set_init_soc(init_soc) + model : pybamm.Model + The PyBaMM model to be used for EIS simulations. + """ + V_cell = pybamm.Variable("Voltage variable [V]") + model.variables["Voltage variable [V]"] = V_cell + V = model.variables["Voltage [V]"] + + # Add algebraic equation for the voltage + model.algebraic[V_cell] = V_cell - V + model.initial_conditions[V_cell] = model.param.ocv_init + + # Create the FunctionControl submodel and extract variables + external_circuit_variables = pybamm.external_circuit.FunctionControl( + model.param, None, model.options, control="algebraic" + ).get_fundamental_variables() + + # Perform the replacement + symbol_replacement_map = { + model.variables[name]: variable + for name, variable in external_circuit_variables.items() + } - if self._built_model is None: - raise ValueError("Model must be built before calling rebuild") - - self.set_params(rebuild=True) - self._mesh = pybamm.Mesh(self.geometry, self.submesh_types, self.var_pts) - self._disc = pybamm.Discretisation( - mesh=self.mesh, - spatial_methods=self.spatial_methods, - check_model=check_model, + # Don't replace initial conditions, as these should not contain + # Variable objects + replacer = SymbolReplacer( + symbol_replacement_map, process_initial_conditions=False ) - self._built_model = self._disc.process_model( - self._model_with_set_params, inplace=False + replacer.process_model(model, inplace=True) + + # Add an algebraic equation for the current density variable + # External circuit submodels are always equations on the current + I_cell = model.variables["Current variable [A]"] + I = model.variables["Current [A]"] + I_applied = pybamm.FunctionParameter( + "Current function [A]", {"Time [s]": pybamm.t} ) + model.algebraic[I_cell] = I - I_applied + model.initial_conditions[I_cell] = 0 - # Clear solver and setup model - self._solver._model_set_up = {} + def clear(self): + """ + Clear any built PyBaMM model. + """ + self._model_with_set_params = None + self._built_model = None + self._built_initial_soc = None + self._mesh = None + self._disc = None - def classify_and_update_parameters(self, parameters: Parameters): + def classify_parameters( + self, parameters: Optional[Parameters] = None, inputs: Optional[Inputs] = None + ): """ - Update the parameter values according to their classification as either - 'rebuild_parameters' which require a model rebuild and - 'standard_parameters' which do not. + Check for any 'rebuild_parameters' which require a model rebuild and + update the unprocessed_parameter_set if a rebuild is required. Parameters ---------- - parameters : pybop.Parameters - + parameters : Parameters, optional + The optimisation parameters. Defaults to None, resulting in the internal + `pybop.Parameters` object to be used. + inputs : Inputs, optional + The input parameters for the simulation (default: None). """ - if parameters is None: - self.parameters = Parameters() - else: - self.parameters = parameters + self.parameters = parameters or self.parameters + # Compile all parameters and inputs parameter_dictionary = self.parameters.as_dict() + parameter_dictionary.update(inputs or {}) rebuild_parameters = { param: parameter_dictionary[param] @@ -275,17 +396,25 @@ def classify_and_update_parameters(self, parameters: Parameters): if param not in self.geometric_parameters } - self.rebuild_parameters.update(rebuild_parameters) - self.standard_parameters.update(standard_parameters) - - # Update the parameter set and geometry for rebuild parameters - if self.rebuild_parameters: - self._parameter_set.update(self.rebuild_parameters) - self._unprocessed_parameter_set = self._parameter_set - self.geometry = self.pybamm_model.default_geometry + # Mark any standard parameters in the active parameter set and pass as inputs + for key in standard_parameters.keys(): + self._parameter_set[key] = "[input]" - # Update the list of parameter names and number of parameters - self._n_parameters = len(self.parameters) + # Clear any built model, update the parameter set and geometry if rebuild required + if rebuild_parameters: + requires_rebuild = False + # A rebuild is required if any of the rebuild parameter values have changed + for key, value in rebuild_parameters.items(): + if value != self._unprocessed_parameter_set[key]: + requires_rebuild = True + if requires_rebuild: + self.clear() + self._geometry = self.pybamm_model.default_geometry + # Update both the active and unprocessed parameter sets for consistency + self._parameter_set.update(rebuild_parameters) + self._unprocessed_parameter_set.update(rebuild_parameters) + + return standard_parameters def reinit( self, inputs: Inputs, t: float = 0.0, x: Optional[np.ndarray] = None @@ -303,9 +432,7 @@ def reinit( if x is None: x = self._built_model.y0 - sol = pybamm.Solution([np.asarray([t])], [x], self._built_model, inputs) - - return TimeSeriesState(sol=sol, inputs=inputs, t=t) + return self.get_state(inputs, t, x) def get_state(self, inputs: Inputs, t: float, x: np.ndarray) -> TimeSeriesState: """ @@ -331,13 +458,13 @@ def step(self, state: TimeSeriesState, time: np.ndarray) -> TimeSeriesState: """ dt = time - state.t new_sol = self._solver.step( - state.sol, self.built_model, dt, npts=2, inputs=state.inputs, save=False + state.sol, self._built_model, dt, npts=2, inputs=state.inputs, save=False ) return TimeSeriesState(sol=new_sol, inputs=state.inputs, t=time) def simulate( - self, inputs: Inputs, t_eval: np.array - ) -> Dict[str, np.ndarray[np.float64]]: + self, inputs: Inputs, t_eval: np.array, initial_state: Optional[dict] = None + ) -> Union[pybamm.Solution, list[np.float64]]: """ Execute the forward model simulation and return the result. @@ -347,6 +474,56 @@ def simulate( The input parameters for the simulation. t_eval : array-like An array of time points at which to evaluate the solution. + initial_state : dict, optional + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + Defaults to None, indicating that the existing initial state of charge (for an ECM) + or initial concentrations (for an EChem model) will be used. + + Returns + ------- + pybamm.Solution + The solution object returned by a PyBaMM simulation, or a pybamm error in the case + where the parameter values are infeasible and infeasible solutions are not allowed. + + Raises + ------ + ValueError + If the model has not been built before simulation. + """ + inputs = self.parameters.verify(inputs) + + # Build or rebuild if required + self.build(inputs=inputs, initial_state=initial_state) + + if not self.check_params( + inputs=inputs, + allow_infeasible_solutions=self.allow_infeasible_solutions, + ): + raise ValueError("These parameter values are infeasible.") + + return self.solver.solve( + self._built_model, + inputs=inputs, + t_eval=[t_eval[0], t_eval[-1]] + if isinstance(self._solver, IDAKLUSolver) + else t_eval, + t_interp=t_eval, + ) + + def simulateEIS( + self, inputs: Inputs, f_eval: list, initial_state: Optional[dict] = None + ) -> dict[str, np.ndarray]: + """ + Compute the forward model simulation with electrochemical impedance spectroscopy + and return the result. + + Parameters + ---------- + inputs : dict or array-like + The input parameters for the simulation. If array-like, it will be + converted to a dictionary using the model's fit keys. + f_eval : array-like + An array of frequency points at which to evaluate the solution. Returns ------- @@ -360,35 +537,86 @@ def simulate( """ inputs = self.parameters.verify(inputs) - if self._built_model is None: - raise ValueError("Model must be built before calling simulate") - else: - if self.rebuild_parameters and not self.standard_parameters: - sol = self.solver.solve(self.built_model, t_eval=t_eval) - - else: - if self.check_params( - inputs=inputs, - allow_infeasible_solutions=self.allow_infeasible_solutions, - ): - try: - sol = self.solver.solve( - self.built_model, inputs=inputs, t_eval=t_eval - ) - except Exception as e: - print(f"Error: {e}") - return {signal: [np.inf] for signal in self.signal} - else: - return {signal: [np.inf] for signal in self.signal} - - y = { - signal: sol[signal].data - for signal in (self.signal + self.additional_variables) - } + # Build or rebuild if required + self.build(inputs=inputs, initial_state=initial_state) + + if not self.check_params( + inputs=inputs, + allow_infeasible_solutions=self.allow_infeasible_solutions, + ): + raise ValueError("These parameter values are infeasible.") + + self.initialise_eis_simulation(inputs) + zs = [self.calculate_impedance(frequency) for frequency in f_eval] - return y + return {"Impedance": np.asarray(zs) * self.z_scale} - def simulateS1(self, inputs: Inputs, t_eval: np.array): + def initialise_eis_simulation(self, inputs: Optional[Inputs] = None): + """ + Initialise the Electrochemical Impedance Spectroscopy (EIS) simulation. + + This method sets up the mass matrix and solver, converts inputs to the appropriate format, + extracts necessary attributes from the model, and prepares matrices for the simulation. + + Parameters + ---------- + inputs : dict (optional) + The input parameters for the simulation. + """ + # Setup mass matrix, solver + self.M = self._built_model.mass_matrix.entries + self._solver.set_up(self._built_model, inputs=inputs) + + # Convert inputs to casadi format if needed + casadi_inputs = ( + casadi.vertcat(*inputs.values()) + if inputs is not None and self._built_model.convert_to_format == "casadi" + else inputs or [] + ) + + # Extract necessary attributes from the model + self.y0 = self._built_model.concatenated_initial_conditions.evaluate( + 0, inputs=inputs + ) + self.J = self._built_model.jac_rhs_algebraic_eval( + 0, self.y0, casadi_inputs + ).sparse() + + # Convert to Compressed Sparse Column format + self.M = csc_matrix(self.M) + self.J = csc_matrix(self.J) + + # Add forcing to the RHS on the current density + self.b = np.zeros(self.y0.shape) + self.b[-1] = -1 + + def calculate_impedance(self, frequency): + """ + Calculate the impedance for a given frequency. + + This method computes the system matrix, solves the linear system, and calculates + the impedance based on the solution. + + Parameters + ---------- + frequency (np.ndarray | list like): The frequency at which to calculate the impedance. + + Returns + ------- + The calculated impedance (complex np.ndarray). + """ + # Compute the system matrix + A = 1.0j * 2 * np.pi * frequency * self.M - self.J + + # Solve the system + x = spsolve(A, self.b) + + # Calculate the impedance + return -x[-2] / x[-1] + + def simulateS1( + self, inputs: Inputs, t_eval: np.array, initial_state: Optional[dict] = None + ): """ Perform the forward model simulation with sensitivities. @@ -399,11 +627,16 @@ def simulateS1(self, inputs: Inputs, t_eval: np.array): t_eval : array-like An array of time points at which to evaluate the solution and its sensitivities. + initial_state : dict, optional + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + Defaults to None, indicating that the existing initial state of charge (for an ECM) + or initial concentrations (for an EChem model) will be used. Returns ------- - tuple - A tuple containing the simulation result and the sensitivities. + pybamm.Solution + The solution object returned by a PyBaMM simulation, or a pybamm error in the case + where the parameter values are infeasible and infeasible solutions are not allowed. Raises ------ @@ -412,67 +645,46 @@ def simulateS1(self, inputs: Inputs, t_eval: np.array): """ inputs = self.parameters.verify(inputs) - if self._built_model is None: - raise ValueError("Model must be built before calling simulate") - else: - if self.rebuild_parameters: - raise ValueError( - "Cannot use sensitivies for parameters which require a model rebuild" - ) + if initial_state is not None or any( + key in self.geometric_parameters for key in inputs.keys() + ): + raise ValueError( + "Cannot use sensitivities for parameters which require a model rebuild" + ) - if self.check_params( - inputs=inputs, - allow_infeasible_solutions=self.allow_infeasible_solutions, - ): - try: - sol = self._solver.solve( - self.built_model, - inputs=inputs, - t_eval=t_eval, - calculate_sensitivities=True, - ) - y = {signal: sol[signal].data for signal in self.signal} - - # Extract the sensitivities and stack them along a new axis for each signal - dy = np.empty( - ( - sol[self.signal[0]].data.shape[0], - self.n_outputs, - self._n_parameters, - ) - ) - - for i, signal in enumerate(self.signal): - dy[:, i, :] = np.stack( - [ - sol[signal].sensitivities[key].toarray()[:, 0] - for key in self.parameters.keys() - ], - axis=-1, - ) - - return y, dy - except Exception as e: - print(f"Error: {e}") - return {signal: [np.inf] for signal in self.signal}, [np.inf] - - else: - return {signal: [np.inf] for signal in self.signal}, [np.inf] + # Build if required + self.build(inputs=inputs, initial_state=initial_state) + + if not self.check_params( + inputs=inputs, + allow_infeasible_solutions=self.allow_infeasible_solutions, + ): + raise ValueError("These parameter values are infeasible.") + + return self._solver.solve( + self._built_model, + inputs=inputs, + t_eval=[t_eval[0], t_eval[-1]] + if isinstance(self._solver, IDAKLUSolver) + else t_eval, + calculate_sensitivities=True, + t_interp=t_eval, + ) def predict( self, - inputs: Inputs = None, - t_eval: np.array = None, - parameter_set: ParameterSet = None, - experiment: Experiment = None, - init_soc: float = None, - ) -> Dict[str, np.ndarray[np.float64]]: + inputs: Optional[Inputs] = None, + t_eval: Optional[np.array] = None, + parameter_set: Optional[ParameterSet] = None, + experiment: Optional[Experiment] = None, + initial_state: Optional[dict] = None, + ) -> dict[str, np.ndarray[np.float64]]: """ Solve the model using PyBaMM's simulation framework and return the solution. This method sets up a PyBaMM simulation by configuring the model, parameters, experiment - (if any), and initial state of charge (if provided). It then solves the simulation and - returns the resulting solution object. + or time vector, and initial state of charge (if provided). Either 't_eval' or 'experiment' + must be provided. It then solves the simulation and returns the resulting solution object. Parameters ---------- @@ -482,20 +694,22 @@ def predict( t_eval : array-like, optional An array of time points at which to evaluate the solution. Defaults to None, which means the time points need to be specified within experiment or elsewhere. - parameter_set : pybamm.ParameterValues, optional - A PyBaMM ParameterValues object or a dictionary containing the parameter values - to use for the simulation. Defaults to the model's current ParameterValues if None. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values to use for the simulation. + Defaults to the model's current ParameterValues if None. experiment : pybamm.Experiment, optional A PyBaMM Experiment object specifying the experimental conditions under which the simulation should be run. Defaults to None, indicating no experiment. - init_soc : float, optional - The initial state of charge for the simulation, as a fraction (between 0 and 1). - Defaults to None. + initial_state : dict, optional + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + Defaults to None, indicating that the existing initial state of charge (for an ECM) + or initial concentrations (for an EChem model) will be used. Returns ------- pybamm.Solution - The solution object returned after solving the simulation. + The solution object returned by a PyBaMM simulation, or a pybamm error in the case + where the parameter values are infeasible and infeasible solutions are not allowed. Raises ------ @@ -504,44 +718,56 @@ def predict( if PyBaMM models are not supported by the current simulation method. """ - inputs = self.parameters.verify(inputs) - - if not self.pybamm_model._built: - self.pybamm_model.build_model() + if self.pybamm_model is None: + raise ValueError( + "The predict method currently only supports PyBaMM models." + ) + elif not self._unprocessed_model._built: # noqa: SLF001 + self._unprocessed_model.build_model() - parameter_set = parameter_set or self._unprocessed_parameter_set + no_parameter_set = parameter_set is None + parameter_set = parameter_set or self._unprocessed_parameter_set.copy() if inputs is not None: + inputs = self.parameters.verify(inputs) parameter_set.update(inputs) - if self.check_params( - inputs=inputs, + if initial_state is not None: + if no_parameter_set: + # Update the default initial state for consistency + self.set_initial_state(initial_state) + + initial_state = self.convert_to_pybamm_initial_state(initial_state) + if isinstance(self.pybamm_model, pybamm.equivalent_circuit.Thevenin): + parameter_set["Initial SoC"] = self._parameter_set["Initial SoC"] + initial_state = None + + if not self.check_params( parameter_set=parameter_set, allow_infeasible_solutions=self.allow_infeasible_solutions, ): - if self._unprocessed_model is not None: - if experiment is None: - return pybamm.Simulation( - self._unprocessed_model, - parameter_values=parameter_set, - ).solve(t_eval=t_eval, initial_soc=init_soc) - else: - return pybamm.Simulation( - self._unprocessed_model, - experiment=experiment, - parameter_values=parameter_set, - ).solve(initial_soc=init_soc) - else: - raise ValueError( - "This sim method currently only supports PyBaMM models" - ) - + raise ValueError("These parameter values are infeasible.") + + if experiment is not None: + return pybamm.Simulation( + model=self._unprocessed_model, + experiment=experiment, + parameter_values=parameter_set, + ).solve(initial_soc=initial_state) + elif t_eval is not None: + return pybamm.Simulation( + model=self._unprocessed_model, + parameter_values=parameter_set, + ).solve(t_eval=t_eval, initial_soc=initial_state) else: - return [np.inf] + raise ValueError( + "The predict method requires either an experiment or t_eval " + "to be specified." + ) def check_params( self, - inputs: Inputs = None, - parameter_set: ParameterSet = None, + inputs: Optional[Inputs] = None, + parameter_set: Optional[ParameterSet] = None, allow_infeasible_solutions: bool = True, ): """ @@ -551,6 +777,8 @@ def check_params( ---------- inputs : Inputs The input parameters for the simulation. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. allow_infeasible_solutions : bool, optional If True, infeasible parameter values will be allowed in the optimisation (default: True). @@ -560,14 +788,20 @@ def check_params( A boolean which signifies whether the parameters are compatible. """ - inputs = self.parameters.verify(inputs) + inputs = self.parameters.verify(inputs) or {} + parameter_set = parameter_set or self._parameter_set return self._check_params( - inputs=inputs, allow_infeasible_solutions=allow_infeasible_solutions + inputs=inputs, + parameter_set=parameter_set, + allow_infeasible_solutions=allow_infeasible_solutions, ) def _check_params( - self, inputs: Inputs = None, allow_infeasible_solutions: bool = True + self, + inputs: Inputs, + parameter_set: ParameterSet, + allow_infeasible_solutions: bool = True, ): """ A compatibility check for the model parameters which can be implemented by subclasses @@ -577,6 +811,8 @@ def _check_params( ---------- inputs : Inputs The input parameters for the simulation. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. allow_infeasible_solutions : bool, optional If True, infeasible parameter values will be allowed in the optimisation (default: True). @@ -585,6 +821,8 @@ def _check_params( bool A boolean which signifies whether the parameters are compatible. """ + if self.param_checker: + return self.param_checker(inputs, allow_infeasible_solutions) return True def copy(self): @@ -598,6 +836,53 @@ def copy(self): """ return copy.copy(self) + def new_copy(self): + """ + Return a new copy of the model, explicitly copying all the mutable attributes + to avoid issues with shared objects. + + Returns + ------- + BaseModel + A new copy of the model. + """ + model_class = type(self) + if self.pybamm_model is None: + model_args = {"parameter_set": self._parameter_set.copy()} + else: + model_args = { + "options": self._unprocessed_model.options, + "parameter_set": self._unprocessed_parameter_set.copy(), + "geometry": self.pybamm_model.default_geometry.copy(), + "submesh_types": self.pybamm_model.default_submesh_types.copy(), + "var_pts": self.pybamm_model.default_var_pts.copy(), + "spatial_methods": self.pybamm_model.default_spatial_methods.copy(), + "solver": self.pybamm_model.default_solver.copy(), + "eis": copy.copy(self.eis), + } + + return model_class(**model_args) + + def get_parameter_info(self, print_info: bool = False): + """ + Extracts the parameter names and types and returns them as a dictionary. + """ + if not self.pybamm_model._built: # noqa: SLF001 + self.pybamm_model.build_model() + + info = self.pybamm_model.get_parameter_info() + + reduced_info = dict() + for param, param_type in info.values(): + param_name = getattr(param, "name", str(param)) + reduced_info[param_name] = param_type + + if print_info: + for param, param_type in info.values(): + print(param, " : ", param_type) + + return reduced_info + def cell_mass(self, parameter_set: ParameterSet = None): """ Calculate the cell mass in kilograms. @@ -606,9 +891,8 @@ def cell_mass(self, parameter_set: ParameterSet = None): Parameters ---------- - parameter_set : dict, optional - A dictionary containing the parameter values necessary for the mass - calculations. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. Raises ------ @@ -625,9 +909,8 @@ def cell_volume(self, parameter_set: ParameterSet = None): Parameters ---------- - parameter_set : dict, optional - A dictionary containing the parameter values necessary for the volume - calculation. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. Raises ------ @@ -636,17 +919,17 @@ def cell_volume(self, parameter_set: ParameterSet = None): """ raise NotImplementedError - def approximate_capacity(self, inputs: Inputs): + def approximate_capacity(self, parameter_set: ParameterSet = None): """ - Calculate a new estimate for the nominal capacity based on the theoretical energy density - and an average voltage. + Calculate a new estimate for the nominal capacity based on the theoretical energy + density and an average voltage. This method must be implemented by subclasses. Parameters ---------- - inputs : Inputs - The parameters that are the inputs of the model. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. Raises ------ @@ -659,14 +942,14 @@ def approximate_capacity(self, inputs: Inputs): def built_model(self): return self._built_model + @property + def built_initial_soc(self): + return self._built_initial_soc + @property def parameter_set(self): return self._parameter_set - @parameter_set.setter - def parameter_set(self, parameter_set): - self._parameter_set = parameter_set.copy() - @property def model_with_set_params(self): return self._model_with_set_params @@ -675,42 +958,26 @@ def model_with_set_params(self): def geometry(self): return self._geometry - @geometry.setter - def geometry(self, geometry: Optional[pybamm.Geometry]): - self._geometry = geometry.copy() if geometry is not None else None - @property def submesh_types(self): return self._submesh_types - @submesh_types.setter - def submesh_types(self, submesh_types: Optional[Dict[str, Any]]): - self._submesh_types = ( - submesh_types.copy() if submesh_types is not None else None - ) - @property def mesh(self): return self._mesh + @property + def disc(self): + return self._disc + @property def var_pts(self): return self._var_pts - @var_pts.setter - def var_pts(self, var_pts: Optional[Dict[str, int]]): - self._var_pts = var_pts.copy() if var_pts is not None else None - @property def spatial_methods(self): return self._spatial_methods - @spatial_methods.setter - def spatial_methods(self, spatial_methods: Optional[Dict[str, Any]]): - self._spatial_methods = ( - spatial_methods.copy() if spatial_methods is not None else None - ) - @property def solver(self): return self._solver diff --git a/pybop/models/empirical/base_ecm.py b/pybop/models/empirical/base_ecm.py index 38d94d147..9b0d9209e 100644 --- a/pybop/models/empirical/base_ecm.py +++ b/pybop/models/empirical/base_ecm.py @@ -1,4 +1,8 @@ +import numpy as np +import pybamm + from pybop.models.base_model import BaseModel, Inputs +from pybop.parameters.parameter_set import ParameterSet class ECircuitModel(BaseModel): @@ -41,27 +45,32 @@ def __init__( var_pts=None, spatial_methods=None, solver=None, + check_params=None, + eis=False, **model_kwargs, ): model_options = dict(build=False) for key, value in model_kwargs.items(): model_options[key] = value - self.pybamm_model = pybamm_model(**model_options) - self._unprocessed_model = self.pybamm_model + pybamm_model = pybamm_model(**model_options) # Correct OCP if set to default if ( parameter_set is not None and "Open-circuit voltage [V]" in parameter_set.keys() ): - default_ocp = self.pybamm_model.default_parameter_values[ + default_ocp = pybamm_model.default_parameter_values[ "Open-circuit voltage [V]" ] if parameter_set["Open-circuit voltage [V]"] == "default": print("Setting open-circuit voltage to default function") parameter_set["Open-circuit voltage [V]"] = default_ocp - super().__init__(name=name, parameter_set=parameter_set) + super().__init__( + name=name, parameter_set=parameter_set, check_params=check_params, eis=eis + ) + self.pybamm_model = pybamm_model + self._unprocessed_model = self.pybamm_model # Set parameters, using either the provided ones or the default self.default_parameter_values = self.pybamm_model.default_parameter_values @@ -69,13 +78,13 @@ def __init__( self._unprocessed_parameter_set = self._parameter_set # Define model geometry and discretization - self.geometry = geometry or self.pybamm_model.default_geometry - self.submesh_types = submesh_types or self.pybamm_model.default_submesh_types - self.var_pts = var_pts or self.pybamm_model.default_var_pts - self.spatial_methods = ( + self._geometry = geometry or self.pybamm_model.default_geometry + self._submesh_types = submesh_types or self.pybamm_model.default_submesh_types + self._var_pts = var_pts or self.pybamm_model.default_var_pts + self._spatial_methods = ( spatial_methods or self.pybamm_model.default_spatial_methods ) - self.solver = solver or self.pybamm_model.default_solver + self._solver = solver or self.pybamm_model.default_solver # Internal attributes for the built model are initialized but not set self._model_with_set_params = None @@ -85,7 +94,12 @@ def __init__( self._disc = None self.geometric_parameters = {} - def _check_params(self, inputs: Inputs = None, allow_infeasible_solutions=True): + def _check_params( + self, + inputs: Inputs, + parameter_set: ParameterSet, + allow_infeasible_solutions: bool = True, + ): """ Check the compatibility of the model parameters. @@ -93,6 +107,8 @@ def _check_params(self, inputs: Inputs = None, allow_infeasible_solutions=True): ---------- inputs : Inputs The input parameters for the simulation. + parameter_set : pybop.ParameterSet + A PyBOP parameter set object or a dictionary containing the parameter values. allow_infeasible_solutions : bool, optional If True, infeasible parameter values will be allowed in the optimisation (default: True). @@ -100,6 +116,90 @@ def _check_params(self, inputs: Inputs = None, allow_infeasible_solutions=True): ------- bool A boolean which signifies whether the parameters are compatible. - """ + if self.param_checker: + return self.param_checker(inputs, allow_infeasible_solutions) return True + + def get_initial_state( + self, + initial_value, + parameter_values=None, + param=None, + options=None, + tol=1e-6, + inputs=None, + ): + """ + Calculate the initial state of charge given an open-circuit voltage, voltage limits + and the open-circuit voltage function defined by the parameter set. + + Parameters + ---------- + initial_value : float + Target initial value. + If float, interpreted as SOC, must be between 0 and 1. + If string e.g. "4 V", interpreted as voltage, must be between V_min and V_max. + parameter_values : :class:`pybamm.ParameterValues` + The parameter values class that will be used for the simulation. Required for + calculating appropriate initial stoichiometries. + param : :class:`pybamm.LithiumIonParameters`, optional + The symbolic parameter set to use for the simulation. + If not provided, the default parameter set will be used. + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. + tol : float, optional + The tolerance for the solver used to compute the initial stoichiometries. + A lower value results in higher precision but may increase computation time. + Default is 1e-6. + + Returns + ------- + initial_soc + The initial state of charge + """ + parameter_values = parameter_values or self._unprocessed_parameter_set + param = param or self.pybamm_model.param + + if isinstance(initial_value, str) and initial_value.endswith("V"): + V_init = float(initial_value[:-1]) + V_min = parameter_values.evaluate(param.voltage_low_cut, inputs=inputs) + V_max = parameter_values.evaluate(param.voltage_high_cut, inputs=inputs) + + if not V_min <= V_init <= V_max: + raise ValueError( + f"Initial voltage {V_init}V is outside the voltage limits " + f"({V_min}, {V_max})" + ) + + # Solve simple model for initial soc based on target voltage + soc_model = pybamm.BaseModel() + soc = pybamm.Variable("soc") + ocv = param.ocv + soc_model.algebraic[soc] = ocv(soc) - V_init + + # initial guess for soc linearly interpolates between 0 and 1 + # based on V linearly interpolating between V_max and V_min + soc_model.initial_conditions[soc] = (V_init - V_min) / (V_max - V_min) + soc_model.variables["soc"] = soc + parameter_values.process_model(soc_model) + initial_soc = ( + pybamm.AlgebraicSolver(tol=tol).solve(soc_model, [0])["soc"].data[0] + ) + + # Ensure that the result lies between 0 and 1 + initial_soc = np.minimum(np.maximum(initial_soc, 0.0), 1.0) + + elif isinstance(initial_value, (int, float)): + if not 0 <= initial_value <= 1: + raise ValueError("Initial SOC should be between 0 and 1") + initial_soc = initial_value + + else: + raise ValueError( + "Initial value must be a float between 0 and 1, " + "or a string ending in 'V'" + ) + + return initial_soc diff --git a/pybop/models/empirical/ecm.py b/pybop/models/empirical/ecm.py index 893b30bc6..95a1a170e 100644 --- a/pybop/models/empirical/ecm.py +++ b/pybop/models/empirical/ecm.py @@ -1,7 +1,6 @@ from pybamm import equivalent_circuit as pybamm_equivalent_circuit from pybop.models.empirical.base_ecm import ECircuitModel -from pybop.parameters.parameter import Inputs class Thevenin(ECircuitModel): @@ -39,27 +38,12 @@ class Thevenin(ECircuitModel): def __init__( self, name="Equivalent Circuit Thevenin Model", + eis=False, **model_kwargs, ): super().__init__( - pybamm_model=pybamm_equivalent_circuit.Thevenin, name=name, **model_kwargs + pybamm_model=pybamm_equivalent_circuit.Thevenin, + name=name, + eis=eis, + **model_kwargs, ) - - def _check_params(self, inputs: Inputs = None, allow_infeasible_solutions=True): - """ - Check the compatibility of the model parameters. - - Parameters - ---------- - inputs : Inputs - The input parameters for the simulation. - allow_infeasible_solutions : bool, optional - If True, infeasible parameter values will be allowed in the optimisation (default: True). - - Returns - ------- - bool - A boolean which signifies whether the parameters are compatible. - - """ - return True diff --git a/pybop/models/lithium_ion/base_echem.py b/pybop/models/lithium_ion/base_echem.py index fd4aa2682..d42b8078b 100644 --- a/pybop/models/lithium_ion/base_echem.py +++ b/pybop/models/lithium_ion/base_echem.py @@ -1,8 +1,10 @@ import warnings +from typing import Optional from pybamm import lithium_ion as pybamm_lithium_ion from pybop.models.base_model import BaseModel, Inputs +from pybop.parameters.parameter_set import ParameterSet class EChemBaseModel(BaseModel): @@ -45,9 +47,10 @@ def __init__( var_pts=None, spatial_methods=None, solver=None, + eis=False, **model_kwargs, ): - super().__init__(name=name, parameter_set=parameter_set) + super().__init__(name=name, parameter_set=parameter_set, eis=eis) model_options = dict(build=False) for key, value in model_kwargs.items(): @@ -61,18 +64,18 @@ def __init__( self._unprocessed_parameter_set = self._parameter_set # Define model geometry and discretization - self.geometry = geometry or self.pybamm_model.default_geometry - self.submesh_types = submesh_types or self.pybamm_model.default_submesh_types - self.var_pts = var_pts or self.pybamm_model.default_var_pts - self.spatial_methods = ( + self._geometry = geometry or self.pybamm_model.default_geometry + self._submesh_types = submesh_types or self.pybamm_model.default_submesh_types + self._var_pts = var_pts or self.pybamm_model.default_var_pts + self._spatial_methods = ( spatial_methods or self.pybamm_model.default_spatial_methods ) if solver is None: - self.solver = self.pybamm_model.default_solver - self.solver.mode = "fast with events" - self.solver.max_step_decrease_count = 1 + self._solver = self.pybamm_model.default_solver + self._solver.mode = "fast with events" + self._solver.max_step_decrease_count = 1 else: - self.solver = solver + self._solver = solver # Internal attributes for the built model are initialized but not set self._model_with_set_params = None @@ -85,7 +88,10 @@ def __init__( self.geometric_parameters = self.set_geometric_parameters() def _check_params( - self, inputs: Inputs = None, parameter_set=None, allow_infeasible_solutions=True + self, + inputs: Inputs, + parameter_set: ParameterSet, + allow_infeasible_solutions: bool = True, ): """ Check compatibility of the model parameters. @@ -94,6 +100,8 @@ def _check_params( ---------- inputs : Inputs The input parameters for the simulation. + parameter_set : pybop.ParameterSet + A PyBOP parameter set object or a dictionary containing the parameter values. allow_infeasible_solutions : bool, optional If True, infeasible parameter values will be allowed in the optimisation (default: True). @@ -102,8 +110,6 @@ def _check_params( bool A boolean which signifies whether the parameters are compatible. """ - parameter_set = parameter_set or self._parameter_set - if self.pybamm_model.options["working electrode"] == "positive": electrode_params = [ ( @@ -136,13 +142,13 @@ def _check_params( ): if self.param_check_counter <= len(electrode_params): infeasibility_warning = "Non-physical point encountered - [{material_vol_fraction} + {porosity}] > 1.0!" - warnings.warn(infeasibility_warning, UserWarning) + warnings.warn(infeasibility_warning, UserWarning, stacklevel=2) self.param_check_counter += 1 return allow_infeasible_solutions return True - def cell_volume(self, parameter_set=None): + def cell_volume(self, parameter_set: Optional[ParameterSet] = None): """ Calculate the total cell volume in m3. @@ -153,8 +159,7 @@ def cell_volume(self, parameter_set=None): Parameters ---------- parameter_set : dict, optional - A dictionary containing the parameter values necessary for the volume - calculation. + A dictionary containing the parameter values necessary for the calculation. Returns ------- @@ -180,7 +185,7 @@ def cell_volume(self, parameter_set=None): # Calculate and return total cell volume return cross_sectional_area * cell_thickness - def cell_mass(self, parameter_set=None): + def cell_mass(self, parameter_set: Optional[ParameterSet] = None): """ Calculate the total cell mass in kilograms. @@ -192,8 +197,7 @@ def cell_mass(self, parameter_set=None): Parameters ---------- parameter_set : dict, optional - A dictionary containing the parameter values necessary for the mass - calculations. + A dictionary containing the parameter values necessary for the calculation. Returns ------- @@ -264,30 +268,27 @@ def area_density(thickness, mass_density): ) return cross_sectional_area * total_area_density - def approximate_capacity(self, inputs: Inputs): + def approximate_capacity(self, parameter_set: Optional[ParameterSet] = None): """ - Calculate and update an estimate for the nominal cell capacity based on the theoretical - energy density and an average voltage. - - The nominal capacity is computed by dividing the theoretical energy (in watt-hours) by - the average open circuit potential (voltage) of the cell. + Calculate an estimate for the nominal cell capacity. The estimate is computed + by dividing the theoretical energy (in watt-hours) by the average open circuit + potential (voltage) of the cell. Parameters ---------- - inputs : Inputs - The parameters that are the inputs of the model. + parameter_set : dict, optional + A dictionary containing the parameter values necessary for the calculation. Returns ------- - None - The nominal cell capacity is updated directly in the model's parameter set. + float + The estimate of the nominal cell capacity. """ - inputs = self.parameters.verify(inputs) - self._parameter_set.update(inputs) + parameter_set = parameter_set or self._parameter_set # Calculate theoretical energy density theoretical_energy = self._electrode_soh.calculate_theoretical_energy( - self._parameter_set + parameter_set ) # Extract stoichiometries and compute mean values @@ -296,25 +297,24 @@ def approximate_capacity(self, inputs: Inputs): max_sto_neg, min_sto_pos, max_sto_pos, - ) = self._electrode_soh.get_min_max_stoichiometries(self._parameter_set) + ) = self._electrode_soh.get_min_max_stoichiometries(parameter_set) mean_sto_neg = (min_sto_neg + max_sto_neg) / 2 mean_sto_pos = (min_sto_pos + max_sto_pos) / 2 # Calculate average voltage - positive_electrode_ocp = self._parameter_set["Positive electrode OCP [V]"] - negative_electrode_ocp = self._parameter_set["Negative electrode OCP [V]"] + positive_electrode_ocp = parameter_set["Positive electrode OCP [V]"] + negative_electrode_ocp = parameter_set["Negative electrode OCP [V]"] try: average_voltage = positive_electrode_ocp( mean_sto_pos ) - negative_electrode_ocp(mean_sto_neg) except Exception as e: - raise ValueError(f"Error in average voltage calculation: {e}") + raise ValueError(f"Error in average voltage calculation: {e}") from e - # Calculate and update nominal capacity - theoretical_capacity = theoretical_energy / average_voltage - self._parameter_set.update( - {"Nominal cell capacity [A.h]": theoretical_capacity} - ) + # Calculate the capacity estimate + approximate_capacity = theoretical_energy / average_voltage + + return approximate_capacity def set_geometric_parameters(self): """ diff --git a/pybop/models/lithium_ion/echem.py b/pybop/models/lithium_ion/echem.py index 8bd8ab636..aac05b738 100644 --- a/pybop/models/lithium_ion/echem.py +++ b/pybop/models/lithium_ion/echem.py @@ -1,8 +1,7 @@ from pybamm import lithium_ion as pybamm_lithium_ion from pybop.models.lithium_ion.base_echem import EChemBaseModel - -from .weppner_huggins import BaseWeppnerHuggins +from pybop.models.lithium_ion.weppner_huggins import BaseWeppnerHuggins class SPM(EChemBaseModel): @@ -39,11 +38,13 @@ class SPM(EChemBaseModel): def __init__( self, name="Single Particle Model", + eis=False, **model_kwargs, ): super().__init__( pybamm_model=pybamm_lithium_ion.SPM, name=name, + eis=eis, **model_kwargs, ) @@ -84,10 +85,11 @@ class SPMe(EChemBaseModel): def __init__( self, name="Single Particle Model with Electrolyte", + eis=False, **model_kwargs, ): super().__init__( - pybamm_model=pybamm_lithium_ion.SPMe, name=name, **model_kwargs + pybamm_model=pybamm_lithium_ion.SPMe, name=name, eis=eis, **model_kwargs ) @@ -127,9 +129,12 @@ class DFN(EChemBaseModel): def __init__( self, name="Doyle-Fuller-Newman", + eis=False, **model_kwargs, ): - super().__init__(pybamm_model=pybamm_lithium_ion.DFN, name=name, **model_kwargs) + super().__init__( + pybamm_model=pybamm_lithium_ion.DFN, name=name, eis=eis, **model_kwargs + ) class MPM(EChemBaseModel): @@ -166,10 +171,12 @@ class MPM(EChemBaseModel): def __init__( self, name="Many Particle Model", + eis=False, **model_kwargs, ): super().__init__( pybamm_model=pybamm_lithium_ion.MPM, + eis=eis, name=name, **model_kwargs, ) @@ -209,11 +216,13 @@ class MSMR(EChemBaseModel): def __init__( self, name="Multi Species Multi Reactions Model", + eis=False, **model_kwargs, ): super().__init__( pybamm_model=pybamm_lithium_ion.MSMR, name=name, + eis=eis, **model_kwargs, ) @@ -242,5 +251,7 @@ class WeppnerHuggins(EChemBaseModel): The solver to use for simulating the model. If None, the default solver from PyBaMM is used. """ - def __init__(self, name="Weppner & Huggins model", **model_kwargs): - super().__init__(pybamm_model=BaseWeppnerHuggins, name=name, **model_kwargs) + def __init__(self, name="Weppner & Huggins model", eis=False, **model_kwargs): + super().__init__( + pybamm_model=BaseWeppnerHuggins, name=name, eis=eis, **model_kwargs + ) diff --git a/pybop/models/lithium_ion/weppner_huggins.py b/pybop/models/lithium_ion/weppner_huggins.py index b8707cca9..9703269d3 100644 --- a/pybop/models/lithium_ion/weppner_huggins.py +++ b/pybop/models/lithium_ion/weppner_huggins.py @@ -99,6 +99,7 @@ def __init__(self, name="Weppner & Huggins model", **model_kwargs): self.variables = { "Voltage [V]": V, "Time [s]": t, + "Current [A]": self.param.current_with_time, } # Set the built property on creation to prevent unnecessary model rebuilds diff --git a/pybop/observers/observer.py b/pybop/observers/observer.py index 1c35c25df..239f352a1 100644 --- a/pybop/observers/observer.py +++ b/pybop/observers/observer.py @@ -22,12 +22,12 @@ class Observer(BaseProblem): The model to observe. check_model : bool, optional Flag to indicate if the model should be checked (default: True). - signal: List[str] + signal: list[str] The signal to observe. - additional_variables : List[str], optional + additional_variables : list[str], optional Additional variables to observe and store in the solution (default: []). - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state, e.g. the initial open-circuit voltage (default: None). """ # define a subtype for covariance matrices for use by derived classes @@ -37,28 +37,21 @@ def __init__( self, parameters: Parameters, model: BaseModel, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[dict] = None, ) -> None: super().__init__( - parameters, model, check_model, signal, additional_variables, init_soc + parameters, model, check_model, signal, additional_variables, initial_state ) - if model._built_model is None: + if model.built_model is None: raise ValueError("Only built models can be used in Observers") - if model.signal is None: - model.signal = self.signal inputs = self.parameters.as_dict("initial") - self._state = model.reinit(inputs) - self._model = model - self._signal = self.signal - self._n_outputs = len(self._signal) + self._state = self.model.reinit(inputs) def reset(self, inputs: Inputs) -> None: - inputs = self.parameters.verify(inputs) - self._state = self._model.reinit(inputs) def observe(self, time: float, value: Optional[np.ndarray] = None) -> float: @@ -97,8 +90,8 @@ def log_likelihood(self, values: dict, times: np.ndarray, inputs: Inputs) -> flo """ inputs = self.parameters.verify(inputs) - if self._n_outputs == 1: - signal = self._signal[0] + if self.n_outputs == 1: + signal = self.signal[0] if len(values[signal]) != len(times): raise ValueError("values and times must have the same length.") log_likelihood = 0.0 @@ -134,7 +127,7 @@ def get_current_covariance(self) -> Covariance: return np.zeros((n, n)) def get_measure(self, x: TimeSeriesState) -> np.ndarray: - measures = [x.sol[s].data[-1] for s in self._signal] + measures = [x.sol[s].data[-1] for s in self.signal] return np.asarray([[m] for m in measures]) def get_current_time(self) -> float: @@ -157,20 +150,22 @@ def evaluate(self, inputs: Inputs): y : np.ndarray The model output y(t) simulated with given inputs. """ + inputs = self.parameters.verify(inputs) + self.reset(inputs) output = {} ys = [] - if hasattr(self, "_dataset"): - for signal in self._signal: + if self._dataset is not None: + for signal in self.signal: ym = self._target[signal] - for i, t in enumerate(self._time_data): + for i, t in enumerate(self._domain_data): self.observe(t, ym[i]) ys.append(self.get_current_measure()) output[signal] = np.vstack(ys) else: - for signal in self._signal: - for t in self._time_data: + for signal in self.signal: + for t in self._domain_data: self.observe(t) ys.append(self.get_current_measure()) output[signal] = np.vstack(ys) diff --git a/pybop/observers/unscented_kalman.py b/pybop/observers/unscented_kalman.py index c7eb18b0b..69e019ee6 100644 --- a/pybop/observers/unscented_kalman.py +++ b/pybop/observers/unscented_kalman.py @@ -1,9 +1,10 @@ from dataclasses import dataclass -from typing import List, Tuple, Union +from typing import Optional, Union import numpy as np import scipy.linalg as linalg +from pybop._dataset import Dataset from pybop.models.base_model import BaseModel, Inputs from pybop.observers.observer import Observer from pybop.parameters.parameter import Parameter @@ -33,45 +34,49 @@ class UnscentedKalmanFilterObserver(Observer): Flag to indicate if the model should be checked (default: True). signal: str The signal to observe. - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state, e.g. the initial open-circuit voltage (default: None). """ Covariance = np.ndarray def __init__( self, - parameters: List[Parameter], + parameters: list[Parameter], model: BaseModel, sigma0: Union[Covariance, float], process: Union[Covariance, float], measure: Union[Covariance, float], - dataset=None, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + dataset: Optional[Dataset] = None, + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[float] = None, ) -> None: + if model is not None: + # Clear any existing built model and its properties + if model.built_model is not None: + model.clear() + + # Build the model from scratch + model.build( + dataset=dataset, + parameters=parameters, + check_model=check_model, + ) + super().__init__( - parameters, model, check_model, signal, additional_variables, init_soc + parameters, model, check_model, signal, additional_variables, initial_state ) if dataset is not None: - self._dataset = dataset.data + # Check that the dataset contains necessary variables + dataset.check(signal=[*self.signal, "Current function [A]"]) - # Check that the dataset contains time and current - dataset.check(self.signal + ["Current function [A]"]) - - self._time_data = self._dataset["Time [s]"] - self.n_time_data = len(self._time_data) + self._dataset = dataset.data + self._domain_data = self._dataset["Time [s]"] + self.n_data = len(self._domain_data) self._target = {signal: self._dataset[signal] for signal in self.signal} - # Add useful parameters to model - if model is not None: - self._model.signal = self.signal - self._model.n_outputs = self.n_outputs - if dataset is not None: - self._model.n_time_data = self.n_time_data - # Observer initiation self._process = process @@ -235,7 +240,7 @@ def reset(self, x: np.ndarray, S: np.ndarray) -> None: @staticmethod def gen_sigma_points( x: np.ndarray, S: np.ndarray, alpha: float, beta: float, states: np.ndarray - ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """ Generates 2L+1 sigma points for the unscented transform, where L is the number of states. @@ -254,11 +259,11 @@ def gen_sigma_points( Returns ------- - List[np.ndarray] + list[np.ndarray] The sigma points - List[float] + list[float] The weights of the sigma points - List[float] + list[float] The weights of the covariance of the sigma points """ # Set the scaling parameters: sigma and eta @@ -290,14 +295,14 @@ def unscented_transform( w_m: np.ndarray, w_c: np.ndarray, sqrtR: np.ndarray, - states: Union[np.ndarray, None] = None, - ) -> Tuple[np.ndarray, np.ndarray]: + states: Optional[np.ndarray] = None, + ) -> tuple[np.ndarray, np.ndarray]: """ Performs the unscented transform Parameters ---------- - sigma_points : List[SigmaPoint] + sigma_points : list[SigmaPoint] The sigma points sqrtR : np.ndarray The square root of the covariance matrix diff --git a/pybop/optimisers/_adamw.py b/pybop/optimisers/_adamw.py index 24e5ec982..34c990206 100644 --- a/pybop/optimisers/_adamw.py +++ b/pybop/optimisers/_adamw.py @@ -26,7 +26,7 @@ class AdamWImpl(PintsOptimiser): m_j' = m_j[i] / (1 - beta1**(1 + i)) v_j' = v_j[i] / (1 - beta2**(1 + i)) - p_j[i] = p_j[i - 1] - alpha * (m_j' / (sqrt(v_j') + eps) + lambda * p_j[i - 1]) + p_j[i] = p_j[i - 1] - alpha * (m_j' / (sqrt(v_j') + eps) + lam * p_j[i - 1]) The initial values of the moments are ``m_j[0] = v_j[0] = 0``, after which they decay with rates ``beta1`` and ``beta2``. The default values for these are, @@ -38,7 +38,7 @@ class AdamWImpl(PintsOptimiser): The parameter ``alpha`` is a step size, which is set as ``min(sigma0)`` in this implementation. - The parameter ``lambda`` is the weight decay rate, which is set to ``0.01`` + The parameter ``lam`` is the weight decay rate, which is set to ``0.01`` by default in this implementation. Finally, ``eps`` is a small constant used to avoid division by zero, set to @@ -86,10 +86,9 @@ def __init__(self, x0, sigma0=0.015, boundaries=None): self._b2 = 0.999 # Step size - self._alpha = np.min(self._sigma0) - + self._alpha = self._sigma0 # Weight decay rate - self.set_lambda() + self._lam = 0.01 # Small number added to avoid divide-by-zero self._eps = np.finfo(float).eps @@ -157,7 +156,7 @@ def tell(self, reply): # Check ask-tell pattern if not self._ready_for_tell: - raise Exception("ask() not called before tell()") + raise RuntimeError("ask() not called before tell()") self._ready_for_tell = False # Unpack reply @@ -186,7 +185,7 @@ def tell(self, reply): # Take step with weight decay self._proposed = self._current - self._alpha * ( - m / (np.sqrt(v) + self._eps) + self._lambda * self._current + m / (np.sqrt(v) + self._eps) + self._lam * self._current ) # Update x_best and f_best @@ -206,17 +205,27 @@ def x_guessed(self): """ return self._current - def set_lambda(self, lambda_: float = 0.01) -> None: + @property + def lam(self): + return self._lam + + @lam.setter + def lam(self, lam: float = 0.01) -> None: """ - Sets the lambda_ decay constant. This is the weight decay rate + Sets the lam decay constant. This is the weight decay rate that helps in finding the optimal solution. """ - if not isinstance(lambda_, (int, float)) or not 0 < lambda_ <= 1: - raise ValueError("lambda_ must be a numeric value between 0 and 1.") + if not isinstance(lam, (int, float)) or not 0 < lam <= 1: + raise ValueError("lam must be a numeric value between 0 and 1.") - self._lambda = float(lambda_) + self._lam = float(lam) - def set_b1(self, b1: float) -> None: + @property + def b1(self): + return self._b1 + + @b1.setter + def b1(self, b1: float) -> None: """ Sets the b1 momentum decay constant. """ @@ -225,7 +234,12 @@ def set_b1(self, b1: float) -> None: self._b1 = float(b1) - def set_b2(self, b2: float) -> None: + @property + def b2(self): + return self._b2 + + @b2.setter + def b2(self, b2: float) -> None: """ Sets the b2 momentum decay constant. """ diff --git a/pybop/optimisers/base_optimiser.py b/pybop/optimisers/base_optimiser.py index 4693468f2..6963c9251 100644 --- a/pybop/optimisers/base_optimiser.py +++ b/pybop/optimisers/base_optimiser.py @@ -1,8 +1,16 @@ import warnings +from typing import Optional import numpy as np -from pybop import BaseCost, BaseLikelihood, DesignCost, Parameter, Parameters +from pybop import ( + BaseCost, + BaseLikelihood, + DesignCost, + Parameter, + Parameters, + WeightedCost, +) class BaseOptimiser: @@ -53,10 +61,11 @@ def __init__( self.parameters = Parameters() self.x0 = None self.bounds = None - self.sigma0 = 0.1 + self.sigma0 = 0.02 self.verbose = False self.log = dict(x=[], x_best=[], cost=[]) self.minimising = True + self._transformation = None self.physical_viability = False self.allow_infeasible_solutions = False self.default_max_iterations = 1000 @@ -64,8 +73,11 @@ def __init__( if isinstance(cost, BaseCost): self.cost = cost + self._transformation = self.cost.transformation self.parameters.join(cost.parameters) self.set_allow_infeasible_solutions() + if isinstance(cost, WeightedCost): + self.minimising = cost.minimising if isinstance(cost, (BaseLikelihood, DesignCost)): self.minimising = False @@ -75,8 +87,9 @@ def __init__( cost_test = cost(self.x0) warnings.warn( "The cost is not an instance of pybop.BaseCost, but let's continue " - + "assuming that it is a callable function to be minimised.", + "assuming that it is a callable function to be minimised.", UserWarning, + stacklevel=2, ) self.cost = cost for i, value in enumerate(self.x0): @@ -85,8 +98,10 @@ def __init__( ) self.minimising = True - except Exception: - raise Exception("The cost is not a recognised cost object or function.") + except Exception as e: + raise Exception( + "The cost is not a recognised cost object or function." + ) from e if not np.isscalar(cost_test) or not np.isreal(cost_test): raise TypeError( @@ -100,7 +115,7 @@ def __init__( self.set_base_options() self._set_up_optimiser() - # Throw an warning if any options remain + # Throw a warning if any options remain if self.unset_options: warnings.warn( f"Unrecognised keyword arguments: {self.unset_options} will not be used.", @@ -114,14 +129,16 @@ def set_base_options(self): """ # Set initial values, if x0 is None, initial values are unmodified. self.parameters.update(initial_values=self.unset_options.pop("x0", None)) - self.x0 = self.parameters.initial_value() + self.x0 = self.parameters.reset_initial_value(apply_transform=True) # Set default bounds (for all or no parameters) - self.bounds = self.unset_options.pop("bounds", self.parameters.get_bounds()) + self.bounds = self.unset_options.pop( + "bounds", self.parameters.get_bounds(apply_transform=True) + ) # Set default initial standard deviation (for all or no parameters) self.sigma0 = self.unset_options.pop( - "sigma0", self.parameters.get_sigma0() or self.sigma0 + "sigma0", self.parameters.get_sigma0(apply_transform=True) or self.sigma0 ) # Set other options @@ -181,19 +198,35 @@ def _run(self): """ raise NotImplementedError - def store_optimised_parameters(self, x): + def log_update(self, x=None, x_best=None, cost=None): """ - Update the problem parameters with optimised values. - - The optimised parameter values are stored within the associated PyBOP parameter class. + Update the log with new values. Parameters ---------- - x : array-like - Optimised parameter values. + x : list or array-like, optional + Parameter values (default: None). + x_best : list or array-like, optional + Paraneter values corresponding to the best cost yet (default: None). + cost : float, optional + Cost value (default: None). """ - for i, param in enumerate(self.cost.parameters): - param.update(value=x[i]) + if x is not None: + if self._transformation: + x = list(x) + for i, xi in enumerate(x): + x[i] = self._transformation.to_model(xi) + self.log["x"].append(x) + + if x_best is not None: + if self._transformation: + x_best = list(x_best) + for i, xi in enumerate(x_best): + x_best[i] = self._transformation.to_model(xi) + self.log["x_best"].append(x_best) + + if cost is not None: + self.log["cost"].append(cost) def check_optimal_parameters(self, x): """ @@ -204,14 +237,14 @@ def check_optimal_parameters(self, x): x : array-like Optimised parameter values. """ - if self.cost.problem._model.check_params( + if self.cost.problem.model.check_params( inputs=x, allow_infeasible_solutions=False ): return else: warnings.warn( "Optimised parameters are not physically viable! \nConsider retrying the optimisation" - + " with a non-gradient-based optimiser and the option allow_infeasible_solutions=False", + " with a non-gradient-based optimiser and the option allow_infeasible_solutions=False", UserWarning, stacklevel=2, ) @@ -240,8 +273,12 @@ def set_allow_infeasible_solutions(self, allow=True): self.physical_viability = allow self.allow_infeasible_solutions = allow - if hasattr(self.cost, "problem") and hasattr(self.cost.problem, "_model"): - self.cost.problem._model.allow_infeasible_solutions = ( + if ( + hasattr(self.cost, "problem") + and hasattr(self.cost.problem, "model") + and self.cost.problem.model is not None + ): + self.cost.problem.model.allow_infeasible_solutions = ( self.allow_infeasible_solutions ) else: @@ -269,8 +306,8 @@ class Result: def __init__( self, x: np.ndarray = None, - final_cost: float = None, - n_iterations: int = None, + final_cost: Optional[float] = None, + n_iterations: Optional[int] = None, scipy_result=None, ): self.x = x diff --git a/pybop/optimisers/base_pints_optimiser.py b/pybop/optimisers/base_pints_optimiser.py index f5698c8ed..c121d074e 100644 --- a/pybop/optimisers/base_pints_optimiser.py +++ b/pybop/optimisers/base_pints_optimiser.py @@ -48,9 +48,6 @@ def __init__(self, cost, pints_optimiser, **optimiser_kwargs): self._evaluations = None self._iterations = None - # PyBOP doesn't currently support the PINTS transformation class - self._transformation = None - self.pints_optimiser = pints_optimiser super().__init__(cost, **optimiser_kwargs) @@ -193,15 +190,14 @@ def _run(self): unchanged_iterations = 0 # Choose method to evaluate - if self._needs_sensitivities: - - def f(x): - L, dl = self.cost.evaluateS1(x) - return (L, dl) if self.minimising else (-L, -dl) - else: - - def f(x, grad=None): - return self.cost(x, grad) if self.minimising else -self.cost(x, grad) + def fun(x): + if self._needs_sensitivities: + L, dl = self.cost(x, calculate_grad=True, apply_transform=True) + else: + L = self.cost(x, apply_transform=True) + dl = None + sign = -1 if not self.minimising else 1 + return (sign * L, sign * dl) if dl is not None else sign * L # Create evaluator object if self._parallel: @@ -212,9 +208,9 @@ def f(x, grad=None): # particles! if isinstance(self.pints_optimiser, PintsPopulationBasedOptimiser): n_workers = min(n_workers, self.pints_optimiser.population_size()) - evaluator = PintsParallelEvaluator(f, n_workers=n_workers) + evaluator = PintsParallelEvaluator(fun, n_workers=n_workers) else: - evaluator = PintsSequentialEvaluator(f) + evaluator = PintsSequentialEvaluator(fun) # Keep track of current best and best-guess scores. fb = fg = np.inf @@ -256,9 +252,11 @@ def f(x, grad=None): # Update counts evaluations += len(fs) iteration += 1 - self.log["x"].append(xs) - self.log["x_best"].append(self.pints_optimiser.x_best()) - self.log["cost"].append(fb if self.minimising else -fb) + self.log_update( + x=xs, + x_best=self.pints_optimiser.x_best(), + cost=fb if self.minimising else -fb, + ) # Check stopping criteria: # Maximum number of iterations @@ -325,7 +323,7 @@ def f(x, grad=None): # Show current parameters x_user = self.pints_optimiser.x_guessed() - if self._transformation is not None: + if self._transformation: x_user = self._transformation.to_model(x_user) for p in x_user: print(PintsStrFloat(p)) @@ -348,7 +346,7 @@ def f(x, grad=None): f = self.pints_optimiser.f_best() # Inverse transform search parameters - if self._transformation is not None: + if self._transformation: x = self._transformation.to_model(x) return Result( diff --git a/pybop/optimisers/pints_optimisers.py b/pybop/optimisers/pints_optimisers.py index 64b77b674..86dba3100 100644 --- a/pybop/optimisers/pints_optimisers.py +++ b/pybop/optimisers/pints_optimisers.py @@ -17,8 +17,9 @@ class GradientDescent(BasePintsOptimiser): Implements a simple gradient descent optimization algorithm. This class extends the gradient descent optimiser from the PINTS library, designed - to minimize a scalar function of one or more variables. Note that this optimiser - does not support boundary constraints. + to minimize a scalar function of one or more variables. + + Note that this optimiser does not support boundary constraints. Parameters ---------- @@ -27,7 +28,7 @@ class GradientDescent(BasePintsOptimiser): x0 : array_like Initial position from which optimisation will start. sigma0 : float - The learning rate / Initial step size (default: 0.02). + The learning rate / Initial step size. See Also -------- @@ -35,8 +36,6 @@ class GradientDescent(BasePintsOptimiser): """ def __init__(self, cost, **optimiser_kwargs): - if "sigma0" not in optimiser_kwargs.keys(): - optimiser_kwargs["sigma0"] = 0.02 # set default super().__init__(cost, PintsGradientDescent, **optimiser_kwargs) @@ -45,8 +44,9 @@ class Adam(BasePintsOptimiser): Implements the Adam optimization algorithm. This class extends the Adam optimiser from the PINTS library, which combines - ideas from RMSProp and Stochastic Gradient Descent with momentum. Note that - this optimiser does not support boundary constraints. + ideas from RMSProp and Stochastic Gradient Descent with momentum. + + Note that this optimiser does not support boundary constraints. Parameters ---------- @@ -81,6 +81,7 @@ class AdamW(BasePintsOptimiser): robust and stable for training deep neural networks, particularly when using larger learning rates. + Note that this optimiser does not support boundary constraints. Parameters ---------- **optimiser_kwargs : optional @@ -223,6 +224,8 @@ class NelderMead(BasePintsOptimiser): either one evaluation, or two sequential evaluations, so that it will not typically benefit from parallelisation. + Note that this optimiser does not support boundary constraints. + Parameters ---------- **optimiser_kwargs : optional @@ -272,7 +275,7 @@ def __init__(self, cost, **optimiser_kwargs): if len(x0) == 1 or len(cost.parameters) == 1: raise ValueError( "CMAES requires optimisation of >= 2 parameters at once. " - + "Please choose another optimiser." + "Please choose another optimiser." ) super().__init__(cost, PintsCMAES, **optimiser_kwargs) diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index 544abfc88..3f9bf9e7b 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -1,5 +1,8 @@ +import warnings +from typing import Union + import numpy as np -from scipy.optimize import OptimizeResult, differential_evolution, minimize +from scipy.optimize import Bounds, OptimizeResult, differential_evolution, minimize from pybop import BaseOptimiser, Result @@ -52,12 +55,18 @@ def _sanitise_inputs(self): # Convert bounds to SciPy format if isinstance(self.bounds, dict): - self._scipy_bounds = [ - (lower, upper) - for lower, upper in zip(self.bounds["lower"], self.bounds["upper"]) - ] - else: + self._scipy_bounds = Bounds( + self.bounds["lower"], self.bounds["upper"], True + ) + elif isinstance(self.bounds, list): + lb, ub = zip(*self.bounds) + self._scipy_bounds = Bounds(lb, ub, True) + elif isinstance(self.bounds, Bounds) or self.bounds is None: self._scipy_bounds = self.bounds + else: + raise TypeError( + "Bounds provided must be either type dict, list or SciPy.optimize.bounds object." + ) def _run(self): """ @@ -70,10 +79,17 @@ def _run(self): """ result = self._run_optimiser() + try: + nit = result.nit + except AttributeError: + nit = -1 + return Result( - x=result.x, + x=self._transformation.to_model(result.x) + if self._transformation + else result.x, final_cost=self.cost(result.x), - n_iterations=result.nit, + n_iterations=nit, scipy_result=result, ) @@ -107,6 +123,7 @@ def __init__(self, cost, **optimiser_kwargs): optimiser_options = dict(method="Nelder-Mead", jac=False) optimiser_options.update(**optimiser_kwargs) super().__init__(cost, **optimiser_options) + self._cost0 = 1.0 def _set_up_optimiser(self): """ @@ -141,6 +158,22 @@ def _set_up_optimiser(self): # Nest this option within an options dictionary for SciPy minimize self._options["options"]["maxiter"] = self.unset_options.pop(key) + def cost_wrapper(self, x): + """ + Scale the cost function, preserving the sign convention, and eliminate nan values + """ + self.log_update(x=[x]) + + if not self._options["jac"]: + cost = self.cost(x) / self._cost0 + if np.isinf(cost): + self.inf_count += 1 + cost = 1 + 0.9**self.inf_count # for fake finite gradient + return cost if self.minimising else -cost + + L, dl = self.cost(x, calculate_grad=True) + return (L, dl) if self.minimising else (-L, -dl) + def _run_optimiser(self): """ Executes the optimisation process using SciPy's minimize function. @@ -150,19 +183,47 @@ def _run_optimiser(self): result : scipy.optimize.OptimizeResult The result of the optimisation including the optimised parameter values and cost. """ + self.inf_count = 0 # Add callback storing history of parameter values - def callback(intermediate_result: OptimizeResult): - self.log["x_best"].append(intermediate_result.x) - self.log["cost"].append( - intermediate_result.fun if self.minimising else -intermediate_result.fun + def base_callback(intermediate_result: Union[OptimizeResult, np.ndarray]): + """ + Log intermediate optimisation solutions. Depending on the + optimisation algorithm, intermediate_result may be either + a OptimizeResult or an array of parameter values, with a + try/except ensuring both cases are handled correctly. + """ + if isinstance(intermediate_result, OptimizeResult): + x_best = intermediate_result.x + cost = intermediate_result.fun + else: + x_best = intermediate_result + cost = self.cost(x_best) + + self.log_update( + x_best=x_best, + cost=(-1 if not self.minimising else 1) * cost * self._cost0, ) + callback = ( + base_callback + if self._options["method"] != "trust-constr" + else lambda x, intermediate_result: base_callback(intermediate_result) + ) + # Compute the absolute initial cost and resample if required self._cost0 = np.abs(self.cost(self.x0)) if np.isinf(self._cost0): - for i in range(1, self.num_resamples): - self.x0 = self.parameters.rvs(1)[0] + for _i in range(1, self.num_resamples): + try: + self.x0 = self.parameters.rvs(apply_transform=True) + except AttributeError: + warnings.warn( + "Parameter does not have a prior distribution. Stopping resampling.", + UserWarning, + stacklevel=2, + ) + break self._cost0 = np.abs(self.cost(self.x0)) if not np.isinf(self._cost0): break @@ -171,27 +232,8 @@ def callback(intermediate_result: OptimizeResult): "The initial parameter values return an infinite cost." ) - # Scale the cost function, preserving the sign convention, and eliminate nan values - self.inf_count = 0 - - if not self._options["jac"]: - - def cost_wrapper(x): - self.log["x"].append([x]) - cost = self.cost(x) / self._cost0 - if np.isinf(cost): - self.inf_count += 1 - cost = 1 + 0.9**self.inf_count # for fake finite gradient - return cost if self.minimising else -cost - elif self._options["jac"] is True: - - def cost_wrapper(x): - self.log["x"].append([x]) - L, dl = self.cost.evaluateS1(x) - return L, dl if self.minimising else -L, -dl - return minimize( - cost_wrapper, + self.cost_wrapper, self.x0, bounds=self._scipy_bounds, callback=callback, @@ -251,9 +293,8 @@ def _set_up_optimiser(self): if self._scipy_bounds is None: raise ValueError("Bounds must be specified for differential_evolution.") else: - if not all( - np.isfinite(value) for pair in self._scipy_bounds for value in pair - ): + bnds = self._scipy_bounds + if not (np.isfinite(bnds.lb).all() and np.isfinite(bnds.ub).all()): raise ValueError("Bounds must be specified for differential_evolution.") # Apply default maxiter and tolerance @@ -302,13 +343,15 @@ def _run_optimiser(self): # Add callback storing history of parameter values def callback(intermediate_result: OptimizeResult): - self.log["x_best"].append(intermediate_result.x) - self.log["cost"].append( - intermediate_result.fun if self.minimising else -intermediate_result.fun + self.log_update( + x_best=intermediate_result.x, + cost=intermediate_result.fun + if self.minimising + else -intermediate_result.fun, ) def cost_wrapper(x): - self.log["x"].append([x]) + self.log_update(x=[x]) return self.cost(x) if self.minimising else -self.cost(x) return differential_evolution( diff --git a/pybop/parameters/parameter.py b/pybop/parameters/parameter.py index 27836fc80..e95cd698b 100644 --- a/pybop/parameters/parameter.py +++ b/pybop/parameters/parameter.py @@ -1,12 +1,13 @@ import warnings from collections import OrderedDict -from typing import Dict, List, Union +from typing import Optional import numpy as np +from pybop import ComposedTransformation, IdentityTransformation from pybop._utils import is_numeric -Inputs = Dict[str, float] +Inputs = dict[str, float] class Parameter: @@ -37,7 +38,13 @@ class Parameter: """ def __init__( - self, name, initial_value=None, true_value=None, prior=None, bounds=None + self, + name, + initial_value=None, + true_value=None, + prior=None, + bounds=None, + transformation=None, ): """ Construct the parameter class with a name, initial value, prior, and bounds. @@ -47,11 +54,15 @@ def __init__( self.true_value = true_value self.initial_value = initial_value self.value = initial_value + self.transformation = transformation self.applied_prior_bounds = False + self.bounds = None + self.lower_bounds = None + self.upper_bounds = None self.set_bounds(bounds) self.margin = 1e-4 - def rvs(self, n_samples, random_state=None): + def rvs(self, n_samples: int = 1, random_state=None, apply_transform: bool = False): """ Draw random samples from the parameter's prior distribution. @@ -61,7 +72,11 @@ def rvs(self, n_samples, random_state=None): Parameters ---------- n_samples : int - The number of samples to draw. + The number of samples to draw (default: 1). + random_state : int, optional + The random state seed for reproducibility (default: None). + apply_transform : bool + If True, the transformation is applied to the output (default: False). Returns ------- @@ -77,6 +92,12 @@ def rvs(self, n_samples, random_state=None): samples, self.lower_bound + offset, self.upper_bound - offset ) + if apply_transform and self.transformation is not None: + samples = list(samples) + for i, x in enumerate(samples): + samples[i] = float(self.transformation.to_search(x)) + return np.asarray(samples) + return samples def update(self, initial_value=None, value=None): @@ -158,18 +179,35 @@ def set_bounds(self, bounds=None, boundary_multiplier=6): self.applied_prior_bounds = True self.lower_bound = self.prior.mean - boundary_multiplier * self.prior.sigma self.upper_bound = self.prior.mean + boundary_multiplier * self.prior.sigma - bounds = [self.lower_bound, self.upper_bound] print("Default bounds applied based on prior distribution.") + else: + self.bounds = None + return - self.bounds = bounds + self.bounds = [self.lower_bound, self.upper_bound] - def get_initial_value(self) -> float: + def get_initial_value(self, apply_transform: bool = False) -> float: """ Return the initial value of each parameter. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). """ if self.initial_value is None: - sample = self.rvs(1) - self.update(initial_value=sample[0]) + if self.prior is not None: + sample = self.rvs(1)[0] + self.update(initial_value=sample) + else: + warnings.warn( + "Initial value and prior are None, proceeding without an initial value.", + UserWarning, + stacklevel=2, + ) + + if apply_transform and self.transformation is not None: + return float(self.transformation.to_search(self.initial_value)) return self.initial_value @@ -191,6 +229,7 @@ def __init__(self, *args): self.param = OrderedDict() for param in args: self.add(param) + self.initial_value() def __getitem__(self, key: str) -> Parameter: """ @@ -219,7 +258,7 @@ def __getitem__(self, key: str) -> Parameter: def __len__(self) -> int: return len(self.param) - def keys(self) -> List: + def keys(self) -> list: """ A list of parameter names """ @@ -245,7 +284,7 @@ def add(self, parameter): if parameter.name in self.param.keys(): raise ValueError( f"There is already a parameter with the name {parameter.name} " - + "in the Parameters object. Please remove the duplicate entry." + "in the Parameters object. Please remove the duplicate entry." ) self.param[parameter.name] = parameter elif isinstance(parameter, dict): @@ -255,7 +294,7 @@ def add(self, parameter): if name in self.param.keys(): raise ValueError( f"There is already a parameter with the name {name} " - + "in the Parameters object. Please remove the duplicate entry." + "in the Parameters object. Please remove the duplicate entry." ) self.param[name] = Parameter(**parameter) else: @@ -287,17 +326,30 @@ def join(self, parameters=None): else: print(f"Discarding duplicate {param.name}.") - def get_bounds(self) -> Dict: + def get_bounds(self, apply_transform: bool = False) -> dict: """ Get bounds, for either all or no parameters. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). """ all_unbounded = True # assumption bounds = {"lower": [], "upper": []} for param in self.param.values(): if param.bounds is not None: - bounds["lower"].append(param.bounds[0]) - bounds["upper"].append(param.bounds[1]) + if apply_transform and param.transformation is not None: + bounds["lower"].append( + float(param.transformation.to_search(param.bounds[0])) + ) + bounds["upper"].append( + float(param.transformation.to_search(param.bounds[1])) + ) + else: + bounds["lower"].append(param.bounds[0]) + bounds["upper"].append(param.bounds[1]) all_unbounded = False else: bounds["lower"].append(-np.inf) @@ -317,12 +369,12 @@ def update(self, initial_values=None, values=None, bounds=None): if values is not None: param.update(value=values[i]) if bounds is not None: - if isinstance(bounds, Dict): + if isinstance(bounds, dict): param.set_bounds(bounds=[bounds["lower"][i], bounds["upper"][i]]) else: param.set_bounds(bounds=bounds[i]) - def rvs(self, n_samples: int) -> List: + def rvs(self, n_samples: int = 1, apply_transform: bool = False) -> np.ndarray: """ Draw random samples from each parameter's prior distribution. @@ -332,7 +384,9 @@ def rvs(self, n_samples: int) -> List: Parameters ---------- n_samples : int - The number of samples to draw. + The number of samples to draw (default: 1). + apply_transform : bool + If True, the transformation is applied to the output (default: False). Returns ------- @@ -342,47 +396,82 @@ def rvs(self, n_samples: int) -> List: all_samples = [] for param in self.param.values(): - samples = param.rvs(n_samples) - - # Constrain samples to be within bounds - if param.bounds is not None: - offset = param.margin * (param.upper_bound - param.lower_bound) - samples = np.clip( - samples, param.lower_bound + offset, param.upper_bound - offset - ) - + samples = param.rvs(n_samples, apply_transform=apply_transform) all_samples.append(samples) - return all_samples + return np.concatenate(all_samples) - def get_sigma0(self) -> List: + def get_sigma0(self, apply_transform: bool = False) -> list: """ Get the standard deviation, for either all or no parameters. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). """ all_have_sigma = True # assumption sigma0 = [] for param in self.param.values(): if hasattr(param.prior, "sigma"): - sigma0.append(param.prior.sigma) + if apply_transform and param.transformation is not None: + sigma0.append( + np.ndarray.item( + param.transformation.convert_standard_deviation( + param.prior.sigma, + param.get_initial_value(apply_transform=True), + ) + ) + ) + else: + sigma0.append(param.prior.sigma) else: all_have_sigma = False if not all_have_sigma: sigma0 = None - return sigma0 - def initial_value(self) -> np.ndarray: + def priors(self) -> list: + """ + Return the prior distribution of each parameter. + """ + return [param.prior for param in self.param.values()] + + def initial_value(self, apply_transform: bool = False) -> np.ndarray: """ Return the initial value of each parameter. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). """ initial_values = [] for param in self.param.values(): - if param.initial_value is None: - initial_value = param.rvs(1)[0] - param.update(initial_value=initial_value) - initial_values.append(param.initial_value) + initial_value = param.get_initial_value(apply_transform=apply_transform) + initial_values.append(initial_value) + + return np.asarray(initial_values) + + def reset_initial_value(self, apply_transform: bool = False) -> np.ndarray: + """ + Reset and return the initial value of each parameter. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). + """ + initial_values = [] + + for param in self.param.values(): + initial_value = param.get_initial_value(apply_transform=apply_transform) + if initial_value is not None: + # Reset the current value as well + param.update(value=param.get_initial_value()) + initial_values.append(initial_value) return np.asarray(initial_values) @@ -408,6 +497,30 @@ def true_value(self) -> np.ndarray: return np.asarray(true_values) + def get_transformations(self): + """ + Get the transformations for each parameter. + """ + transformations = [] + + for param in self.param.values(): + transformations.append(param.transformation) + + return transformations + + def construct_transformation(self): + """ + Create a ComposedTransformation object from the individual parameter transformations. + """ + transformations = self.get_transformations() + if not transformations or all(t is None for t in transformations): + return None + + valid_transformations = [ + t if t is not None else IdentityTransformation() for t in transformations + ] + return ComposedTransformation(valid_transformations) + def get_bounds_for_plotly(self): """ Retrieve parameter bounds in the format expected by Plotly. @@ -417,7 +530,7 @@ def get_bounds_for_plotly(self): bounds : numpy.ndarray An array of shape (n_parameters, 2) containing the bounds for each parameter. """ - bounds = np.empty((len(self), 2)) + bounds = np.zeros((len(self), 2)) for i, param in enumerate(self.param.values()): if param.applied_prior_bounds: @@ -427,14 +540,14 @@ def get_bounds_for_plotly(self): UserWarning, stacklevel=2, ) - elif param.bounds is not None: + if param.bounds is not None: bounds[i] = param.bounds else: raise ValueError("All parameters require bounds for plotting.") return bounds - def as_dict(self, values=None) -> Dict: + def as_dict(self, values=None) -> dict: """ Parameters ---------- @@ -456,7 +569,7 @@ def as_dict(self, values=None) -> Dict: values = self.true_value() return {key: values[i] for i, key in enumerate(self.param.keys())} - def verify(self, inputs: Union[Inputs, None] = None): + def verify(self, inputs: Optional[Inputs] = None): """ Verify that the inputs are an Inputs dictionary or numeric values which can be used to construct an Inputs dictionary @@ -465,9 +578,11 @@ def verify(self, inputs: Union[Inputs, None] = None): ---------- inputs : Inputs or numeric """ - if inputs is None or isinstance(inputs, Dict): + if inputs is None or isinstance(inputs, dict): return inputs - elif (isinstance(inputs, list) and all(is_numeric(x) for x in inputs)) or all( + if isinstance(inputs, np.ndarray) and inputs.ndim == 0: + inputs = inputs[np.newaxis] + if (isinstance(inputs, list) and all(is_numeric(x) for x in inputs)) or all( is_numeric(x) for x in list(inputs) ): return self.as_dict(inputs) @@ -475,3 +590,18 @@ def verify(self, inputs: Union[Inputs, None] = None): raise TypeError( f"Inputs must be a dictionary or numeric. Received {type(inputs)}" ) + + def __repr__(self): + """ + Return a string representation of the Parameters instance. + + Returns + ------- + str + A string including the number of parameters and a summary of each parameter. + """ + param_summary = "\n".join( + f" {name}: prior= {param.prior}, value={param.value}, bounds={param.bounds}" + for name, param in self.param.items() + ) + return f"Parameters({len(self)}):\n{param_summary}" diff --git a/pybop/parameters/parameter_set.py b/pybop/parameters/parameter_set.py index 821faf682..8cfc2c026 100644 --- a/pybop/parameters/parameter_set.py +++ b/pybop/parameters/parameter_set.py @@ -1,6 +1,5 @@ import json import types -from typing import List from pybamm import LithiumIonParameters, ParameterValues, parameter_sets @@ -35,7 +34,7 @@ def __setitem__(self, key, value): def __getitem__(self, key): return self.params[key] - def keys(self) -> List: + def keys(self) -> list: """ A list of parameter names """ @@ -139,7 +138,7 @@ def export_parameters(self, output_json_path, fit_params=None): # Update parameter set if fit_params is not None: - for i, param in enumerate(fit_params): + for _i, param in enumerate(fit_params): exportable_params.update({param.name: param.value}) # Replace non-serializable values @@ -209,31 +208,46 @@ def set_formation_concentrations(parameter_set): Compute the concentration of lithium in the positive electrode assuming that all lithium in the active material originated from the positive electrode. + Only perform the calculation if an initial concentration exists for both + electrodes, i.e. it is not a half cell. + Parameters ---------- parameter_set : pybamm.ParameterValues A PyBaMM parameter set containing standard lithium ion parameters. """ - # Obtain the total amount of lithium in the active material - Q_Li_particles_init = parameter_set.evaluate( - LithiumIonParameters().Q_Li_particles_init - ) - - # Convert this total amount to a concentration in the positive electrode - c_init = ( - Q_Li_particles_init - * 3600 - / ( - parameter_set["Positive electrode active material volume fraction"] - * parameter_set["Positive electrode thickness [m]"] - * parameter_set["Electrode height [m]"] - * parameter_set["Electrode width [m]"] - * parameter_set["Faraday constant [C.mol-1]"] + if ( + all( + key in parameter_set.keys() + for key in [ + "Initial concentration in negative electrode [mol.m-3]", + "Initial concentration in positive electrode [mol.m-3]", + ] + ) + and parameter_set["Initial concentration in negative electrode [mol.m-3]"] > 0 + ): + # Obtain the total amount of lithium in the active material + Q_Li_particles_init = parameter_set.evaluate( + LithiumIonParameters().Q_Li_particles_init ) - ) - # Update the initial lithium concentrations - parameter_set.update({"Initial concentration in negative electrode [mol.m-3]": 0}) - parameter_set.update( - {"Initial concentration in positive electrode [mol.m-3]": c_init} - ) + # Convert this total amount to a concentration in the positive electrode + c_init = ( + Q_Li_particles_init + * 3600 + / ( + parameter_set["Positive electrode active material volume fraction"] + * parameter_set["Positive electrode thickness [m]"] + * parameter_set["Electrode height [m]"] + * parameter_set["Electrode width [m]"] + * parameter_set["Faraday constant [C.mol-1]"] + ) + ) + + # Update the initial lithium concentrations + parameter_set.update( + {"Initial concentration in negative electrode [mol.m-3]": 0} + ) + parameter_set.update( + {"Initial concentration in positive electrode [mol.m-3]": c_init} + ) diff --git a/pybop/parameters/priors.py b/pybop/parameters/priors.py index 10472e17f..5c40c42c1 100644 --- a/pybop/parameters/priors.py +++ b/pybop/parameters/priors.py @@ -1,3 +1,5 @@ +from typing import Union + import numpy as np import scipy.stats as stats @@ -56,6 +58,38 @@ def logpdf(self, x): """ return self.prior.logpdf(x, loc=self.loc, scale=self.scale) + def icdf(self, q): + """ + Calculates the inverse cumulative distribution function (CDF) of the distribution at q. + + Parameters + ---------- + q : float + The point(s) at which to evaluate the inverse CDF. + + Returns + ------- + float + The inverse cumulative distribution function value at q. + """ + return self.prior.ppf(q, scale=self.scale, loc=self.loc) + + def cdf(self, x): + """ + Calculates the cumulative distribution function (CDF) of the distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the CDF. + + Returns + ------- + float + The cumulative distribution function value at x. + """ + return self.prior.cdf(x, scale=self.scale, loc=self.loc) + def rvs(self, size=1, random_state=None): """ Generates random variates from the distribution. @@ -90,6 +124,66 @@ def rvs(self, size=1, random_state=None): loc=self.loc, scale=self.scale, size=size, random_state=random_state ) + def __call__(self, x): + """ + Evaluates the distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the distribution. + + Returns + ------- + float + The value(s) of the distribution at x. + """ + inputs = self.verify(x) + return self.logpdf(inputs) + + def logpdfS1(self, x): + """ + Evaluates the first derivative of the distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + inputs = self.verify(x) + return self._logpdfS1(inputs) + + def _logpdfS1(self, x): + """ + Evaluates the first derivative of the distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + raise NotImplementedError + + def verify(self, x): + """ + Verifies that the input is a numpy array and converts it if necessary. + """ + if isinstance(x, dict): + x = np.asarray(list(x.values())) + elif not isinstance(x, np.ndarray): + x = np.asarray(x) + return x + def __repr__(self): """ Returns a string representation of the object. @@ -137,10 +231,31 @@ class Gaussian(BasePrior): """ def __init__(self, mean, sigma, random_state=None): + super().__init__() self.name = "Gaussian" self.loc = mean self.scale = sigma self.prior = stats.norm + self._offset = -0.5 * np.log(2 * np.pi * self.scale**2) + self.sigma2 = self.scale**2 + self._multip = -1 / (2.0 * self.sigma2) + self._n_parameters = 1 + + def _logpdfS1(self, x): + """ + Evaluates the first derivative of the gaussian (log) distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + return self.__call__(x), -(x - self.loc) * self._multip class Uniform(BasePrior): @@ -159,12 +274,32 @@ class Uniform(BasePrior): """ def __init__(self, lower, upper, random_state=None): + super().__init__() self.name = "Uniform" self.lower = lower self.upper = upper self.loc = lower self.scale = upper - lower self.prior = stats.uniform + self._n_parameters = 1 + + def _logpdfS1(self, x): + """ + Evaluates the first derivative of the log uniform distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + log_pdf = self.__call__(x) + dlog_pdf = np.zeros_like(x) + return log_pdf, dlog_pdf @property def mean(self): @@ -195,7 +330,106 @@ class Exponential(BasePrior): """ def __init__(self, scale, loc=0, random_state=None): + super().__init__() self.name = "Exponential" self.loc = loc self.scale = scale self.prior = stats.expon + self._n_parameters = 1 + + def _logpdfS1(self, x): + """ + Evaluates the first derivative of the log exponential distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + log_pdf = self.__call__(x) + dlog_pdf = -1 / self.scale * np.ones_like(x) + return log_pdf, dlog_pdf + + +class JointLogPrior(BasePrior): + """ + Represents a joint prior distribution composed of multiple prior distributions. + + Parameters + ---------- + priors : BasePrior + One or more prior distributions to combine into a joint distribution. + """ + + def __init__(self, *priors: BasePrior): + super().__init__() + + if not all(isinstance(prior, BasePrior) for prior in priors): + raise ValueError("All priors must be instances of BasePrior") + + self._n_parameters = len(priors) + self._priors: list[BasePrior] = list(priors) + + def logpdf(self, x: Union[float, np.ndarray]) -> float: + """ + Evaluates the joint log-prior distribution at a given point. + + Parameters + ---------- + x : Union[float, np.ndarray] + The point(s) at which to evaluate the distribution. The length of `x` + should match the total number of parameters in the joint distribution. + + Returns + ------- + float + The joint log-probability density of the distribution at `x`. + """ + if len(x) != self._n_parameters: + raise ValueError( + f"Input x must have length {self._n_parameters}, got {len(x)}" + ) + + return sum(prior(x) for prior, x in zip(self._priors, x)) + + def _logpdfS1(self, x: Union[float, np.ndarray]) -> tuple[float, np.ndarray]: + """ + Evaluates the first derivative of the joint log-prior distribution at a given point. + + Parameters + ---------- + x : Union[float, np.ndarray] + The point(s) at which to evaluate the first derivative. The length of `x` + should match the total number of parameters in the joint distribution. + + Returns + ------- + Tuple[float, np.ndarray] + A tuple containing the log-probability density and its first derivative at `x`. + """ + if len(x) != self._n_parameters: + raise ValueError( + f"Input x must have length {self._n_parameters}, got {len(x)}" + ) + + log_probs = [] + derivatives = [] + + for prior, xi in zip(self._priors, x): + p, dp = prior.logpdfS1(xi) + log_probs.append(p) + derivatives.append(dp) + + output = sum(log_probs) + doutput = np.array(derivatives) + + return output, doutput + + def __repr__(self) -> str: + priors_repr = ", ".join([repr(prior) for prior in self._priors]) + return f"{self.__class__.__name__}(priors: [{priors_repr}])" diff --git a/pybop/plotting/nyquist.py b/pybop/plotting/nyquist.py new file mode 100644 index 000000000..c55652935 --- /dev/null +++ b/pybop/plotting/nyquist.py @@ -0,0 +1,139 @@ +from pybop import StandardPlot +from pybop.parameters.parameter import Inputs + + +def nyquist(problem, problem_inputs: Inputs = None, show=True, **layout_kwargs): + """ + Generates Nyquist plots for the given problem by evaluating the model's output and target values. + + Parameters + ---------- + problem : pybop.BaseProblem + An instance of a problem class (e.g., `pybop.EISProblem`) that contains the parameters and methods + for evaluation and target retrieval. + problem_inputs : Inputs, optional + Input parameters for the problem. If not provided, the default parameters from the problem + instance will be used. These parameters are verified before use (default is None). + show : bool, optional + If True, the plots will be displayed. + **layout_kwargs : dict, optional + Additional keyword arguments for customising the plot layout. These arguments are passed to + `fig.update_layout()`. + + Returns + ------- + list + A list of plotly `Figure` objects, each representing a Nyquist plot for the model's output and target values. + + Notes + ----- + - The function extracts the real part of the impedance from the model's output and the real and imaginary parts + of the impedance from the target output. + - For each signal in the problem, a Nyquist plot is created with the model's impedance plotted as a scatter plot. + - An additional trace for the reference (target output) is added to the plot. + - The plot layout can be customised using `layout_kwargs`. + + Example + ------- + >>> problem = pybop.EISProblem() + >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") + >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. + """ + if problem_inputs is None: + problem_inputs = problem.parameters.as_dict() + else: + problem_inputs = problem.parameters.verify(problem_inputs) + + model_output = problem.evaluate(problem_inputs) + domain_data = model_output["Impedance"].real + target_output = problem.get_target() + + figure_list = [] + for i in problem.signal: + default_layout_options = dict( + title="Nyquist Plot", + font=dict(family="Arial", size=14), + plot_bgcolor="white", + paper_bgcolor="white", + xaxis=dict( + title=dict(text="Zre / Ω", font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + ), + yaxis=dict( + title=dict(text="-Zim / Ω", font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + scaleanchor="x", + scaleratio=1, + ), + legend=dict( + x=0.02, + y=0.98, + bgcolor="rgba(255, 255, 255, 0.5)", + bordercolor="black", + borderwidth=1, + ), + width=600, + height=600, + ) + + plot_dict = StandardPlot( + x=domain_data, + y=-model_output[i].imag, + layout_options=default_layout_options, + trace_names="Model", + ) + + plot_dict.traces[0].update( + mode="lines+markers", + line=dict(color="blue", width=2), + marker=dict(size=8, color="blue", symbol="circle"), + ) + + target_trace = plot_dict.create_trace( + x=target_output[i].real, + y=-target_output[i].imag, + name="Reference", + mode="markers", + marker=dict(size=8, color="red", symbol="circle-open"), + showlegend=True, + ) + plot_dict.traces.append(target_trace) + + fig = plot_dict(show=False) + + # Add minor gridlines + fig.update_xaxes( + showgrid=True, + gridwidth=1, + gridcolor="lightgray", + minor=dict(showgrid=True, gridwidth=0.5, gridcolor="lightgray"), + ) + fig.update_yaxes( + showgrid=True, + gridwidth=1, + gridcolor="lightgray", + minor=dict(showgrid=True, gridwidth=0.5, gridcolor="lightgray"), + ) + + # Overwrite with user-kwargs + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + figure_list.append(fig) + + return figure_list diff --git a/pybop/plotting/plot2d.py b/pybop/plotting/plot2d.py index ee8d70573..09a49c0ad 100644 --- a/pybop/plotting/plot2d.py +++ b/pybop/plotting/plot2d.py @@ -1,16 +1,16 @@ -import sys import warnings +from typing import Union import numpy as np from scipy.interpolate import griddata -from pybop import BaseOptimiser, Optimisation, PlotlyManager +from pybop import BaseCost, BaseOptimiser, Optimisation, PlotlyManager def plot2d( cost_or_optim, gradient: bool = False, - bounds: np.ndarray = None, + bounds: Union[np.ndarray, None] = None, steps: int = 10, show: bool = True, use_optim_log: bool = False, @@ -64,15 +64,16 @@ def plot2d( cost = cost_or_optim plot_optim = False - if len(cost.parameters) < 2: + if isinstance(cost, BaseCost) and len(cost.parameters) < 2: raise ValueError("This cost function takes fewer than 2 parameters.") additional_values = [] - if len(cost.parameters) > 2: + if isinstance(cost, BaseCost) and len(cost.parameters) > 2: warnings.warn( "This cost function requires more than 2 parameters. " - + "Plotting in 2d with fixed values for the additional parameters.", + "Plotting in 2d with fixed values for the additional parameters.", UserWarning, + stacklevel=2, ) for ( i, @@ -101,17 +102,17 @@ def plot2d( if gradient: grad_parameter_costs = [] - # Determine the number of gradient outputs from cost.evaluateS1 + # Determine the number of gradient outputs from cost.compute num_gradients = len( - cost.evaluateS1(np.asarray([x[0], y[0]] + additional_values))[1] + cost(np.asarray([x[0], y[0]] + additional_values), calculate_grad=True)[1] ) # Create an array to hold each gradient output & populate grads = [np.zeros((len(y), len(x))) for _ in range(num_gradients)] for i, xi in enumerate(x): for j, yj in enumerate(y): - (*current_grads,) = cost.evaluateS1( - np.asarray([xi, yj] + additional_values) + (*current_grads,) = cost( + np.asarray([xi, yj] + additional_values), calculate_grad=True )[1] for k, grad_output in enumerate(current_grads): grads[k][j, i] = grad_output @@ -147,8 +148,8 @@ def plot2d( title_y=0.9, width=600, height=600, - xaxis=dict(range=bounds[0]), - yaxis=dict(range=bounds[1]), + xaxis=dict(range=bounds[0], showexponent="last", exponentformat="e"), + yaxis=dict(range=bounds[1], showexponent="last", exponentformat="e"), ) if hasattr(cost, "parameters"): name = cost.parameters.keys() @@ -202,9 +203,7 @@ def plot2d( # Update the layout and display the figure fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() if gradient: @@ -223,9 +222,7 @@ def plot2d( ) grad_fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - grad_fig.show("svg") - elif show: + if show: grad_fig.show() # append grad_fig to list diff --git a/pybop/plotting/plot_convergence.py b/pybop/plotting/plot_convergence.py index f8ec6948c..5380ec0e6 100644 --- a/pybop/plotting/plot_convergence.py +++ b/pybop/plotting/plot_convergence.py @@ -1,5 +1,3 @@ -import sys - from pybop import StandardPlot @@ -35,7 +33,9 @@ def plot_convergence(optim, show=True, **layout_kwargs): x=iteration_numbers, y=cost_log, layout_options=dict( - xaxis_title="Iteration", yaxis_title="Cost", title="Convergence" + xaxis_title="Iteration", + yaxis_title="Cost", + title="Convergence", ), trace_names=optim.name(), ) @@ -43,9 +43,7 @@ def plot_convergence(optim, show=True, **layout_kwargs): # Generate and display the figure fig = plot_dict(show=False) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig diff --git a/pybop/plotting/plot_dataset.py b/pybop/plotting/plot_dataset.py index 70573e476..f13b86ac7 100644 --- a/pybop/plotting/plot_dataset.py +++ b/pybop/plotting/plot_dataset.py @@ -1,11 +1,7 @@ -import sys - from pybop import StandardPlot, plot_trajectories -def plot_dataset( - dataset, signal=["Voltage [V]"], trace_names=None, show=True, **layout_kwargs -): +def plot_dataset(dataset, signal=None, trace_names=None, show=True, **layout_kwargs): """ Quickly plot a PyBOP Dataset using Plotly. @@ -31,7 +27,9 @@ def plot_dataset( """ # Get data dictionary - dataset.check(signal) + if signal is None: + signal = ["Voltage [V]"] + dataset.check(signal=signal) # Compile ydata and labels or legend y = [dataset[s] for s in signal] @@ -54,9 +52,7 @@ def plot_dataset( yaxis_title=yaxis_title, ) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig diff --git a/pybop/plotting/plot_parameters.py b/pybop/plotting/plot_parameters.py index 94cb71cb4..161e28bf1 100644 --- a/pybop/plotting/plot_parameters.py +++ b/pybop/plotting/plot_parameters.py @@ -1,5 +1,3 @@ -import sys - from pybop import GaussianLogLikelihood, StandardSubplot @@ -76,9 +74,7 @@ def plot_parameters(optim, show=True, **layout_kwargs): # Generate the figure and update the layout fig = plot_dict(show=False) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig diff --git a/pybop/plotting/plot_problem.py b/pybop/plotting/plot_problem.py index fb8759c98..9f48bd97e 100644 --- a/pybop/plotting/plot_problem.py +++ b/pybop/plotting/plot_problem.py @@ -1,5 +1,3 @@ -import sys - import numpy as np from pybop import DesignProblem, FittingProblem, StandardPlot @@ -37,7 +35,7 @@ def quick_plot(problem, problem_inputs: Inputs = None, show=True, **layout_kwarg problem_inputs = problem.parameters.verify(problem_inputs) # Extract the time data and evaluate the model's output and target values - xaxis_data = problem.time_data() + xaxis_data = problem.domain_data model_output = problem.evaluate(problem_inputs) target_output = problem.get_target() @@ -53,13 +51,13 @@ def quick_plot(problem, problem_inputs: Inputs = None, show=True, **layout_kwarg # Create a plotting dictionary if isinstance(problem, DesignProblem): trace_name = "Optimised" - opt_time_data = model_output["Time [s]"] + opt_domain_data = model_output["Time [s]"] else: trace_name = "Model" - opt_time_data = xaxis_data + opt_domain_data = xaxis_data plot_dict = StandardPlot( - x=opt_time_data, + x=opt_domain_data, y=model_output[i], layout_options=default_layout_options, trace_names=trace_name, @@ -100,9 +98,7 @@ def quick_plot(problem, problem_inputs: Inputs = None, show=True, **layout_kwarg # Generate the figure and update the layout fig = plot_dict(show=False) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() figure_list.append(fig) diff --git a/pybop/plotting/plotly_manager.py b/pybop/plotting/plotly_manager.py index 7b4b079a4..d8ad14c88 100644 --- a/pybop/plotting/plotly_manager.py +++ b/pybop/plotting/plotly_manager.py @@ -71,14 +71,13 @@ def prompt_for_plotly_installation(self): print("Installation cancelled by user.") sys.exit(1) # Exit if user cancels installation - def install_plotly(self): + @staticmethod + def install_plotly(): """ Install the Plotly package using pip. Exit if installation fails. """ try: - subprocess.check_call( - [sys.executable, "-m", "pip", "install", "plotly", "kaleido"] - ) + subprocess.check_call([sys.executable, "-m", "pip", "install", "plotly"]) except subprocess.CalledProcessError as e: print(f"Error installing plotly: {e}") sys.exit(1) # Exit if installation fails @@ -119,7 +118,7 @@ def check_browser_availability(self): if self.pio and self.pio.renderers.default == "browser": try: webbrowser.get() - except webbrowser.Error: + except webbrowser.Error as e: raise Exception( "\n **Browser Not Found** \nFor Windows users, in order to view figures in the browser using Plotly, " "you need to set the environment variable BROWSER equal to the " @@ -129,4 +128,4 @@ def check_browser_availability(self): "\n\nThen reactivate your virtual environment. Alternatively, you can use a " "different Plotly renderer. For more information see: " "https://plotly.com/python/renderers/#setting-the-default-renderer" - ) + ) from e diff --git a/pybop/plotting/quick_plot.py b/pybop/plotting/standard_plots.py similarity index 86% rename from pybop/plotting/quick_plot.py rename to pybop/plotting/standard_plots.py index 5be353a62..737df2196 100644 --- a/pybop/plotting/quick_plot.py +++ b/pybop/plotting/standard_plots.py @@ -1,5 +1,4 @@ import math -import sys import textwrap import numpy as np @@ -9,14 +8,27 @@ DEFAULT_LAYOUT_OPTIONS = dict( title=None, title_x=0.5, - xaxis=dict(title=None, titlefont_size=12, tickfont_size=12), - yaxis=dict(title=None, titlefont_size=12, tickfont_size=12), + xaxis=dict( + title=None, + showexponent="last", + exponentformat="e", + titlefont_size=12, + tickfont_size=12, + ), + yaxis=dict( + title=None, + showexponent="last", + exponentformat="e", + titlefont_size=12, + tickfont_size=12, + ), legend=dict(x=1, y=1, xanchor="right", yanchor="top", font_size=12), showlegend=True, autosize=False, - width=1024, - height=576, + width=600, + height=600, margin=dict(l=10, r=10, b=10, t=75, pad=4), + plot_bgcolor="white", ) DEFAULT_SUBPLOT_OPTIONS = dict( start_cell="bottom-left", @@ -57,24 +69,32 @@ def __init__( x, y, layout=None, - layout_options=DEFAULT_LAYOUT_OPTIONS.copy(), - trace_options=DEFAULT_TRACE_OPTIONS.copy(), + layout_options=None, + trace_options=None, trace_names=None, trace_name_width=40, ): self.x = x self.y = y self.layout = layout - self.layout_options = layout_options + self.trace_name_width = trace_name_width + + # Set default layout options and update if provided + if self.layout is None: + self.layout_options = DEFAULT_LAYOUT_OPTIONS.copy() + if layout_options: + self.layout_options.update(layout_options) + + # Set default trace options and update if provided self.trace_options = DEFAULT_TRACE_OPTIONS.copy() - if trace_options is not None: - for arg, value in trace_options.items(): - self.trace_options[arg] = value + if trace_options: + self.trace_options.update(trace_options) + + # Check trace_names and set attribute if isinstance(trace_names, str): self.trace_names = [trace_names] else: self.trace_names = trace_names - self.trace_name_width = trace_name_width # Check type and dimensions of data # What we want is a list of 'things plotly can take', e.g. numpy arrays or lists of numbers @@ -113,10 +133,7 @@ def __init__( # Create layout if self.layout is None: - self.layout = self.go.Layout(self.layout_options) - if self.layout_options is not None: - for arg, value in self.layout_options.items(): - self.layout[arg] = value + self.layout = self.go.Layout(**self.layout_options) # Wrap trace names if self.trace_names is not None: @@ -146,9 +163,7 @@ def __call__(self, show=True): If True, the figure is shown upon creation (default: True). """ fig = self.go.Figure(data=self.traces, layout=self.layout) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig @@ -246,9 +261,9 @@ def __init__( num_cols=None, axis_titles=None, layout=None, - layout_options=DEFAULT_LAYOUT_OPTIONS.copy(), - subplot_options=DEFAULT_SUBPLOT_OPTIONS.copy(), - trace_options=DEFAULT_SUBPLOT_TRACE_OPTIONS.copy(), + layout_options=DEFAULT_LAYOUT_OPTIONS, + subplot_options=DEFAULT_SUBPLOT_OPTIONS, + trace_options=DEFAULT_SUBPLOT_TRACE_OPTIONS, trace_names=None, trace_name_width=40, ): @@ -267,7 +282,7 @@ def __init__( elif self.num_cols is None: self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) self.axis_titles = axis_titles - self.subplot_options = DEFAULT_SUBPLOT_OPTIONS.copy() + self.subplot_options = subplot_options.copy() if subplot_options is not None: for arg, value in subplot_options.items(): self.subplot_options[arg] = value @@ -285,7 +300,11 @@ def __call__(self, show): If True, the figure is shown upon creation (default: True). """ fig = self.make_subplots( - rows=self.num_rows, cols=self.num_cols, **self.subplot_options + rows=self.num_rows, + cols=self.num_cols, + horizontal_spacing=0.1, + vertical_spacing=0.15, + **self.subplot_options, ) fig.update_layout(self.layout_options) @@ -297,11 +316,15 @@ def __call__(self, show): if self.axis_titles and idx < len(self.axis_titles): x_title, y_title = self.axis_titles[idx] fig.update_xaxes(title_text=x_title, row=row, col=col) - fig.update_yaxes(title_text=y_title, row=row, col=col) - - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + fig.update_yaxes( + title_text=y_title, + row=row, + col=col, + showexponent="last", + exponentformat="e", + ) + + if show: fig.show() return fig @@ -339,9 +362,7 @@ def plot_trajectories(x, y, trace_names=None, show=True, **layout_kwargs): # Generate the figure and update the layout fig = plot_dict(show=False) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig diff --git a/pybop/problems/base_problem.py b/pybop/problems/base_problem.py index 4d9d85194..94c278976 100644 --- a/pybop/problems/base_problem.py +++ b/pybop/problems/base_problem.py @@ -1,3 +1,8 @@ +from typing import Optional + +import numpy as np +from pybamm import IDAKLUSolver + from pybop import BaseModel, Dataset, Parameter, Parameters from pybop.parameters.parameter import Inputs @@ -14,23 +19,29 @@ class BaseProblem: The model to be used for the problem (default: None). check_model : bool, optional Flag to indicate if the model should be checked (default: True). - signal: List[str] + signal: list[str] The signal to observe. - additional_variables : List[str], optional + additional_variables : list[str], optional Additional variables to observe and store in the solution (default: []). - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state (default: None). """ def __init__( self, - parameters, - model=None, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + parameters: Parameters, + model: Optional[BaseModel] = None, + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[dict] = None, ): + signal = signal or ["Voltage [V]"] + if isinstance(signal, str): + signal = [signal] + elif not all(isinstance(item, str) for item in signal): + raise ValueError("Signal should be either a string or list of strings.") + # Check if parameters is a list of pybop.Parameter objects if isinstance(parameters, list): if all(isinstance(param, Parameter) for param in parameters): @@ -49,28 +60,62 @@ def __init__( ) self.parameters = parameters + self.parameters.reset_initial_value() + self._model = model + self.eis = False + self.domain = "Time [s]" self.check_model = check_model - if isinstance(signal, str): - signal = [signal] - elif not all(isinstance(item, str) for item in signal): - raise ValueError("Signal should be either a string or list of strings.") - self.signal = signal - self.init_soc = init_soc - self.n_outputs = len(self.signal) - self._time_data = None + self.signal = signal or ["Voltage [V]"] + self.set_initial_state(initial_state) + self._dataset = None + self._domain_data = None self._target = None + self.verbose = False + self.failure_output = np.asarray([np.inf]) + if isinstance(self._model, BaseModel): + self.eis = self.model.eis + self.domain = "Frequency [Hz]" if self.eis else "Time [s]" + + # Add domain and remove duplicates + self.additional_variables = additional_variables or [] + self.additional_variables.extend([self.domain, "Current [A]"]) + self.additional_variables = list(set(self.additional_variables)) + + # If model.solver is IDAKLU, set output vars for improved performance + self.output_vars = tuple(self.signal + self.additional_variables) + if self._model is not None and isinstance(self._model.solver, IDAKLUSolver): + self._solver_copy = self._model.solver.copy() + self._model.solver = IDAKLUSolver( + atol=self._solver_copy.atol, + rtol=self._solver_copy.rtol, + root_method=self._solver_copy.root_method, + root_tol=self._solver_copy.root_tol, + extrap_tol=self._solver_copy.extrap_tol, + options=self._solver_copy._options, # noqa: SLF001 + output_variables=self.output_vars, + ) - if isinstance(model, BaseModel): - self.additional_variables = additional_variables - else: - self.additional_variables = [] + def set_initial_state(self, initial_state: Optional[dict] = None): + """ + Set the initial state to be applied to evaluations of the problem. + + Parameters + ---------- + initial_state : dict, optional + A valid initial state (default: None). + """ + self.initial_state = initial_state @property def n_parameters(self): return len(self.parameters) - def evaluate(self, inputs: Inputs): + @property + def n_outputs(self): + return len(self.signal) + + def evaluate(self, inputs: Inputs, eis=False): """ Evaluate the model with the given parameters and return the signal. @@ -94,7 +139,7 @@ def evaluateS1(self, inputs: Inputs): Parameters ---------- inputs : Inputs - Parameters for evaluation of the model. + Parameters for evaluation of the model. Raises ------ @@ -103,17 +148,6 @@ def evaluateS1(self, inputs: Inputs): """ raise NotImplementedError - def time_data(self): - """ - Returns the time data. - - Returns - ------- - np.ndarray - The time array. - """ - return self._time_data - def get_target(self): """ Return the target dataset. @@ -125,13 +159,13 @@ def get_target(self): """ return self._target - def set_target(self, dataset): + def set_target(self, dataset: Dataset): """ Set the target dataset. Parameters ---------- - target : np.ndarray + target : Dataset The target dataset array. """ if self.signal is None: @@ -139,8 +173,25 @@ def set_target(self, dataset): if not isinstance(dataset, Dataset): raise ValueError("Dataset must be a pybop Dataset object.") + self._domain_data = dataset[self.domain] self._target = {signal: dataset[signal] for signal in self.signal} @property def model(self): return self._model + + @property + def target(self): + return self._target + + @property + def domain_data(self): + return self._domain_data + + @domain_data.setter + def domain_data(self, domain_data): + self._domain_data = domain_data + + @property + def dataset(self): + return self._dataset diff --git a/pybop/problems/design_problem.py b/pybop/problems/design_problem.py index a1efa22fd..726ec8c8f 100644 --- a/pybop/problems/design_problem.py +++ b/pybop/problems/design_problem.py @@ -1,7 +1,13 @@ +import warnings +from typing import Optional + import numpy as np -from pybop import BaseProblem +from pybop import BaseModel, BaseProblem, Experiment, Parameters +from pybop.models.empirical.base_ecm import ECircuitModel +from pybop.models.lithium_ion.base_echem import EChemBaseModel from pybop.parameters.parameter import Inputs +from pybop.parameters.parameter_set import set_formation_concentrations class DesignProblem(BaseProblem): @@ -22,55 +28,79 @@ class DesignProblem(BaseProblem): Flag to indicate if the model parameters should be checked for feasibility each iteration (default: True). signal : str, optional The signal to fit (default: "Voltage [V]"). - additional_variables : List[str], optional + additional_variables : list[str], optional Additional variables to observe and store in the solution (default additions are: ["Time [s]", "Current [A]"]). - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state (default: {"Initial SoC": 1.0}). + update_capacity : bool, optional + If True, the nominal capacity is updated with an approximate value for each design. """ def __init__( self, - model, - parameters, - experiment, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + model: BaseModel, + parameters: Parameters, + experiment: Optional[Experiment], + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[dict] = None, + update_capacity: bool = False, ): - # Add time and current and remove duplicates - additional_variables.extend(["Time [s]", "Current [A]"]) - additional_variables = list(set(additional_variables)) - super().__init__( - parameters, - model, - check_model, - signal, - additional_variables, - init_soc, + parameters, model, check_model, signal, additional_variables, initial_state ) self.experiment = experiment - - # Build the model if required - if experiment is not None: - # Leave the build until later to apply the experiment - self._model.classify_and_update_parameters(self.parameters) - - elif self._model._built_model is None: - self._model.build( - experiment=self.experiment, - parameters=self.parameters, - check_model=self.check_model, - init_soc=self.init_soc, + self.warning_patterns = [ + "Ah is greater than", + "Non-physical point encountered", + ] + + # Set whether to update the nominal capacity along with the design parameters + if update_capacity is True: + nominal_capacity_warning = ( + "The nominal capacity is approximated for each evaluation." + ) + else: + nominal_capacity_warning = ( + "The nominal capacity is fixed at the initial model value." ) + warnings.warn(nominal_capacity_warning, UserWarning, stacklevel=2) + self.update_capacity = update_capacity # Add an example dataset for plotting comparison sol = self.evaluate(self.parameters.as_dict("initial")) - self._time_data = sol["Time [s]"] + self._domain_data = sol["Time [s]"] self._target = {key: sol[key] for key in self.signal} self._dataset = None + def set_initial_state(self, initial_state: dict): + """ + Set the initial state to be applied to evaluations of the problem. + + Parameters + ---------- + initial_state : dict, optional + A valid initial state (default: None). + """ + if initial_state is None: + if isinstance(self.model, ECircuitModel): + initial_state = {"Initial SoC": self.model.parameter_set["Initial SoC"]} + else: + initial_state = {"Initial SoC": 1.0} # default value + elif "Initial open-circuit voltage [V]" in initial_state.keys(): + warnings.warn( + "It is usually better to define an initial state of charge as the " + "initial_state for a DesignProblem because this state will scale with " + "design properties such as the capacity of the battery, as opposed to the " + "initial open-circuit voltage which may correspond to a different state " + "of charge for each design.", + UserWarning, + stacklevel=1, + ) + + self.initial_state = initial_state + def evaluate(self, inputs: Inputs): """ Evaluate the model with the given parameters and return the signal. @@ -87,18 +117,38 @@ def evaluate(self, inputs: Inputs): """ inputs = self.parameters.verify(inputs) - sol = self._model.predict( - inputs=inputs, - experiment=self.experiment, - init_soc=self.init_soc, - ) - - if sol == [np.inf]: - return sol - - else: - predictions = {} - for signal in self.signal + self.additional_variables: - predictions[signal] = sol[signal].data - - return predictions + # Update the active parameter set + parameter_set = self.model.parameter_set + if isinstance(self._model, EChemBaseModel): + set_formation_concentrations(parameter_set) + parameter_set.update(inputs) + if self.update_capacity: + approximate_capacity = self.model.approximate_capacity(parameter_set) + parameter_set.update({"Nominal cell capacity [A.h]": approximate_capacity}) + + try: + with warnings.catch_warnings(): + for pattern in self.warning_patterns: + warnings.filterwarnings( + "error", category=UserWarning, message=pattern + ) + + sol = self._model.predict( + parameter_set=parameter_set, + experiment=self.experiment, + initial_state=self.initial_state, + ) + + # Catch infeasible solutions and return infinity + except (UserWarning, Exception) as e: + if self.verbose: + print(f"Ignoring this sample due to: {e}") + return { + signal: np.asarray(np.ones(2) * -np.inf) + for signal in [*self.signal, *self.additional_variables] + } + + return { + signal: sol[signal].data + for signal in self.signal + self.additional_variables + } diff --git a/pybop/problems/fitting_problem.py b/pybop/problems/fitting_problem.py index b27955479..25609b6b1 100644 --- a/pybop/problems/fitting_problem.py +++ b/pybop/problems/fitting_problem.py @@ -1,7 +1,10 @@ +import warnings +from typing import Optional + import numpy as np -from pybop import BaseProblem -from pybop.parameters.parameter import Inputs +from pybop import BaseModel, BaseProblem, Dataset +from pybop.parameters.parameter import Inputs, Parameters class FittingProblem(BaseProblem): @@ -18,64 +21,91 @@ class FittingProblem(BaseProblem): An object or list of the parameters for the problem. dataset : Dataset Dataset object containing the data to fit the model to. + check_model : bool, optional + Flag to indicate if the model should be checked (default: True). signal : str, optional The variable used for fitting (default: "Voltage [V]"). - additional_variables : List[str], optional + additional_variables : list[str], optional Additional variables to observe and store in the solution (default additions are: ["Time [s]"]). - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state, e.g. the initial open-circuit voltage (default: None). + + Additional Attributes + --------------------- + dataset : dictionary + The dictionary from a Dataset object containing the signal keys and values to fit the model to. + domain_data : np.ndarray + The domain points in the dataset. + n_domain_data : int + The number of domain points. + target : np.ndarray + The target values of the signals. """ def __init__( self, - model, - parameters, - dataset, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + model: BaseModel, + parameters: Parameters, + dataset: Dataset, + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[dict] = None, ): - # Add time and remove duplicates - additional_variables.extend(["Time [s]"]) - additional_variables = list(set(additional_variables)) - super().__init__( - parameters, model, check_model, signal, additional_variables, init_soc + parameters, model, check_model, signal, additional_variables, initial_state ) self._dataset = dataset.data - self.parameters.initial_value() + self._n_parameters = len(self.parameters) - # Check that the dataset contains time and current - dataset.check(self.signal + ["Current function [A]"]) + # Check that the dataset contains necessary variables + dataset.check(domain=self.domain, signal=[*self.signal, "Current function [A]"]) - # Unpack time and target data - self._time_data = self._dataset["Time [s]"] - self.n_time_data = len(self._time_data) + # Unpack domain and target data + self._domain_data = self._dataset[self.domain] + self.n_data = len(self._domain_data) self.set_target(dataset) - # Add useful parameters to model - if model is not None: - self._model.signal = self.signal - self._model.additional_variables = self.additional_variables - self._model.n_outputs = self.n_outputs - self._model.n_time_data = self.n_time_data - + if self._model is not None: # Build the model from scratch - if self._model._built_model is not None: - self._model._model_with_set_params = None - self._model._built_model = None - self._model._built_initial_soc = None - self._model._mesh = None - self._model._disc = None + if self._model.built_model is not None: + self._model.clear() self._model.build( dataset=self._dataset, parameters=self.parameters, check_model=self.check_model, - init_soc=self.init_soc, + initial_state=self.initial_state, + ) + + def set_initial_state(self, initial_state: Optional[dict] = None): + """ + Set the initial state to be applied to evaluations of the problem. + + Parameters + ---------- + initial_state : dict, optional + A valid initial state (default: None). + """ + if initial_state is not None and "Initial SoC" in initial_state.keys(): + warnings.warn( + "It is usually better to define an initial open-circuit voltage as the " + "initial_state for a FittingProblem because this value can typically be " + "obtained from the data, unlike the intrinsic initial state of charge. " + "In the case where the fitting parameters do not change the OCV-SOC " + "relationship, the initial state of charge may be passed to the model " + 'using, for example, `model.set_initial_state({"Initial SoC": 1.0})` ' + "before constructing the FittingProblem.", + UserWarning, + stacklevel=1, ) - def evaluate(self, inputs: Inputs): + self.initial_state = initial_state + + def evaluate( + self, + inputs: Inputs, + update_capacity=False, + ) -> dict[str, np.ndarray[np.float64]]: """ Evaluate the model with the given parameters and return the signal. @@ -87,24 +117,56 @@ def evaluate(self, inputs: Inputs): Returns ------- y : np.ndarray - The model output y(t) simulated with given inputs. + The simulated model output y(t) for self.eis == False, and y(ω) for self.eis == True for the given inputs. """ inputs = self.parameters.verify(inputs) + if self.eis: + return self._evaluateEIS(inputs, update_capacity=update_capacity) + else: + try: + sol = self._model.simulate( + inputs=inputs, + t_eval=self._domain_data, + initial_state=self.initial_state, + ) + except Exception as e: + if self.verbose: + print(f"Simulation error: {e}") + return {signal: self.failure_output for signal in self.signal} + + return { + signal: sol[signal].data + for signal in (self.signal + self.additional_variables) + } + + def _evaluateEIS( + self, inputs: Inputs, update_capacity=False + ) -> dict[str, np.ndarray[np.float64]]: + """ + Evaluate the model with the given parameters and return the signal. - requires_rebuild = False - for key, value in inputs.items(): - if key in self._model.rebuild_parameters: - current_value = self.parameters[key].value - if value != current_value: - self.parameters[key].update(value=value) - requires_rebuild = True - - if requires_rebuild: - self._model.rebuild(parameters=self.parameters) + Parameters + ---------- + inputs : Inputs + Parameters for evaluation of the model. - y = self._model.simulate(inputs=inputs, t_eval=self._time_data) + Returns + ------- + y : np.ndarray + The simulated model output y(ω) for the given inputs. + """ + try: + sol = self._model.simulateEIS( + inputs=inputs, + f_eval=self._domain_data, + initial_state=self.initial_state, + ) + except Exception as e: + if self.verbose: + print(f"Simulation error: {e}") + return {signal: self.failure_output for signal in self.signal} - return y + return sol def evaluateS1(self, inputs: Inputs): """ @@ -117,20 +179,43 @@ def evaluateS1(self, inputs: Inputs): Returns ------- - tuple - A tuple containing the simulation result y(t) and the sensitivities dy/dx(t) evaluated - with given inputs. + tuple[dict, np.ndarray] + A tuple containing the simulation result y(t) as a dictionary and the sensitivities + dy/dx(t) evaluated with given inputs. """ inputs = self.parameters.verify(inputs) + self.parameters.update(values=list(inputs.values())) - if self._model.rebuild_parameters: - raise RuntimeError( - "Gradient not available when using geometric parameters." + try: + sol = self._model.simulateS1( + inputs=inputs, + t_eval=self._domain_data, + initial_state=self.initial_state, + ) + except Exception as e: + print(f"Error: {e}") + return { + signal: self.failure_output for signal in self.signal + }, self.failure_output + + y = {signal: sol[signal].data for signal in self.signal} + + # Extract the sensitivities and stack them along a new axis for each signal + dy = np.empty( + ( + sol[self.signal[0]].data.shape[0], + self.n_outputs, + self._n_parameters, ) - - y, dy = self._model.simulateS1( - inputs=inputs, - t_eval=self._time_data, ) - return (y, np.asarray(dy)) + for i, signal in enumerate(self.signal): + dy[:, i, :] = np.stack( + [ + sol[signal].sensitivities[key].toarray()[:, 0] + for key in self.parameters.keys() + ], + axis=-1, + ) + + return y, np.asarray(dy) diff --git a/pybop/problems/multi_fitting_problem.py b/pybop/problems/multi_fitting_problem.py new file mode 100644 index 000000000..d0bb997e7 --- /dev/null +++ b/pybop/problems/multi_fitting_problem.py @@ -0,0 +1,141 @@ +from typing import Optional + +import numpy as np + +from pybop import BaseProblem, Dataset +from pybop.parameters.parameter import Inputs, Parameters + + +class MultiFittingProblem(BaseProblem): + """ + Problem class for joining mulitple fitting problems into one combined fitting problem. + + Extends `BaseProblem` in a similar way to FittingProblem but for multiple parameter + estimation problems, which must first be defined individually. + + Additional Attributes + --------------------- + problems : pybop.FittingProblem + The individual PyBOP fitting problems. + """ + + def __init__(self, *args): + self.problems = [] + models_to_check = [] + for problem in args: + self.problems.append(problem) + if problem.model is not None: + models_to_check.append(problem.model) + + # Check that there are no copies of the same model + if len(set(models_to_check)) < len(models_to_check): + raise ValueError("Make a new_copy of the model for each problem.") + + # Compile the set of parameters, ignoring duplicates + combined_parameters = Parameters() + for problem in self.problems: + combined_parameters.join(problem.parameters) + + # Combine the target datasets + combined_domain_data = [] + combined_signal = [] + for problem in self.problems: + for signal in problem.signal: + combined_domain_data.extend(problem.domain_data) + combined_signal.extend(problem.target[signal]) + + super().__init__( + parameters=combined_parameters, + model=None, + signal=["Combined signal"], + ) + + combined_dataset = Dataset( + { + self.domain: np.asarray(combined_domain_data), + "Combined signal": np.asarray(combined_signal), + } + ) + self._dataset = combined_dataset.data + self.parameters.initial_value() + + # Unpack domain and target data + self._domain_data = self._dataset[self.domain] + self.n_domain_data = len(self._domain_data) + self.set_target(combined_dataset) + + def set_initial_state(self, initial_state: Optional[dict] = None): + """ + Set the initial state to be applied to evaluations of the problem. + + Parameters + ---------- + initial_state : dict, optional + A valid initial state (default: None). + """ + for problem in self.problems: + problem.set_initial_state(initial_state) + + def evaluate(self, inputs: Inputs, eis=False): + """ + Evaluate the model with the given parameters and return the signal. + + Parameters + ---------- + inputs : Inputs + Parameters for evaluation of the model. + + Returns + ------- + y : np.ndarray + The model output y(t) simulated with given inputs. + """ + inputs = self.parameters.verify(inputs) + self.parameters.update(values=list(inputs.values())) + + combined_signal = [] + + for problem in self.problems: + problem_inputs = problem.parameters.as_dict() + signal_values = problem.evaluate(problem_inputs) + + # Collect signals + for signal in problem.signal: + combined_signal.extend(signal_values[signal]) + + return {"Combined signal": np.asarray(combined_signal)} + + def evaluateS1(self, inputs: Inputs): + """ + Evaluate the model with the given parameters and return the signal and its derivatives. + + Parameters + ---------- + inputs : Inputs + Parameters for evaluation of the model. + + Returns + ------- + tuple[dict, np.ndarray] + A tuple containing the simulation result y(t) as a dictionary and the sensitivities + dy/dx(t) evaluated with given inputs. + """ + inputs = self.parameters.verify(inputs) + self.parameters.update(values=list(inputs.values())) + + combined_signal = [] + all_derivatives = [] + + for problem in self.problems: + problem_inputs = problem.parameters.as_dict() + signal_values, dyi = problem.evaluateS1(problem_inputs) + + # Collect signals and derivatives + for signal in problem.signal: + combined_signal.extend(signal_values[signal]) + all_derivatives.append(dyi) + + y = {"Combined signal": np.asarray(combined_signal)} + dy = np.concatenate(all_derivatives) if all_derivatives else None + + return (y, dy) diff --git a/pybop/samplers/__init__.py b/pybop/samplers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pybop/samplers/base_pints_sampler.py b/pybop/samplers/base_pints_sampler.py new file mode 100644 index 000000000..76446e198 --- /dev/null +++ b/pybop/samplers/base_pints_sampler.py @@ -0,0 +1,293 @@ +import logging +from functools import partial +from typing import Optional + +import numpy as np +from pints import ( + MultiSequentialEvaluator, + ParallelEvaluator, + SequentialEvaluator, + SingleChainMCMC, +) + +from pybop import BaseCost, BaseSampler, LogPosterior + + +class BasePintsSampler(BaseSampler): + """ + Base class for PINTS samplers. + + This class extends the BaseSampler class to provide a common interface for + PINTS samplers. The class provides a sample() method that can be used to + sample from the posterior distribution using a PINTS sampler. + """ + + def __init__( + self, + log_pdf: LogPosterior, + sampler, + chains: int = 1, + warm_up=None, + x0=None, + cov0=0.1, + **kwargs, + ): + """ + Initialise the base PINTS sampler. + + Args: + log_pdf (pybop.LogPosterior or List[pybop.LogPosterior]): The distribution(s) to be sampled. + chains (int): Number of chains to be used. + sampler: The sampler class to be used. + x0 (list): Initial states for the chains. + cov0: Initial standard deviation for the chains. + kwargs: Additional keyword arguments. + """ + super().__init__(log_pdf, x0, cov0) + + # Set kwargs + self._max_iterations = kwargs.get("max_iterations", 500) + self._log_to_screen = kwargs.get("log_to_screen", True) + self._log_filename = kwargs.get("log_filename", None) + self._initial_phase_iterations = kwargs.get("initial_phase_iterations", 250) + self._chains_in_memory = kwargs.get("chains_in_memory", True) + self._chain_files = kwargs.get("chain_files", None) + self._evaluation_files = kwargs.get("evaluation_files", None) + self._parallel = kwargs.get("parallel", False) + self._verbose = kwargs.get("verbose", False) + self._iteration = 0 + self._warm_up = warm_up + self.n_parameters = ( + self._log_pdf[0].n_parameters + if isinstance(self._log_pdf, list) + else self._log_pdf.n_parameters + ) + + # Check log_pdf + if isinstance(self._log_pdf, BaseCost): + self._multi_log_pdf = False + else: + if len(self._log_pdf) != chains: + raise ValueError("Number of log pdf's must match number of chains") + + first_pdf_parameters = self._log_pdf[0].n_parameters + for pdf in self._log_pdf: + if not isinstance(pdf, BaseCost): + raise ValueError("All log pdf's must be instances of BaseCost") + if pdf.n_parameters != first_pdf_parameters: + raise ValueError( + "All log pdf's must have the same number of parameters" + ) + + self._multi_log_pdf = True + + # Number of chains + self._n_chains = chains + if self._n_chains < 1: + raise ValueError("Number of chains must be greater than 0") + + # Check initial conditions + if self._x0.size != self.n_parameters: + raise ValueError("x0 must have the same number of parameters as log_pdf") + if len(self._x0) != self._n_chains or len(self._x0) == 1: + self._x0 = np.tile(self._x0, (self._n_chains, 1)) + + # Single chain vs multiple chain samplers + self._single_chain = issubclass(sampler, SingleChainMCMC) + + # Construct the samplers object + if self._single_chain: + self._n_samplers = self._n_chains + self._samplers = [sampler(x0, sigma0=self._cov0) for x0 in self._x0] + else: + self._n_samplers = 1 + self._samplers = [sampler(self._n_chains, self._x0, self._cov0)] + + # Check for sensitivities from sampler and set evaluation + self._needs_sensitivities = self._samplers[0].needs_sensitivities() + + # Check initial phase + self._initial_phase = self._samplers[0].needs_initial_phase() + if self._initial_phase: + self.set_initial_phase_iterations() + + # Parallelisation (Might be able to move into parent class) + self._n_workers = 1 + self.set_parallel(self._parallel) + + def run(self) -> Optional[np.ndarray]: + """ + Executes the Monte Carlo sampling process and generates samples + from the posterior distribution. + + This method orchestrates the entire sampling process, managing + iterations, evaluations, logging, and stopping criteria. It + initialises the necessary structures, handles both single and + multi-chain scenarios, and manages parallel or sequential + evaluation based on the configuration. + + Returns: + np.ndarray: A numpy array containing the samples from the + posterior distribution if chains are stored in memory, + otherwise returns None. + + Raises: + ValueError: If no stopping criterion is set (i.e., + _max_iterations is None). + + Details: + - Checks and ensures at least one stopping criterion is set. + - Initialises iterations, evaluations, and other required + structures. + - Sets up the evaluator (parallel or sequential) based on the + configuration. + - Handles the initial phase, if applicable, and manages + intermediate steps in the sampling process. + - Logs progress and relevant information based on the logging + configuration. + - Iterates through the sampling process, evaluating the log + PDF, updating chains, and managing the stopping criteria. + - Finalises and returns the collected samples, or None if + chains are not stored in memory. + """ + + self._initialise_logging() + self._check_stopping_criteria() + + # Initialise iterations and evaluations + self._iteration = 0 + + evaluator = self._create_evaluator() + self._check_initial_phase() + self._initialise_storage() + + running = True + while running: + if ( + self._initial_phase + and self._iteration == self._initial_phase_iterations + ): + self._end_initial_phase() + + xs = self._ask_for_samples() + self.fxs = evaluator.evaluate(xs) + self._evaluations += len(self.fxs) + + if self._single_chain: + self._process_single_chain() + self._intermediate_step = min(self._n_samples) <= self._iteration + else: + self._process_multi_chain() + + if self._intermediate_step: + continue + + self._iteration += 1 + if self._log_to_screen and self._verbose: + logging.info(f"Iteration: {self._iteration}") # TODO: Add more info + + if self._max_iterations and self._iteration >= self._max_iterations: + running = False + + self._finalise_logging() + + if self._warm_up: + self._samples = self._samples[:, self._warm_up :, :] + + return self._samples if self._chains_in_memory else None + + def _process_single_chain(self): + self.fxs_iterator = iter(self.fxs) + for i in list(self._active): + reply = self._samplers[i].tell(next(self.fxs_iterator)) + if reply: + y, fy, accepted = reply + y_store = self._inverse_transform( + y, self._log_pdf[i] if self._multi_log_pdf else self._log_pdf + ) + if self._chains_in_memory: + self._samples[i][self._n_samples[i]] = y_store + else: + self._samples[i] = y_store + + if accepted: + self._sampled_logpdf[i] = ( + fy[0] if self._needs_sensitivities else fy + ) # Not storing sensitivities + if self._prior: + self._sampled_prior[i] = self._prior(y) + + e = self._sampled_logpdf[i] + if self._prior: + e = [ + e, + self._sampled_logpdf[i] - self._sampled_prior[i], + self._sampled_prior[i], + ] + + self._evaluations[i][self._n_samples[i]] = e + self._n_samples[i] += 1 + if self._n_samples[i] == self._max_iterations: + self._active.remove(i) + + def _process_multi_chain(self): + reply = self._samplers[0].tell(self.fxs) + self._intermediate_step = reply is None + if reply: + ys, fys, accepted = reply + ys_store = np.asarray( + [self._inverse_transform(y, self._log_pdf) for y in ys] + ) + if self._chains_in_memory: + self._samples[:, self._iteration] = ys_store + else: + self._samples = ys_store + + es = [] + for i, _y in enumerate(ys): + if accepted[i]: + self._sampled_logpdf[i] = ( + fys[0][i] if self._needs_sensitivities else fys[i] + ) + if self._prior: + self._sampled_prior[i] = self._prior(ys[i]) + e = self._sampled_logpdf[i] + if self._prior: + e = [ + e, + self._sampled_logpdf[i] - self._sampled_prior[i], + self._sampled_prior[i], + ] + es.append(e) + + for i, e in enumerate(es): + self._evaluations[i, self._iteration] = e + + def _check_stopping_criteria(self): + has_stopping_criterion = False + has_stopping_criterion |= self._max_iterations is not None + if not has_stopping_criterion: + raise ValueError("At least one stopping criterion must be set.") + + def _create_evaluator(self): + f = self._log_pdf + # Check for sensitivities from sampler and set evaluator + if self._needs_sensitivities: + if not self._multi_log_pdf: + f = partial(f, calculate_grad=True) + else: + f = [partial(pdf, calculate_grad=True) for pdf in f] + + if self._parallel: + if not self._multi_log_pdf: + self._n_workers = min(self._n_workers, self._n_chains) + return ParallelEvaluator(f, n_workers=self._n_workers) + else: + return ( + SequentialEvaluator(f) + if not self._multi_log_pdf + else MultiSequentialEvaluator(f) + ) + + def _inverse_transform(self, y, log_pdf): + return log_pdf.transformation.to_model(y) if log_pdf.transformation else y diff --git a/pybop/samplers/base_sampler.py b/pybop/samplers/base_sampler.py new file mode 100644 index 000000000..a46143e87 --- /dev/null +++ b/pybop/samplers/base_sampler.py @@ -0,0 +1,165 @@ +import logging +from typing import Union + +import numpy as np +from pints import ParallelEvaluator + +from pybop import LogPosterior, Parameters + + +class BaseSampler: + """ + Base class for Monte Carlo samplers. + """ + + def __init__(self, log_pdf: LogPosterior, x0, cov0: Union[np.ndarray, float]): + """ + Initialise the base sampler. + + Parameters + ---------------- + log_pdf (pybop.LogPosterior or List[pybop.LogPosterior]): The posterior or PDF to be sampled. + x0: List-like initial condition for Monte Carlo sampling. + cov0: The covariance matrix to be sampled. + """ + self._log_pdf = log_pdf + self._cov0 = cov0 + + # Set up parameters based on log_pdf + self.parameters = ( + log_pdf.parameters if isinstance(log_pdf, LogPosterior) else Parameters() + ) + + # Initialize x0 + self._x0 = ( + self.parameters.initial_value() + if x0 is None + else np.asarray([x0], dtype=float) + ) + + def run(self) -> np.ndarray: + """ + Sample from the posterior distribution. + + Returns: + np.ndarray: Samples from the posterior distribution. + """ + raise NotImplementedError + + def set_initial_phase_iterations(self, iterations=250): + """ + Set the number of iterations for the initial phase of the sampler. + + Args: + iterations (int): Number of iterations for the initial phase. + """ + self._initial_phase_iterations = iterations + + def set_max_iterations(self, iterations=500): + """ + Set the maximum number of iterations for the sampler. + + Args: + iterations (int): Maximum number of iterations. + """ + iterations = int(iterations) + if iterations < 1: + raise ValueError("Number of iterations must be greater than 0") + + self._max_iterations = iterations + + def set_parallel(self, parallel=False): + """ + Enable or disable parallel evaluation. + Credit: PINTS + + Parameters + ---------- + parallel : bool or int, optional + If True, use as many worker processes as there are CPU cores. If an integer, use that many workers. + If False or 0, disable parallelism (default: False). + """ + if parallel is True: + self._parallel = True + self._n_workers = ParallelEvaluator.cpu_count() + elif parallel >= 1: + self._parallel = True + self._n_workers = int(parallel) + else: + self._parallel = False + self._n_workers = 1 + + def _ask_for_samples(self): + if self._single_chain: + return [self._samplers[i].ask() for i in self._active] + else: + return self._samplers[0].ask() + + def _check_initial_phase(self): + # Set initial phase if needed + if self._initial_phase: + for sampler in self._samplers: + sampler.set_initial_phase(True) + + def _end_initial_phase(self): + for sampler in self._samplers: + sampler.set_initial_phase(False) + if self._log_to_screen: + logging.info("Initial phase completed.") + + def _initialise_storage(self): + self._prior = None + if isinstance(self._log_pdf, LogPosterior): + self._prior = self._log_pdf.prior + + # Storage of the received samples + self._sampled_logpdf = np.zeros(self._n_chains) + self._sampled_prior = np.zeros(self._n_chains) + + # Pre-allocate arrays for chain storage + self._samples = np.zeros( + (self._n_chains, self._max_iterations, self.n_parameters) + ) + + # Pre-allocate arrays for evaluation storage + if self._prior: + # Store posterior, likelihood, prior + self._evaluations = np.zeros((self._n_chains, self._max_iterations, 3)) + else: + # Store pdf + self._evaluations = np.zeros((self._n_chains, self._max_iterations)) + + # From PINTS: + # Some samplers need intermediate steps, where `None` is returned instead + # of a sample. But samplers can run asynchronously, so that one can return + # `None` while another returns a sample. To deal with this, we maintain a + # list of 'active' samplers that have not reached `max_iterations`, + # and store the number of samples so far in each chain. + if self._single_chain: + self._active = list(range(self._n_chains)) + self._n_samples = [0] * self._n_chains + + def _initialise_logging(self): + logging.basicConfig(format="%(message)s", level=logging.INFO) + + if self._log_to_screen: + logging.info("Using " + str(self._samplers[0].name())) + logging.info("Generating " + str(self._n_chains) + " chains.") + if self._parallel: + logging.info( + f"Running in parallel with {self._n_workers} worker processes." + ) + else: + logging.info("Running in sequential mode.") + if self._chain_files: + logging.info("Writing chains to " + self._chain_files[0] + " etc.") + if self._evaluation_files: + logging.info( + "Writing evaluations to " + self._evaluation_files[0] + " etc." + ) + + def _finalise_logging(self): + if self._log_to_screen: + logging.info( + f"Halting: Maximum number of iterations ({self._iteration}) reached." + ) diff --git a/pybop/samplers/mcmc_sampler.py b/pybop/samplers/mcmc_sampler.py new file mode 100644 index 000000000..933c36957 --- /dev/null +++ b/pybop/samplers/mcmc_sampler.py @@ -0,0 +1,105 @@ +from pybop import AdaptiveCovarianceMCMC + + +class MCMCSampler: + """ + A high-level class for MCMC sampling. + + This class provides an alternative API to the `PyBOP.Sampler()` API, + specifically allowing for single user-friendly interface for the + optimisation process. + """ + + def __init__( + self, + log_pdf, + chains, + sampler=AdaptiveCovarianceMCMC, + x0=None, + cov0=None, + **kwargs, + ): + """ + Initialize the MCMCSampler. + + Parameters + ---------- + log_pdf : pybop.BaseCost + The log-probability density function to be sampled. + chains : int + The number of MCMC chains to be run. + sampler : pybop.MCMCSampler, optional + The MCMC sampler class to be used. Defaults to `pybop.MCMC`. + x0 : np.ndarray, optional + Initial positions for the MCMC chains. Defaults to None. + cov0 : np.ndarray, optional + Initial step sizes for the MCMC chains. Defaults to None. + **kwargs : dict + Additional keyword arguments to pass to the sampler. + + Raises + ------ + ValueError + If the sampler could not be constructed due to an exception. + """ + + self.sampler = sampler(log_pdf, chains, x0=x0, sigma0=cov0, **kwargs) + + def run(self): + """ + Run the MCMC sampling process. + + Returns + ------- + list + The result of the sampling process. + """ + return self.sampler.run() + + def __getattr__(self, attr): + """ + Delegate attribute access to the underlying sampler if the attribute + is not found in the MCMCSampler instance. + + Parameters + ---------- + attr : str + The attribute name to be accessed. + + Returns + ------- + Any + The attribute value from the underlying sampler. + + Raises + ------ + AttributeError + If the attribute is not found in both the MCMCSampler instance + and the underlying sampler. + """ + if "sampler" in self.__dict__ and hasattr(self.sampler, attr): + return getattr(self.sampler, attr) + raise AttributeError( + f"'{self.__class__.__name__}' object has no attribute '{attr}'" + ) + + def __setattr__(self, name: str, value) -> None: + """ + Delegate attribute setting to the underlying sampler if the attribute + exists in the sampler and not in the MCMCSampler instance. + + Parameters + ---------- + name : str + The attribute name to be set. + value : Any + The value to be set to the attribute. + """ + if ( + name in self.__dict__ + or "sampler" not in self.__dict__ + or not hasattr(self.sampler, name) + ): + object.__setattr__(self, name, value) + else: + setattr(self.sampler, name, value) diff --git a/pybop/samplers/pints_samplers.py b/pybop/samplers/pints_samplers.py new file mode 100644 index 000000000..481a5eaeb --- /dev/null +++ b/pybop/samplers/pints_samplers.py @@ -0,0 +1,607 @@ +from pints import MALAMCMC as PintsMALAMCMC +from pints import AdaptiveCovarianceMCMC as PintsAdaptiveCovarianceMCMC +from pints import DifferentialEvolutionMCMC as PintsDifferentialEvolutionMCMC +from pints import DramACMC as PintsDramACMC +from pints import DreamMCMC as PintsDREAM +from pints import EmceeHammerMCMC as PintsEmceeHammerMCMC +from pints import HaarioACMC as PintsHaarioACMC +from pints import HaarioBardenetACMC as PintsHaarioBardenetACMC +from pints import HamiltonianMCMC as PintsHamiltonianMCMC +from pints import MetropolisRandomWalkMCMC as PintsMetropolisRandomWalkMCMC +from pints import MonomialGammaHamiltonianMCMC as PintsMonomialGammaHamiltonianMCMC +from pints import NoUTurnMCMC +from pints import PopulationMCMC as PintsPopulationMCMC +from pints import RaoBlackwellACMC as PintsRaoBlackwellACMC +from pints import RelativisticMCMC as PintsRelativisticMCMC +from pints import SliceDoublingMCMC as PintsSliceDoublingMCMC +from pints import SliceRankShrinkingMCMC as PintsSliceRankShrinkingMCMC +from pints import SliceStepoutMCMC as PintsSliceStepoutMCMC + +from pybop import BasePintsSampler + + +class NUTS(BasePintsSampler): + """ + Implements the No-U-Turn Sampler (NUTS) algorithm. + + This class extends the NUTS sampler from the PINTS library. + NUTS is a Markov chain Monte Carlo (MCMC) method for sampling + from a probability distribution. It is an extension of the + Hamiltonian Monte Carlo (HMC) method, which uses a dynamic + integration time to explore the parameter space more efficiently. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the NUTS sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, NoUTurnMCMC, chains=chains, x0=x0, cov0=cov0, **kwargs + ) + + +class DREAM(BasePintsSampler): + """ + Implements the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. + + This class extends the DREAM sampler from the PINTS library. + DREAM is a Markov chain Monte Carlo (MCMC) method for sampling + from a probability distribution. It combines the Differential + Evolution (DE) algorithm with the Adaptive Metropolis (AM) algorithm + to explore the parameter space more efficiently. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the DREAM sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__(log_pdf, PintsDREAM, chains=chains, x0=x0, cov0=cov0, **kwargs) + + +class AdaptiveCovarianceMCMC(BasePintsSampler): + """ + Implements the Adaptive Covariance Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method adapts the proposal distribution covariance matrix + during the sampling process to improve efficiency and convergence. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsAdaptiveCovarianceMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class DifferentialEvolutionMCMC(BasePintsSampler): + """ + Implements the Differential Evolution Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Differential Evolution MCMC sampler from the PINTS library. + This MCMC method uses the Differential Evolution algorithm to explore the + parameter space more efficiently by evolving a population of chains. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Differential Evolution MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsDifferentialEvolutionMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class DramACMC(BasePintsSampler): + """ + Implements the Delayed Rejection Adaptive Metropolis (DRAM) Adaptive Covariance Markov Chain + Monte Carlo (MCMC) algorithm. + + This class extends the DRAM Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method combines Delayed Rejection with Adaptive Metropolis to enhance + the efficiency and robustness of the sampling process. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the DRAM Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsDramACMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class EmceeHammerMCMC(BasePintsSampler): + """ + Implements the Emcee Hammer Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Emcee Hammer MCMC sampler from the PINTS library. + The Emcee Hammer is an affine-invariant ensemble sampler for MCMC, which is + particularly effective for high-dimensional parameter spaces. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Emcee Hammer MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsEmceeHammerMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class HaarioACMC(BasePintsSampler): + """ + Implements the Haario Adaptive Covariance Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Haario Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method adapts the proposal distribution's covariance matrix based on the + history of the chain, improving sampling efficiency and convergence. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Haario Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsHaarioACMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class HaarioBardenetACMC(BasePintsSampler): + """ + Implements the Haario-Bardenet Adaptive Covariance Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Haario-Bardenet Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method combines the adaptive covariance approach with an additional + mechanism to improve performance in high-dimensional parameter spaces. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Haario-Bardenet Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsHaarioBardenetACMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class HamiltonianMCMC(BasePintsSampler): + """ + Implements the Hamiltonian Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Hamiltonian MCMC sampler from the PINTS library. + This MCMC method uses Hamiltonian dynamics to propose new states, + allowing for efficient exploration of high-dimensional parameter spaces. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Hamiltonian MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsHamiltonianMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class MALAMCMC(BasePintsSampler): + """ + Implements the Metropolis Adjusted Langevin Algorithm (MALA) Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the MALA MCMC sampler from the PINTS library. + This MCMC method combines the Metropolis-Hastings algorithm with + Langevin dynamics to improve sampling efficiency and convergence. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the MALA MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsMALAMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class MetropolisRandomWalkMCMC(BasePintsSampler): + """ + Implements the Metropolis Random Walk Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Metropolis Random Walk MCMC sampler from the PINTS library. + This classic MCMC method uses a simple random walk proposal distribution + and the Metropolis-Hastings acceptance criterion. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Metropolis Random Walk MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsMetropolisRandomWalkMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class MonomialGammaHamiltonianMCMC(BasePintsSampler): + """ + Implements the Monomial Gamma Hamiltonian Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Monomial Gamma Hamiltonian MCMC sampler from the PINTS library. + This MCMC method uses Hamiltonian dynamics with a monomial gamma distribution + for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Monomial Gamma Hamiltonian MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsMonomialGammaHamiltonianMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class PopulationMCMC(BasePintsSampler): + """ + Implements the Population Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Population MCMC sampler from the PINTS library. + This MCMC method uses a population of chains at different temperatures + to explore the parameter space more efficiently and avoid local minima. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Population MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsPopulationMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class RaoBlackwellACMC(BasePintsSampler): + """ + Implements the Rao-Blackwell Adaptive Covariance Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Rao-Blackwell Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method improves sampling efficiency by combining Rao-Blackwellisation + with adaptive covariance strategies. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Rao-Blackwell Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsRaoBlackwellACMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class RelativisticMCMC(BasePintsSampler): + """ + Implements the Relativistic Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Relativistic MCMC sampler from the PINTS library. + This MCMC method uses concepts from relativistic mechanics to propose new states, + allowing for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Relativistic MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsRelativisticMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class SliceDoublingMCMC(BasePintsSampler): + """ + Implements the Slice Doubling Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Slice Doubling MCMC sampler from the PINTS library. + This MCMC method uses slice sampling with a doubling procedure to propose new states, + allowing for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Slice Doubling MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsSliceDoublingMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class SliceRankShrinkingMCMC(BasePintsSampler): + """ + Implements the Slice Rank Shrinking Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Slice Rank Shrinking MCMC sampler from the PINTS library. + This MCMC method uses slice sampling with a rank shrinking procedure to propose new states, + allowing for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Slice Rank Shrinking MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsSliceRankShrinkingMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class SliceStepoutMCMC(BasePintsSampler): + """ + Implements the Slice Stepout Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Slice Stepout MCMC sampler from the PINTS library. + This MCMC method uses slice sampling with a stepout procedure to propose new states, + allowing for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Slice Stepout MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsSliceStepoutMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) diff --git a/pybop/transformation/base_transformation.py b/pybop/transformation/base_transformation.py new file mode 100644 index 000000000..40488949e --- /dev/null +++ b/pybop/transformation/base_transformation.py @@ -0,0 +1,160 @@ +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import Union + +import numpy as np + + +class Transformation(ABC): + """ + Abstract base class for transformations between two parameter spaces: the model + parameter space and a search space. + + If `transform` is an instance of a `Transformation` class, you can apply the + transformation of a parameter vector from the model space `p` to the search + space `q` using `q = transform.to_search(p)` and the inverse using `p = transform.to_model(q)`. + + Based on pints.transformation method. + + References + ---------- + .. [1] Erik Jorgensen and Asger Roer Pedersen. "How to Obtain Those Nasty Standard Errors From Transformed Data." + http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.9023 + .. [2] Kaare Brandt Petersen and Michael Syskind Pedersen. "The Matrix Cookbook." 2012. + """ + + # ---- To be implemented with Monte Carlo PR ------ # + # def convert_log_prior(self, log_prior): + # """Returns a transformed log-prior class.""" + # return TransformedLogPrior(log_prior, self) + + def convert_covariance_matrix(self, cov: np.ndarray, q: np.ndarray) -> np.ndarray: + """ + Converts a covariance matrix `covariance` from the model space to the search space + around a parameter vector `q` in the search space. + """ + jac_inv = np.linalg.pinv(self.jacobian(q)) + return jac_inv @ cov @ jac_inv.T + + def convert_standard_deviation( + self, std: Union[float, np.ndarray], q: np.ndarray + ) -> np.ndarray: + """ + Converts standard deviation `std`, either a scalar or a vector, from the model space + to the search space around a parameter vector `q` in the search space. + """ + if isinstance(q, (int, float)): + q = np.asarray([q]) + jac_inv = np.linalg.pinv(self.jacobian(q)) + cov = jac_inv @ jac_inv.T + return std * np.sqrt(np.diagonal(cov)) + + @abstractmethod + def jacobian(self, q: np.ndarray) -> np.ndarray: + """Returns the Jacobian matrix of the transformation at the parameter vector `q`.""" + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, Sequence[np.ndarray]]: + """ + Computes the Jacobian matrix and its partial derivatives at the parameter vector `q`. + + Returns a tuple `(jacobian, hessian)`. + """ + raise NotImplementedError("jacobian_S1 method must be implemented if used.") + + def log_jacobian_det(self, q: np.ndarray) -> float: + """ + Returns the logarithm of the absolute value of the determinant of the Jacobian matrix + at the parameter vector `q`. + """ + raise NotImplementedError( + "log_jacobian_det method must be implemented if used." + ) + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """ + Computes the logarithm of the absolute value of the determinant of the Jacobian, + and returns it along with its partial derivatives. + """ + raise NotImplementedError( + "log_jacobian_det_S1 method must be implemented if used." + ) + + @property + def n_parameters(self): + return self._n_parameters + + def to_model(self, q: np.ndarray) -> np.ndarray: + """Transforms a parameter vector `q` from the search space to the model space.""" + return self._transform(q, method="to_model") + + def to_search(self, p: np.ndarray) -> np.ndarray: + """Transforms a parameter vector `p` from the model space to the search space.""" + return self._transform(p, method="to_search") + + @abstractmethod + def _transform(self, x: np.ndarray, method: str) -> np.ndarray: + """ + Transforms a parameter vector `x` from the search space to the model space if `method` + is "to_model", or from the model space to the search space if `method` is "to_search". + """ + + def is_elementwise(self) -> bool: + """ + Returns `True` if the transformation is element-wise, meaning it can be applied + element-by-element independently. + """ + raise NotImplementedError("is_elementwise method must be implemented if used.") + + def verify_input( + self, inputs: Union[float, int, list[float], np.ndarray, dict[str, float]] + ) -> np.ndarray: + """Set and validate the transformation parameter.""" + if isinstance(inputs, (float, int)): + return np.full(self._n_parameters, float(inputs)) + + if isinstance(inputs, dict): + inputs = list(inputs.values()) + + try: + input_array = np.asarray(inputs, dtype=float) + except (ValueError, TypeError) as e: + raise TypeError( + "Transform must be a float, int, list, numpy array, or dictionary" + ) from e + + if input_array.size != self._n_parameters: + raise ValueError(f"Transform must have {self._n_parameters} elements") + + return input_array + + +# ---- To be implemented with Monte Carlo PR ------ # +# class TransformedLogPDF(BaseCost): +# """Transformed log-PDF class.""" +# def __init__(self, log_pdf, transformation): +# self._log_pdf = log_pdf +# self._transformation = transformation + +# def __call__(self, q): +# p = self._transformation.to_model(q) +# log_pdf = self._log_pdf(p) + +# # Calculate the PDF using change of variable +# # Wikipedia: https://w.wiki/UsJ +# log_jacobian_det = self._transformation.log_jacobian_det(q) +# return log_pdf + log_jacobian_det + +# def _evaluateS1(self, x): +# p = self._transformation.to_model(x) +# log_pdf, log_pdf_derivatives = self._log_pdf._evaluateS1(p) +# log_jacobian_det, log_jacobian_det_derivatives = self._transformation.log_jacobian_det_S1(x) +# return log_pdf + log_jacobian_det, log_pdf_derivatives + log_jacobian_det_derivatives + +# class TransformedLogPrior: +# """Transformed log-prior class.""" +# def __init__(self, log_prior, transformation): +# self._log_prior = log_prior +# self._transformation = transformation + +# def __call__(self, q): +# return self diff --git a/pybop/transformation/transformations.py b/pybop/transformation/transformations.py new file mode 100644 index 000000000..8951c41db --- /dev/null +++ b/pybop/transformation/transformations.py @@ -0,0 +1,371 @@ +from typing import Union + +import numpy as np + +from pybop import Transformation + + +class IdentityTransformation(Transformation): + """ + This class implements a trivial transformation where the model parameter space + and the search space are identical. It extends the base Transformation class. + + The transformation is defined as: + - to_search: y = x + - to_model: x = y + + Key properties: + 1. Jacobian: Identity matrix + 2. Log determinant of Jacobian: Always 0 + 3. Elementwise: True (each output dimension depends only on the corresponding input dimension) + + Use cases: + 1. When no transformation is needed between spaces + 2. As a placeholder in composite transformations + 3. For testing and benchmarking other transformations + + Note: While this transformation doesn't change the parameters, it still provides + all the methods required by the Transformation interface, making it useful in + scenarios where a transformation object is expected but no actual transformation + is needed. + + Initially based on pints.IdentityTransformation method. + """ + + def __init__(self, n_parameters: int = 1): + self._n_parameters = n_parameters + + def is_elementwise(self) -> bool: + """See :meth:`Transformation.is_elementwise()`.""" + return True + + def jacobian(self, q: np.ndarray) -> np.ndarray: + """See :meth:`Transformation.jacobian()`.""" + return np.eye(self._n_parameters) + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """See :meth:`Transformation.jacobian_S1()`.""" + n = self._n_parameters + return self.jacobian(q), np.zeros((n, n, n)) + + def log_jacobian_det(self, q: np.ndarray) -> float: + """See :meth:`Transformation.log_jacobian_det()`.""" + return 0.0 + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """See :meth:`Transformation.log_jacobian_det_S1()`.""" + return self.log_jacobian_det(q), np.zeros(self._n_parameters) + + def _transform(self, x: np.ndarray, method: str) -> np.ndarray: + """See :meth:`Transformation._transform`.""" + return np.asarray(x) + + +class ScaledTransformation(Transformation): + """ + This class implements a linear transformation between the model parameter space + and a search space, using a coefficient (scale factor) and an intercept (offset). + It extends the base Transformation class. + + The transformation is defined as: + - to_search: y = coefficient * (x + intercept) + - to_model: x = y / coefficient - intercept + + Where: + - x is in the model parameter space + - y is in the search space + - coefficient is the scaling factor + - intercept is the offset + + This transformation is useful for scaling and shifting parameters to a more + suitable range for optimisation algorithms. + + Based on pints.ScaledTransformation class. + """ + + def __init__( + self, + coefficient: Union[list, float, np.ndarray], + intercept: Union[list, float, np.ndarray] = 0, + n_parameters: int = 1, + ): + self._n_parameters = n_parameters + self.intercept = self.verify_input(intercept) + self.coefficient = self.verify_input(coefficient) + self.inverse_coeff = np.reciprocal(self.coefficient) + + def is_elementwise(self) -> bool: + """See :meth:`Transformation.is_elementwise()`.""" + return True + + def jacobian(self, q: np.ndarray) -> np.ndarray: + """See :meth:`Transformation.jacobian()`.""" + return np.diag(self.inverse_coeff) + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """See :meth:`Transformation.jacobian_S1()`.""" + n = self._n_parameters + return self.jacobian(q), np.zeros((n, n, n)) + + def log_jacobian_det(self, q: np.ndarray) -> float: + """See :meth:`Transformation.log_jacobian_det()`.""" + return np.log(np.abs(self.coefficient)).sum() + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """See :meth:`Transformation.log_jacobian_det_S1()`.""" + return self.log_jacobian_det(q), np.zeros(self._n_parameters) + + def _transform(self, x: np.ndarray, method: str) -> np.ndarray: + """See :meth:`Transformation._transform`.""" + x = self.verify_input(x) + if method == "to_model": + return x * self.inverse_coeff - self.intercept + elif method == "to_search": + return self.coefficient * (x + self.intercept) + else: + raise ValueError(f"Unknown method: {method}") + + +class LogTransformation(Transformation): + """ + This class implements a logarithmic transformation between the model parameter space + and a search space. It extends the base Transformation class. + + The transformation is defined as: + - to_search: y = log(x) + - to_model: x = exp(y) + + Where: + - x is in the model parameter space (strictly positive) + - y is in the search space (can be any real number) + + This transformation is particularly useful for: + 1. Parameters that are strictly positive and may span several orders of magnitude. + 2. Converting multiplicative processes to additive ones in the search space. + 3. Ensuring positivity constraints without explicit bounds in optimisation. + + Note: Care should be taken when using this transformation, as it can introduce + bias in the parameter estimates if not accounted for properly in the likelihood + or cost function. Simply, E[log(x)] <= log(E[x]) as per to Jensen's inequality. + For more information, see Jensen's inequality: + https://en.wikipedia.org/w/index.php?title=Jensen%27s_inequality&oldid=1212437916#Probabilistic_form + + Initially based on pints.LogTransformation class. + """ + + def __init__(self, n_parameters: int = 1): + self._n_parameters = n_parameters + + def is_elementwise(self) -> bool: + """See :meth:`Transformation.is_elementwise()`.""" + return True + + def jacobian(self, q: np.ndarray) -> np.ndarray: + """See :meth:`Transformation.jacobian()`.""" + return np.diag(np.exp(q)) + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """See :meth:`Transformation.jacobian_S1()`.""" + n = self._n_parameters + jac = self.jacobian(q) + jac_S1 = np.zeros((n, n, n)) + rn = np.arange(n) + jac_S1[rn, rn, rn] = np.diagonal(jac) + return jac, jac_S1 + + def log_jacobian_det(self, q: np.ndarray) -> float: + """See :meth:`Transformation.log_jacobian_det()`.""" + return np.sum(q) + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """See :meth:`Transformation.log_jacobian_det_S1()`.""" + logjacdet = self.log_jacobian_det(q) + dlogjacdet = np.ones(self._n_parameters) + return logjacdet, dlogjacdet + + def _transform(self, x: np.ndarray, method: str) -> np.ndarray: + """See :meth:`Transformation._transform`.""" + x = self.verify_input(x) + if method == "to_model": + return np.exp(x) + elif method == "to_search": + return np.log(x) + else: + raise ValueError(f"Unknown method: {method}") + + +class ComposedTransformation(Transformation): + """ + N-dimensional Transformation composed of one or more other N_i-dimensional + sub-transformations, where the sum of N_i equals N. + + This class allows for the composition of multiple transformations, each potentially + operating on a different number of dimensions. The total dimensionality of the + composed transformation is the sum of the dimensionalities of its components. + + The dimensionality of the individual transformations does not have to be + the same, i.e., N_i != N_j is allowed. + + Extends pybop.Transformation. Initially based on pints.ComposedTransformation class. + """ + + def __init__(self, transformations: list[Transformation]): + if not transformations: + raise ValueError("Must have at least one sub-transformation.") + self._transformations = [] + self._n_parameters = 0 + self._is_elementwise = True + for transformation in transformations: + self._append_transformation(transformation) + + def _append_transformation(self, transformation: Transformation): + """ + Append a transformation to the internal list of transformations. + + Parameters + ---------- + transformation : Transformation + Transformation to append. + + Raises + ------ + ValueError + If the appended object is not a Transformation. + """ + if not isinstance(transformation, Transformation): + raise TypeError("The appended object must be a Transformation.") + self._transformations.append(transformation) + self._n_parameters += transformation.n_parameters + self._is_elementwise = self._is_elementwise and transformation.is_elementwise() + + def append(self, transformation: Transformation): + """ + Append a new transformation to the existing composition. + + Parameters + ---------- + transformation : Transformation + The transformation to append. + """ + self._append_transformation(transformation) + + def is_elementwise(self) -> bool: + """See :meth:`Transformation.is_elementwise()`.""" + return self._is_elementwise + + def jacobian(self, q: np.ndarray) -> np.ndarray: + """ + Compute the elementwise Jacobian of the composed transformation. + + Parameters + ---------- + q : np.ndarray + Input array. + + Returns + ------- + np.ndarray + Diagonal matrix representing the elementwise Jacobian. + """ + q = self.verify_input(q) + diag = np.zeros(self._n_parameters) + lo = 0 + + for transformation in self._transformations: + hi = lo + transformation.n_parameters + diag[lo:hi] = np.diagonal(transformation.jacobian(q[lo:hi])) + lo = hi + + return np.diag(diag) + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """See :meth:`Transformation.jacobian_S1()`.""" + q = self.verify_input(q) + output_S1 = np.zeros( + (self._n_parameters, self._n_parameters, self._n_parameters) + ) + lo = 0 + + for transformation in self._transformations: + hi = lo + transformation.n_parameters + _, jac_S1 = transformation.jacobian_S1(q[lo:hi]) + for i, jac_S1_i in enumerate(jac_S1): + output_S1[lo + i, lo:hi, lo:hi] = jac_S1_i + lo = hi + + return self.jacobian(q), output_S1 + + def log_jacobian_det(self, q: np.ndarray) -> float: + """ + Compute the elementwise logarithm of the determinant of the Jacobian. + + Parameters + ---------- + q : np.ndarray + Input array. + + Returns + ------- + float + Sum of log determinants of individual transformations. + """ + q = self.verify_input(q) + return sum( + transformation.log_jacobian_det(q[lo : lo + transformation.n_parameters]) + for lo, transformation in self._iter_transformations() + ) + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """ + Compute the elementwise logarithm of the determinant of the Jacobian and its first-order sensitivities. + + Parameters + ---------- + q : np.ndarray + Input array. + + Returns + ------- + Tuple[float, np.ndarray] + Tuple of sum of log determinants and concatenated first-order sensitivities. + """ + q = self.verify_input(q) + output = 0.0 + output_S1 = np.zeros(self._n_parameters) + lo = 0 + + for transformation in self._transformations: + hi = lo + transformation.n_parameters + j, j_S1 = transformation.log_jacobian_det_S1(q[lo:hi]) + output += j + output_S1[lo:hi] = j_S1 + lo = hi + + return output, output_S1 + + def _transform(self, data: np.ndarray, method: str) -> np.ndarray: + """See :meth:`Transformation._transform`.""" + data = self.verify_input(data) + output = np.zeros_like(data) + lo = 0 + + for transformation in self._transformations: + hi = lo + transformation.n_parameters + output[lo:hi] = getattr(transformation, method)(data[lo:hi]) + lo = hi + + return output + + def _iter_transformations(self): + """ + Iterate over the transformations in the composition. + + Yields + ------ + Tuple[int, Transformation] + Tuple of starting index and transformation object for each sub-transformation. + """ + lo = 0 + for transformation in self._transformations: + yield lo, transformation + lo += transformation.n_parameters diff --git a/pyproject.toml b/pyproject.toml index 105ad38de..a81ed61d9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "pybop" -version = "24.6.1" +version = "24.9.0" authors = [ {name = "The PyBOP Team"}, ] @@ -26,7 +26,7 @@ classifiers = [ ] requires-python = ">=3.9, <3.13" dependencies = [ - "pybamm>=24.5", + "pybamm[cite]>=24.9", "numpy>=1.16, <2.0", "scipy>=1.3", "pints>=0.5", @@ -34,12 +34,7 @@ dependencies = [ ] [project.optional-dependencies] -# Split kaleido into two dependencies to avoid Windows hang -# See: https://github.com/plotly/Kaleido/issues/110 -plot = ["plotly>=5.0", - "kaleido==0.1.0.post1; sys_platform == 'win32'", - "kaleido>=0.2; sys_platform != 'win32'", -] +plot = ["plotly>=5.0"] docs = [ "pydata-sphinx-theme", "sphinx>=6", @@ -84,8 +79,24 @@ extend-exclude = ["__init__.py"] fix = true [tool.ruff.lint] -extend-select = ["I"] +select = [ + "A", # flake8-builtins: Check for Python builtins being used as variables or parameters + "B", # flake8-bugbear: Find likely bugs and design problems + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes: Detect various errors by parsing the source file + "I", # isort: Check and enforce import ordering + "ISC", # flake8-implicit-str-concat: Check for implicit string concatenation + "TID", # flake8-tidy-imports: Validate import hygiene + "UP", # pyupgrade: Automatically upgrade syntax for newer versions of Python + "SLF001", # flake8-string-format: Check for private object name access +] + ignore = ["E501","E741"] [tool.ruff.lint.per-file-ignores] +"tests/*" = ["SLF001"] "**.ipynb" = ["E402", "E703"] + +[tool.ruff.lint.flake8-tidy-imports] +ban-relative-imports = "all" diff --git a/tests/examples/test_examples.py b/tests/examples/test_examples.py index 1c45be840..2bebc6fc6 100644 --- a/tests/examples/test_examples.py +++ b/tests/examples/test_examples.py @@ -12,14 +12,14 @@ class TestExamples: """ def list_of_examples(): - list = [] + examples_list = [] path_to_example_scripts = os.path.join( pybop.script_path, "..", "examples", "scripts" ) for example in os.listdir(path_to_example_scripts): if example.endswith(".py"): - list.append(os.path.join(path_to_example_scripts, example)) - return list + examples_list.append(os.path.join(path_to_example_scripts, example)) + return examples_list @pytest.mark.parametrize("example", list_of_examples()) @pytest.mark.examples diff --git a/tests/integration/test_eis_parameterisation.py b/tests/integration/test_eis_parameterisation.py new file mode 100644 index 000000000..aea988b92 --- /dev/null +++ b/tests/integration/test_eis_parameterisation.py @@ -0,0 +1,185 @@ +import numpy as np +import pytest + +import pybop + + +class TestEISParameterisation: + """ + A class to test the eis parameterisation methods. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.sigma0 = 5e-4 + self.ground_truth = np.clip( + np.asarray([0.55, 0.55]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=0.4, + a_max=0.75, + ) + + @pytest.fixture + def model(self): + parameter_set = pybop.ParameterSet.pybamm("Chen2020") + x = self.ground_truth + parameter_set.update( + { + "Negative electrode active material volume fraction": x[0], + "Positive electrode active material volume fraction": x[1], + } + ) + return pybop.lithium_ion.SPM( + parameter_set=parameter_set, + eis=True, + options={"surface form": "differential"}, + ) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.75), + bounds=[0.375, 0.775], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.75), + bounds=[0.375, 0.775], + ), + ) + + @pytest.fixture(params=[0.5]) + def init_soc(self, request): + return request.param + + @pytest.fixture( + params=[ + pybop.GaussianLogLikelihood, + pybop.SumSquaredError, + pybop.Minkowski, + pybop.LogPosterior, + ] + ) + def cost(self, request): + return request.param + + def noise(self, sigma, values): + # Generate real part noise + real_noise = np.random.normal(0, sigma, values) + + # Generate imaginary part noise + imag_noise = np.random.normal(0, sigma, values) + + # Combine them into a complex noise + return real_noise + 1j * imag_noise + + @pytest.fixture( + params=[ + pybop.SciPyDifferentialEvolution, + pybop.CMAES, + pybop.CuckooSearch, + pybop.XNES, + ] + ) + def optimiser(self, request): + return request.param + + @pytest.fixture + def optim(self, optimiser, model, parameters, cost, init_soc): + n_frequency = 15 + # Set frequency set + f_eval = np.logspace(-4, 5, n_frequency) + + # Form dataset + solution = self.get_data(model, init_soc, f_eval) + dataset = pybop.Dataset( + { + "Frequency [Hz]": f_eval, + "Current function [A]": np.ones(n_frequency) * 0.0, + "Impedance": solution["Impedance"] + + self.noise(self.sigma0, len(solution["Impedance"])), + } + ) + + # Define the problem + signal = ["Impedance"] + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) + + # Construct the cost + if cost is pybop.GaussianLogLikelihoodKnownSigma: + cost = cost(problem, sigma0=self.sigma0) + elif cost is pybop.GaussianLogLikelihood: + cost = cost(problem, sigma0=self.sigma0 * 4) # Initial sigma0 guess + elif cost is pybop.LogPosterior: + cost = cost( + pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=self.sigma0) + ) + elif cost in [pybop.SumofPower, pybop.Minkowski]: + cost = cost(problem, p=2) + else: + cost = cost(problem) + + # Construct optimisation object + common_args = { + "cost": cost, + "max_iterations": 250, + "absolute_tolerance": 1e-6, + "max_unchanged_iterations": 35, + "sigma0": [0.05, 0.05, 1e-3] + if isinstance(cost, pybop.GaussianLogLikelihood) + else 0.05, + } + + if isinstance(cost, pybop.LogPosterior): + for i in cost.parameters.keys(): + cost.parameters[i].prior = pybop.Uniform( + 0.2, 2.0 + ) # Increase range to avoid prior == np.inf + + # Create optimiser + optim = optimiser(**common_args) + return optim + + @pytest.mark.integration + def test_eis_optimisers(self, optim): + x0 = optim.parameters.initial_value() + + # Add sigma0 to ground truth for GaussianLogLikelihood + if isinstance(optim.cost, pybop.GaussianLogLikelihood): + self.ground_truth = np.concatenate( + (self.ground_truth, np.asarray([self.sigma0])) + ) + + initial_cost = optim.cost(x0) + x, final_cost = optim.run() + + # Assertions + if np.allclose(x0, self.ground_truth, atol=1e-5): + raise AssertionError("Initial guess is too close to ground truth") + + # Assert on identified values, without sigma for GaussianLogLikelihood + # as the sigma values are small (5e-4), this is a difficult identification process + # and requires a high number of iterations, and parameter dependent step sizes. + if optim.minimising: + assert initial_cost > final_cost + else: + assert initial_cost < final_cost + np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) + + def get_data(self, model, init_soc, f_eval): + initial_state = {"Initial SoC": init_soc} + sim = model.simulateEIS( + inputs={ + "Negative electrode active material volume fraction": self.ground_truth[ + 0 + ], + "Positive electrode active material volume fraction": self.ground_truth[ + 1 + ], + }, + f_eval=f_eval, + initial_state=initial_state, + ) + + return sim diff --git a/tests/integration/test_model_experiment_changes.py b/tests/integration/test_model_experiment_changes.py index 7bcc33ddb..fec94452b 100644 --- a/tests/integration/test_model_experiment_changes.py +++ b/tests/integration/test_model_experiment_changes.py @@ -39,20 +39,21 @@ def test_changing_experiment(self, parameters): # Change the experiment and check that the results are different. parameter_set = pybop.ParameterSet.pybamm("Chen2020") - init_soc = 0.5 + parameter_set.update(parameters.as_dict("true")) + initial_state = {"Initial SoC": 0.5} model = pybop.lithium_ion.SPM(parameter_set=parameter_set) t_eval = np.arange(0, 3600, 2) # Default 1C discharge to cut-off voltage - solution_1 = model.predict(init_soc=init_soc, t_eval=t_eval) - cost_1 = self.final_cost(solution_1, model, parameters, init_soc) + solution_1 = model.predict(initial_state=initial_state, t_eval=t_eval) + cost_1 = self.final_cost(solution_1, model, parameters) experiment = pybop.Experiment(["Charge at 1C until 4.1 V (2 seconds period)"]) solution_2 = model.predict( - init_soc=init_soc, + initial_state=initial_state, experiment=experiment, inputs=parameters.as_dict("true"), ) - cost_2 = self.final_cost(solution_2, model, parameters, init_soc) + cost_2 = self.final_cost(solution_2, model, parameters) with np.testing.assert_raises(AssertionError): np.testing.assert_array_equal( @@ -69,16 +70,17 @@ def test_changing_model(self, parameters): # Change the model and check that the results are different. parameter_set = pybop.ParameterSet.pybamm("Chen2020") - init_soc = 0.5 + parameter_set.update(parameters.as_dict("true")) + initial_state = {"Initial SoC": 0.5} experiment = pybop.Experiment(["Charge at 1C until 4.1 V (2 seconds period)"]) model = pybop.lithium_ion.SPM(parameter_set=parameter_set) - solution_1 = model.predict(init_soc=init_soc, experiment=experiment) - cost_1 = self.final_cost(solution_1, model, parameters, init_soc) + solution_1 = model.predict(initial_state=initial_state, experiment=experiment) + cost_1 = self.final_cost(solution_1, model, parameters) model = pybop.lithium_ion.SPMe(parameter_set=parameter_set) - solution_2 = model.predict(init_soc=init_soc, experiment=experiment) - cost_2 = self.final_cost(solution_2, model, parameters, init_soc) + solution_2 = model.predict(initial_state=initial_state, experiment=experiment) + cost_2 = self.final_cost(solution_2, model, parameters) with np.testing.assert_raises(AssertionError): np.testing.assert_array_equal( @@ -90,7 +92,7 @@ def test_changing_model(self, parameters): np.testing.assert_allclose(cost_1, 0, atol=1e-5) np.testing.assert_allclose(cost_2, 0, atol=1e-5) - def final_cost(self, solution, model, parameters, init_soc): + def final_cost(self, solution, model, parameters): # Compute the cost corresponding to a particular solution dataset = pybop.Dataset( { @@ -100,10 +102,58 @@ def final_cost(self, solution, model, parameters, init_soc): } ) signal = ["Voltage [V]"] - problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=init_soc - ) + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.RootMeanSquaredError(problem) optim = pybop.PSO(cost) x, final_cost = optim.run() return final_cost + + @pytest.mark.integration + def test_multi_fitting_problem(self): + parameter_set = pybop.ParameterSet.pybamm("Chen2020") + parameters = pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + true_value=parameter_set[ + "Negative electrode active material volume fraction" + ], + ) + + model_1 = pybop.lithium_ion.SPM(parameter_set=parameter_set) + experiment_1 = pybop.Experiment( + ["Discharge at 1C until 3 V (4 seconds period)"] + ) + solution_1 = model_1.predict(experiment=experiment_1) + dataset_1 = pybop.Dataset( + { + "Time [s]": solution_1["Time [s]"].data, + "Current function [A]": solution_1["Current [A]"].data, + "Voltage [V]": solution_1["Voltage [V]"].data, + } + ) + + model_2 = pybop.lithium_ion.SPMe(parameter_set=parameter_set.copy()) + experiment_2 = pybop.Experiment( + ["Discharge at 3C until 3 V (4 seconds period)"] + ) + solution_2 = model_2.predict(experiment=experiment_2) + dataset_2 = pybop.Dataset( + { + "Time [s]": solution_2["Time [s]"].data, + "Current function [A]": solution_2["Current [A]"].data, + "Voltage [V]": solution_2["Voltage [V]"].data, + } + ) + + # Define a problem for each dataset and combine them into one + problem_1 = pybop.FittingProblem(model_1, parameters, dataset_1) + problem_2 = pybop.FittingProblem(model_2, parameters, dataset_2) + problem = pybop.MultiFittingProblem(problem_1, problem_2) + cost = pybop.RootMeanSquaredError(problem) + + # Test with a gradient and non-gradient-based optimiser + for optimiser in [pybop.SNES, pybop.IRPropMin]: + optim = optimiser(cost) + x, final_cost = optim.run() + np.testing.assert_allclose(x, parameters.true_value, atol=2e-5) + np.testing.assert_allclose(final_cost, 0, atol=2e-5) diff --git a/tests/integration/test_monte_carlo.py b/tests/integration/test_monte_carlo.py new file mode 100644 index 000000000..6d2a51219 --- /dev/null +++ b/tests/integration/test_monte_carlo.py @@ -0,0 +1,136 @@ +import numpy as np +import pybamm +import pytest + +import pybop +from pybop import ( + DREAM, + DifferentialEvolutionMCMC, + HaarioACMC, + HaarioBardenetACMC, + MetropolisRandomWalkMCMC, + PopulationMCMC, +) + + +class Test_Sampling_SPM: + """ + A class to test the MCMC samplers on a physics-based model. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.ground_truth = np.clip( + np.asarray([0.55, 0.55]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=0.4, + a_max=0.75, + ) + + @pytest.fixture + def model(self): + parameter_set = pybop.ParameterSet.pybamm("Chen2020") + x = self.ground_truth + parameter_set.update( + { + "Negative electrode active material volume fraction": x[0], + "Positive electrode active material volume fraction": x[1], + } + ) + solver = pybamm.IDAKLUSolver() + return pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.7), + bounds=[0.375, 0.725], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.7), + # no bounds + ), + ) + + @pytest.fixture(params=[0.5]) + def init_soc(self, request): + return request.param + + @pytest.fixture( + params=[ + pybop.GaussianLogLikelihoodKnownSigma, + ] + ) + def cost_class(self, request): + return request.param + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.fixture + def spm_likelihood(self, model, parameters, cost_class, init_soc): + # Form dataset + solution = self.get_data(model, init_soc) + dataset = pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(0.002, len(solution["Time [s]"].data)), + } + ) + + # Define the cost to optimise + problem = pybop.FittingProblem(model, parameters, dataset) + return cost_class(problem, sigma0=0.002) + + @pytest.mark.parametrize( + "quick_sampler", + [ + DREAM, + DifferentialEvolutionMCMC, + HaarioACMC, + HaarioBardenetACMC, + MetropolisRandomWalkMCMC, + PopulationMCMC, + ], + ) + @pytest.mark.integration + def test_sampling_spm(self, quick_sampler, spm_likelihood): + posterior = pybop.LogPosterior(spm_likelihood) + + # set common args + common_args = { + "log_pdf": posterior, + "chains": 3, + "warm_up": 250, + "max_iterations": 550, + } + + if issubclass(quick_sampler, DifferentialEvolutionMCMC): + common_args["warm_up"] = 750 + common_args["max_iterations"] = 900 + # construct and run + sampler = quick_sampler(**common_args) + results = sampler.run() + + # Assert both final sample and posterior mean + x = np.mean(results, axis=1) + for i in range(len(x)): + np.testing.assert_allclose(x[i], self.ground_truth, atol=2.5e-2) + np.testing.assert_allclose(results[i][-1], self.ground_truth, atol=2.0e-2) + + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} + experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 4 minutes (12 second period)", + "Charge at 0.5C for 4 minutes (12 second period)", + ), + ] + ) + sim = model.predict(initial_state=initial_state, experiment=experiment) + return sim diff --git a/tests/integration/test_monte_carlo_thevenin.py b/tests/integration/test_monte_carlo_thevenin.py new file mode 100644 index 000000000..6a9046f62 --- /dev/null +++ b/tests/integration/test_monte_carlo_thevenin.py @@ -0,0 +1,146 @@ +import numpy as np +import pytest + +import pybop +from pybop import ( + MALAMCMC, + NUTS, + DramACMC, + HamiltonianMCMC, + MonomialGammaHamiltonianMCMC, + RaoBlackwellACMC, + RelativisticMCMC, + SliceDoublingMCMC, + SliceRankShrinkingMCMC, + SliceStepoutMCMC, +) + + +class TestSamplingThevenin: + """ + A class to test a subset of samplers on the simple Thevenin Model. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.sigma0 = 1e-3 + self.ground_truth = np.clip( + np.asarray([0.05, 0.05]) + np.random.normal(loc=0.0, scale=0.01, size=2), + a_min=0.0, + a_max=0.1, + ) + self.fast_samplers = [ + MALAMCMC, + RaoBlackwellACMC, + SliceDoublingMCMC, + SliceStepoutMCMC, + DramACMC, + ] + + @pytest.fixture + def model(self): + parameter_set = pybop.ParameterSet( + json_path="examples/scripts/parameters/initial_ecm_parameters.json" + ) + parameter_set.import_parameters() + parameter_set.params.update( + { + "C1 [F]": 1000, + "R0 [Ohm]": self.ground_truth[0], + "R1 [Ohm]": self.ground_truth[1], + } + ) + + return pybop.empirical.Thevenin(parameter_set=parameter_set) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "R0 [Ohm]", prior=pybop.Uniform(1e-2, 8e-2), bounds=[1e-2, 8e-2] + ), + pybop.Parameter( + "R1 [Ohm]", prior=pybop.Uniform(1e-2, 8e-2), bounds=[1e-2, 8e-2] + ), + ) + + @pytest.fixture(params=[0.5]) + def init_soc(self, request): + return request.param + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.fixture + def likelihood(self, model, parameters, init_soc): + # Form dataset + solution = self.get_data(model, init_soc) + dataset = pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(self.sigma0, len(solution["Time [s]"].data)), + } + ) + + # Define the cost to optimise + problem = pybop.FittingProblem(model, parameters, dataset) + return pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.0075) + + # Parameterize the samplers + @pytest.mark.parametrize( + "sampler", + [ + NUTS, + HamiltonianMCMC, + MonomialGammaHamiltonianMCMC, + RelativisticMCMC, + SliceRankShrinkingMCMC, + MALAMCMC, + RaoBlackwellACMC, + SliceDoublingMCMC, + SliceStepoutMCMC, + DramACMC, + ], + ) + @pytest.mark.integration + def test_sampling_thevenin(self, sampler, likelihood): + posterior = pybop.LogPosterior(likelihood) + + # set common args + common_args = { + "log_pdf": posterior, + "chains": 1, + "warm_up": 250, + "max_iterations": 500, + "cov0": [3e-4, 3e-4], + } + if sampler in self.fast_samplers: + common_args["warm_up"] = 600 + common_args["max_iterations"] = 1200 + + # construct and run + sampler = sampler(**common_args) + if isinstance(sampler, SliceRankShrinkingMCMC): + sampler._samplers[0].set_hyper_parameters([1e-3]) + results = sampler.run() + + # Assert both final sample and posterior mean + x = np.mean(results, axis=1) + for i in range(len(x)): + np.testing.assert_allclose(x[i], self.ground_truth, atol=1.5e-2) + np.testing.assert_allclose(results[i][-1], self.ground_truth, atol=1e-2) + + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} + experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 2 minutes (4 second period)", + "Rest for 1 minute (4 second period)", + ), + ] + ) + sim = model.predict(initial_state=initial_state, experiment=experiment) + return sim diff --git a/tests/integration/test_observer_parameterisation.py b/tests/integration/test_observer_parameterisation.py new file mode 100644 index 000000000..143a1535f --- /dev/null +++ b/tests/integration/test_observer_parameterisation.py @@ -0,0 +1,103 @@ +import numpy as np +import pytest + +import pybop +from examples.standalone.model import ExponentialDecay + + +class TestObservers: + """ + A class to run integration tests on the Observers class. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.ground_truth = np.clip( + np.array([0.1, 1.0]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=[0.04, 0.2], + a_max=[0.85, 1.15], + ) + + @pytest.fixture + def parameter_set(self): + return {"k": self.ground_truth[0], "y0": self.ground_truth[1]} + + @pytest.fixture + def model(self, parameter_set): + return ExponentialDecay(parameter_set=parameter_set, n_states=1) + + @pytest.fixture + def parameters(self, parameter_set): + return pybop.Parameters( + pybop.Parameter( + "k", + prior=pybop.Gaussian(0.1, 0.05), + bounds=[0, 1], + true_value=parameter_set["k"], + ), + pybop.Parameter( + "y0", + prior=pybop.Gaussian(1, 0.05), + bounds=[0, 3], + true_value=parameter_set["y0"], + ), + ) + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.mark.integration + def test_observer_exponential_decay(self, parameters, model): + # Make a prediction with measurement noise + sigma = 1e-2 + t_eval = np.linspace(0, 20, 10) + values = model.predict(t_eval=t_eval)["2y"].data + corrupt_values = values + self.noise(sigma, len(t_eval)) + + # Form dataset + dataset = pybop.Dataset( + { + "Time [s]": t_eval, + "Current function [A]": 0 * t_eval, # placeholder + "2y": corrupt_values, + } + ) + + # Define the UKF observer + signal = ["2y"] + n_states = model.n_states + n_signals = len(signal) + covariance = np.diag([sigma**2] * n_states) + process_noise = np.diag([1e-6] * n_states) + measurement_noise = np.diag([sigma**2] * n_signals) + observer = pybop.UnscentedKalmanFilterObserver( + parameters, + model, + covariance, + process_noise, + measurement_noise, + dataset, + signal=signal, + ) + + # Generate cost function, and optimisation class + cost = pybop.ObserverCost(observer) + optim = pybop.CMAES(cost, verbose=True) + + # Initial Cost + x0 = cost.parameters.initial_value() + initial_cost = optim.cost(x0) + + # Run optimisation + x, final_cost = optim.run() + print("Estimated parameters:", x) + + # Assertions + if not np.allclose(x0, self.ground_truth, atol=1e-5): + if optim.minimising: + assert initial_cost > final_cost + else: + assert initial_cost < final_cost + else: + raise ValueError("Initial value is the same as the ground truth value.") + np.testing.assert_allclose(x, parameters.true_value(), atol=1.5e-2) diff --git a/tests/integration/test_optimisation_options.py b/tests/integration/test_optimisation_options.py index 47782a03a..258bdc17b 100644 --- a/tests/integration/test_optimisation_options.py +++ b/tests/integration/test_optimisation_options.py @@ -20,6 +20,13 @@ def setup(self): @pytest.fixture def model(self): parameter_set = pybop.ParameterSet.pybamm("Chen2020") + x = self.ground_truth + parameter_set.update( + { + "Negative electrode active material volume fraction": x[0], + "Positive electrode active material volume fraction": x[1], + } + ) return pybop.lithium_ion.SPM(parameter_set=parameter_set) @pytest.fixture @@ -53,8 +60,8 @@ def noise(self, sigma, values): @pytest.fixture def spm_costs(self, model, parameters, cost_class): # Form dataset - init_soc = 0.5 - solution = self.get_data(model, parameters, self.ground_truth, init_soc) + initial_state = {"Initial SoC": 0.5} + solution = self.get_data(model, initial_state) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, @@ -65,7 +72,7 @@ def spm_costs(self, model, parameters, cost_class): ) # Define the cost to optimise - problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc) + problem = pybop.FittingProblem(model, parameters, dataset) if cost_class in [pybop.GaussianLogLikelihoodKnownSigma]: return cost_class(problem, sigma0=0.002) else: @@ -93,7 +100,7 @@ def test_optimisation_f_guessed(self, f_guessed, spm_costs): # Set parallelisation if not on Windows if sys.platform != "win32": - optim.set_parallel(True) + optim.set_parallel(1) initial_cost = optim.cost(x0) x, final_cost = optim.run() @@ -104,10 +111,12 @@ def test_optimisation_f_guessed(self, f_guessed, spm_costs): assert initial_cost > final_cost else: assert initial_cost < final_cost + else: + raise ValueError("Initial value is the same as the ground truth value.") np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) - def get_data(self, model, parameters, x, init_soc): - model.classify_and_update_parameters(parameters) + def get_data(self, model, initial_state): + # Update the initial state and save the ground truth initial concentrations experiment = pybop.Experiment( [ ( @@ -117,5 +126,5 @@ def get_data(self, model, parameters, x, init_soc): ] * 2 ) - sim = model.predict(init_soc=init_soc, experiment=experiment, inputs=x) + sim = model.predict(initial_state=initial_state, experiment=experiment) return sim diff --git a/tests/integration/test_spm_parameterisations.py b/tests/integration/test_spm_parameterisations.py index d7acaf7d7..d6c4c7861 100644 --- a/tests/integration/test_spm_parameterisations.py +++ b/tests/integration/test_spm_parameterisations.py @@ -1,4 +1,5 @@ import numpy as np +import pybamm import pytest import pybop @@ -12,13 +13,22 @@ class Test_SPM_Parameterisation: @pytest.fixture(autouse=True) def setup(self): self.sigma0 = 0.002 - self.ground_truth = np.asarray([0.55, 0.55]) + np.random.normal( - loc=0.0, scale=0.05, size=2 + self.ground_truth = np.clip( + np.asarray([0.55, 0.55]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=0.4, + a_max=0.75, ) @pytest.fixture def model(self): parameter_set = pybop.ParameterSet.pybamm("Chen2020") + x = self.ground_truth + parameter_set.update( + { + "Negative electrode active material volume fraction": x[0], + "Positive electrode active material volume fraction": x[1], + } + ) return pybop.lithium_ion.SPM(parameter_set=parameter_set) @pytest.fixture @@ -27,7 +37,7 @@ def parameters(self): pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Uniform(0.4, 0.75), - bounds=[0.375, 0.75], + bounds=[0.375, 0.775], ), pybop.Parameter( "Positive electrode active material volume fraction", @@ -46,19 +56,36 @@ def init_soc(self, request): pybop.GaussianLogLikelihood, pybop.RootMeanSquaredError, pybop.SumSquaredError, - pybop.MAP, + pybop.SumofPower, + pybop.Minkowski, + pybop.LogPosterior, ] ) - def cost_class(self, request): + def cost(self, request): return request.param def noise(self, sigma, values): return np.random.normal(0, sigma, values) + @pytest.fixture( + params=[ + pybop.SciPyDifferentialEvolution, + pybop.CuckooSearch, + pybop.NelderMead, + pybop.IRPropMin, + pybop.AdamW, + pybop.CMAES, + pybop.SNES, + pybop.XNES, + ] + ) + def optimiser(self, request): + return request.param + @pytest.fixture - def spm_costs(self, model, parameters, cost_class, init_soc): + def optim(self, optimiser, model, parameters, cost, init_soc): # Form dataset - solution = self.get_data(model, parameters, self.ground_truth, init_soc) + solution = self.get_data(model, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, @@ -68,64 +95,57 @@ def spm_costs(self, model, parameters, cost_class, init_soc): } ) - # Define the cost to optimise - problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc) - if cost_class in [pybop.GaussianLogLikelihoodKnownSigma]: - return cost_class(problem, sigma0=self.sigma0) - elif cost_class in [pybop.GaussianLogLikelihood]: - return cost_class(problem, sigma0=self.sigma0 * 4) # Initial sigma0 guess - elif cost_class in [pybop.MAP]: - return cost_class( - problem, pybop.GaussianLogLikelihoodKnownSigma, sigma0=self.sigma0 + # IDAKLU Solver for Gradient-based optimisers + if optimiser in [pybop.AdamW, pybop.IRPropMin]: + model.solver = pybamm.IDAKLUSolver() + + # Define the problem + problem = pybop.FittingProblem(model, parameters, dataset) + + # Construct the cost + if cost is pybop.GaussianLogLikelihoodKnownSigma: + cost = cost(problem, sigma0=self.sigma0) + elif cost is pybop.GaussianLogLikelihood: + cost = cost(problem, sigma0=self.sigma0 * 4) # Initial sigma0 guess + elif cost is pybop.LogPosterior: + cost = cost( + pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=self.sigma0) ) + elif cost in [pybop.SumofPower, pybop.Minkowski]: + cost = cost(problem, p=2.5) else: - return cost_class(problem) + cost = cost(problem) - @pytest.mark.parametrize( - "optimiser", - [ - pybop.SciPyDifferentialEvolution, - pybop.AdamW, - pybop.CMAES, - pybop.CuckooSearch, - pybop.IRPropMin, - pybop.NelderMead, - pybop.SNES, - pybop.XNES, - ], - ) - @pytest.mark.integration - def test_spm_optimisers(self, optimiser, spm_costs): - x0 = spm_costs.parameters.initial_value() + # Construct optimisation object common_args = { - "cost": spm_costs, + "cost": cost, "max_iterations": 250, "absolute_tolerance": 1e-6, + "max_unchanged_iterations": 55, + "sigma0": [0.05, 0.05, 1e-3] + if isinstance(cost, pybop.GaussianLogLikelihood) + else 0.05, } - # Add sigma0 to ground truth for GaussianLogLikelihood - if isinstance(spm_costs, pybop.GaussianLogLikelihood): - self.ground_truth = np.concatenate( - (self.ground_truth, np.asarray([self.sigma0])) - ) - if isinstance(spm_costs, pybop.MAP): - for i in spm_costs.parameters.keys(): - spm_costs.parameters[i].prior = pybop.Uniform( - 0.4, 2.0 + if isinstance(cost, pybop.LogPosterior): + for i in cost.parameters.keys(): + cost.parameters[i].prior = pybop.Uniform( + 0.2, 2.0 ) # Increase range to avoid prior == np.inf + # Set sigma0 and create optimiser - sigma0 = 0.05 if isinstance(spm_costs, pybop.MAP) else None - optim = optimiser(sigma0=sigma0, **common_args) + optim = optimiser(**common_args) + return optim - # Set max unchanged iterations for BasePintsOptimisers - if issubclass(optimiser, pybop.BasePintsOptimiser): - optim.set_max_unchanged_iterations(iterations=55) + @pytest.mark.integration + def test_spm_optimisers(self, optim): + x0 = optim.parameters.initial_value() - # AdamW will use lowest sigma0 for learning rate, so allow more iterations - if issubclass(optimiser, (pybop.AdamW, pybop.IRPropMin)) and isinstance( - spm_costs, pybop.GaussianLogLikelihood - ): - optim = optimiser(max_unchanged_iterations=75, **common_args) + # Add sigma0 to ground truth for GaussianLogLikelihood + if isinstance(optim.cost, pybop.GaussianLogLikelihood): + self.ground_truth = np.concatenate( + (self.ground_truth, np.asarray([self.sigma0])) + ) initial_cost = optim.cost(x0) x, final_cost = optim.run() @@ -134,7 +154,7 @@ def test_spm_optimisers(self, optimiser, spm_costs): if np.allclose(x0, self.ground_truth, atol=1e-5): raise AssertionError("Initial guess is too close to ground truth") - if isinstance(spm_costs, pybop.GaussianLogLikelihood): + if isinstance(optim.cost, pybop.GaussianLogLikelihood): np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) np.testing.assert_allclose(x[-1], self.sigma0, atol=5e-4) else: @@ -146,10 +166,9 @@ def test_spm_optimisers(self, optimiser, spm_costs): np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) @pytest.fixture - def spm_two_signal_cost(self, parameters, model, cost_class): + def spm_two_signal_cost(self, parameters, model, cost): # Form dataset - init_soc = 0.5 - solution = self.get_data(model, parameters, self.ground_truth, init_soc) + solution = self.get_data(model, init_soc=0.5) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, @@ -165,18 +184,18 @@ def spm_two_signal_cost(self, parameters, model, cost_class): # Define the cost to optimise signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] - problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=init_soc - ) - - if cost_class in [pybop.GaussianLogLikelihoodKnownSigma]: - return cost_class(problem, sigma0=self.sigma0) - elif cost_class in [pybop.MAP]: - return cost_class( - problem, pybop.GaussianLogLikelihoodKnownSigma, sigma0=self.sigma0 + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) + + if cost is pybop.GaussianLogLikelihoodKnownSigma: + return cost(problem, sigma0=self.sigma0) + elif cost is pybop.GaussianLogLikelihood: + return cost(problem, sigma0=self.sigma0 * 4) # Initial sigma0 guess + elif cost is pybop.LogPosterior: + return cost( + pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=self.sigma0) ) else: - return cost_class(problem) + return cost(problem) @pytest.mark.parametrize( "multi_optimiser", @@ -191,19 +210,25 @@ def test_multiple_signals(self, multi_optimiser, spm_two_signal_cost): x0 = spm_two_signal_cost.parameters.initial_value() combined_sigma0 = np.asarray([self.sigma0, self.sigma0]) + common_args = { + "cost": spm_two_signal_cost, + "max_iterations": 250, + "absolute_tolerance": 1e-6, + "max_unchanged_iterations": 55, + "sigma0": [0.035, 0.035, 6e-3, 6e-3] + if isinstance(spm_two_signal_cost, pybop.GaussianLogLikelihood) + else None, + } + # Test each optimiser - optim = multi_optimiser( - cost=spm_two_signal_cost, - sigma0=0.03, - max_iterations=250, - ) + optim = multi_optimiser(**common_args) # Add sigma0 to ground truth for GaussianLogLikelihood if isinstance(spm_two_signal_cost, pybop.GaussianLogLikelihood): self.ground_truth = np.concatenate((self.ground_truth, combined_sigma0)) if issubclass(multi_optimiser, pybop.BasePintsOptimiser): - optim.set_max_unchanged_iterations(iterations=35, absolute_tolerance=1e-5) + optim.set_max_unchanged_iterations(iterations=35, absolute_tolerance=1e-6) initial_cost = optim.cost(optim.parameters.initial_value()) x, final_cost = optim.run() @@ -232,7 +257,7 @@ def test_model_misparameterisation(self, parameters, model, init_soc): second_model = pybop.lithium_ion.SPMe(parameter_set=second_parameter_set) # Form dataset - solution = self.get_data(second_model, parameters, self.ground_truth, init_soc) + solution = self.get_data(second_model, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, @@ -242,7 +267,7 @@ def test_model_misparameterisation(self, parameters, model, init_soc): ) # Define the cost to optimise - problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc) + problem = pybop.FittingProblem(model, parameters, dataset) cost = pybop.RootMeanSquaredError(problem) # Select optimiser @@ -262,8 +287,8 @@ def test_model_misparameterisation(self, parameters, model, init_soc): with np.testing.assert_raises(AssertionError): np.testing.assert_allclose(x, self.ground_truth, atol=2e-2) - def get_data(self, model, parameters, x, init_soc): - model.classify_and_update_parameters(parameters) + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} experiment = pybop.Experiment( [ ( @@ -272,5 +297,5 @@ def get_data(self, model, parameters, x, init_soc): ), ] ) - sim = model.predict(init_soc=init_soc, experiment=experiment, inputs=x) + sim = model.predict(initial_state=initial_state, experiment=experiment) return sim diff --git a/tests/integration/test_thevenin_parameterisation.py b/tests/integration/test_thevenin_parameterisation.py index 98dde5cbc..75530c892 100644 --- a/tests/integration/test_thevenin_parameterisation.py +++ b/tests/integration/test_thevenin_parameterisation.py @@ -11,8 +11,10 @@ class TestTheveninParameterisation: @pytest.fixture(autouse=True) def setup(self): - self.ground_truth = np.asarray([0.05, 0.05]) + np.random.normal( - loc=0.0, scale=0.01, size=2 + self.ground_truth = np.clip( + np.asarray([0.05, 0.05]) + np.random.normal(loc=0.0, scale=0.01, size=2), + a_min=0.0, + a_max=0.1, ) @pytest.fixture @@ -21,7 +23,13 @@ def model(self): json_path="examples/scripts/parameters/initial_ecm_parameters.json" ) parameter_set.import_parameters() - parameter_set.params.update({"C1 [F]": 1000}) + parameter_set.params.update( + { + "C1 [F]": 1000, + "R0 [Ohm]": self.ground_truth[0], + "R1 [Ohm]": self.ground_truth[1], + } + ) return pybop.empirical.Thevenin(parameter_set=parameter_set) @pytest.fixture @@ -46,7 +54,7 @@ def cost_class(self, request): @pytest.fixture def cost(self, model, parameters, cost_class): # Form dataset - solution = self.get_data(model, parameters, self.ground_truth) + solution = self.get_data(model) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, @@ -90,10 +98,11 @@ def test_optimisers_on_simple_model(self, optimiser, cost): assert initial_cost > final_cost else: assert initial_cost < final_cost + else: + raise ValueError("Initial value is the same as the ground truth value.") np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) - def get_data(self, model, parameters, x): - model.classify_and_update_parameters(parameters) + def get_data(self, model): experiment = pybop.Experiment( [ ( @@ -102,5 +111,5 @@ def get_data(self, model, parameters, x): ), ] ) - sim = model.predict(experiment=experiment, inputs=x) + sim = model.predict(experiment=experiment) return sim diff --git a/tests/integration/test_transformation.py b/tests/integration/test_transformation.py new file mode 100644 index 000000000..8939e28e5 --- /dev/null +++ b/tests/integration/test_transformation.py @@ -0,0 +1,114 @@ +import numpy as np +import pytest + +import pybop + + +class TestTransformation: + """ + A class for integration tests of the transformation methods. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.ground_truth = np.clip( + np.array([0.5, 0.1]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=[0.375, 0.02], + a_max=[0.7, 0.5], + ) + + @pytest.fixture + def model(self): + parameter_set = pybop.ParameterSet.pybamm("Chen2020") + x = self.ground_truth + parameter_set.update( + { + "Positive electrode active material volume fraction": x[0], + "Positive electrode conductivity [S.m-1]": x[1], + } + ) + return pybop.lithium_ion.SPMe(parameter_set=parameter_set) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.7), + bounds=[0.375, 0.725], + transformation=pybop.ScaledTransformation( + coefficient=1 / 0.35, intercept=-0.375 + ), + ), + pybop.Parameter( + "Positive electrode conductivity [S.m-1]", + prior=pybop.Uniform(0.05, 0.45), + bounds=[0.02, 0.5], + transformation=pybop.LogTransformation(), + ), + ) + + @pytest.fixture(params=[0.5]) + def init_soc(self, request): + return request.param + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.fixture + def cost(self, model, parameters, init_soc): + # Form dataset + solution = self.get_data(model, init_soc) + dataset = pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(0.002, len(solution["Time [s]"].data)), + } + ) + + # Define the cost to optimise + problem = pybop.FittingProblem(model, parameters, dataset) + return pybop.RootMeanSquaredError(problem) + + @pytest.mark.parametrize( + "optimiser", + [ + pybop.AdamW, + pybop.CMAES, + ], + ) + @pytest.mark.integration + def test_spm_transformation(self, optimiser, cost): + x0 = cost.parameters.initial_value() + optim = optimiser( + cost=cost, + sigma0=0.1, + max_unchanged_iterations=35, + absolute_tolerance=1e-6, + max_iterations=250, + ) + + initial_cost = optim.cost(x0) + x, final_cost = optim.run() + + # Assertions + if not np.allclose(x0, self.ground_truth, atol=1e-5): + assert initial_cost > final_cost + np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) + else: + raise ValueError("Initial value is the same as the ground truth value.") + + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} + experiment = pybop.Experiment( + [ + ( + "Discharge at 1C for 3 minutes (5 second period)", + "Charge at 1C for 3 minutes (5 second period)", + ), + ] + ) + sim = model.predict(initial_state=initial_state, experiment=experiment) + return sim diff --git a/tests/integration/test_weighted_cost.py b/tests/integration/test_weighted_cost.py new file mode 100644 index 000000000..03d963fc3 --- /dev/null +++ b/tests/integration/test_weighted_cost.py @@ -0,0 +1,186 @@ +import numpy as np +import pytest + +import pybop + + +class TestWeightedCost: + """ + A class to test the weighted cost function. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.sigma0 = 0.002 + self.ground_truth = np.clip( + np.asarray([0.55, 0.55]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=0.4, + a_max=0.75, + ) + + @pytest.fixture + def model(self): + parameter_set = pybop.ParameterSet.pybamm("Chen2020") + x = self.ground_truth + parameter_set.update( + { + "Negative electrode active material volume fraction": x[0], + "Positive electrode active material volume fraction": x[1], + } + ) + return pybop.lithium_ion.SPM(parameter_set=parameter_set) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.75), + bounds=[0.375, 0.75], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.75), + # no bounds + ), + ) + + @pytest.fixture(params=[0.4]) + def init_soc(self, request): + return request.param + + @pytest.fixture( + params=[ + ( + pybop.GaussianLogLikelihoodKnownSigma, + pybop.RootMeanSquaredError, + pybop.SumSquaredError, + pybop.LogPosterior, + ) + ] + ) + def cost_class(self, request): + return request.param + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.fixture + def weighted_fitting_cost(self, model, parameters, cost_class, init_soc): + # Form dataset + solution = self.get_data(model, init_soc) + dataset = pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(self.sigma0, len(solution["Time [s]"].data)), + } + ) + + # Define the cost to optimise + problem = pybop.FittingProblem(model, parameters, dataset) + costs = [] + for cost in cost_class: + if issubclass(cost, pybop.LogPosterior): + costs.append( + cost( + pybop.GaussianLogLikelihoodKnownSigma( + problem, sigma0=self.sigma0 + ), + ) + ) + elif issubclass(cost, pybop.BaseLikelihood): + costs.append(cost(problem, sigma0=self.sigma0)) + else: + costs.append(cost(problem)) + + return pybop.WeightedCost(*costs, weights=[0.1, 1, 0.5, 0.6]) + + @pytest.mark.integration + def test_fitting_costs(self, weighted_fitting_cost): + x0 = weighted_fitting_cost.parameters.initial_value() + optim = pybop.CuckooSearch( + cost=weighted_fitting_cost, + sigma0=0.03, + max_iterations=250, + max_unchanged_iterations=35, + ) + + initial_cost = optim.cost(optim.parameters.initial_value()) + x, final_cost = optim.run() + + # Assertions + if not np.allclose(x0, self.ground_truth, atol=1e-5): + if optim.minimising: + assert initial_cost > final_cost + else: + assert initial_cost < final_cost + np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) + + @pytest.fixture( + params=[ + ( + pybop.GravimetricEnergyDensity, + pybop.VolumetricEnergyDensity, + ) + ] + ) + def design_cost(self, request): + return request.param + + @pytest.fixture + def weighted_design_cost(self, model, design_cost): + initial_state = {"Initial SoC": 1.0} + parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode thickness [m]", + prior=pybop.Gaussian(5e-05, 5e-06), + bounds=[2e-06, 10e-05], + ), + pybop.Parameter( + "Negative electrode thickness [m]", + prior=pybop.Gaussian(5e-05, 5e-06), + bounds=[2e-06, 10e-05], + ), + ) + experiment = pybop.Experiment( + ["Discharge at 1C until 3.5 V (5 seconds period)"], + ) + + problem = pybop.DesignProblem( + model, parameters, experiment=experiment, initial_state=initial_state + ) + costs = [cost(problem) for cost in design_cost] + + return pybop.WeightedCost(*costs, weights=[1.0, 0.1]) + + @pytest.mark.integration + def test_design_costs(self, weighted_design_cost): + cost = weighted_design_cost + optim = pybop.CuckooSearch( + cost, + max_iterations=15, + allow_infeasible_solutions=False, + ) + initial_values = optim.parameters.initial_value() + initial_cost = optim.cost(initial_values) + x, final_cost = optim.run() + + # Assertions + assert initial_cost < final_cost + for i, _ in enumerate(x): + assert x[i] > initial_values[i] + + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} + experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (4 second period)", + "Charge at 0.5C for 3 minutes (4 second period)", + ), + ] + ) + sim = model.predict(initial_state=initial_state, experiment=experiment) + return sim diff --git a/tests/plotting/test_plotly_manager.py b/tests/plotting/test_plotly_manager.py index ba0adbd8b..418a0b322 100644 --- a/tests/plotting/test_plotly_manager.py +++ b/tests/plotting/test_plotly_manager.py @@ -21,7 +21,7 @@ def plotly_installed(): # If Plotly is not installed initially, install it if not initially_installed: - subprocess.check_call([python_executable, "-m", "pip", "install", "plotly"]) + PlotlyManager.install_plotly() # Yield control back to the tests yield @@ -50,7 +50,7 @@ def uninstall_plotly_if_installed(): # If Plotly was uninstalled for the test, reinstall it afterwards if was_installed: - subprocess.check_call([python_executable, "-m", "pip", "install", "plotly"]) + PlotlyManager.install_plotly() # Reset the default renderer for tests plotly.io.renderers.default = None @@ -121,7 +121,6 @@ def is_package_installed(package_name): def dataset(plotly_installed): # Construct and simulate model model = pybop.lithium_ion.SPM() - model.parameter_set = model.pybamm_model.default_parameter_values solution = model.predict(t_eval=np.linspace(0, 10, 100)) # Form dataset diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 0fc7c546d..2aad7169b 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -1,4 +1,7 @@ +from copy import copy + import numpy as np +import pybamm import pytest import pybop @@ -15,20 +18,24 @@ def __init__(self, problem, sigma0): pass @pytest.fixture - def model(self): - return pybop.lithium_ion.SPM() + def model(self, ground_truth): + solver = pybamm.IDAKLUSolver() + model = pybop.lithium_ion.SPM(solver=solver) + model.parameter_set["Negative electrode active material volume fraction"] = ( + ground_truth + ) + return model @pytest.fixture def ground_truth(self): return 0.52 @pytest.fixture - def parameters(self, ground_truth): + def parameters(self): return pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.5, 0.01), bounds=[0.375, 0.625], - initial_value=ground_truth, ) @pytest.fixture @@ -40,13 +47,7 @@ def experiment(self): ) @pytest.fixture - def dataset(self, model, experiment, ground_truth): - model.parameter_set = model.pybamm_model.default_parameter_values - model.parameter_set.update( - { - "Negative electrode active material volume fraction": ground_truth, - } - ) + def dataset(self, model, experiment): solution = model.predict(experiment=experiment) return pybop.Dataset( { @@ -64,29 +65,30 @@ def signal(self): def problem(self, model, parameters, dataset, signal, request): cut_off = request.param model.parameter_set.update({"Lower voltage cut-off [V]": cut_off}) - problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=1.0 - ) - problem.dataset = dataset # add this to pass the pybop dataset to cost + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) return problem @pytest.fixture( params=[ pybop.RootMeanSquaredError, pybop.SumSquaredError, + pybop.Minkowski, + pybop.SumofPower, pybop.ObserverCost, - pybop.MAP, + pybop.LogPosterior, ] ) def cost(self, problem, request): cls = request.param if cls in [pybop.SumSquaredError, pybop.RootMeanSquaredError]: return cls(problem) - elif cls in [pybop.MAP]: - return cls(problem, pybop.GaussianLogLikelihoodKnownSigma) - elif cls in [pybop.ObserverCost]: + elif cls is pybop.LogPosterior: + return cls(pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.002)) + elif cls in [pybop.Minkowski, pybop.SumofPower]: + return cls(problem, p=2) + elif cls is pybop.ObserverCost: inputs = problem.parameters.initial_value() - state = problem._model.reinit(inputs) + state = problem.model.reinit(inputs) n = len(state) sigma_diag = [0.0] * n sigma_diag[0] = 1e-4 @@ -96,11 +98,11 @@ def cost(self, problem, request): process_diag[1] = 1e-4 sigma0 = np.diag(sigma_diag) process = np.diag(process_diag) - dataset = problem.dataset + dataset = pybop.Dataset(data_dictionary=problem.dataset) return cls( pybop.UnscentedKalmanFilterObserver( problem.parameters, - problem._model, + problem.model, sigma0=sigma0, process=process, measure=1e-4, @@ -115,43 +117,6 @@ def test_base(self, problem): assert base_cost.problem == problem with pytest.raises(NotImplementedError): base_cost([0.5]) - with pytest.raises(NotImplementedError): - base_cost.evaluateS1([0.5]) - - @pytest.mark.unit - def test_error_in_cost_calculation(self, problem): - class RaiseErrorCost(pybop.BaseCost): - def _evaluate(self, inputs, grad=None): - raise ValueError("Error test.") - - def _evaluateS1(self, inputs): - raise ValueError("Error test.") - - cost = RaiseErrorCost(problem) - with pytest.raises(ValueError, match="Error in cost calculation: Error test."): - cost([0.5]) - with pytest.raises(ValueError, match="Error in cost calculation: Error test."): - cost.evaluateS1([0.5]) - - @pytest.mark.unit - def test_MAP(self, problem): - # Incorrect likelihood - with pytest.raises( - ValueError, - match="An error occurred when constructing the Likelihood class:", - ): - pybop.MAP(problem, pybop.SumSquaredError) - - # Incorrect construction of likelihood - with pytest.raises( - ValueError, - match="An error occurred when constructing the Likelihood class: could not convert string to float: 'string'", - ): - pybop.MAP(problem, pybop.GaussianLogLikelihoodKnownSigma, sigma0="string") - - # Incorrect likelihood - with pytest.raises(ValueError, match="must be a subclass of BaseLikelihood"): - pybop.MAP(problem, self.InvalidLikelihood, sigma0=0.1) @pytest.mark.unit def test_costs(self, cost): @@ -162,16 +127,12 @@ def test_costs(self, cost): higher_cost = cost([0.55]) lower_cost = cost([0.52]) assert higher_cost > lower_cost or ( - higher_cost == lower_cost and higher_cost == np.inf + higher_cost == lower_cost and not np.isfinite(higher_cost) ) # Test type of returned value assert np.isscalar(cost([0.5])) - if isinstance(cost, pybop.ObserverCost): - with pytest.raises(NotImplementedError): - cost.evaluateS1([0.5]) - # Test UserWarnings if isinstance(cost, (pybop.SumSquaredError, pybop.RootMeanSquaredError)): assert cost([0.5]) >= 0 @@ -182,37 +143,75 @@ def test_costs(self, cost): cost.set_fail_gradient(10) assert cost._de == 10 - if isinstance(cost, pybop.SumSquaredError): - e, de = cost.evaluateS1([0.5]) + if not isinstance(cost, (pybop.ObserverCost, pybop.LogPosterior)): + e, de = cost([0.5], calculate_grad=True) assert np.isscalar(e) - assert type(de) == np.ndarray + assert isinstance(de, np.ndarray) # Test exception for non-numeric inputs with pytest.raises( TypeError, match="Inputs must be a dictionary or numeric." ): - cost.evaluateS1(["StringInputShouldNotWork"]) + cost(["StringInputShouldNotWork"], calculate_grad=True) with pytest.warns(UserWarning) as record: - cost.evaluateS1([1.1]) + cost([1.1], calculate_grad=True) for i in range(len(record)): assert "Non-physical point encountered" in str(record[i].message) # Test infeasible locations - cost.problem._model.allow_infeasible_solutions = False + cost.problem.model.allow_infeasible_solutions = False assert cost([1.1]) == np.inf - assert cost.evaluateS1([1.1]) == (np.inf, cost._de) + assert cost([1.1], calculate_grad=True) == (np.inf, cost._de) assert cost([0.01]) == np.inf - assert cost.evaluateS1([0.01]) == (np.inf, cost._de) + assert cost([0.01], calculate_grad=True) == (np.inf, cost._de) # Test exception for non-numeric inputs with pytest.raises(TypeError, match="Inputs must be a dictionary or numeric."): cost(["StringInputShouldNotWork"]) - # Test treatment of simulations that terminated early - # by variation of the cut-off voltage. + # Test ValueError for none dy w/ calculate_grad == True + if not isinstance(cost, pybop.ObserverCost): + with pytest.raises( + ValueError, + match="Forward model sensitivities need to be provided alongside `calculate_grad=True` for `cost.compute`.", + ): + cost.compute([1.1], dy=None, calculate_grad=True) + + @pytest.mark.unit + def test_minkowski(self, problem): + # Incorrect order + with pytest.raises(ValueError, match="The order of the Minkowski distance"): + pybop.Minkowski(problem, p=-1) + with pytest.raises( + ValueError, + match="For p = infinity, an implementation of the Chebyshev distance is required.", + ): + pybop.Minkowski(problem, p=np.inf) + + @pytest.mark.unit + def test_sumofpower(self, problem): + # Incorrect order + with pytest.raises( + ValueError, match="The order of 'p' must be greater than 0." + ): + pybop.SumofPower(problem, p=-1) + + with pytest.raises(ValueError, match="p = np.inf is not yet supported."): + pybop.SumofPower(problem, p=np.inf) + + @pytest.fixture + def design_problem(self, parameters, experiment, signal): + model = pybop.lithium_ion.SPM() + return pybop.DesignProblem( + model, + parameters, + experiment, + signal=signal, + initial_state={"Initial SoC": 0.5}, + ) @pytest.mark.parametrize( "cost_class", @@ -223,21 +222,9 @@ def test_costs(self, cost): ], ) @pytest.mark.unit - def test_design_costs( - self, - cost_class, - model, - parameters, - experiment, - signal, - ): - # Construct Problem - problem = pybop.DesignProblem( - model, parameters, experiment, signal=signal, init_soc=0.5 - ) - + def test_design_costs(self, cost_class, design_problem): # Construct Cost - cost = cost_class(problem) + cost = cost_class(design_problem) if cost_class in [pybop.DesignCost]: with pytest.raises(NotImplementedError): @@ -253,7 +240,7 @@ def test_design_costs( assert cost([-0.1]) == -np.inf # Should not be a viable design # Test infeasible locations - cost.problem._model.allow_infeasible_solutions = False + cost.problem.model.allow_infeasible_solutions = False assert cost([1.1]) == -np.inf # Test exception for non-numeric inputs @@ -263,5 +250,156 @@ def test_design_costs( cost(["StringInputShouldNotWork"]) # Compute after updating nominal capacity - cost = cost_class(problem, update_capacity=True) - assert np.isfinite(cost([0.4])) + design_problem.update_capacity = True + cost = cost_class(design_problem) + cost([0.4]) + + @pytest.fixture + def noisy_problem(self, ground_truth, parameters, experiment): + model = pybop.lithium_ion.SPM() + model.parameter_set["Negative electrode active material volume fraction"] = ( + ground_truth + ) + sol = model.predict(experiment=experiment) + noisy_dataset = pybop.Dataset( + { + "Time [s]": sol["Time [s]"].data, + "Current function [A]": sol["Current [A]"].data, + "Voltage [V]": sol["Voltage [V]"].data + + np.random.normal(0, 0.02, len(sol["Time [s]"].data)), + } + ) + return pybop.FittingProblem(model, parameters, noisy_dataset) + + @pytest.mark.unit + def test_weighted_fitting_cost(self, noisy_problem): + problem = noisy_problem + cost1 = pybop.SumSquaredError(problem) + cost2 = pybop.RootMeanSquaredError(problem) + + # Test with and without weights + weighted_cost = pybop.WeightedCost(cost1, cost2) + np.testing.assert_array_equal(weighted_cost.weights, np.ones(2)) + weighted_cost = pybop.WeightedCost(cost1, cost2, weights=[1, 1]) + np.testing.assert_array_equal(weighted_cost.weights, np.ones(2)) + weighted_cost = pybop.WeightedCost(cost1, cost2, weights=np.array([1, 1])) + np.testing.assert_array_equal(weighted_cost.weights, np.ones(2)) + with pytest.raises( + TypeError, + match="All costs must be instances of BaseCost.", + ): + pybop.WeightedCost(cost1.problem) + with pytest.raises( + ValueError, + match="Weights must be numeric values.", + ): + pybop.WeightedCost(cost1, cost2, weights="Invalid string") + with pytest.raises( + ValueError, + match="Number of weights must match number of costs.", + ): + pybop.WeightedCost(cost1, cost2, weights=[1]) + + # Test with identical problems + weight = 100 + weighted_cost_2 = pybop.WeightedCost(cost1, cost2, weights=[1, weight]) + assert weighted_cost_2.has_identical_problems is True + assert weighted_cost_2.has_separable_problem is False + assert weighted_cost_2.problem is problem + assert weighted_cost_2([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost_2([0.6]), + cost1([0.6]) + weight * cost2([0.6]), + atol=1e-5, + ) + + # Test with different problems + cost3 = pybop.RootMeanSquaredError(copy(problem)) + weighted_cost_3 = pybop.WeightedCost(cost1, cost3, weights=[1, weight]) + assert weighted_cost_3.has_identical_problems is False + assert weighted_cost_3.has_separable_problem is False + assert weighted_cost_3.problem is None + assert weighted_cost_3([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost_3([0.6]), + cost1([0.6]) + weight * cost3([0.6]), + atol=1e-5, + ) + + errors_2, sensitivities_2 = weighted_cost_2([0.5], calculate_grad=True) + errors_3, sensitivities_3 = weighted_cost_3([0.5], calculate_grad=True) + np.testing.assert_allclose(errors_2, errors_3, atol=1e-5) + np.testing.assert_allclose(sensitivities_2, sensitivities_3, atol=1e-5) + + # Test LogPosterior explicitly + cost4 = pybop.LogPosterior(pybop.GaussianLogLikelihood(problem)) + weighted_cost_4 = pybop.WeightedCost(cost1, cost4, weights=[1, -1 / weight]) + assert weighted_cost_4.has_identical_problems is True + assert weighted_cost_4.has_separable_problem is False + sigma = 0.01 + assert np.isfinite(cost4.parameters["Sigma for output 1"].prior.logpdf(sigma)) + assert np.isfinite(weighted_cost_4([0.5, sigma])) + np.testing.assert_allclose( + weighted_cost_4([0.6, sigma]), + cost1([0.6, sigma]) - 1 / weight * cost4([0.6, sigma]), + atol=1e-5, + ) + + @pytest.mark.unit + def test_weighted_design_cost(self, design_problem): + cost1 = pybop.GravimetricEnergyDensity(design_problem) + cost2 = pybop.VolumetricEnergyDensity(design_problem) + + # Test DesignCosts with identical problems + weighted_cost = pybop.WeightedCost(cost1, cost2) + assert weighted_cost.has_identical_problems is True + assert weighted_cost.has_separable_problem is False + assert weighted_cost.problem is design_problem + assert weighted_cost([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost([0.6]), + cost1([0.6]) + cost2([0.6]), + atol=1e-5, + ) + + # Test DesignCosts with different problems + cost3 = pybop.VolumetricEnergyDensity(copy(design_problem)) + weighted_cost = pybop.WeightedCost(cost1, cost3) + assert weighted_cost.has_identical_problems is False + assert weighted_cost.has_separable_problem is False + for i, _ in enumerate(weighted_cost.costs): + assert isinstance(weighted_cost.costs[i].problem, pybop.DesignProblem) + + assert weighted_cost([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost([0.6]), + cost1([0.6]) + cost2([0.6]), + atol=1e-5, + ) + + @pytest.mark.unit + def test_weighted_design_cost_with_update_capacity(self, design_problem): + design_problem.update_capacity = True + cost1 = pybop.GravimetricEnergyDensity(design_problem) + cost2 = pybop.VolumetricEnergyDensity(design_problem) + weighted_cost = pybop.WeightedCost(cost1, cost2, weights=[1, 1]) + + assert weighted_cost.has_identical_problems is True + assert weighted_cost.has_separable_problem is False + assert weighted_cost.problem is design_problem + assert weighted_cost([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost([0.6]), + cost1([0.6]) + cost2([0.6]), + atol=1e-5, + ) + + @pytest.mark.unit + def test_mixed_problem_classes(self, problem, design_problem): + cost1 = pybop.SumSquaredError(problem) + cost2 = pybop.GravimetricEnergyDensity(design_problem) + with pytest.raises( + TypeError, + match="All problems must be of the same class type.", + ): + pybop.WeightedCost(cost1, cost2) diff --git a/tests/unit/test_dataset.py b/tests/unit/test_dataset.py index 9c4eac13d..bb881429c 100644 --- a/tests/unit/test_dataset.py +++ b/tests/unit/test_dataset.py @@ -13,14 +13,13 @@ class TestDataset: def test_dataset(self): # Construct and simulate model model = pybop.lithium_ion.SPM() - model.parameter_set = model.pybamm_model.default_parameter_values solution = model.predict(t_eval=np.linspace(0, 10, 100)) # Form dataset data_dictionary = { "Time [s]": solution["Time [s]"].data, "Current [A]": solution["Current [A]"].data, - "Terminal voltage [V]": solution["Terminal voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data, } dataset = pybop.Dataset(data_dictionary) @@ -55,4 +54,15 @@ def test_dataset(self): dataset["Time"] # Test conversion of single signal to list - assert dataset.check(signal="Terminal voltage [V]") + assert dataset.check() + + # Form frequency dataset + data_dictionary = { + "Frequency [Hz]": np.linspace(-10, 0, 10), + "Current [A]": np.zeros(10), + "Impedance": np.zeros(10), + } + frequency_dataset = pybop.Dataset(data_dictionary) + + with pytest.raises(ValueError, match="Frequencies cannot be negative."): + frequency_dataset.check(domain="Frequency [Hz]", signal="Impedance") diff --git a/tests/unit/test_likelihoods.py b/tests/unit/test_likelihoods.py index aa68cc0e5..a22c63e35 100644 --- a/tests/unit/test_likelihoods.py +++ b/tests/unit/test_likelihoods.py @@ -10,38 +10,37 @@ class TestLikelihoods: """ @pytest.fixture - def model(self): - return pybop.lithium_ion.SPM() + def model(self, ground_truth): + model = pybop.lithium_ion.SPM() + model.parameter_set.update( + { + "Negative electrode active material volume fraction": ground_truth, + } + ) + return model @pytest.fixture def ground_truth(self): return 0.52 @pytest.fixture - def parameters(self, ground_truth): + def parameters(self): return pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.5, 0.01), bounds=[0.375, 0.625], - initial_value=ground_truth, ) @pytest.fixture def experiment(self): return pybop.Experiment( [ - ("Discharge at 1C for 10 minutes (20 second period)"), + ("Discharge at 1C for 1 minutes (5 second period)"), ] ) @pytest.fixture - def dataset(self, model, experiment, ground_truth): - model.parameter_set = model.pybamm_model.default_parameter_values - model.parameter_set.update( - { - "Negative electrode active material volume fraction": ground_truth, - } - ) + def dataset(self, model, experiment): solution = model.predict(experiment=experiment) return pybop.Dataset( { @@ -54,16 +53,12 @@ def dataset(self, model, experiment, ground_truth): @pytest.fixture def one_signal_problem(self, model, parameters, dataset): signal = ["Voltage [V]"] - return pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=1.0 - ) + return pybop.FittingProblem(model, parameters, dataset, signal=signal) @pytest.fixture def two_signal_problem(self, model, parameters, dataset): signal = ["Time [s]", "Voltage [V]"] - return pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=1.0 - ) + return pybop.FittingProblem(model, parameters, dataset, signal=signal) @pytest.mark.parametrize( "problem_name, n_outputs", @@ -75,9 +70,9 @@ def test_base_likelihood_init(self, problem_name, n_outputs, request): likelihood = pybop.BaseLikelihood(problem) assert likelihood.problem == problem assert likelihood.n_outputs == n_outputs - assert likelihood.n_time_data == problem.n_time_data + assert likelihood.n_data == problem.n_data assert likelihood.n_parameters == 1 - assert np.array_equal(likelihood._target, problem._target) + assert np.array_equal(likelihood.target, problem.target) @pytest.mark.unit def test_base_likelihood_call_raises_not_implemented_error( @@ -85,7 +80,7 @@ def test_base_likelihood_call_raises_not_implemented_error( ): likelihood = pybop.BaseLikelihood(one_signal_problem) with pytest.raises(NotImplementedError): - likelihood(np.array([0.5, 0.5])) + likelihood(np.array([0.5])) @pytest.mark.unit def test_likelihood_check_sigma0(self, one_signal_problem): @@ -120,19 +115,25 @@ def test_gaussian_log_likelihood_known_sigma(self, problem_name, request): problem, sigma0=np.array([1.0]) ) result = likelihood(np.array([0.5])) - grad_result, grad_likelihood = likelihood.evaluateS1(np.array([0.5])) + grad_result, grad_likelihood = likelihood(np.array([0.5]), calculate_grad=True) assert isinstance(result, float) np.testing.assert_allclose(result, grad_result, atol=1e-5) - assert np.all(grad_likelihood <= 0) + # Since 0.5 < ground_truth, the likelihood should be increasing + assert grad_likelihood >= 0 @pytest.mark.unit def test_gaussian_log_likelihood(self, one_signal_problem): likelihood = pybop.GaussianLogLikelihood(one_signal_problem) - result = likelihood(np.array([0.5, 0.5])) - grad_result, grad_likelihood = likelihood.evaluateS1(np.array([0.5, 0.5])) + result = likelihood(np.array([0.8, 0.2])) + grad_result, grad_likelihood = likelihood( + np.array([0.8, 0.2]), calculate_grad=True + ) assert isinstance(result, float) np.testing.assert_allclose(result, grad_result, atol=1e-5) - assert np.all(grad_likelihood <= 0) + # Since 0.8 > ground_truth, the likelihood should be decreasing + assert grad_likelihood[0] <= 0 + # Since sigma < 0.5, the likelihood should be decreasing + assert grad_likelihood[1] <= 0 # Test construction with sigma as a Parameter sigma = pybop.Parameter("sigma", prior=pybop.Uniform(0.4, 0.6)) @@ -161,13 +162,9 @@ def test_gaussian_log_likelihood_dsigma_scale(self, one_signal_problem): @pytest.mark.unit def test_gaussian_log_likelihood_returns_negative_inf(self, one_signal_problem): likelihood = pybop.GaussianLogLikelihood(one_signal_problem) - assert likelihood(np.array([-0.5, -0.5])) == -np.inf # negative sigma value - assert ( - likelihood.evaluateS1(np.array([-0.5, -0.5]))[0] == -np.inf - ) # negative sigma value assert likelihood(np.array([0.01, 0.1])) == -np.inf # parameter value too small assert ( - likelihood.evaluateS1(np.array([0.01, 0.1]))[0] == -np.inf + likelihood(np.array([0.01, 0.1]), calculate_grad=True)[0] == -np.inf ) # parameter value too small @pytest.mark.unit @@ -179,5 +176,5 @@ def test_gaussian_log_likelihood_known_sigma_returns_negative_inf( ) assert likelihood(np.array([0.01])) == -np.inf # parameter value too small assert ( - likelihood.evaluateS1(np.array([0.01]))[0] == -np.inf + likelihood(np.array([0.01]), calculate_grad=True)[0] == -np.inf ) # parameter value too small diff --git a/tests/unit/test_models.py b/tests/unit/test_models.py index b12b3639e..02c321171 100644 --- a/tests/unit/test_models.py +++ b/tests/unit/test_models.py @@ -1,3 +1,6 @@ +import sys +from io import StringIO + import numpy as np import pybamm import pytest @@ -57,18 +60,6 @@ def model(self, request): model = request.param return model.copy() - @pytest.mark.unit - def test_simulate_without_build_model(self, model): - with pytest.raises( - ValueError, match="Model must be built before calling simulate" - ): - model.simulate(None, None) - - with pytest.raises( - ValueError, match="Model must be built before calling simulate" - ): - model.simulateS1(None, None) - @pytest.mark.unit def test_non_default_solver(self): solver = pybamm.CasadiSolver( @@ -83,11 +74,20 @@ def test_non_default_solver(self): @pytest.mark.unit def test_predict_without_pybamm(self, model): - model._unprocessed_model = None + model.pybamm_model = None - with pytest.raises(ValueError): + with pytest.raises( + ValueError, + match="The predict method currently only supports PyBaMM models.", + ): model.predict(None, None) + # Test new_copy() without pybamm_model + if not isinstance(model, pybop.lithium_ion.MSMR): + new_model = model.new_copy() + assert new_model.pybamm_model is not None + assert new_model.parameter_set is not None + @pytest.mark.unit def test_predict_with_inputs(self, model): # Define inputs @@ -113,6 +113,12 @@ def test_predict_with_inputs(self, model): res = model.predict(t_eval=t_eval, inputs=inputs) assert len(res["Voltage [V]"].data) == 100 + with pytest.raises( + ValueError, + match="The predict method requires either an experiment or t_eval to be specified.", + ): + model.predict(inputs=inputs) + @pytest.mark.unit def test_predict_without_allow_infeasible_solutions(self, model): if isinstance(model, (pybop.lithium_ion.SPM, pybop.lithium_ion.SPMe)): @@ -123,35 +129,39 @@ def test_predict_without_allow_infeasible_solutions(self, model): "Positive electrode active material volume fraction": 0.9, } - res = model.predict(t_eval=t_eval, inputs=inputs) - assert np.isinf(res).any() + with pytest.raises( + ValueError, match="These parameter values are infeasible." + ): + model.predict(t_eval=t_eval, inputs=inputs) @pytest.mark.unit def test_build(self, model): - model.build() - assert model.built_model is not None + if isinstance(model, pybop.lithium_ion.SPMe): + model.build(initial_state={"Initial SoC": 1.0}) - # Test that the model can be built again - model.build() - assert model.built_model is not None + # Test attributes with init_soc + assert model.built_model is not None + assert model.disc is not None + assert model.built_initial_soc is not None + else: + model.build() + assert model.built_model is not None + + # Test that the model can be built again + model.build() + assert model.built_model is not None @pytest.mark.unit def test_rebuild(self, model): - # Test rebuild before build - with pytest.raises( - ValueError, match="Model must be built before calling rebuild" - ): - model.rebuild() - model.build() initial_built_model = model._built_model assert model._built_model is not None - model.set_params() + model.set_parameters() assert model.model_with_set_params is not None # Test that the model can be built again - model.rebuild() + model.build() rebuilt_model = model._built_model assert rebuilt_model is not None @@ -180,18 +190,18 @@ def test_parameter_set_definition(self): # Test initilisation with different types of parameter set param_dict = {"Nominal cell capacity [A.h]": 5} model = pybop.BaseModel(parameter_set=None) - assert model._parameter_set is None + assert model.parameter_set is None model = pybop.BaseModel(parameter_set=param_dict) parameter_set = pybamm.ParameterValues(param_dict) - assert model._parameter_set == parameter_set + assert model.parameter_set == parameter_set model = pybop.BaseModel(parameter_set=parameter_set) - assert model._parameter_set == parameter_set + assert model.parameter_set == parameter_set pybop_parameter_set = pybop.ParameterSet(params_dict=param_dict) model = pybop.BaseModel(parameter_set=pybop_parameter_set) - assert model._parameter_set == parameter_set + assert model.parameter_set == parameter_set @pytest.mark.unit def test_rebuild_geometric_parameters(self): @@ -220,17 +230,23 @@ def test_rebuild_geometric_parameters(self): t_eval = np.linspace(0, 100, 100) out_init = initial_built_model.predict(t_eval=t_eval) + with pytest.raises( + ValueError, + match="Cannot use sensitivities for parameters which require a model rebuild", + ): + model.simulateS1(t_eval=t_eval, inputs=parameters.as_dict()) + # Test that the model can be rebuilt with different geometric parameters parameters["Positive particle radius [m]"].update(5e-06) parameters["Negative electrode thickness [m]"].update(45e-06) - model.rebuild(parameters=parameters) + model.build(parameters=parameters) rebuilt_model = model assert rebuilt_model._built_model is not None # Test model geometry assert ( - rebuilt_model._mesh["negative electrode"].nodes[1] - != initial_built_model._mesh["negative electrode"].nodes[1] + rebuilt_model.mesh["negative electrode"].nodes[1] + != initial_built_model.mesh["negative electrode"].nodes[1] ) assert ( rebuilt_model.geometry["negative electrode"]["x_n"]["max"] @@ -243,8 +259,8 @@ def test_rebuild_geometric_parameters(self): ) assert ( - rebuilt_model._mesh["positive particle"].nodes[1] - != initial_built_model._mesh["positive particle"].nodes[1] + rebuilt_model.mesh["positive particle"].nodes[1] + != initial_built_model.mesh["positive particle"].nodes[1] ) # Compare model results @@ -271,7 +287,7 @@ def test_reinit(self): state = model.reinit(inputs={}) np.testing.assert_array_almost_equal(state.as_ndarray(), np.array([[y0]])) - model.classify_and_update_parameters(pybop.Parameters(pybop.Parameter("y0"))) + model.classify_parameters(pybop.Parameters(pybop.Parameter("y0"))) state = model.reinit(inputs=[1]) np.testing.assert_array_almost_equal(state.as_ndarray(), np.array([[y0]])) @@ -292,11 +308,33 @@ def test_simulate(self): t_eval = np.linspace(0, 10, 100) expected = y0 * np.exp(-k * t_eval) solved = model.simulate(inputs, t_eval) - np.testing.assert_array_almost_equal(solved["y_0"], expected, decimal=5) + np.testing.assert_array_almost_equal(solved["y_0"].data, expected, decimal=5) with pytest.raises(ValueError): ExponentialDecay(n_states=-1) + @pytest.mark.unit + def test_simulateEIS(self): + # Test EIS on SPM + model = pybop.lithium_ion.SPM(eis=True) + + # Construct frequencies and solve + f_eval = np.linspace(100, 1000, 5) + sol = model.simulateEIS(inputs={}, f_eval=f_eval) + assert np.isfinite(sol["Impedance"]).all() + + # Test infeasible parameter values + model.allow_infeasible_solutions = False + inputs = { + "Negative electrode active material volume fraction": 0.9, + "Positive electrode active material volume fraction": 0.9, + } + # Rebuild model + model.build(inputs=inputs) + + with pytest.raises(ValueError, match="These parameter values are infeasible."): + model.simulateEIS(f_eval=f_eval, inputs=inputs) + @pytest.mark.unit def test_basemodel(self): base = pybop.BaseModel() @@ -311,8 +349,8 @@ def test_basemodel(self): with pytest.raises(NotImplementedError): base.approximate_capacity(x) - base.classify_and_update_parameters(parameters=None) - assert base._n_parameters == 0 + base.classify_parameters(parameters=None) + assert isinstance(base.parameters, pybop.Parameters) @pytest.mark.unit def test_thevenin_model(self): @@ -329,6 +367,38 @@ def test_thevenin_model(self): == model.pybamm_model.default_parameter_values["Open-circuit voltage [V]"] ) + model.predict(initial_state={"Initial SoC": 0.5}, t_eval=np.arange(0, 10, 5)) + assert model.parameter_set["Initial SoC"] == 0.5 + + model.set_initial_state({"Initial SoC": parameter_set["Initial SoC"] / 2}) + assert model.parameter_set["Initial SoC"] == parameter_set["Initial SoC"] / 2 + model.set_initial_state( + { + "Initial open-circuit voltage [V]": parameter_set[ + "Lower voltage cut-off [V]" + ] + } + ) + np.testing.assert_allclose(model.parameter_set["Initial SoC"], 0.0, atol=1e-2) + model.set_initial_state( + { + "Initial open-circuit voltage [V]": parameter_set[ + "Upper voltage cut-off [V]" + ] + } + ) + np.testing.assert_allclose(model.parameter_set["Initial SoC"], 1.0, atol=1e-2) + + with pytest.raises(ValueError, match="outside the voltage limits"): + model.set_initial_state({"Initial open-circuit voltage [V]": -1.0}) + with pytest.raises(ValueError, match="Initial SOC should be between 0 and 1"): + model.set_initial_state({"Initial SoC": -1.0}) + with pytest.raises( + ValueError, + match="Initial value must be a float between 0 and 1, or a string ending in 'V'", + ): + model.set_initial_state({"Initial SoC": "invalid string"}) + @pytest.mark.unit def test_check_params(self): base = pybop.BaseModel() @@ -338,6 +408,38 @@ def test_check_params(self): with pytest.raises(TypeError, match="Inputs must be a dictionary or numeric."): base.check_params(inputs=["unexpected_string"]) + @pytest.mark.unit + def test_base_ecircuit_model(self): + def check_params(inputs: dict, allow_infeasible_solutions: bool): + return True if inputs is None else inputs["a"] < 2 + + base_ecircuit_model = pybop.empirical.ECircuitModel( + pybamm_model=pybamm.equivalent_circuit.Thevenin, + check_params=check_params, + ) + assert base_ecircuit_model.check_params({"a": 1}) + + base_ecircuit_model = pybop.empirical.ECircuitModel( + pybamm_model=pybamm.equivalent_circuit.Thevenin, + ) + assert base_ecircuit_model.check_params() + + @pytest.mark.unit + def test_userdefined_check_params(self): + def check_params(inputs: dict, allow_infeasible_solutions: bool): + return True if inputs is None else inputs["a"] < 2 + + for model in [ + pybop.BaseModel(check_params=check_params), + pybop.empirical.Thevenin(check_params=check_params), + ]: + assert model.check_params(inputs={"a": 1}) + assert not model.check_params(inputs={"a": 2}) + with pytest.raises( + TypeError, match="Inputs must be a dictionary or numeric." + ): + model.check_params(inputs=["unexpected_string"]) + @pytest.mark.unit def test_non_converged_solution(self): model = pybop.lithium_ion.DFN() @@ -368,3 +470,101 @@ def test_non_converged_solution(self): for key in problem.signal: assert np.allclose(output.get(key, [])[0], output.get(key, [])) assert np.allclose(output_S1.get(key, [])[0], output_S1.get(key, [])) + + @pytest.mark.unit + def test_set_initial_state(self): + t_eval = np.linspace(0, 10, 100) + + model = pybop.lithium_ion.SPM() + model.build(initial_state={"Initial SoC": 0.7}) + values_1 = model.predict(t_eval=t_eval) + + model = pybop.lithium_ion.SPM() + model.build(initial_state={"Initial SoC": 0.4}) + model.set_initial_state({"Initial SoC": 0.7}) + values_2 = model.predict(t_eval=t_eval) + + np.testing.assert_allclose( + values_1["Voltage [V]"].data, values_2["Voltage [V]"].data, atol=1e-8 + ) + + init_ocp_p = model.parameter_set["Positive electrode OCP [V]"](0.7) + init_ocp_n = model.parameter_set["Negative electrode OCP [V]"](0.7) + model.set_initial_state( + {"Initial open-circuit voltage [V]": init_ocp_p - init_ocp_n} + ) + values_3 = model.predict(t_eval=t_eval) + + np.testing.assert_allclose( + values_1["Voltage [V]"].data, values_3["Voltage [V]"].data, atol=0.05 + ) + + with pytest.raises(ValueError, match="Expecting only one initial state."): + model.set_initial_state( + {"Initial open-circuit voltage [V]": 3.7, "Initial SoC": 0.7} + ) + with pytest.raises(ValueError, match="Unrecognised initial state"): + model.set_initial_state({"Initial voltage [V]": 3.7}) + + @pytest.mark.unit + def test_get_parameter_info(self, model): + if isinstance(model, pybop.empirical.Thevenin): + # Test at least one model without a built pybamm model + model = pybop.empirical.Thevenin(build=False) + + parameter_info = model.get_parameter_info() + assert isinstance(parameter_info, dict) + + captured_output = StringIO() + sys.stdout = captured_output + + model.get_parameter_info(print_info=True) + sys.stdout = sys.__stdout__ + + printed_messaage = captured_output.getvalue().strip() + + for key, value in parameter_info.items(): + assert key in printed_messaage + assert value in printed_messaage + + @pytest.mark.unit + def test_set_current_function(self): + dataset_1 = pybop.Dataset( + { + "Time [s]": np.linspace(0, 10, 100), + "Current function [A]": 3.0 * np.ones(100), + } + ) + dataset_2 = pybop.Dataset( + { + "Time [s]": np.linspace(0, 5, 100), + "Current function [A]": 6.0 * np.ones(100), + } + ) + + model = pybop.lithium_ion.SPM() + model.set_current_function(dataset=dataset_1) + values_1 = model.predict(t_eval=dataset_1["Time [s]"]) + + np.testing.assert_allclose( + values_1["Current [A]"].data, + dataset_1["Current function [A]"].data, + atol=1e-8, + ) + + model.set_current_function(dataset=dataset_2) + values_2 = model.predict(t_eval=dataset_2["Time [s]"]) + + np.testing.assert_allclose( + values_2["Current [A]"].data, + dataset_2["Current function [A]"].data, + atol=1e-8, + ) + + values_3 = model.simulate(inputs={}, t_eval=dataset_2["Time [s]"]) + + np.testing.assert_allclose( + values_3["Current [A]"].data, + dataset_2["Current function [A]"].data, + atol=1e-8, + ) diff --git a/tests/unit/test_observer_unscented_kalman.py b/tests/unit/test_observer_unscented_kalman.py index a6217cbb8..c83fae315 100644 --- a/tests/unit/test_observer_unscented_kalman.py +++ b/tests/unit/test_observer_unscented_kalman.py @@ -170,3 +170,22 @@ def test_wrong_input_shapes(self, model, parameters): pybop.UnscentedKalmanFilterObserver( parameters, model, sigma0, process, measure, signal=signal ) + + @pytest.mark.unit + def test_without_signal(self): + model = pybop.lithium_ion.SPM() + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.5, 0.05), + ) + ) + model.build(parameters=parameters) + n = model.n_states + sigma0 = np.diag([1e-4] * n) + process = np.diag([1e-4] * n) + measure = np.diag([1e-4]) + observer = pybop.UnscentedKalmanFilterObserver( + parameters, model, sigma0, process, measure + ) + assert observer.signal == ["Voltage [V]"] diff --git a/tests/unit/test_observers.py b/tests/unit/test_observers.py index 2d2e3bc6e..2eda85612 100644 --- a/tests/unit/test_observers.py +++ b/tests/unit/test_observers.py @@ -1,5 +1,4 @@ import numpy as np -import pybamm import pytest import pybop @@ -18,22 +17,18 @@ def parameters(self): "k", prior=pybop.Gaussian(0.1, 0.05), bounds=[0, 1], - initial_value=0.1, ), pybop.Parameter( "y0", prior=pybop.Gaussian(1, 0.05), bounds=[0, 3], - initial_value=1.0, ), ) @pytest.fixture(params=[1, 2]) def model(self, parameters, request): - model = ExponentialDecay( - parameter_set=pybamm.ParameterValues({"k": "[input]", "y0": "[input]"}), - n_states=request.param, - ) + parameter_set = {"k": 0.1, "y0": 1.0} + model = ExponentialDecay(parameter_set=parameter_set, n_states=request.param) model.build(parameters=parameters) return model @@ -41,7 +36,7 @@ def model(self, parameters, request): def test_observer(self, model, parameters): n = model.n_states observer = pybop.Observer(parameters, model, signal=["2y"]) - t_eval = np.linspace(0, 1, 100) + t_eval = np.linspace(0, 20, 10) expected = parameters["y0"].initial_value * np.exp( -parameters["k"].initial_value * t_eval ) @@ -59,9 +54,11 @@ def test_observer(self, model, parameters): ) # Test with invalid inputs - with pytest.raises(ValueError): + with pytest.raises(ValueError, match="Time must be increasing."): observer.observe(-1) - with pytest.raises(ValueError): + with pytest.raises( + ValueError, match="values and times must have the same length." + ): observer.log_likelihood( {"2y": t_eval}, np.array([1]), inputs=observer._state.inputs ) @@ -71,7 +68,7 @@ def test_observer(self, model, parameters): assert np.shape(covariance) == (n, n) # Test evaluate with different inputs - observer._time_data = t_eval + observer.domain_data = t_eval observer.evaluate(parameters.as_dict()) observer.evaluate(parameters.current_value()) @@ -88,5 +85,26 @@ def test_observer(self, model, parameters): @pytest.mark.unit def test_unbuilt_model(self, parameters): model = ExponentialDecay() - with pytest.raises(ValueError): + with pytest.raises( + ValueError, match="Only built models can be used in Observers" + ): pybop.Observer(parameters, model) + + @pytest.mark.unit + def test_observer_inputs(self): + initial_state = {"Initial open-circuit voltage [V]": 4.0} + t_eval = np.linspace(0, 1, 100) + model = ExponentialDecay(n_states=1) + model.build() + observer = pybop.Observer( + pybop.Parameters(), model, signal=["y_0", "2y"], initial_state=initial_state + ) + assert observer.initial_state == initial_state + + with pytest.raises( + ValueError, + match="Observer.log_likelihood is currently restricted to single output models.", + ): + observer.log_likelihood( + {"2y": t_eval}, t_eval, inputs=observer._state.inputs + ) diff --git a/tests/unit/test_optimisation.py b/tests/unit/test_optimisation.py index 8444159b9..8fb53f78f 100644 --- a/tests/unit/test_optimisation.py +++ b/tests/unit/test_optimisation.py @@ -1,3 +1,5 @@ +import io +import sys import warnings import numpy as np @@ -226,34 +228,34 @@ def test_optimiser_kwargs(self, cost, optimiser): # Test AdamW hyperparameters if optimiser in [pybop.AdamW]: - optim = optimiser(cost=cost, b1=0.9, b2=0.999, lambda_=0.1) - optim.pints_optimiser.set_b1(0.9) - optim.pints_optimiser.set_b2(0.9) - optim.pints_optimiser.set_lambda(0.1) + optim = optimiser(cost=cost, b1=0.9, b2=0.999, lam=0.1) + optim.pints_optimiser.b1 = 0.9 + optim.pints_optimiser.b2 = 0.9 + optim.pints_optimiser.lam = 0.1 - assert optim.pints_optimiser._b1 == 0.9 - assert optim.pints_optimiser._b2 == 0.9 - assert optim.pints_optimiser._lambda == 0.1 + assert optim.pints_optimiser.b1 == 0.9 + assert optim.pints_optimiser.b2 == 0.9 + assert optim.pints_optimiser.lam == 0.1 # Incorrect values - for i, match in (("Value", -1),): + for i, _match in (("Value", -1),): with pytest.raises( Exception, match="must be a numeric value between 0 and 1." ): - optim.pints_optimiser.set_b1(i) + optim.pints_optimiser.b1 = i with pytest.raises( Exception, match="must be a numeric value between 0 and 1." ): - optim.pints_optimiser.set_b2(i) + optim.pints_optimiser.b2 = i with pytest.raises( Exception, match="must be a numeric value between 0 and 1." ): - optim.pints_optimiser.set_lambda(i) + optim.pints_optimiser.lam = i # Check defaults assert optim.pints_optimiser.n_hyper_parameters() == 5 assert optim.pints_optimiser.x_guessed() == optim.pints_optimiser._x0 - with pytest.raises(Exception): + with pytest.raises(RuntimeError): optim.pints_optimiser.tell([0.1]) else: @@ -277,6 +279,11 @@ def test_scipy_minimize_with_jac(self, cost): optim = pybop.SciPyMinimize(cost=cost, method="L-BFGS-B", jac=True, maxiter=10) optim.run() assert optim.result.scipy_result.success is True + # Check constraint-based methods, which have different callbacks / returns + for method in ["trust-constr", "SLSQP", "COBYLA"]: + optim = pybop.SciPyMinimize(cost=cost, method=method, maxiter=10) + optim.run() + assert optim.result.scipy_result.success with pytest.raises( ValueError, @@ -326,12 +333,8 @@ def test_default_optimiser(self, cost): optim = pybop.Optimisation(cost=cost) assert optim.name() == "Exponential Natural Evolution Strategy (xNES)" - # Test incorrect setting attribute - with pytest.raises( - AttributeError, - match="'Optimisation' object has no attribute 'not_a_valid_attribute'", - ): - optim.not_a_valid_attribute + # Test getting incorrect attribute + assert not hasattr(optim, "not_a_valid_attribute") @pytest.mark.unit def test_incorrect_optimiser_class(self, cost): @@ -356,6 +359,7 @@ class RandomClass: [ (0.85, 0.2, False), (0.85, 0.001, True), + (1.0, 0.5, False), ], ) def test_scipy_prior_resampling( @@ -389,8 +393,56 @@ def test_scipy_prior_resampling( else: opt.run() + # Test cost_wrapper inf return + cost = opt.cost_wrapper(np.array([0.9])) + assert cost in [1.729, 1.81, 1.9] + + @pytest.mark.unit + def test_scipy_noprior(self, model, dataset): + # Test that Scipy minimize handles no-priors correctly + # Set up the parameter with no prior + parameter = pybop.Parameter( + "Negative electrode active material volume fraction", + initial_value=1, # Intentionally infeasible! + bounds=[0.55, 0.95], + ) + + # Define the problem and cost + problem = pybop.FittingProblem(model, parameter, dataset) + cost = pybop.SumSquaredError(problem) + + # Create the optimisation class with infeasible solutions disabled + opt = pybop.SciPyMinimize( + cost=cost, + allow_infeasible_solutions=False, + max_iterations=1, + ) + with pytest.raises( + ValueError, + match="The initial parameter values return an infinite cost.", + ): + opt.run() + + @pytest.mark.unit + def test_scipy_bounds(self, cost): + # Create the optimisation class with incorrect bounds type + with pytest.raises( + TypeError, + match="Bounds provided must be either type dict, list or SciPy.optimize.bounds object.", + ): + pybop.SciPyMinimize( + cost=cost, + bounds="This is a bad bound", + max_iterations=1, + ) + @pytest.mark.unit def test_halting(self, cost): + # Add a parameter transformation + cost.parameters[ + "Negative electrode active material volume fraction" + ].transformation = pybop.IdentityTransformation() + # Test max evalutions optim = pybop.GradientDescent(cost=cost, max_evaluations=1, verbose=True) x, __ = optim.run() @@ -421,12 +473,23 @@ def test_halting(self, cost): with pytest.raises(ValueError): optim.set_max_evaluations(-1) - optim = pybop.Optimisation(cost=cost) + # Reset optim + optim = pybop.Optimisation(cost=cost, sigma0=0.015, verbose=True) - # Trigger threshold + # Confirm setting threshold == None optim.set_threshold(None) + assert optim._threshold is None + + # Confirm threshold halts + # Redirect stdout to capture print output + captured_output = io.StringIO() + sys.stdout = captured_output optim.set_threshold(np.inf) optim.run() + assert ( + captured_output.getvalue().strip() + == "Halt: Objective function crossed threshold: inf." + ) optim.set_max_unchanged_iterations() # Test callback and halting output diff --git a/tests/unit/test_parameters.py b/tests/unit/test_parameters.py index ebfccea12..775180f28 100644 --- a/tests/unit/test_parameters.py +++ b/tests/unit/test_parameters.py @@ -1,3 +1,4 @@ +import numpy as np import pytest import pybop @@ -130,8 +131,8 @@ def test_parameters_construction(self, parameter): with pytest.raises( ValueError, match="There is already a parameter with the name " - + "Negative electrode active material volume fraction" - + " in the Parameters object. Please remove the duplicate entry.", + "Negative electrode active material volume fraction" + " in the Parameters object. Please remove the duplicate entry.", ): params.add(parameter) @@ -158,8 +159,8 @@ def test_parameters_construction(self, parameter): with pytest.raises( ValueError, match="There is already a parameter with the name " - + "Negative electrode active material volume fraction" - + " in the Parameters object. Please remove the duplicate entry.", + "Negative electrode active material volume fraction" + " in the Parameters object. Please remove the duplicate entry.", ): params.add( dict( @@ -205,6 +206,17 @@ def test_parameters_update(self, parameter): params.update(bounds=dict(lower=[0.37], upper=[0.7])) assert parameter.bounds == [0.37, 0.7] + @pytest.mark.unit + def test_parameters_rvs(self, parameter): + parameter.transformation = pybop.ScaledTransformation( + coefficient=0.2, intercept=-1 + ) + params = pybop.Parameters(parameter) + params.construct_transformation() + samples = params.rvs(n_samples=500, apply_transform=True) + assert (samples >= -0.125).all() and (samples <= -0.06).all() + parameter.transformation = None + @pytest.mark.unit def test_get_sigma(self, parameter): params = pybop.Parameters(parameter) @@ -212,3 +224,27 @@ def test_get_sigma(self, parameter): parameter.prior = None assert params.get_sigma0() is None + + @pytest.mark.unit + def test_initial_values_without_attributes(self): + # Test without initial values + parameter = pybop.Parameters( + pybop.Parameter( + "Negative electrode conductivity [S.m-1]", + ) + ) + with pytest.warns( + UserWarning, + match="Initial value and prior are None, proceeding without an initial value.", + ): + sample = parameter.initial_value() + + np.testing.assert_equal(sample, np.array([None])) + + @pytest.mark.unit + def test_parameters_repr(self, parameter): + params = pybop.Parameters(parameter) + assert ( + repr(params) + == "Parameters(1):\n Negative electrode active material volume fraction: prior= Gaussian, loc: 0.6, scale: 0.02, value=0.6, bounds=[0.375, 0.7]" + ) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 4c7e14d42..27f5b7941 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -66,7 +66,7 @@ def test_dataset_plots(self, dataset): dataset["Voltage [V]"], trace_names=["Time [s]", "Voltage [V]"], ) - pybop.plot_dataset(dataset, signal=["Voltage [V]"]) + pybop.plot_dataset(dataset) @pytest.fixture def fitting_problem(self, model, parameters, dataset): @@ -82,6 +82,7 @@ def experiment(self): @pytest.fixture def design_problem(self, model, parameters, experiment): + model = pybop.lithium_ion.SPM() return pybop.DesignProblem(model, parameters, experiment) @pytest.mark.unit @@ -217,3 +218,38 @@ def test_plot2d_prior_bounds(self, model, dataset): ): warnings.simplefilter("always") pybop.plot2d(cost) + + @pytest.mark.unit + def test_nyquist(self): + # Define model + model = pybop.lithium_ion.SPM( + eis=True, options={"surface form": "differential"} + ) + + # Fitting parameters + parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode thickness [m]", + prior=pybop.Gaussian(60e-6, 1e-6), + bounds=[10e-6, 80e-6], + ) + ) + + # Form dataset + dataset = pybop.Dataset( + { + "Frequency [Hz]": np.logspace(-4, 5, 10), + "Current function [A]": np.ones(10) * 0.0, + "Impedance": np.ones(10) * 0.0, + } + ) + + signal = ["Impedance"] + # Generate problem, cost function, and optimisation class + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) + + # Plot the nyquist + pybop.nyquist(problem, problem_inputs=[60e-6], title="Optimised Comparison") + + # Without inputs + pybop.nyquist(problem, title="Optimised Comparison") diff --git a/tests/unit/test_posterior.py b/tests/unit/test_posterior.py new file mode 100644 index 000000000..b43e21ecd --- /dev/null +++ b/tests/unit/test_posterior.py @@ -0,0 +1,133 @@ +import numpy as np +import pytest +import scipy.stats as st + +import pybop + + +class TestLogPosterior: + """ + Class for log posterior unit tests + """ + + @pytest.fixture + def model(self): + return pybop.lithium_ion.SPM() + + @pytest.fixture + def ground_truth(self): + return 0.52 + + @pytest.fixture + def parameters(self, ground_truth): + return pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.5, 0.01), + bounds=[0.375, 0.625], + initial_value=ground_truth, + ) + + @pytest.fixture + def experiment(self): + return pybop.Experiment( + [ + ("Discharge at 1C for 1 minutes (5 second period)"), + ] + ) + + @pytest.fixture + def dataset(self, model, experiment, ground_truth): + model._parameter_set = model.pybamm_model.default_parameter_values + model._parameter_set.update( + { + "Negative electrode active material volume fraction": ground_truth, + } + ) + solution = model.predict(experiment=experiment) + return pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Terminal voltage [V]"].data, + } + ) + + @pytest.fixture + def one_signal_problem(self, model, parameters, dataset): + return pybop.FittingProblem(model, parameters, dataset) + + @pytest.fixture + def likelihood(self, one_signal_problem): + return pybop.GaussianLogLikelihoodKnownSigma(one_signal_problem, sigma0=0.01) + + @pytest.fixture + def prior(self): + return pybop.Gaussian(0.5, 0.01) + + @pytest.mark.unit + def test_log_posterior_construction(self, likelihood, prior): + # Test log posterior construction + posterior = pybop.LogPosterior(likelihood, prior) + + assert posterior._log_likelihood == likelihood + assert posterior._prior == prior + + # Test log posterior construction without parameters + likelihood.parameters.priors = None + + with pytest.raises(TypeError, match="'NoneType' object is not callable"): + pybop.LogPosterior(likelihood, log_prior=None) + + @pytest.mark.unit + def test_log_posterior_construction_no_prior(self, likelihood): + # Test log posterior construction without prior + posterior = pybop.LogPosterior(likelihood, None) + + assert posterior._prior is not None + assert isinstance(posterior._prior, pybop.JointLogPrior) + + for i, p in enumerate(posterior._prior._priors): + assert p == posterior._log_likelihood.problem.parameters.priors()[i] + + @pytest.fixture + def posterior(self, likelihood, prior): + return pybop.LogPosterior(likelihood, prior) + + @pytest.mark.unit + def test_log_posterior(self, posterior): + # Test log posterior + x = np.array([0.50]) + assert np.allclose(posterior(x), 51.5236, atol=2e-2) + + # Test log posterior evaluateS1 + p, dp = posterior(x, calculate_grad=True) + assert np.allclose(p, 51.5236, atol=2e-2) + assert np.allclose(dp, 2.0, atol=2e-2) + + # Get log likelihood and log prior + likelihood = posterior.likelihood + prior = posterior.prior + + assert likelihood == posterior._log_likelihood + assert prior == posterior._prior + + @pytest.fixture + def posterior_uniform_prior(self, likelihood): + return pybop.LogPosterior(likelihood, pybop.Uniform(0.45, 0.55)) + + @pytest.mark.unit + def test_log_posterior_inf(self, posterior_uniform_prior): + # Test prior np.inf + assert not np.isfinite(posterior_uniform_prior([1])) + assert not np.isfinite(posterior_uniform_prior([1], calculate_grad=True)[0]) + + @pytest.mark.unit + def test_non_logpdfS1_prior(self, likelihood): + # Scipy distribution + prior = st.norm(0.8, 0.01) + posterior = pybop.LogPosterior(likelihood, log_prior=prior) + p, dp = posterior([0.6], calculate_grad=True) + + # Assert to PyBOP.Gaussian + p2, dp2 = pybop.Gaussian(0.8, 0.01).logpdfS1(0.6) + np.testing.assert_allclose(dp, dp2, atol=2e-3) diff --git a/tests/unit/test_priors.py b/tests/unit/test_priors.py index b773049f8..3e9184374 100644 --- a/tests/unit/test_priors.py +++ b/tests/unit/test_priors.py @@ -21,13 +21,23 @@ def Uniform(self): def Exponential(self): return pybop.Exponential(scale=1) + @pytest.fixture + def JointPrior1(self, Gaussian, Uniform): + return pybop.JointLogPrior(Gaussian, Uniform) + + @pytest.fixture + def JointPrior2(self, Gaussian, Exponential): + return pybop.JointLogPrior(Gaussian, Exponential) + @pytest.mark.unit def test_base_prior(self): base = pybop.BasePrior() assert isinstance(base, pybop.BasePrior) + with pytest.raises(NotImplementedError): + base._logpdfS1(0.0) @pytest.mark.unit - def test_priors(self, Gaussian, Uniform, Exponential): + def test_priors(self, Gaussian, Uniform, Exponential, JointPrior1, JointPrior2): # Test pdf np.testing.assert_allclose(Gaussian.pdf(0.5), 0.3989422804014327, atol=1e-4) np.testing.assert_allclose(Uniform.pdf(0.5), 1, atol=1e-4) @@ -38,6 +48,69 @@ def test_priors(self, Gaussian, Uniform, Exponential): np.testing.assert_allclose(Uniform.logpdf(0.5), 0, atol=1e-4) np.testing.assert_allclose(Exponential.logpdf(1), -1, atol=1e-4) + # Test icdf + np.testing.assert_allclose(Gaussian.icdf(0.5), 0.5, atol=1e-4) + np.testing.assert_allclose(Uniform.icdf(0.5), 0.5, atol=1e-4) + np.testing.assert_allclose(Exponential.icdf(0.5), 0.6931471805599453, atol=1e-4) + + # Test cdf + np.testing.assert_allclose(Gaussian.cdf(0.5), 0.5, atol=1e-4) + np.testing.assert_allclose(Uniform.cdf(0.5), 0.5, atol=1e-4) + np.testing.assert_allclose(Exponential.cdf(1), 0.6321205588285577, atol=1e-4) + + # Test __call__ + assert Gaussian(0.5) == Gaussian.logpdf(0.5) + assert Uniform(0.5) == Uniform.logpdf(0.5) + assert Exponential(1) == Exponential.logpdf(1) + assert JointPrior1([0.5, 0.5]) == Gaussian.logpdf(0.5) + Uniform.logpdf(0.5) + assert JointPrior2([0.5, 1]) == Gaussian.logpdf(0.5) + Exponential.logpdf(1) + + # Test Gaussian.logpdfS1 + p, dp = Gaussian.logpdfS1(0.5) + assert p == Gaussian.logpdf(0.5) + assert dp == 0.0 + + # Test Uniform.logpdfS1 + p, dp = Uniform.logpdfS1(0.5) + assert p == Uniform.logpdf(0.5) + assert dp == 0.0 + + # Test Exponential.logpdfS1 + p, dp = Exponential.logpdfS1(1) + assert p == Exponential.logpdf(1) + assert dp == Exponential.logpdf(1) + + # Test JointPrior1.logpdfS1 + p, dp = JointPrior1.logpdfS1([0.5, 0.5]) + assert p == Gaussian.logpdf(0.5) + Uniform.logpdf(0.5) + np.testing.assert_allclose(dp, np.array([0.0, 0.0]), atol=1e-4) + + # Test JointPrior.logpdfS1 + p, dp = JointPrior2.logpdfS1([0.5, 1]) + assert p == Gaussian.logpdf(0.5) + Exponential.logpdf(1) + np.testing.assert_allclose( + dp, np.array([0.0, Exponential.logpdf(1)]), atol=1e-4 + ) + + # Test JointPrior1 non-symmetric + with pytest.raises(AssertionError): + np.testing.assert_allclose( + JointPrior1([0.4, 0.5]), JointPrior1([0.5, 0.4]), atol=1e-4 + ) + + # Test JointPrior2 non-symmetric + with pytest.raises(AssertionError): + np.testing.assert_allclose( + JointPrior2([0.4, 1]), JointPrior2([1, 0.4]), atol=1e-4 + ) + + # Test JointPrior with incorrect dimensions + with pytest.raises(ValueError, match="Input x must have length 2, got 1"): + JointPrior1([0.4]) + + with pytest.raises(ValueError, match="Input x must have length 2, got 1"): + JointPrior1.logpdfS1([0.4]) + # Test properties assert Uniform.mean == (Uniform.upper - Uniform.lower) / 2 np.testing.assert_allclose( @@ -74,10 +147,14 @@ def test_exponential_rvs(self, Exponential): assert abs(mean - 1) < 0.2 @pytest.mark.unit - def test_repr(self, Gaussian, Uniform, Exponential): + def test_repr(self, Gaussian, Uniform, Exponential, JointPrior1): assert repr(Gaussian) == "Gaussian, loc: 0.5, scale: 1" assert repr(Uniform) == "Uniform, loc: 0, scale: 1" assert repr(Exponential) == "Exponential, loc: 0, scale: 1" + assert ( + repr(JointPrior1) + == "JointLogPrior(priors: [Gaussian, loc: 0.5, scale: 1, Uniform, loc: 0, scale: 1])" + ) @pytest.mark.unit def test_invalid_size(self, Gaussian, Uniform, Exponential): @@ -87,3 +164,14 @@ def test_invalid_size(self, Gaussian, Uniform, Exponential): Uniform.rvs(-1) with pytest.raises(ValueError): Exponential.rvs(-1) + + @pytest.mark.unit + def test_incorrect_composed_priors(self, Gaussian, Uniform): + with pytest.raises( + ValueError, match="All priors must be instances of BasePrior" + ): + pybop.JointLogPrior(Gaussian, Uniform, "string") + with pytest.raises( + ValueError, match="All priors must be instances of BasePrior" + ): + pybop.JointLogPrior(Gaussian, Uniform, 0.5) diff --git a/tests/unit/test_problem.py b/tests/unit/test_problem.py index c2c40a038..c02a0aa17 100644 --- a/tests/unit/test_problem.py +++ b/tests/unit/test_problem.py @@ -45,7 +45,6 @@ def experiment(self): @pytest.fixture def dataset(self, model, experiment): - model.parameter_set = model.pybamm_model.default_parameter_values x0 = np.array([2e-5, 0.5e-5]) model.parameter_set.update( { @@ -71,7 +70,7 @@ def test_base_problem(self, parameters, model, dataset): # Construct Problem problem = pybop.BaseProblem(parameters, model=model) - assert problem._model == model + assert problem.model == model with pytest.raises(NotImplementedError): problem.evaluate([1e-5, 1e-5]) @@ -109,11 +108,29 @@ def test_base_problem(self, parameters, model, dataset): @pytest.mark.unit def test_fitting_problem(self, parameters, dataset, model, signal): + with pytest.warns(UserWarning) as record: + problem = pybop.FittingProblem( + model, + parameters, + dataset, + signal=signal, + initial_state={"Initial SoC": 0.8}, + ) + assert "It is usually better to define an initial open-circuit voltage" in str( + record[0].message + ) + # Construct Problem - problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) + problem = pybop.FittingProblem( + model, + parameters, + dataset, + signal=signal, + initial_state={"Initial open-circuit voltage [V]": 4.0}, + ) - assert problem._model == model - assert problem._model._built_model is not None + assert problem.model == model + assert problem.model.built_model is not None # Test get target target = problem.get_target()["Voltage [V]"] @@ -130,6 +147,14 @@ def test_fitting_problem(self, parameters, dataset, model, signal): # Test model.simulate model.simulate(inputs=[1e-5, 1e-5], t_eval=np.linspace(0, 10, 100)) + # Test model.simulate with an initial state + problem.evaluate(inputs=[1e-5, 1e-5]) + + # Test try-except + problem.verbose = True + out = problem.evaluate(inputs=[0.0, 0.0]) + assert not np.isfinite(out["Voltage [V]"]) + # Test problem construction errors for bad_dataset in [ pybop.Dataset({"Time [s]": np.array([0])}), @@ -162,19 +187,120 @@ def test_fitting_problem(self, parameters, dataset, model, signal): with pytest.raises(ValueError): pybop.FittingProblem(model, parameters, bad_dataset, signal=two_signals) + @pytest.mark.unit + def test_fitting_problem_eis(self, parameters): + model = pybop.lithium_ion.SPM(eis=True) + + dataset = pybop.Dataset( + { + "Frequency [Hz]": np.logspace(-4, 5, 30), + "Current function [A]": np.ones(30) * 0.0, + "Impedance": np.ones(30) * 0.0, + } + ) + + # Construct Problem + problem = pybop.FittingProblem( + model, + parameters, + dataset, + signal=["Impedance"], + initial_state={"Initial open-circuit voltage [V]": 4.0}, + ) + assert problem.eis == model.eis + assert problem.domain == "Frequency [Hz]" + + # Test try-except + problem.verbose = True + out = problem.evaluate(inputs=[0.0, 0.0]) + assert not np.isfinite(out["Impedance"]) + + @pytest.mark.unit + def test_multi_fitting_problem(self, model, parameters, dataset, signal): + problem_1 = pybop.FittingProblem(model, parameters, dataset, signal=signal) + + with pytest.raises( + ValueError, match="Make a new_copy of the model for each problem." + ): + pybop.MultiFittingProblem(problem_1, problem_1) + + # Generate a second fitting problem + model = model.new_copy() + experiment = pybop.Experiment( + ["Discharge at 1C for 5 minutes (1 second period)"] + ) + values = model.predict( + initial_state={"Initial SoC": 0.8}, experiment=experiment + ) + dataset_2 = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data, + } + ) + problem_2 = pybop.FittingProblem(model, parameters, dataset_2, signal=signal) + combined_problem = pybop.MultiFittingProblem(problem_1, problem_2) + + assert combined_problem._model is None + + assert len(combined_problem._dataset["Time [s]"]) == len( + problem_1._dataset["Time [s]"] + ) + len(problem_2._dataset["Time [s]"]) + assert len(combined_problem._dataset["Combined signal"]) == len( + problem_1._dataset[signal] + ) + len(problem_2._dataset[signal]) + + y = combined_problem.evaluate(inputs=[1e-5, 1e-5]) + assert len(y["Combined signal"]) == len( + combined_problem._dataset["Combined signal"] + ) + @pytest.mark.unit def test_design_problem(self, parameters, experiment, model): + with pytest.warns(UserWarning) as record: + problem = pybop.DesignProblem( + model, + parameters, + experiment, + update_capacity=True, + ) + assert "The nominal capacity is approximated for each evaluation." in str( + record[0].message + ) + + with pytest.warns(UserWarning) as record: + problem = pybop.DesignProblem( + model, + parameters, + experiment, + initial_state={"Initial open-circuit voltage [V]": 4.0}, + ) + assert "It is usually better to define an initial state of charge" in str( + record[0].message + ) + assert "The nominal capacity is fixed at the initial model value." in str( + record[1].message + ) + # Construct Problem problem = pybop.DesignProblem(model, parameters, experiment) - assert problem._model == model + assert problem.model == model assert ( - problem._model._built_model is None + problem.model.built_model is None ) # building postponed with input experiment + assert problem.initial_state == {"Initial SoC": 1.0} + + # Test evaluation + problem.evaluate(inputs=[1e-5, 1e-5]) + problem.evaluate(inputs=[3e-5, 3e-5]) - # Test model.predict - model.predict(inputs=[1e-5, 1e-5], experiment=experiment) - model.predict(inputs=[3e-5, 3e-5], experiment=experiment) + # Test initial SoC from parameter_set + model = pybop.empirical.Thevenin() + model.parameter_set["Initial SoC"] = 0.8 + problem = pybop.DesignProblem(model, pybop.Parameters(), experiment) + assert problem.initial_state == {"Initial SoC": 0.8} @pytest.mark.unit def test_problem_construct_with_model_predict( @@ -191,7 +317,7 @@ def test_problem_construct_with_model_predict( # Test problem evaluate problem_output = problem.evaluate([2e-5, 2e-5]) - assert problem._model._built_model is not None + assert problem.model.built_model is not None with pytest.raises(AssertionError): assert_allclose( out["Voltage [V]"].data, diff --git a/tests/unit/test_sampling.py b/tests/unit/test_sampling.py new file mode 100644 index 000000000..e854d24ab --- /dev/null +++ b/tests/unit/test_sampling.py @@ -0,0 +1,388 @@ +import copy +import logging +from unittest.mock import call, patch + +import numpy as np +import pytest +from pints import ParallelEvaluator + +import pybop +from pybop import ( + DREAM, + MALAMCMC, + NUTS, + AdaptiveCovarianceMCMC, + DifferentialEvolutionMCMC, + DramACMC, + EmceeHammerMCMC, + HaarioACMC, + HaarioBardenetACMC, + HamiltonianMCMC, + MetropolisRandomWalkMCMC, + MonomialGammaHamiltonianMCMC, + PopulationMCMC, + RaoBlackwellACMC, + RelativisticMCMC, + SliceDoublingMCMC, + SliceRankShrinkingMCMC, + SliceStepoutMCMC, +) + + +class TestPintsSamplers: + """ + Class for testing the Pints-based MCMC Samplers + """ + + @pytest.fixture + def dataset(self): + return pybop.Dataset( + { + "Time [s]": np.linspace(0, 360, 10), + "Current function [A]": np.zeros(10), + "Voltage [V]": np.ones(10), + } + ) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.6, 0.2), + bounds=[0.58, 0.62], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.55, 0.05), + bounds=[0.53, 0.57], + ), + ) + + @pytest.fixture + def model(self): + return pybop.lithium_ion.SPM() + + @pytest.fixture + def log_posterior(self, model, parameters, dataset): + problem = pybop.FittingProblem( + model, + parameters, + dataset, + ) + likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.01) + prior1 = pybop.Gaussian(0.7, 0.02) + prior2 = pybop.Gaussian(0.6, 0.02) + composed_prior = pybop.JointLogPrior(prior1, prior2) + log_posterior = pybop.LogPosterior(likelihood, composed_prior) + + return log_posterior + + @pytest.fixture + def x0(self): + return [0.68, 0.58] + + @pytest.fixture + def chains(self): + return 3 + + @pytest.fixture + def multi_samplers(self): + return (pybop.DREAM, pybop.EmceeHammerMCMC, pybop.DifferentialEvolutionMCMC) + + @pytest.fixture( + params=[ + NUTS, + DREAM, + AdaptiveCovarianceMCMC, + DifferentialEvolutionMCMC, + DramACMC, + EmceeHammerMCMC, + HaarioACMC, + HaarioBardenetACMC, + HamiltonianMCMC, + MALAMCMC, + MetropolisRandomWalkMCMC, + MonomialGammaHamiltonianMCMC, + PopulationMCMC, + RaoBlackwellACMC, + RelativisticMCMC, + SliceDoublingMCMC, + SliceRankShrinkingMCMC, + SliceStepoutMCMC, + ] + ) + def MCMC(self, request): + return request.param + + @pytest.mark.unit + def test_initialisation_and_run( + self, log_posterior, x0, chains, MCMC, multi_samplers + ): + sampler = pybop.MCMCSampler( + log_pdf=log_posterior, + chains=chains, + sampler=MCMC, + x0=x0, + max_iterations=1, + verbose=True, + ) + assert sampler._n_chains == chains + assert sampler._log_pdf == log_posterior + if isinstance(sampler.sampler, multi_samplers): + np.testing.assert_allclose(sampler._samplers[0]._x0[0], x0) + else: + np.testing.assert_allclose(sampler._samplers[0]._x0, x0) + + # Test incorrect __getattr__ + with pytest.raises( + AttributeError, match="'MCMCSampler' object has no attribute 'test'" + ): + sampler.__getattr__("test") + + # Test __setattr__ + sampler.some_attribute = 1 + assert sampler.some_attribute == 1 + sampler.verbose = True + assert sampler.verbose is True + + # Run the sampler + samples = sampler.run() + assert samples is not None + assert samples.shape == (chains, 1, 2) + + @pytest.mark.unit + def test_single_parameter_sampling(self, model, dataset, MCMC, chains): + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.6, 0.2), + bounds=[0.58, 0.62], + ) + ) + problem = pybop.FittingProblem( + model, + parameters, + dataset, + ) + likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.01) + log_posterior = pybop.LogPosterior(likelihood) + + # Skip RelativisticMCMC as it requires > 1 parameter + if issubclass(MCMC, RelativisticMCMC): + return + + # Construct and run + sampler = pybop.MCMCSampler( + log_pdf=log_posterior, + chains=chains, + sampler=MCMC, + max_iterations=1, + verbose=True, + ) + sampler.run() + + @pytest.mark.unit + def test_multi_log_pdf(self, log_posterior, x0, chains): + multi_log_posterior = [log_posterior, log_posterior, log_posterior] + sampler = pybop.MCMCSampler( + log_pdf=multi_log_posterior, + chains=chains, + sampler=HamiltonianMCMC, + x0=x0, + max_iterations=1, + ) + assert sampler._n_chains == chains + assert sampler._log_pdf == multi_log_posterior + + # Run the sampler + samples = sampler.run() + assert samples is not None + assert samples.shape == (chains, 1, 2) + + # Test incorrect multi log pdf + incorrect_multi_log_posterior = [log_posterior, log_posterior, chains] + with pytest.raises( + ValueError, match="All log pdf's must be instances of BaseCost" + ): + sampler = pybop.MCMCSampler( + log_pdf=incorrect_multi_log_posterior, + chains=chains, + sampler=HaarioBardenetACMC, + x0=x0, + max_iterations=1, + ) + + # Test incorrect number of parameters + new_multi_log_posterior = copy.copy(log_posterior) + new_multi_log_posterior.parameters = [ + new_multi_log_posterior.parameters[ + "Positive electrode active material volume fraction" + ] + ] + with pytest.raises( + ValueError, match="All log pdf's must have the same number of parameters" + ): + sampler = pybop.MCMCSampler( + log_pdf=[log_posterior, log_posterior, new_multi_log_posterior], + chains=chains, + sampler=HaarioBardenetACMC, + x0=x0, + max_iterations=1, + ) + + @pytest.mark.unit + def test_invalid_initialisation(self, log_posterior, x0): + with pytest.raises(ValueError, match="Number of chains must be greater than 0"): + AdaptiveCovarianceMCMC( + log_pdf=log_posterior, + chains=0, + x0=x0, + ) + + with pytest.raises( + ValueError, match="Number of log pdf's must match number of chains" + ): + AdaptiveCovarianceMCMC( + log_pdf=[log_posterior, log_posterior, log_posterior], + chains=2, + x0=x0, + ) + + with pytest.raises( + ValueError, match="x0 must have the same number of parameters as log_pdf" + ): + AdaptiveCovarianceMCMC( + log_pdf=[log_posterior, log_posterior, log_posterior], + chains=3, + x0=[0.4, 0.4, 0.4, 0.4], + ) + + # SingleChain & MultiChain Sampler + @pytest.mark.parametrize( + "sampler", + [ + AdaptiveCovarianceMCMC, + DifferentialEvolutionMCMC, + ], + ) + @pytest.mark.unit + def test_no_chains_in_memory(self, log_posterior, x0, chains, sampler): + sampler = sampler( + log_pdf=log_posterior, + chains=chains, + x0=x0, + max_iterations=1, + chains_in_memory=False, + ) + assert sampler._chains_in_memory is False + + # Run the sampler + samples = sampler.run() + assert sampler._samples is not None + assert samples is None + + @patch("logging.basicConfig") + @patch("logging.info") + @pytest.mark.unit + def test_initialise_logging( + self, mock_info, mock_basicConfig, log_posterior, x0, chains + ): + sampler = AdaptiveCovarianceMCMC( + log_pdf=log_posterior, + chains=chains, + x0=x0, + parallel=True, + evaluation_files=["eval1.txt", "eval2.txt"], + chain_files=["chain1.txt", "chain2.txt"], + ) + + # Set parallel workers + sampler.set_parallel(parallel=2) + sampler._initialise_logging() + + # Check if basicConfig was called with correct arguments + mock_basicConfig.assert_called_once_with( + format="%(message)s", level=logging.INFO + ) + + # Check if correct messages were called + expected_calls = [ + call("Using Haario-Bardenet adaptive covariance MCMC"), + call("Generating 3 chains."), + call("Running in parallel with 2 worker processes."), + call("Writing chains to chain1.txt etc."), + call("Writing evaluations to eval1.txt etc."), + ] + mock_info.assert_has_calls(expected_calls, any_order=False) + + # Test when _log_to_screen is False + sampler._log_to_screen = False + sampler._initialise_logging() + assert mock_info.call_count == len(expected_calls) # No additional calls + + @pytest.mark.unit + def test_check_stopping_criteria(self, log_posterior, x0, chains): + sampler = AdaptiveCovarianceMCMC( + log_pdf=log_posterior, + chains=chains, + x0=x0, + ) + # Set stopping criteria + sampler.set_max_iterations(10) + assert sampler._max_iterations == 10 + + # Remove stopping criteria + sampler._max_iterations = None + with pytest.raises( + ValueError, match="At least one stopping criterion must be set." + ): + sampler._check_stopping_criteria() + + # Incorrect stopping criteria + with pytest.raises( + ValueError, match="Number of iterations must be greater than 0" + ): + sampler.set_max_iterations(-1) + + @pytest.mark.unit + def test_set_parallel(self, log_posterior, x0, chains): + sampler = AdaptiveCovarianceMCMC( + log_pdf=log_posterior, + chains=chains, + x0=x0, + ) + + # Disable parallelism + sampler.set_parallel(False) + assert sampler._parallel is False + assert sampler._n_workers == 1 + + # Enable parallelism + sampler.set_parallel(True) + assert sampler._parallel is True + + # Enable parallelism with number of workers + sampler.set_parallel(2) + assert sampler._parallel is True + assert sampler._n_workers == 2 + + # Test evaluator construction + sampler.set_parallel(2) + evaluator = sampler._create_evaluator() + assert isinstance(evaluator, ParallelEvaluator) + + @pytest.mark.unit + def test_base_sampler(self, log_posterior, x0): + sampler = pybop.BaseSampler(log_posterior, x0, cov0=0.1) + with pytest.raises(NotImplementedError): + sampler.run() + + @pytest.mark.unit + def test_MCMC_sampler(self, log_posterior, x0, chains): + with pytest.raises(TypeError): + pybop.MCMCSampler( + log_pdf=log_posterior, + chains=chains, + sampler=log_posterior, # Incorrect sampler + ) diff --git a/tests/unit/test_solvers.py b/tests/unit/test_solvers.py new file mode 100644 index 000000000..3fe318378 --- /dev/null +++ b/tests/unit/test_solvers.py @@ -0,0 +1,75 @@ +import numpy as np +import pybamm +import pytest + +import pybop + + +class TestSolvers: + """ + A class to test the forward model solver interface + """ + + @pytest.fixture( + params=[ + pybamm.IDAKLUSolver(atol=1e-4, rtol=1e-4), + pybamm.CasadiSolver(atol=1e-4, rtol=1e-4, mode="safe"), + pybamm.CasadiSolver(atol=1e-4, rtol=1e-4, mode="fast with events"), + ] + ) + def solver(self, request): + solver = request.param + return solver.copy() + + @pytest.fixture + def model(self, solver): + parameter_set = pybop.ParameterSet.pybamm("Marquis2019") + model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) + return model + + @pytest.mark.unit + def test_solvers_with_model_predict(self, model, solver): + assert model.solver == solver + assert model.solver.atol == 1e-4 + assert model.solver.rtol == 1e-4 + + # Ensure solver is functional + sol = model.predict(t_eval=np.linspace(0, 1, 100)) + assert np.isfinite(sol["Voltage [V]"].data).all() + + signals = ["Voltage [V]", "Bulk open-circuit voltage [V]"] + additional_vars = [ + "Maximum negative particle concentration", + "Positive electrode volume-averaged concentration [mol.m-3]", + ] + + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode conductivity [S.m-1]", prior=pybop.Uniform(0.1, 100) + ) + ) + dataset = pybop.Dataset( + { + "Time [s]": sol["Time [s]"].data, + "Current function [A]": sol["Current [A]"].data, + "Voltage [V]": sol["Voltage [V]"].data, + "Bulk open-circuit voltage [V]": sol[ + "Bulk open-circuit voltage [V]" + ].data, + } + ) + problem = pybop.FittingProblem( + model, + parameters=parameters, + dataset=dataset, + signal=signals, + additional_variables=additional_vars, + ) + + y = problem.evaluate(inputs={"Negative electrode conductivity [S.m-1]": 10}) + + for signal in signals: + assert np.isfinite(y[signal].data).all() + + if isinstance(model.solver, pybamm.IDAKLUSolver): + assert model.solver.output_variables is not None diff --git a/tests/unit/test_standalone.py b/tests/unit/test_standalone.py index 2d5727b60..2f6caceb7 100644 --- a/tests/unit/test_standalone.py +++ b/tests/unit/test_standalone.py @@ -73,14 +73,14 @@ def test_standalone_problem(self): # Test the Problem with a Cost rmse_cost = pybop.RootMeanSquaredError(problem) rmse_x = rmse_cost([1, 2]) - rmse_grad_x = rmse_cost.evaluateS1([1, 2]) + rmse_grad_x = rmse_cost([1, 2], calculate_grad=True) np.testing.assert_allclose(rmse_x, 3.05615, atol=1e-2) np.testing.assert_allclose(rmse_grad_x[1], [-0.54645, 0.0], atol=1e-2) # Test the sensitivities sums_cost = pybop.SumSquaredError(problem) - x = sums_cost.evaluateS1([1, 2]) + x = sums_cost([1, 2], calculate_grad=True) np.testing.assert_allclose(x[0], 934.006734006734, atol=1e-2) np.testing.assert_allclose(x[1], [-334.006734, 0.0], atol=1e-2) diff --git a/tests/unit/test_transformations.py b/tests/unit/test_transformations.py new file mode 100644 index 000000000..daec0c8d6 --- /dev/null +++ b/tests/unit/test_transformations.py @@ -0,0 +1,237 @@ +import inspect + +import numpy as np +import pytest + +import pybop + + +class TestTransformation: + """ + A class to test the transformations. + """ + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Identity", + transformation=pybop.IdentityTransformation(), + ), + pybop.Parameter( + "Scaled", + transformation=pybop.ScaledTransformation(coefficient=2.0, intercept=1), + ), + pybop.Parameter( + "Log", + transformation=pybop.LogTransformation(), + ), + ) + + @pytest.mark.unit + def test_identity_transformation(self, parameters): + q = np.asarray([5.0]) + transformation = parameters["Identity"].transformation + assert np.array_equal(transformation.to_model(q), q) + assert np.array_equal(transformation.to_search(q), q) + assert transformation.log_jacobian_det(q) == 0.0 + assert transformation.log_jacobian_det_S1(q) == (0.0, np.zeros(1)) + assert transformation.n_parameters == 1 + assert transformation.is_elementwise() + + jac, jac_S1 = transformation.jacobian_S1(q) + assert np.array_equal(jac, np.eye(1)) + assert np.array_equal(jac_S1, np.zeros((1, 1, 1))) + + # Test covariance transformation + cov = np.array([[0.5]]) + q = np.array([5.0]) + cov_transformed = transformation.convert_covariance_matrix(cov, q) + assert np.array_equal(cov_transformed, cov) + + @pytest.mark.unit + def test_scaled_transformation(self, parameters): + q = np.asarray([2.5]) + transformation = parameters["Scaled"].transformation + p = transformation.to_model(q) + assert np.allclose(p, (q / 2.0) - 1.0) + assert transformation.n_parameters == 1 + assert transformation.is_elementwise() + + q_transformed = transformation.to_search(p) + assert np.allclose(q_transformed, q) + assert np.allclose( + transformation.log_jacobian_det(q), np.sum(np.log(np.abs(2.0))) + ) + log_jac_det_S1 = transformation.log_jacobian_det_S1(q) + assert log_jac_det_S1[0] == np.sum(np.log(np.abs(2.0))) + assert log_jac_det_S1[1] == np.zeros(1) + + jac, jac_S1 = transformation.jacobian_S1(q) + assert np.array_equal(jac, np.diag([0.5])) + assert np.array_equal(jac_S1, np.zeros((1, 1, 1))) + + # Test covariance transformation + cov = np.array([[0.5]]) + cov_transformed = transformation.convert_covariance_matrix(cov, q) + assert np.array_equal(cov_transformed, cov * transformation.coefficient**2) + + # Test incorrect transform + with pytest.raises(ValueError, match="Unknown method:"): + transformation._transform(q, "bad-string") + + @pytest.mark.unit + def test_log_transformation(self, parameters): + q = np.asarray([10]) + transformation = parameters["Log"].transformation + p = transformation.to_model(q) + assert np.allclose(p, np.exp(q)) + assert transformation.n_parameters == 1 + + q_transformed = transformation.to_search(p) + assert np.allclose(q_transformed, q) + assert np.allclose(transformation.log_jacobian_det(q), np.sum(q)) + + log_jac_det_S1 = transformation.log_jacobian_det_S1(q) + n = transformation._n_parameters + assert log_jac_det_S1[0] == np.sum(q) + assert log_jac_det_S1[1] == np.ones(n) + + jac, jac_S1 = transformation.jacobian_S1(q) + assert np.array_equal(jac, np.diag(np.exp(q))) + jac_S1_def = np.zeros((n, n, n)) + rn = np.arange(n) + jac_S1_def[rn, rn, rn] = np.diagonal(jac) + assert np.array_equal(jac_S1, jac_S1_def) + + # Test covariance transformation + cov = np.array([[0.5]]) + cov_transformed = transformation.convert_covariance_matrix(cov, q) + assert np.array_equal(cov_transformed, cov * np.exp(q) ** -2) + + # Test incorrect transform + with pytest.raises(ValueError, match="Unknown method:"): + transformation._transform(q, "bad-string") + + @pytest.mark.unit + def test_composed_transformation(self, parameters): + # Test elementwise transformations + transformation = pybop.ComposedTransformation( + [parameters["Identity"].transformation, parameters["Scaled"].transformation] + ) + # Test appending transformations + transformation.append(parameters["Log"].transformation) + assert transformation.is_elementwise() is True + + q = np.asarray([5.0, 2.5, 10]) + p = transformation.to_model(q) + np.testing.assert_allclose( + p, np.asarray([5.0, ((q[1] / 2.0) - 1.0), np.exp(q[2])]) + ) + jac = transformation.jacobian(q) + jac_S1 = transformation.jacobian_S1(q) + np.testing.assert_allclose( + jac, + np.asarray([[1, 0.0, 0.0], [0.0, 0.5, 0.0], [0.0, 0.0, 2.202647e04]]), + rtol=1e-6, + ) + np.testing.assert_allclose(jac_S1[0], jac) + assert jac_S1[1].shape == (3, 3, 3) + np.testing.assert_allclose(jac_S1[1][2, 2, 2], 22026.4657948067) + np.testing.assert_allclose(jac_S1[1][0, :, :], np.zeros((3, 3))) + np.testing.assert_allclose(jac_S1[1][1, :, :], np.zeros((3, 3))) + + correct_output = np.sum(np.log(np.abs(2.0))) + np.sum(10) + log_jac_det = transformation.log_jacobian_det(q) + assert log_jac_det == correct_output + + log_jac_det_S1 = transformation.log_jacobian_det_S1(q) + assert log_jac_det_S1[0] == correct_output + np.testing.assert_allclose(log_jac_det_S1[1], np.asarray([0.0, 0.0, 1.0])) + + # Test composed with no transformations + with pytest.raises( + ValueError, match="Must have at least one sub-transformation." + ): + pybop.ComposedTransformation([]) + + # Test incorrect append object + with pytest.raises( + TypeError, match="The appended object must be a Transformation." + ): + pybop.ComposedTransformation( + [parameters["Identity"].transformation, "string"] + ) + + @pytest.mark.unit + def test_verify_input(self, parameters): + q = np.asarray([5.0]) + q_dict = {"Identity": q[0]} + transformation = parameters["Scaled"].transformation + assert transformation.verify_input(q) == q + assert transformation.verify_input(q.tolist()) == q + assert transformation.verify_input(q_dict) == q + + with pytest.raises( + TypeError, match="Transform must be a float, int, list, numpy array," + ): + transformation.verify_input("string") + + with pytest.raises(ValueError, match="Transform must have"): + transformation.verify_input(np.array([1.0, 2.0, 3.0])) + + +class TestBaseTransformation: + """ + A class to test the abstract base transformation class. + """ + + @pytest.fixture + def ConcreteTransformation(self): + class ConcreteTransformation(pybop.Transformation): + def jacobian(self, q): + pass + + def _transform(self, q, method): + pass + + return ConcreteTransformation() + + @pytest.mark.unit + def test_abstract_base_transformation(self): + with pytest.raises(TypeError): + pybop.Transformation() + + @pytest.mark.unit + def test_abstract_methods(self): + abstract_methods = ["jacobian", "_transform"] + for method in abstract_methods: + assert hasattr(pybop.Transformation, method) + assert getattr(pybop.Transformation, method).__isabstractmethod__ + + @pytest.mark.unit + def test_concrete_methods(self): + concrete_methods = [ + "convert_covariance_matrix", + "convert_standard_deviation", + "log_jacobian_det", + "to_model", + "to_search", + ] + for method in concrete_methods: + assert hasattr(pybop.Transformation, method) + assert not inspect.isabstract(getattr(pybop.Transformation, method)) + + @pytest.mark.unit + def test_not_implemented_methods(self, ConcreteTransformation): + not_implemented_methods = [ + "jacobian_S1", + "log_jacobian_det_S1", + ] + for method_name in not_implemented_methods: + with pytest.raises(NotImplementedError): + method = getattr(ConcreteTransformation, method_name) + method(np.array([1.0])) + + with pytest.raises(NotImplementedError): + ConcreteTransformation.is_elementwise()