From 906c911afc3873bf6f61bb9afc6c52449f733fc3 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 16:12:40 -0400 Subject: [PATCH 01/36] Update README.md --- README.md | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 85b9b1e8..340eeb45 100644 --- a/README.md +++ b/README.md @@ -5,16 +5,15 @@ espaloma [![Total alerts](https://img.shields.io/lgtm/alerts/g/choderalab/espaloma.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/choderalab/espaloma/alerts/) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/choderalab/espaloma.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/choderalab/espaloma/context:python) -Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithms - -Rather than: - -molecule ---(atom typing schemes)---> atom-types ---(atom typing schemes)---> bond-, angle-, torsion-types ---(table lookup)---> force field parameters - -we want to have - -molecule ---(graph nets)---> atom-embedding ---(pooling)---> hypernode-embedding ---(feedforward neural networks)---> force field parameters - +Extensible Surrogate Potential of Optimized by Message-passing Algorithms + +# Abstract +Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. +Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. +Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules _atom types_ for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. +Here, we propose an alternative approach that uses _graph neural networks_ to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. +Since all stages are built from smooth neural functions, the entire process---spanning chemical perception to parameter assignment---is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. +We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn and extend existing molecular mechanics force fields, construct entirely new force fields applicable to both biopolymers and small molecules from quantum chemical calculations, and even learn to accurately predict free energies from experimental observables. # Manifest From 29dbdb1e1955d281009c67245f7ae8812f7e7dca Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 16:14:37 -0400 Subject: [PATCH 02/36] Add files via upload --- 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4La1nmuwj&@Yu((P*M}xnVWKC1H zH@HX)Nn;v+MXc7jSMSyzz({C9K9bEj!q?SXR2nB=rzPP#7h8P#4L^zyvFfumN0Yc- z*Yjl82~D|QhrQqP=JKjc>1y5Z0%w75z@F>bJanv$fio%Q5Pm<_E#4v!gvs`Wp9Y(j zxK{%^5VMQ@X~fH<>6MPNwCmc4y=J75pM665hVMvHPC6^V)}xQ%>+C;)P8j#)@IRl+ z0bCE=6|8rX)ofen^Ele(PeYN=k&1-izX?7$qi&smI{q2w8q~6vtd>Dnj2cWI>7W}G ztRIKniyH}= zZ8p+{hj=Gb`oXB8*?c%uJM|=dOShOSA51W31@5~{Cwq~5M&!kG-t!Qjrb6!0TR%0u z* H@02Ws-5#((2Qb!oLS#_60^lOrYap|4%GQuA4(BTRp<0BjQl6XDdk;oLuURi+ zm;TNoKyX)k$@%4(QU8=x#mH{gzrzeKWPO?{{!_4D`oi6@6Kw!#$>_4CjQe`bQCXqa z8E{CtDvcX3QNV!ZYWYhP%t7b3>$Eg?bcWKn8hO*$3tGFn@yOfyD28R^Nxb;d{|Ih}}32I-##u z`fbwhHfSa&FMgX!znh&9QDiSjln3HyQq-Fz>#_pX z-#aH2H6);k*Ribzi(ho9Eo505o#{*L!?2dIjVVn>D#y=1zI&F9{s%!~(Xf22m?0Z@ zK;JzAGc5YD6A7QPE74`su@2FbuZsR0x!y>c261GZhr6)!(L-Os;=sAnK zR;ewkLxp^6)DYkvGY+J7i9&RjP%Pf1nEO&EzhtT% zk~z^gGrZz-&6M93q~XGMf%p1AuYsiE^LO+WBa-4-1PBp!f4>=>e$-j&kS#&4M`6+{ zba6lpq%f@sv>Q$=Y%0(c&1tc($NgVZzX&`$;MQHIm#>)xUbOEsTM!hQ$1|-eY~`;7 zyG5()Q9UgP<3J9@<)1GiF(%>^E+q3Y^%RLh#_%WmM!JL+NEKm&kh8moF7w!A!hs`b~| zdA7UXTn+iTtARnPHSH?P<8V2(T^BfhErBF#jz-5ehS$BoW|iu~lC-n4M04-$xyikA zp3UX4-70 z1kb`(93MRD*8di_Wy6y+`_@GtR)wzq6ZqUze!Il`=&c;wxoey^w_K26+`gig<%ahA zo14{l0WWXbd(|xN6!2Pu8{dq>_-eZk$k+dQ7+dt%lI!UdrAMjXg(@VRmSh62_IWvf zml%_-;)233S+i%ddyYs5$|;<^y!E#gH>h%UQe5!ULS)k4iTx@~e=qjg26hAQchQ?= zo*!qDe5@zq>@3rZ>tc5+tw||NJZ_v+JoEOaoYs!RjcI(`>TCA-TfJ1--#FW@w#wyB z@ZMP;4a1K>g0U#iyt2dI8locQ|c7jT){_2X}1 z_tjLkY+sv}aj1oJ-cDV`AmbHR{klzUX9)RElsL2YNY}c#yXSpTW@_O&FoEHGnRfWP zH^;S}trO-_`Y@|!#&jLC?RPIna%^b=#oU!A8`q)5+!;{JN;bSuO(+@7~1WPnPkp-dx2lgDb3|S zD&DrVORu^1k6TQqV_W`xIa#Zc6YYT;_zFRXge*Phv_O}|@t#GW~sa0|`zW(pk=&Z-**p{6PbeDa)c6VpAwz&D;JG=Hq zehcV1aBt0Cue(}I24NOQe&mWe?p22;NARc}uzESse_)B{HMkOy9(>-PnCO5L=T70jn5>E-`YTuP0IbOf zuG=6kadTL7)TurNgcYF@c8Gh;*Pd olTg9;=ucTUaS@h&5KL#N1boFyt=akR{09LblyZ`_I literal 0 HcmV?d00001 From 94d9aeccf36356e1b5fa08206a1f0e1cd2407e71 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 16:15:20 -0400 Subject: [PATCH 03/36] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 340eeb45..bd83728b 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,8 @@ espaloma Extensible Surrogate Potential of Optimized by Message-passing Algorithms +![abstract](docs/_static/espaloma_abstract_v2-2.png) + # Abstract Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. From 4280759a22f69d9af43575cd3f4e443a27aee616 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 16:16:44 -0400 Subject: [PATCH 04/36] Update README.md --- README.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index bd83728b..09ec416c 100644 --- a/README.md +++ b/README.md @@ -5,10 +5,10 @@ espaloma [![Total alerts](https://img.shields.io/lgtm/alerts/g/choderalab/espaloma.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/choderalab/espaloma/alerts/) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/choderalab/espaloma.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/choderalab/espaloma/context:python) -Extensible Surrogate Potential of Optimized by Message-passing Algorithms - ![abstract](docs/_static/espaloma_abstract_v2-2.png) +Extensible Surrogate Potential of Optimized by Message-passing Algorithms + # Abstract Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. @@ -49,6 +49,9 @@ This software is licensed under [MIT license](https://opensource.org/licenses/MI Copyright (c) 2020, Chodera Lab at Memorial Sloan Kettering Cancer Center and Authors: Authors: -- Yuanqing Wang +- [Yuanqing Wang](wangyq.net) [![GitHub](https://img.shields.io/badge/-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/yuanqing-wang/) +[![Twitter](https://img.shields.io/badge/-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/yuanqingwang) +[![LinkedIn](https://img.shields.io/badge/-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/yuanqing-wang/) +[![Google](https://img.shields.io/badge/-4285F4?style=for-the-badge&logo=google&logoColor=white)](https://scholar.google.com/citations?user=Njp5EY4AAAAJ&hl=en) - Josh Fass - John D. Chodera From f1d8a3325b30ed5c48453b4689d6aeacd9e6eb06 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 16:17:22 -0400 Subject: [PATCH 05/36] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 09ec416c..22913774 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ espaloma Extensible Surrogate Potential of Optimized by Message-passing Algorithms -# Abstract +# Paper Abstract Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules _atom types_ for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. From 3184b6dfbf78726f334b5668f8f9240309cd6808 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 23:40:12 -0400 Subject: [PATCH 06/36] Create sphinx.yml --- .github/workflows/sphinx.yml | 42 ++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 .github/workflows/sphinx.yml diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml new file mode 100644 index 00000000..de486c1c --- /dev/null +++ b/.github/workflows/sphinx.yml @@ -0,0 +1,42 @@ +name: "Build Doc" +on: +- push + +jobs: + docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.7 + uses: actions/setup-python@v2 + with: + python-version: 3.7 + + - uses: conda-incubator/setup-miniconda@v2 + with: + installer-url: ${{ matrix.conda-installer }} + python-version: ${{ matrix.python-version }} + activate-environment: test + channel-priority: true + environment-file: devtools/conda-envs/espaloma.yaml + auto-activate-base: false + use-mamba: true + + - name: Decrypt and check OE license + shell: bash -l {0} + env: + OE_LICENSE_TEXT: ${{ secrets.OE_LICENSE }} + run: | + echo "${OE_LICENSE_TEXT}" > ${OE_LICENSE} + python -c "import openeye; assert openeye.OEChemIsLicensed()" + + - name: Install package + shell: bash -l {0} + run: | + python -m pip install --no-deps . + + - name: Deploy + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/_build/html From a3703e0203d4c401184781659620785e5701e5d0 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 23:45:36 -0400 Subject: [PATCH 07/36] Update CI.yaml --- .github/workflows/CI.yaml | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml index 1398821a..c7e7a817 100644 --- a/.github/workflows/CI.yaml +++ b/.github/workflows/CI.yaml @@ -1,16 +1,5 @@ name: CI -on: [push] -jobs: - build: - name: Push Sphinx Pages - runs-on: ubuntu-latest - steps: - - uses: seanzhengw/sphinx-pages@master - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - create_readme: true - on: pull_request: branches: From b09962e449ad14c8ccab2f706e3e449a81ec94fa Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 23:55:50 -0400 Subject: [PATCH 08/36] Update getting_started.rst --- docs/getting_started.rst | 71 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 70 insertions(+), 1 deletion(-) diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 707a0b64..05d1418a 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -1,4 +1,73 @@ Getting Started =============== -This page details how to get started with espaloma. +![abstract](docs/_static/espaloma_abstract_v2-2.png) + +# Paper Abstract +Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. +Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. +Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules _atom types_ for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. +Here, we propose an alternative approach that uses _graph neural networks_ to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. +Since all stages are built from smooth neural functions, the entire process---spanning chemical perception to parameter assignment---is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. +We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn and extend existing molecular mechanics force fields, construct entirely new force fields applicable to both biopolymers and small molecules from quantum chemical calculations, and even learn to accurately predict free energies from experimental observables. + +# Minimal Example +``` +import torch, dgl, espaloma as esp + +# retrieve QM dataset used to train OpenFF 1.0.0 ("parsley") small molecule force field +dataset = esp.data.dataset.GraphDataset.load("parsley").view(batch_size=128) + +# define Espaloma stage I: graph -> atom latent representation +representation = esp.nn.Sequential( + layer=esp.nn.layers.dgl_legacy.gn("SAGEConv"), # use SAGEConv implementation in DGL + config=[128, "relu", 128, "relu", 128, "relu"], # 3 layers, 128 units, ReLU activation +) + +# define Espaloma stage II and III: +# atom latent representation -> bond, angle, and torsion representation and parameters +readout = esp.nn.readout.janossy.JanossyPooling( + in_features=128, + config=[128, "relu", 128, "relu", 128, "relu"], + out_features={ # define modular MM parameters Espaloma will assign + 1: {"e": 1, "s": 1}, + 2: {"coefficients": 2}, # bond linear combination + 3: {"coefficients": 3}, # angle linear combination + 4: {"k": 6}, # torsion barrier heights (can be positive or negative) + }, +) + +# compose all three Espaloma stages into an end-to-end model +espaloma_model = torch.nn.Sequential( + representation, + readout, + esp.mm.geometry.GeometryInGraph(), + esp.mm.energy.EnergyInGraph(), + esp.nn.readout.charge_equilibrium.ChargeEquilibrium(), +) + +# define training metric +metrics = [ + esp.metrics.GraphMetric( + base_metric=torch.nn.MSELoss(), # use mean-squared error loss + between=['u', "u_ref"], # between predicted and QM energies + level="g", + ) + esp.metrics.GraphMetric( + base_metric=torch.nn.MSELoss(), # use mean-squared error loss + between=['q', "q_hat"], # between predicted and reference charges + level="n1", + ) +] + +# fit Espaloma model to training data +results = esp.Train( + ds_tr=dataset, net=espaloma_model, metrics=metrics, + device=torch.device('cuda:0'), n_epochs=5000, + optimizer=lambda net: torch.optim.Adam(net.parameters(), 1e-3), # use Adam optimizer +).run() +``` + + + + From 11328b6fb4fc7ab5ad4ecd40d855ede1574e72a2 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 23:58:41 -0400 Subject: [PATCH 09/36] Update getting_started.rst --- docs/getting_started.rst | 103 ++++++++++++++++++++------------------- 1 file changed, 53 insertions(+), 50 deletions(-) diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 05d1418a..07632cff 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -3,7 +3,8 @@ Getting Started ![abstract](docs/_static/espaloma_abstract_v2-2.png) -# Paper Abstract +Paper Abstract +-------------- Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules _atom types_ for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. @@ -11,62 +12,64 @@ Here, we propose an alternative approach that uses _graph neural networks_ to pe Since all stages are built from smooth neural functions, the entire process---spanning chemical perception to parameter assignment---is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn and extend existing molecular mechanics force fields, construct entirely new force fields applicable to both biopolymers and small molecules from quantum chemical calculations, and even learn to accurately predict free energies from experimental observables. -# Minimal Example -``` -import torch, dgl, espaloma as esp +Minimal Example +--------------- +:: -# retrieve QM dataset used to train OpenFF 1.0.0 ("parsley") small molecule force field -dataset = esp.data.dataset.GraphDataset.load("parsley").view(batch_size=128) + import torch, dgl, espaloma as esp -# define Espaloma stage I: graph -> atom latent representation -representation = esp.nn.Sequential( - layer=esp.nn.layers.dgl_legacy.gn("SAGEConv"), # use SAGEConv implementation in DGL - config=[128, "relu", 128, "relu", 128, "relu"], # 3 layers, 128 units, ReLU activation -) + # retrieve QM dataset used to train OpenFF 1.0.0 ("parsley") small molecule force field + dataset = esp.data.dataset.GraphDataset.load("parsley").view(batch_size=128) -# define Espaloma stage II and III: -# atom latent representation -> bond, angle, and torsion representation and parameters -readout = esp.nn.readout.janossy.JanossyPooling( - in_features=128, - config=[128, "relu", 128, "relu", 128, "relu"], - out_features={ # define modular MM parameters Espaloma will assign - 1: {"e": 1, "s": 1}, - 2: {"coefficients": 2}, # bond linear combination - 3: {"coefficients": 3}, # angle linear combination - 4: {"k": 6}, # torsion barrier heights (can be positive or negative) - }, -) - -# compose all three Espaloma stages into an end-to-end model -espaloma_model = torch.nn.Sequential( - representation, - readout, - esp.mm.geometry.GeometryInGraph(), - esp.mm.energy.EnergyInGraph(), - esp.nn.readout.charge_equilibrium.ChargeEquilibrium(), -) + # define Espaloma stage I: graph -> atom latent representation + representation = esp.nn.Sequential( + layer=esp.nn.layers.dgl_legacy.gn("SAGEConv"), # use SAGEConv implementation in DGL + config=[128, "relu", 128, "relu", 128, "relu"], # 3 layers, 128 units, ReLU activation + ) -# define training metric -metrics = [ - esp.metrics.GraphMetric( - base_metric=torch.nn.MSELoss(), # use mean-squared error loss - between=['u', "u_ref"], # between predicted and QM energies - level="g", + # define Espaloma stage II and III: + # atom latent representation -> bond, angle, and torsion representation and parameters + readout = esp.nn.readout.janossy.JanossyPooling( + in_features=128, + config=[128, "relu", 128, "relu", 128, "relu"], + out_features={ # define modular MM parameters Espaloma will assign + 1: {"e": 1, "s": 1}, + 2: {"coefficients": 2}, # bond linear combination + 3: {"coefficients": 3}, # angle linear combination + 4: {"k": 6}, # torsion barrier heights (can be positive or negative) + }, ) - esp.metrics.GraphMetric( - base_metric=torch.nn.MSELoss(), # use mean-squared error loss - between=['q', "q_hat"], # between predicted and reference charges - level="n1", + + # compose all three Espaloma stages into an end-to-end model + espaloma_model = torch.nn.Sequential( + representation, + readout, + esp.mm.geometry.GeometryInGraph(), + esp.mm.energy.EnergyInGraph(), + esp.nn.readout.charge_equilibrium.ChargeEquilibrium(), ) -] -# fit Espaloma model to training data -results = esp.Train( - ds_tr=dataset, net=espaloma_model, metrics=metrics, - device=torch.device('cuda:0'), n_epochs=5000, - optimizer=lambda net: torch.optim.Adam(net.parameters(), 1e-3), # use Adam optimizer -).run() -``` + # define training metric + metrics = [ + esp.metrics.GraphMetric( + base_metric=torch.nn.MSELoss(), # use mean-squared error loss + between=['u', "u_ref"], # between predicted and QM energies + level="g", + ) + esp.metrics.GraphMetric( + base_metric=torch.nn.MSELoss(), # use mean-squared error loss + between=['q', "q_hat"], # between predicted and reference charges + level="n1", + ) + ] + + # fit Espaloma model to training data + results = esp.Train( + ds_tr=dataset, net=espaloma_model, metrics=metrics, + device=torch.device('cuda:0'), n_epochs=5000, + optimizer=lambda net: torch.optim.Adam(net.parameters(), 1e-3), # use Adam optimizer + ).run() + From 14e1eb871f8d792b8b1976472e9c14d44a7d8be9 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 23:59:23 -0400 Subject: [PATCH 10/36] Update getting_started.rst --- docs/getting_started.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 07632cff..5ea15bde 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -1,7 +1,7 @@ Getting Started =============== -![abstract](docs/_static/espaloma_abstract_v2-2.png) +..image:: docs/_static/espaloma_abstract_v2-2.png Paper Abstract -------------- From 47093902ba9771c314348b3562f5dd00a2399b43 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Mon, 27 Sep 2021 23:59:48 -0400 Subject: [PATCH 11/36] Update getting_started.rst --- docs/getting_started.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 5ea15bde..6630476e 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -1,7 +1,7 @@ Getting Started =============== -..image:: docs/_static/espaloma_abstract_v2-2.png +..image:: /docs/_static/espaloma_abstract_v2-2.png Paper Abstract -------------- From 62a1616e7a47a966dc7d56d037fa05122af60106 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 00:00:00 -0400 Subject: [PATCH 12/36] Update getting_started.rst --- docs/getting_started.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 6630476e..6069943e 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -1,7 +1,7 @@ Getting Started =============== -..image:: /docs/_static/espaloma_abstract_v2-2.png +..image:: _static/espaloma_abstract_v2-2.png Paper Abstract -------------- From 086be0a6af6c1a586058ed5381a2f617cde83061 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 00:00:14 -0400 Subject: [PATCH 13/36] Update getting_started.rst --- docs/getting_started.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 6069943e..568bbed8 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -1,7 +1,7 @@ Getting Started =============== -..image:: _static/espaloma_abstract_v2-2.png +.. image:: _static/espaloma_abstract_v2-2.png Paper Abstract -------------- From 822c64d1029854afad2fdfd1ed78d73fb02c19cb Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 00:01:28 -0400 Subject: [PATCH 14/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 8 -------- 1 file changed, 8 deletions(-) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index de486c1c..d2fac41e 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -22,14 +22,6 @@ jobs: auto-activate-base: false use-mamba: true - - name: Decrypt and check OE license - shell: bash -l {0} - env: - OE_LICENSE_TEXT: ${{ secrets.OE_LICENSE }} - run: | - echo "${OE_LICENSE_TEXT}" > ${OE_LICENSE} - python -c "import openeye; assert openeye.OEChemIsLicensed()" - - name: Install package shell: bash -l {0} run: | From 54a4c979915ae38b17efcc6770c3efd2549f5663 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 00:06:20 -0400 Subject: [PATCH 15/36] Update README.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 22913774..8f28db4f 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,17 @@ -espaloma +espaloma: **E**xtensible **S**urrogate **P**otenti**al** **O**ptimized by **M**essage-passing **A**lgorithms ============================== [//]: # (Badges) [![CI](https://github.com/choderalab/espaloma/actions/workflows/CI.yaml/badge.svg?branch=master)](https://github.com/choderalab/espaloma/actions/workflows/CI.yaml) [![Total alerts](https://img.shields.io/lgtm/alerts/g/choderalab/espaloma.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/choderalab/espaloma/alerts/) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/choderalab/espaloma.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/choderalab/espaloma/context:python) + + +Source code for [Wang Y, Fass J, and Chodera JD "End-to-End Differentiable Construction of Molecular Mechanics Force Fields."](https://arxiv.org/abs/2010.01196) + ![abstract](docs/_static/espaloma_abstract_v2-2.png) -Extensible Surrogate Potential of Optimized by Message-passing Algorithms + # Paper Abstract Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. From 37577b478858d0a0013d0c2f251682c26ee81800 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 00:09:55 -0400 Subject: [PATCH 16/36] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8f28db4f..cdc5a925 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ espaloma: **E**xtensible **S**urrogate **P**otenti**al** **O**ptimized by **M**e [![CI](https://github.com/choderalab/espaloma/actions/workflows/CI.yaml/badge.svg?branch=master)](https://github.com/choderalab/espaloma/actions/workflows/CI.yaml) [![Total alerts](https://img.shields.io/lgtm/alerts/g/choderalab/espaloma.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/choderalab/espaloma/alerts/) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/choderalab/espaloma.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/choderalab/espaloma/context:python) - +[![docs stable](https://img.shields.io/badge/docs-stable-5077AB.svg?logo=read%20the%20docs)](www.espaloma.wangyq.net/) Source code for [Wang Y, Fass J, and Chodera JD "End-to-End Differentiable Construction of Molecular Mechanics Force Fields."](https://arxiv.org/abs/2010.01196) From 2aa2c4e870773c18adc2f1053879c9d96d56695a Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 00:10:29 -0400 Subject: [PATCH 17/36] Update README.md --- README.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/README.md b/README.md index cdc5a925..3e7339ac 100644 --- a/README.md +++ b/README.md @@ -53,9 +53,6 @@ This software is licensed under [MIT license](https://opensource.org/licenses/MI Copyright (c) 2020, Chodera Lab at Memorial Sloan Kettering Cancer Center and Authors: Authors: -- [Yuanqing Wang](wangyq.net) [![GitHub](https://img.shields.io/badge/-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/yuanqing-wang/) -[![Twitter](https://img.shields.io/badge/-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/yuanqingwang) -[![LinkedIn](https://img.shields.io/badge/-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/yuanqing-wang/) -[![Google](https://img.shields.io/badge/-4285F4?style=for-the-badge&logo=google&logoColor=white)](https://scholar.google.com/citations?user=Njp5EY4AAAAJ&hl=en) +- [Yuanqing Wang](wangyq.net) - Josh Fass - John D. Chodera From 1114f5caababbd6901ca8cd7bcd37400af207867 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 00:11:05 -0400 Subject: [PATCH 18/36] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3e7339ac..e77ce0af 100644 --- a/README.md +++ b/README.md @@ -53,6 +53,6 @@ This software is licensed under [MIT license](https://opensource.org/licenses/MI Copyright (c) 2020, Chodera Lab at Memorial Sloan Kettering Cancer Center and Authors: Authors: -- [Yuanqing Wang](wangyq.net) +- [Yuanqing Wang](http://www.wangyq.net) - Josh Fass - John D. Chodera From 5ea3c652aae4bb0341ceada6b744b198ea19482b Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 00:40:52 -0400 Subject: [PATCH 19/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index d2fac41e..b8d52008 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -26,7 +26,9 @@ jobs: shell: bash -l {0} run: | python -m pip install --no-deps . - + - name: Compile + run: | + cd docs && make html - name: Deploy uses: peaceiris/actions-gh-pages@v3 with: From d7c251e12d9927b842e09a4bfdae3e53e5cb43e3 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 09:52:17 -0400 Subject: [PATCH 20/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index b8d52008..505b5186 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -28,6 +28,7 @@ jobs: python -m pip install --no-deps . - name: Compile run: | + pip install sphinx sphinx-rtd-theme cd docs && make html - name: Deploy uses: peaceiris/actions-gh-pages@v3 From 5f5683bc3e9445e1d81eab22b2138dd15651d5f6 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 10:21:01 -0400 Subject: [PATCH 21/36] Update conf.py --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 3064c21b..1a8abb26 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,7 +16,7 @@ import os import sys -sys.path.insert(0, os.path.abspath('../espaloma')) +sys.path.insert(0, os.path.abspath('..')) import espaloma from espaloma import mm, nn, data, graphs From df02b5842aceb23a37f5f450153bfd4809d19504 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 10:55:06 -0400 Subject: [PATCH 22/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index 505b5186..f6035616 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -28,7 +28,7 @@ jobs: python -m pip install --no-deps . - name: Compile run: | - pip install sphinx sphinx-rtd-theme + python -m pip install sphinx sphinx-rtd-theme cd docs && make html - name: Deploy uses: peaceiris/actions-gh-pages@v3 From 9137e154393f7650475fa6b780a92a54db59a0f5 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 11:32:32 -0400 Subject: [PATCH 23/36] Update espaloma.yaml --- devtools/conda-envs/espaloma.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/devtools/conda-envs/espaloma.yaml b/devtools/conda-envs/espaloma.yaml index 674400cd..68e6fee5 100644 --- a/devtools/conda-envs/espaloma.yaml +++ b/devtools/conda-envs/espaloma.yaml @@ -4,6 +4,7 @@ channels: - dglteam - openeye - defaults + - anaconda dependencies: # Base dependencies - python @@ -29,3 +30,5 @@ dependencies: - nose-timer - coverage - qcportal + - sphinx + - sphinx_rtd_theme From dd584ecf9a97912595f043fd94fbd269167ceaa2 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 11:32:55 -0400 Subject: [PATCH 24/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index f6035616..b8d52008 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -28,7 +28,6 @@ jobs: python -m pip install --no-deps . - name: Compile run: | - python -m pip install sphinx sphinx-rtd-theme cd docs && make html - name: Deploy uses: peaceiris/actions-gh-pages@v3 From 5f1c9034505a3ed858e43d64b4549eae8dc9aa5c Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Tue, 28 Sep 2021 13:35:57 -0400 Subject: [PATCH 25/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index b8d52008..08555c66 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -26,6 +26,7 @@ jobs: shell: bash -l {0} run: | python -m pip install --no-deps . + conda install -y -c anaconda sphinx - name: Compile run: | cd docs && make html From 657d514f29eb6014313dc58489385130ca475bb1 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Wed, 29 Sep 2021 09:55:47 -0400 Subject: [PATCH 26/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index 08555c66..f6035616 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -26,9 +26,9 @@ jobs: shell: bash -l {0} run: | python -m pip install --no-deps . - conda install -y -c anaconda sphinx - name: Compile run: | + python -m pip install sphinx sphinx-rtd-theme cd docs && make html - name: Deploy uses: peaceiris/actions-gh-pages@v3 From 655c704ee7c579d666f483796653f80fec4f116a Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Wed, 29 Sep 2021 11:44:52 -0400 Subject: [PATCH 27/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index f6035616..2fb364c2 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -28,6 +28,7 @@ jobs: python -m pip install --no-deps . - name: Compile run: | + shell: bash -l {0} python -m pip install sphinx sphinx-rtd-theme cd docs && make html - name: Deploy From 356a7589b6ddb573d2bba133904ee7963052db6d Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Wed, 29 Sep 2021 17:18:04 -0400 Subject: [PATCH 28/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index 2fb364c2..be40de94 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -28,7 +28,7 @@ jobs: python -m pip install --no-deps . - name: Compile run: | - shell: bash -l {0} + shell: bash -l {0} python -m pip install sphinx sphinx-rtd-theme cd docs && make html - name: Deploy From 8769f1f422834cc5c26ebb860d879a8c049f2ad7 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Thu, 30 Sep 2021 16:21:07 -0400 Subject: [PATCH 29/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index be40de94..61e4b9c4 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -27,8 +27,8 @@ jobs: run: | python -m pip install --no-deps . - name: Compile - run: | shell: bash -l {0} + run: | python -m pip install sphinx sphinx-rtd-theme cd docs && make html - name: Deploy From a87b3b22be6418ccfd02efb0dc845c6cf9a96e3f Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Thu, 30 Sep 2021 17:16:13 -0400 Subject: [PATCH 30/36] Update conf.py --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 1a8abb26..dd134917 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -51,7 +51,7 @@ 'sphinx.ext.intersphinx', 'sphinx.ext.extlinks', 'sphinx.ext.coverage', - 'numpydoc', + # 'numpydoc', ] autosummary_generate = True From 54209cc1a8c9f02c3933f8077e3767fda40d7e68 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Thu, 30 Sep 2021 17:43:35 -0400 Subject: [PATCH 31/36] Update conf.py --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index dd134917..1a8abb26 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -51,7 +51,7 @@ 'sphinx.ext.intersphinx', 'sphinx.ext.extlinks', 'sphinx.ext.coverage', - # 'numpydoc', + 'numpydoc', ] autosummary_generate = True From ad2fd04d0f45c8560c119042a433154176eb09a0 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Thu, 30 Sep 2021 17:43:54 -0400 Subject: [PATCH 32/36] Update sphinx.yml --- .github/workflows/sphinx.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index 61e4b9c4..09e75009 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -29,7 +29,7 @@ jobs: - name: Compile shell: bash -l {0} run: | - python -m pip install sphinx sphinx-rtd-theme + python -m pip install sphinx sphinx-rtd-theme numpydoc cd docs && make html - name: Deploy uses: peaceiris/actions-gh-pages@v3 From a969d89114667572d39e018ac4c41a28e31c1d83 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Sun, 3 Oct 2021 01:11:27 -0400 Subject: [PATCH 33/36] change to John's parametrization for torsion --- espaloma/mm/functional.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/espaloma/mm/functional.py b/espaloma/mm/functional.py index 56b76d5d..82c7b9f7 100644 --- a/espaloma/mm/functional.py +++ b/espaloma/mm/functional.py @@ -161,12 +161,12 @@ def periodic( k = k[:, None, :].repeat(1, x.shape[1], 1) - energy = (k * (1.0 + cos_n_theta_minus_phases)).sum(dim=-1) + # energy = (k * (1.0 + cos_n_theta_minus_phases)).sum(dim=-1) - # energy = ( - # torch.nn.functional.relu(k) * (cos_n_theta_minus_phases + 1.0) - # -torch.nn.functional.relu(0.0-k) * (cos_n_theta_minus_phases - 1.0) - # ).sum(dim=-1) + energy = ( + torch.nn.functional.relu(k) * (cos_n_theta_minus_phases + 1.0) + -torch.nn.functional.relu(0.0-k) * (cos_n_theta_minus_phases - 1.0) + ).sum(dim=-1) return energy From 73163598ec9bbc8edaaa739637b0fb6a12a9da6c Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Sun, 3 Oct 2021 01:13:34 -0400 Subject: [PATCH 34/36] test positive torsion k --- espaloma/mm/tests/test_openmm_consistency.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/espaloma/mm/tests/test_openmm_consistency.py b/espaloma/mm/tests/test_openmm_consistency.py index aef0c238..df5e4302 100644 --- a/espaloma/mm/tests/test_openmm_consistency.py +++ b/espaloma/mm/tests/test_openmm_consistency.py @@ -48,7 +48,7 @@ def _create_torsion_sim( # TODO: mark this properly: want to test periodicities 1..6, +ve, -ve k # @pytest.mark.parametrize(periodicity=[1,2,3,4,5,6], k=[-10 * omm_energy_unit, +10 * omm_energy_unit]) def test_periodic_torsion( - periodicity=4, k=-10 * omm_energy_unit, n_samples=100 + periodicity=4, k=10 * omm_energy_unit, n_samples=100 ): """ Using simulated torsion scan, test if espaloma torsion energies and OpenMM torsion energies agree. From 265888840262b2af7f3699858d86bff38333bb80 Mon Sep 17 00:00:00 2001 From: Yuanqing Wang Date: Sun, 3 Oct 2021 01:22:12 -0400 Subject: [PATCH 35/36] split page build --- .github/workflows/CI.yaml | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml index 1398821a..c7e7a817 100644 --- a/.github/workflows/CI.yaml +++ b/.github/workflows/CI.yaml @@ -1,16 +1,5 @@ name: CI -on: [push] -jobs: - build: - name: Push Sphinx Pages - runs-on: ubuntu-latest - steps: - - uses: seanzhengw/sphinx-pages@master - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - create_readme: true - on: pull_request: branches: From 4e67a58ff155e19c4c726f9f49ce7df44ce90f83 Mon Sep 17 00:00:00 2001 From: yuanqing-wang Date: Sun, 3 Oct 2021 17:45:23 -0400 Subject: [PATCH 36/36] exp as a module --- espaloma/nn/readout/janossy.py | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/espaloma/nn/readout/janossy.py b/espaloma/nn/readout/janossy.py index eb69f924..d482317e 100644 --- a/espaloma/nn/readout/janossy.py +++ b/espaloma/nn/readout/janossy.py @@ -423,3 +423,32 @@ def forward(self, g): ) return g + + +class ExpCoefficients(torch.nn.Module): + def forward(self, g): + import math + g.nodes['n2'].data['coefficients'] = g.nodes['n2'].data['log_coefficients'].exp() + g.nodes['n3'].data['coefficients'] = g.nodes['n3'].data['log_coefficients'].exp() + return g + +class LinearMixtureToOriginal(torch.nn.Module): + def forward(self, g): + import math + g.nodes['n2'].data['k'], g.nodes['n2'].data['eq'] = esp.mm.functional.linear_mixture_to_original( + g.nodes['n2'].data['coefficients'][:, 0][:, None], + g.nodes['n2'].data['coefficients'][:, 1][:, None], + 1.5, 6.0, + ) + + g.nodes['n3'].data['k'], g.nodes['n3'].data['eq'] = esp.mm.functional.linear_mixture_to_original( + g.nodes['n3'].data['coefficients'][:, 0][:, None], + g.nodes['n3'].data['coefficients'][:, 1][:, None], + 0.0, math.pi + ) + + g.nodes['n3'].data.pop('coefficients') + g.nodes['n2'].data.pop('coefficients') + return g + +