Merge pull request #119 from aai-institute/feature/renaming-to-contin…

…uiti Rename continuity to continuiti.
aai-institute · Apr 11, 2024 · ae51e89 · ae51e89
2 parents b6a2cd9 + 97f1bda
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diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -1,6 +1,6 @@
 # Contributing
 
-Continuity aims to be a repository of architectures and benchmarks for
+**continuiti** aims to be a repository of architectures and benchmarks for
 operator learning with neural networks and its applications.
 
 Contributions are welcome from anyone in the form of pull requests,
@@ -86,7 +86,7 @@ during the test run.
 Because we want documentation to include the full dataset, we commit notebooks
 with their outputs running with full datasets to the repo. The notebooks are
 then added by CI to the section
-[Examples](https://aai-institute.github.io/continuity/examples.html) of the
+[Examples](https://aai-institute.github.io/continuiti/examples.html) of the
 documentation.
 
 ### Hiding cells in notebooks

diff --git a/README.md b/README.md
@@ -1,40 +1,40 @@
 <div align="center">
-<img alt="Continuity" src="https://aai-institute.github.io/continuity/img/icon.png" width="100">
+<img alt="continuiti" src="https://aai-institute.github.io/continuiti/img/icon.png" width="100">
 
-<h1>Continuity</h1>
+<h1>continuiti</h1>
 
 Learning function operators with neural networks.
 
 [![PyTorch](https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/)
-[![Documentation](https://img.shields.io/badge/Documentation-blue)](https://aai-institute.github.io/continuity/)
-[![Test](https://github.com/aai-institute/continuity/actions/workflows/test.yml/badge.svg)](https://github.com/aai-institute/continuity/actions/workflows/test.yml)
+[![Documentation](https://img.shields.io/badge/Documentation-blue)](https://aai-institute.github.io/continuiti/)
+[![Test](https://github.com/aai-institute/continuiti/actions/workflows/test.yml/badge.svg)](https://github.com/aai-institute/continuiti/actions/workflows/test.yml)
 </div>
 
-**Continuity** is a Python package for machine learning on function operators.
+**continuiti** is a Python package for machine learning on function operators.
 It implements various neural operator architectures (e.g., DeepONets),
 physics-informed loss functions to train based on PDEs, and a collection of
 examples and benchmarks.
 
 ## Installation
 Clone the repository and install the package using pip.
 ```
-git clone https://github.com/aai-institute/continuity.git
-cd continuity
+git clone https://github.com/aai-institute/continuiti.git
+cd continuiti
 pip install -e .
 ```
 
 ## Usage
-Our [Documentation](https://aai-institute.github.io/continuity/) contains a
+Our [Documentation](https://aai-institute.github.io/continuiti/) contains a
 verbose introduction to operator learning, a collection of examples using
-Continuity, and a class documentation.
+continuiti, and a class documentation.
 
-In general, the operator syntax in Continuity is
+In general, the operator syntax in **continuiti** is
 ```python
 v = operator(x, u(x), y)
 ```
 mapping a function `u` (evaluated at `x`) to function `v` (evaluated in `y`).
 For more details, see
-[Learning Operators](https://aai-institute.github.io/continuity/operators/index.html).
+[Learning Operators](https://aai-institute.github.io/continuiti/operators/index.html).
 
 ## Contributing
 Contributions are welcome from anyone in the form of pull requests, bug reports

diff --git a/benchmarks/flame/run_flame.py b/benchmarks/flame/run_flame.py
@@ -1,7 +1,7 @@
 from functools import partial
-from continuity.benchmarks.run import BenchmarkRunner, RunConfig
-from continuity.benchmarks import Flame
-from continuity.operators import ConvolutionalNeuralNetwork
+from continuiti.benchmarks.run import BenchmarkRunner, RunConfig
+from continuiti.benchmarks import Flame
+from continuiti.operators import ConvolutionalNeuralNetwork
 
 
 def run():

diff --git a/benchmarks/html/table.html b/benchmarks/html/table.html
@@ -1,25 +1,25 @@
 <link rel="stylesheet" href="style.css">
-<h2><a href="../api/continuity/benchmarks/#continuity.benchmarks.SineRegular">SineRegular</a></h2>
+<h2><a href="../api/continuiti/benchmarks/#continuiti.benchmarks.SineRegular">SineRegular</a></h2>
 <table class="benchmark-table">
 <thead>
 <tr><th>Operator</th><th>Params</th><th>Learning Curve</th><th>loss/train</th><th>loss/test</th></tr>
 </thead>
 <tbody>
-<tr><th><a href="../api/continuity/operators/#continuity.operators.FourierNeuralOperator" >FourierNeuralOperator</a><div class="div-params">(depth=1, width=4)</div></th><td>305</td><td width="150px"><img height="60px" src="img/SineRegular_FourierNeuralOperator.svg"></td><td>3.43e-15</td><td><b>3.61e-15</b></td></tr>
-<tr><th><a href="../api/continuity/operators/#continuity.operators.DeepNeuralOperator" >DeepNeuralOperator</a><div class="div-params">(depth=32, width=64)</div></th><td>133249</td><td width="150px"><img height="60px" src="img/SineRegular_DeepNeuralOperator.svg"></td><td>6.95e-07</td><td>7.36e-07</td></tr>
-<tr><th><a href="../api/continuity/operators/#continuity.operators.BelNet" >BelNet</a><div class="div-params">(D_1=4, D_2=8, K=8, N_1=16, N_2=16)</div></th><td>7768</td><td width="150px"><img height="60px" src="img/SineRegular_BelNet.svg"></td><td>1.77e-05</td><td>1.81e-05</td></tr>
-<tr><th><a href="../api/continuity/operators/#continuity.operators.DeepONet" >DeepONet</a><div class="div-params">(basis_functions=32, branch_depth=8, branch_width=32, trunk_depth=8, trunk_width=32)</div></th><td>18016</td><td width="150px"><img height="60px" src="img/SineRegular_DeepONet.svg"></td><td>2.5e-05</td><td>2.57e-05</td></tr>
+<tr><th><a href="../api/continuiti/operators/#continuiti.operators.FourierNeuralOperator" >FourierNeuralOperator</a><div class="div-params">(depth=1, width=4)</div></th><td>305</td><td width="150px"><img height="60px" src="img/SineRegular_FourierNeuralOperator.svg"></td><td>3.43e-15</td><td><b>3.61e-15</b></td></tr>
+<tr><th><a href="../api/continuiti/operators/#continuiti.operators.DeepNeuralOperator" >DeepNeuralOperator</a><div class="div-params">(depth=32, width=64)</div></th><td>133249</td><td width="150px"><img height="60px" src="img/SineRegular_DeepNeuralOperator.svg"></td><td>6.95e-07</td><td>7.36e-07</td></tr>
+<tr><th><a href="../api/continuiti/operators/#continuiti.operators.BelNet" >BelNet</a><div class="div-params">(D_1=4, D_2=8, K=8, N_1=16, N_2=16)</div></th><td>7768</td><td width="150px"><img height="60px" src="img/SineRegular_BelNet.svg"></td><td>1.77e-05</td><td>1.81e-05</td></tr>
+<tr><th><a href="../api/continuiti/operators/#continuiti.operators.DeepONet" >DeepONet</a><div class="div-params">(basis_functions=32, branch_depth=8, branch_width=32, trunk_depth=8, trunk_width=32)</div></th><td>18016</td><td width="150px"><img height="60px" src="img/SineRegular_DeepONet.svg"></td><td>2.5e-05</td><td>2.57e-05</td></tr>
 </tbody>
 </table>
-<h2><a href="../api/continuity/benchmarks/#continuity.benchmarks.SineUniform">SineUniform</a></h2>
+<h2><a href="../api/continuiti/benchmarks/#continuiti.benchmarks.SineUniform">SineUniform</a></h2>
 <table class="benchmark-table">
 <thead>
 <tr><th>Operator</th><th>Params</th><th>Learning Curve</th><th>loss/train</th><th>loss/test</th></tr>
 </thead>
 <tbody>
-<tr><th><a href="../api/continuity/operators/#continuity.operators.DeepNeuralOperator" >DeepNeuralOperator</a><div class="div-params">(depth=8, width=64)</div></th><td>33409</td><td width="150px"><img height="60px" src="img/SineUniform_DeepNeuralOperator.svg"></td><td>0.000239</td><td><b>0.000397</b></td></tr>
-<tr><th><a href="../api/continuity/operators/#continuity.operators.BelNet" >BelNet</a><div class="div-params">(D_1=8, D_2=4, K=16, N_1=16, N_2=8)</div></th><td>11512</td><td width="150px"><img height="60px" src="img/SineUniform_BelNet.svg"></td><td>0.000317</td><td>0.000572</td></tr>
-<tr><th><a href="../api/continuity/operators/#continuity.operators.DeepONet" >DeepONet</a><div class="div-params">(basis_functions=32, branch_depth=8, branch_width=32, trunk_depth=8, trunk_width=32)</div></th><td>18016</td><td width="150px"><img height="60px" src="img/SineUniform_DeepONet.svg"></td><td>0.00313</td><td>0.00533</td></tr>
-<tr><th><a href="../api/continuity/operators/#continuity.operators.FourierNeuralOperator" >FourierNeuralOperator</a><div class="div-params">(depth=3, width=4)</div></th><td>889</td><td width="150px"><img height="60px" src="img/SineUniform_FourierNeuralOperator.svg"></td><td>0.199</td><td>0.207</td></tr>
+<tr><th><a href="../api/continuiti/operators/#continuiti.operators.DeepNeuralOperator" >DeepNeuralOperator</a><div class="div-params">(depth=8, width=64)</div></th><td>33409</td><td width="150px"><img height="60px" src="img/SineUniform_DeepNeuralOperator.svg"></td><td>0.000239</td><td><b>0.000397</b></td></tr>
+<tr><th><a href="../api/continuiti/operators/#continuiti.operators.BelNet" >BelNet</a><div class="div-params">(D_1=8, D_2=4, K=16, N_1=16, N_2=8)</div></th><td>11512</td><td width="150px"><img height="60px" src="img/SineUniform_BelNet.svg"></td><td>0.000317</td><td>0.000572</td></tr>
+<tr><th><a href="../api/continuiti/operators/#continuiti.operators.DeepONet" >DeepONet</a><div class="div-params">(basis_functions=32, branch_depth=8, branch_width=32, trunk_depth=8, trunk_width=32)</div></th><td>18016</td><td width="150px"><img height="60px" src="img/SineUniform_DeepONet.svg"></td><td>0.00313</td><td>0.00533</td></tr>
+<tr><th><a href="../api/continuiti/operators/#continuiti.operators.FourierNeuralOperator" >FourierNeuralOperator</a><div class="div-params">(depth=3, width=4)</div></th><td>889</td><td width="150px"><img height="60px" src="img/SineUniform_FourierNeuralOperator.svg"></td><td>0.199</td><td>0.207</td></tr>
 </tbody>
 </table>
diff --git a/benchmarks/navierstokes/plot_navierstokes.py b/benchmarks/navierstokes/plot_navierstokes.py
@@ -1,7 +1,7 @@
 import torch
 import matplotlib.pyplot as plt
-from continuity.benchmarks import NavierStokes
-from continuity.operators import FourierNeuralOperator
+from continuiti.benchmarks import NavierStokes
+from continuiti.operators import FourierNeuralOperator
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 

diff --git a/benchmarks/navierstokes/run_navierstokes.py b/benchmarks/navierstokes/run_navierstokes.py
@@ -1,7 +1,7 @@
 from functools import partial
-from continuity.benchmarks.run import BenchmarkRunner, RunConfig
-from continuity.benchmarks import NavierStokes
-from continuity.operators import FourierNeuralOperator
+from continuiti.benchmarks.run import BenchmarkRunner, RunConfig
+from continuiti.benchmarks import NavierStokes
+from continuiti.operators import FourierNeuralOperator
 
 config = RunConfig(
     benchmark_factory=NavierStokes,

diff --git a/benchmarks/process.py b/benchmarks/process.py
@@ -1,4 +1,4 @@
-from continuity.benchmarks.run.table import BenchmarkTable
+from continuiti.benchmarks.run.table import BenchmarkTable
 
 
 if __name__ == "__main__":

diff --git a/benchmarks/run_all.py b/benchmarks/run_all.py
@@ -2,9 +2,9 @@
 import torch
 from functools import partial
 from multiprocessing import Pool
-from continuity.benchmarks.run import BenchmarkRunner, RunConfig
-from continuity.benchmarks import SineRegular, SineUniform
-from continuity.operators import (
+from continuiti.benchmarks.run import BenchmarkRunner, RunConfig
+from continuiti.benchmarks import SineRegular, SineUniform
+from continuiti.operators import (
     DeepONet,
     BelNet,
     FourierNeuralOperator,

diff --git a/benchmarks/run_optuna.py b/benchmarks/run_optuna.py
@@ -1,8 +1,8 @@
 import optuna
 from functools import partial
-from continuity.benchmarks.run import BenchmarkRunner, RunConfig
-from continuity.benchmarks import SineRegular
-from continuity.operators import (
+from continuiti.benchmarks.run import BenchmarkRunner, RunConfig
+from continuiti.benchmarks import SineRegular
+from continuiti.operators import (
     FourierNeuralOperator,
 )
 

diff --git a/benchmarks/run_single.py b/benchmarks/run_single.py
@@ -1,6 +1,6 @@
-from continuity.benchmarks.run import BenchmarkRunner, RunConfig
-from continuity.benchmarks import SineRegular
-from continuity.operators import DeepNeuralOperator
+from continuiti.benchmarks.run import BenchmarkRunner, RunConfig
+from continuiti.benchmarks import SineRegular
+from continuiti.operators import DeepNeuralOperator
 
 config = RunConfig(
     benchmark_factory=SineRegular,

diff --git a/docs/benchmarks/index.md b/docs/benchmarks/index.md
@@ -4,14 +4,14 @@ of different operator architectures on various problems.
 The benchmarks are implemented in the `benchmarks` directory and we refer to
 this directory for detailed information on how the benchmarks are run.
 
-## [NavierStokes](../api/continuity/benchmarks/#continuity.benchmarks.NavierStokes)
+## [NavierStokes](../api/continuiti/benchmarks/#continuiti.benchmarks.NavierStokes)
 
 Reference: _Li, Zongyi, et al. "Fourier neural operator for parametric partial
 differential equations." arXiv preprint arXiv:2010.08895 (2020)_ _Table 1 ($\nu$ = 1e−5  T=20  N=1000)_
 
 _reported for_ FNO-3D: __0.1893__ (rel. test error)
 
-[FourierNeuralOperator](../api/continuity/operators/#continuity.operators.FourierNeuralOperator):
+[FourierNeuralOperator](../api/continuiti/operators/#continuiti.operators.FourierNeuralOperator):
 0.0185 (rel. train error)  __0.1841__ (rel. test error)
 
 <table>

diff --git a/docs/examples/index.md b/docs/examples/index.md
@@ -3,7 +3,7 @@ title: Examples
 ---
 
 This is a collection of notebooks that showcase various applications of
-Continuity.
+continuiti.
 
 ::cards:: cols=2
 

diff --git a/docs/getting-started/first-steps.md b/docs/getting-started/first-steps.md
@@ -7,7 +7,7 @@ alias:
 
 # First Steps
 
-**Continuity** aims to implement recent advances in learning function operators,
+**continuiti** aims to implement recent advances in learning function operators,
 i.e., mappings of (continuous) functions. If you are not familiar with the
 concepts of operator learning, the page [[operators]] should introduce the
 key concepts.
@@ -16,7 +16,7 @@ key concepts.
 
 If you are familiar with the idea of operator learning (or just want to dive
 right into code), you can start by browsing our examples illustrating
-Continuity's capabilities, either:
+continuiti's capabilities, either:
 
 - On the documentation page under <a href="../../examples">Examples</a>.
 - Locally, by starting a jupyter server and navigating to the `examples` directory.
diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md
@@ -1,20 +1,20 @@
 ---
-title: Installing Continuity
+title: Installing continuiti
 alias:
     name: installation
-    text: Installing Continuity
+    text: Installing continuiti
 ---
 
-# Installing Continuity
+# Installing continuiti
 
 To install the latest development version use:
 ```
-git clone https://github.com/aai-institute/continuity.git
-cd continuity
+git clone https://github.com/aai-institute/continuiti.git
+cd continuiti
 pip install -e .
 ```
 
 ## Dependencies
 
-Continuity requires Python>=3.9 and is built on top of
+**continuiti** requires Python>=3.9 and is built on top of
 [PyTorch](https://pytorch.org/).
diff --git a/docs/index.md b/docs/index.md
@@ -4,16 +4,16 @@ title: Home
 
 <div align="center">
 
-<img alt="Continuity" src="https://aai-institute.github.io/continuity/img/icon.png" width="100">
+<img alt="continuiti" src="https://aai-institute.github.io/continuiti/img/icon.png" width="100">
 
-<h1>Continuity</h1>
+<h1>continuiti</h1>
 
 <i>Learning function operators with neural networks.</i>
 
 </div>
 
 
-**Continuity** is a Python package for machine learning on function operators.
+**continuiti** is a Python package for machine learning on function operators.
 It implements various neural operator architectures (e.g., DeepONets),
 physics-informed loss functions to train based on PDEs, and a collection of
 examples and benchmarks.
@@ -31,11 +31,11 @@ examples and benchmarks.
 
 - title: Examples
   content: >
-    Some notebooks using Continuity
+    Some notebooks using continuiti
   url: examples/index.md
 
 - title: Browse the API
   content: Full class documentation
-  url: api/continuity/index.md
+  url: api/continuiti/index.md
 
 ::/cards::
diff --git a/docs/operators/architectures.md b/docs/operators/architectures.md
@@ -7,14 +7,14 @@ alias:
 
 # Architectures
 
-Continuity implements the following neural operator architectures:
+**continuiti** implements the following neural operator architectures:
 
-- [DeepONet](../../api/continuity/operators/deeponet/)
-- [Fourier Neural Operator (FNO)](../../api/continuity/operators/fno/)
-- [BelNet](../../api/continuity/operators/belnet/)
+- [DeepONet](../../api/continuiti/operators/deeponet/)
+- [Fourier Neural Operator (FNO)](../../api/continuiti/operators/fno/)
+- [BelNet](../../api/continuiti/operators/belnet/)
 
-- [Deep Neural Operator (DNO)](../../api/continuity/operators/dno/)
-- [Convolutional Neural Network (CNN)](../../api/continuity/operators/cnn/)
-- a generic [NeuralOperator](../../api/continuity/operators/neuraloperator/) class
+- [Deep Neural Operator (DNO)](../../api/continuiti/operators/dno/)
+- [Convolutional Neural Network (CNN)](../../api/continuiti/operators/cnn/)
+- a generic [NeuralOperator](../../api/continuiti/operators/neuraloperator/) class
 
 and more to come...
diff --git a/docs/operators/index.md b/docs/operators/index.md
@@ -55,7 +55,7 @@ We generally refer to such a neural network $G_\theta$ as a *neural operator*.
 
 ## Discretization
 
-In Continuity, we use the general approach of mapping function
+In continuiti, we use the general approach of mapping function
 evaluations to represent both input and output functions $u$ and $v$ in
 a discretized form.
 
@@ -100,6 +100,6 @@ applications to physics-informed training, super-resolution, and more.
 See our <a href="../examples">Examples</a> section for more on this.
 
 ## Further Reading
-Follow our introduction to <a href="../examples/functions">Functions</a> in Continuity
+Follow our introduction to <a href="../examples/functions">Functions</a> in continuiti
 and proceed with the <a href="../examples/training">Training</a> example to learn
-more about operator learning in Continuity.
+more about operator learning in continuiti.
diff --git a/examples/fno.ipynb b/examples/fno.ipynb
@@ -7,7 +7,7 @@
     "# Fourier Neural Operator (FNO)\n",
     "\n",
     "This example demonstrates the use of `FourierLayer` and and\n",
-    "`FourierNeuralOperator` in Continuity.\n",
+    "`FourierNeuralOperator` in continuiti.\n",
     "\n",
     "The FNO architecture was proposed in\n",
     "[Z. Li et al., 2020](https://arxiv.org/abs/2010.08895) and gained a lot of\n",
@@ -45,11 +45,11 @@
    "outputs": [],
    "source": [
     "import torch\n",
-    "from continuity.data import OperatorDataset\n",
-    "from continuity.discrete import RegularGridSampler\n",
-    "from continuity.operators import FourierNeuralOperator\n",
-    "from continuity.operators.fourierlayer import FourierLayer\n",
-    "from continuity.trainer import Trainer"
+    "from continuiti.data import OperatorDataset\n",
+    "from continuiti.discrete import RegularGridSampler\n",
+    "from continuiti.operators import FourierNeuralOperator\n",
+    "from continuiti.operators.fourierlayer import FourierLayer\n",
+    "from continuiti.trainer import Trainer"
    ]
   },
   {