diff --git a/README.md b/README.md index 5b5aac5..c668415 100644 --- a/README.md +++ b/README.md @@ -83,6 +83,17 @@ Or sh ./ts-dart/scripts/train_tsdart.sh ``` +## Compiling Document Environment +Once you have already installed ts-dart in your conda environment. +```bash +python -m pip install -U sphinx +pip install sphinx-rtd-theme +pip install nbconvert nbformat +pip install pandoc +pip install sphinx-design +make html +``` + ## More instructions TS-DART refers to the preprint [10.26434/chemrxiv-2024-r8gjv](https://chemrxiv.org/engage/chemrxiv/article-details/65adf0b966c1381729fb4c11). diff --git a/docs/_build/doctrees/_example/muller-example.doctree b/docs/_build/doctrees/_example/muller-example.doctree deleted file mode 100644 index e456abe..0000000 Binary files a/docs/_build/doctrees/_example/muller-example.doctree and /dev/null differ diff --git a/docs/_build/doctrees/_example/quadruple-well-example.doctree b/docs/_build/doctrees/_example/quadruple-well-example.doctree deleted file mode 100644 index d3e4ae3..0000000 Binary files a/docs/_build/doctrees/_example/quadruple-well-example.doctree and /dev/null differ diff --git a/docs/_build/doctrees/chapters/installation.doctree b/docs/_build/doctrees/chapters/installation.doctree deleted file mode 100644 index b30429f..0000000 Binary files a/docs/_build/doctrees/chapters/installation.doctree and /dev/null differ diff --git a/docs/_build/doctrees/chapters/intro.doctree b/docs/_build/doctrees/chapters/intro.doctree deleted file mode 100644 index 38c4a1a..0000000 Binary files a/docs/_build/doctrees/chapters/intro.doctree and /dev/null differ diff --git a/docs/_build/doctrees/chapters/tutorials.doctree b/docs/_build/doctrees/chapters/tutorials.doctree deleted file mode 100644 index 61c65c8..0000000 Binary files a/docs/_build/doctrees/chapters/tutorials.doctree and /dev/null differ diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle deleted file mode 100644 index 047059c..0000000 Binary files a/docs/_build/doctrees/environment.pickle and /dev/null differ diff --git a/docs/_build/doctrees/index.doctree b/docs/_build/doctrees/index.doctree deleted file mode 100644 index 324bf66..0000000 Binary files a/docs/_build/doctrees/index.doctree and /dev/null differ diff --git a/docs/_build/doctrees/source/modules.doctree b/docs/_build/doctrees/source/modules.doctree deleted file mode 100644 index 3d9e6d8..0000000 Binary files a/docs/_build/doctrees/source/modules.doctree and /dev/null differ diff --git a/docs/_build/doctrees/source/tsdart.doctree b/docs/_build/doctrees/source/tsdart.doctree deleted file mode 100644 index f6d00f6..0000000 Binary files a/docs/_build/doctrees/source/tsdart.doctree and /dev/null differ diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo deleted file mode 100644 index b2c9779..0000000 --- a/docs/_build/html/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: fca715a87e6687f8584f71a09f20b6ce -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_example/muller-example.html b/docs/_build/html/_example/muller-example.html deleted file mode 100644 index 25ac484..0000000 --- a/docs/_build/html/_example/muller-example.html +++ /dev/null @@ -1,412 +0,0 @@ - - - - - - - Muller potential — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- - 
import numpy as np
-import torch
-import torch.nn as nn
-from matplotlib import pyplot as plt
-
-from torch.utils.data.dataloader import DataLoader
-from torch.utils.data import random_split
-
-from tsdart.utils import set_random_seed
-from tsdart.loss import Prototypes
-from tsdart.model import TSDART, TSDARTLayer, TSDARTEstimator
-from tsdart.dataprocessing import Preprocessing
-
-
-
if torch.cuda.is_available():
-    device = torch.device('cuda')
-    print('cuda is available')
-else:
-    device = torch.device('cpu')
-    print('cpu')
-
-
-
cpu
-
-
-
-

Muller potential

-
A = np.array([-10,-5,-17/2,0.75])
-a = np.array([-1,-1,-6.5,0.7])
-b = np.array([0,0,11,0.6])
-c = np.array([-10,-10,-6.5,0.7])
-xbar = np.array([1,0,-0.5,-1])
-ybar = np.array([0,0.5,1.5,1])
-
-def V(x,y):
-    s = 0.
-    for i in range(4):
-        s += A[i]*np.exp(a[i]*(x-xbar[i])**2+b[i]*(x-xbar[i])*(y-ybar[i])+c[i]*(y-ybar[i])**2)
-    return s
-
-fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(2)
-ax.set_aspect('equal')
-x = np.arange(-1.7,1.2+0.01,0.01)
-y = np.arange(-0.35,2.1+0.01,0.01)
-xx,yy = np.meshgrid(x,y)
-z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
-z = z - z.min()
-z = z*1/0.9 # temperature is 0.4.
-z = np.ma.masked_greater(z, 10)
-c = ax.contourf(x,y,z,cmap='rainbow',levels=20,zorder=1)
-ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=0.2)
-cb = fig.colorbar(c)
-#ax.grid(True)
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('free energy/kT',fontsize=12)
-
-ax.set_xlim(-1.5,1.15)
-ax.set_ylim(-0.3,2.1)
-
-ax.set_yticks([0,1,2])
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-ax.set_xlabel('x1',fontsize=12)
-ax.set_ylabel('x2',fontsize=12)
-
-
-
Text(0, 0.5, 'x2')
-
-
-../_images/output_3_1.png -
-
-

Create dataset

-
data = np.load('../data/muller/muller.npy')
-
-pre = Preprocessing(dtype=np.float32)
-dataset = pre.create_dataset(lag_time=1,data=data)
-
-
-
-
-

2 states model

-
set_random_seed(1)
-
-val = int(len(dataset)*0.10)
-train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val])
-
-loader_train = DataLoader(train_data, batch_size=1000, shuffle=True)
-loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False)
-
-lobe = TSDARTLayer([2,20,20,20,10,2],n_states=2)
-lobe = lobe.to(device=device)
-### 50 epochs for fully optimization
-tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=2, pretrain=50)
-tsdart_model = tsdart.fit(loader_train, n_epochs=100, validation_loader=loader_val).fetch_model()
-
-
-
tsdart_estimator = TSDARTEstimator(tsdart_model)
-ood_scores = tsdart_estimator.fit(data).ood_scores
-
-
-
A = np.array([-10,-5,-17/2,0.75])
-a = np.array([-1,-1,-6.5,0.7])
-b = np.array([0,0,11,0.6])
-c = np.array([-10,-10,-6.5,0.7])
-xbar = np.array([1,0,-0.5,-1])
-ybar = np.array([0,0.5,1.5,1])
-
-def V(x,y):
-    s = 0.
-    for i in range(4):
-        s += A[i]*np.exp(a[i]*(x-xbar[i])**2+b[i]*(x-xbar[i])*(y-ybar[i])+c[i]*(y-ybar[i])**2)
-    return s
-
-fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(2)
-ax.set_aspect('equal')
-x = np.arange(-1.7,1.2+0.01,0.01)
-y = np.arange(-0.35,2.1+0.01,0.01)
-xx,yy = np.meshgrid(x,y)
-z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
-z = z - z.min()
-z = z*1/0.9 # temperature is 0.4.
-z = np.ma.masked_greater(z, 10)
-
-c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('ood scores',fontsize=12)
-
-ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1)
-
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-
-ax.set_xlim(-1.5,1.15)
-ax.set_ylim(-0.3,2.1)
-
-ax.set_yticks([0,1,2])
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-ax.set_xlabel('x1',fontsize=12)
-ax.set_ylabel('x2',fontsize=12)
-
-
-
Text(0, 0.5, 'x2')
-
-
-../_images/output_9_1.png -
features = tsdart_model.transform(data,return_type='hypersphere_embs')
-state_centers = tsdart_estimator.fit(data).state_centers
-
-
-
fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(1)
-ax.set_aspect('equal')
-
-c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('ood scores',fontsize=12)
-
-ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--')
-ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--')
-
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-
-ax.set_xlim(-1.1,1.1)
-ax.set_ylim(-1.1,1.1)
-
-ax.set_xticks([-1,0,1])
-ax.set_yticks([-1,0,1])
-
-ax.set_xlabel('z1',fontsize=12)
-ax.set_ylabel('z2',fontsize=12)
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-
-../_images/output_11_0.png -
-
-

3 states model

-
set_random_seed(1)
-
-val = int(len(dataset)*0.10)
-train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val])
-
-loader_train = DataLoader(train_data, batch_size=1000, shuffle=True)
-loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False)
-
-lobe = TSDARTLayer([2,20,20,20,10,2],n_states=3)
-lobe = lobe.to(device=device)
-### 50 epochs for fully optimization
-tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=3, pretrain=50)
-tsdart_model = tsdart.fit(loader_train, n_epochs=100, validation_loader=loader_val).fetch_model()
-
-
-
tsdart_estimator = TSDARTEstimator(tsdart_model)
-ood_scores = tsdart_estimator.fit(data).ood_scores
-
-
-
A = np.array([-10,-5,-17/2,0.75])
-a = np.array([-1,-1,-6.5,0.7])
-b = np.array([0,0,11,0.6])
-c = np.array([-10,-10,-6.5,0.7])
-xbar = np.array([1,0,-0.5,-1])
-ybar = np.array([0,0.5,1.5,1])
-
-def V(x,y):
-    s = 0.
-    for i in range(4):
-        s += A[i]*np.exp(a[i]*(x-xbar[i])**2+b[i]*(x-xbar[i])*(y-ybar[i])+c[i]*(y-ybar[i])**2)
-    return s
-
-fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(2)
-ax.set_aspect('equal')
-x = np.arange(-1.7,1.2+0.01,0.01)
-y = np.arange(-0.35,2.1+0.01,0.01)
-xx,yy = np.meshgrid(x,y)
-z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
-z = z - z.min()
-z = z*1/0.9 # temperature is 0.4.
-z = np.ma.masked_greater(z, 10)
-
-c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('ood scores',fontsize=12)
-
-ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1)
-
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-
-ax.set_xlim(-1.5,1.15)
-ax.set_ylim(-0.3,2.1)
-
-ax.set_yticks([0,1,2])
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-ax.set_xlabel('x1',fontsize=12)
-ax.set_ylabel('x2',fontsize=12)
-
-
-
Text(0, 0.5, 'x2')
-
-
-../_images/output_15_1.png -
features = tsdart_model.transform(data,return_type='hypersphere_embs')
-state_centers = tsdart_estimator.fit(data).state_centers
-
-
-
fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(1)
-ax.set_aspect('equal')
-
-c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('ood scores',fontsize=12)
-
-ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--')
-ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--')
-ax.plot([0,state_centers[2,0]],[0,state_centers[2,1]],linewidth=2,color='black',linestyle='--')
-
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-
-ax.set_xlim(-1.1,1.1)
-ax.set_ylim(-1.1,1.1)
-
-ax.set_xticks([-1,0,1])
-ax.set_yticks([-1,0,1])
-
-ax.set_xlabel('z1',fontsize=12)
-ax.set_ylabel('z2',fontsize=12)
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-
-../_images/output_17_0.png -
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_example/quadruple-well-example.html b/docs/_build/html/_example/quadruple-well-example.html deleted file mode 100644 index 0f04737..0000000 --- a/docs/_build/html/_example/quadruple-well-example.html +++ /dev/null @@ -1,492 +0,0 @@ - - - - - - - Quadruple-well potential — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- - 
import numpy as np
-import torch
-import torch.nn as nn
-from matplotlib import pyplot as plt
-
-from torch.utils.data.dataloader import DataLoader
-from torch.utils.data import random_split
-
-from tsdart.utils import set_random_seed
-from tsdart.loss import Prototypes
-from tsdart.model import TSDART, TSDARTLayer, TSDARTEstimator
-from tsdart.dataprocessing import Preprocessing
-
-
-
if torch.cuda.is_available():
-    device = torch.device('cuda')
-    print('cuda is available')
-else:
-    device = torch.device('cpu')
-    print('cpu')
-
-
-
cpu
-
-
-
-

Quadruple-well potential

-
# quadruple-well potential
-# See "RPnet: a reverse-projection-based neural network for coarse-graining metastable conformational states for protein dynamics" for simulation details.
-# The temperature is specified as 0.4
-
-V = lambda x,y: 1/8*(np.cos(x/6)-3*np.sin(x/3)+5)*(np.cos(y/6)-np.sin(y/3)+3)
-
-fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(1)
-ax.set_aspect('equal')
-
-x = np.arange(0,30+0.1,0.1)
-y = np.arange(0,30+0.1,0.1)
-xx,yy = np.meshgrid(x,y)
-z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
-z = z - z.min()
-z = z*1/0.4 # temperature is 0.4.
-z = np.ma.masked_greater(z,10)
-
-c = ax.contourf(x,y,z,cmap='rainbow',levels=20,zorder=1)
-ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=0.2)
-cb = fig.colorbar(c)
-#ax.grid(True)
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('free energy/kT',fontsize=12)
-
-ax.set_xlim(0,30)
-ax.set_ylim(0,30)
-
-ax.set_xticks([0,10,20,30])
-ax.set_yticks([0,10,20,30])
-
-ax.set_xlabel('x',fontsize=12)
-ax.set_ylabel('y',fontsize=12)
-
-
-
Text(0, 0.5, 'y')
-
-
-../_images/output_3_1.png -
-
-

Create dataset

-
data = np.load('../data/quadruple-well/quadruple-well.npy')
-
-pre = Preprocessing(dtype=np.float32)
-dataset = pre.create_dataset(lag_time=10,data=data)
-
-
-
-
-

2 states model

-
set_random_seed(1)
-
-val = int(len(dataset)*0.10)
-train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val])
-
-loader_train = DataLoader(train_data, batch_size=1000, shuffle=True)
-loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False)
-
-lobe = TSDARTLayer([2,20,20,20,10,2],n_states=2)
-lobe = lobe.to(device=device)
-
-tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=2, pretrain=10)
-tsdart_model = tsdart.fit(loader_train, n_epochs=20, validation_loader=loader_val).fetch_model()
-
-
-
tsdart_estimator = TSDARTEstimator(tsdart_model)
-ood_scores = tsdart_estimator.fit(data).ood_scores
-
-
-
# quadruple-well potential
-V = lambda x,y: 1/8*(np.cos(x/6)-3*np.sin(x/3)+5)*(np.cos(y/6)-np.sin(y/3)+3)
-
-fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(1)
-ax.set_aspect('equal')
-
-x = np.arange(0,30+0.1,0.1)
-y = np.arange(0,30+0.1,0.1)
-xx,yy = np.meshgrid(x,y)
-z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
-z = z - z.min()
-z = z*1/0.4 # temperature is 0.4.
-z = np.ma.masked_greater(z,10)
-
-c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('ood scores',fontsize=12)
-
-ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1)
-
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-
-ax.set_xlim(0,30)
-ax.set_ylim(0,30)
-
-ax.set_xticks([0,10,20,30])
-ax.set_yticks([0,10,20,30])
-
-ax.set_xlabel('x',fontsize=12)
-ax.set_ylabel('y',fontsize=12)
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-
-../_images/output_9_0.png -
features = tsdart_model.transform(data,return_type='hypersphere_embs')
-state_centers = tsdart_estimator.fit(data).state_centers
-
-
-
fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(1)
-ax.set_aspect('equal')
-
-c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('ood scores',fontsize=12)
-
-ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--')
-ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--')
-
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-
-ax.set_xlim(-1.1,1.1)
-ax.set_ylim(-1.1,1.1)
-
-ax.set_xticks([-1,0,1])
-ax.set_yticks([-1,0,1])
-
-ax.set_xlabel('z1',fontsize=12)
-ax.set_ylabel('z2',fontsize=12)
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-
-../_images/output_11_0.png -
-
-

3 states model

-
set_random_seed(1)
-
-val = int(len(dataset)*0.10)
-train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val])
-
-loader_train = DataLoader(train_data, batch_size=1000, shuffle=True)
-loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False)
-
-lobe = TSDARTLayer([2,20,20,20,10,2],n_states=3)
-lobe = lobe.to(device=device)
-
-tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=3, pretrain=10)
-tsdart_model = tsdart.fit(loader_train, n_epochs=20, validation_loader=loader_val).fetch_model()
-
-
-
tsdart_estimator = TSDARTEstimator(tsdart_model)
-ood_scores = tsdart_estimator.fit(data).ood_scores
-
-
-
# quadruple-well potential
-V = lambda x,y: 1/8*(np.cos(x/6)-3*np.sin(x/3)+5)*(np.cos(y/6)-np.sin(y/3)+3)
-
-fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(1)
-ax.set_aspect('equal')
-
-x = np.arange(0,30+0.1,0.1)
-y = np.arange(0,30+0.1,0.1)
-xx,yy = np.meshgrid(x,y)
-z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
-z = z - z.min()
-z = z*1/0.4 # temperature is 0.4.
-z = np.ma.masked_greater(z,10)
-
-c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('ood scores',fontsize=12)
-
-ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1)
-
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-
-ax.set_xlim(0,30)
-ax.set_ylim(0,30)
-
-ax.set_xticks([0,10,20,30])
-ax.set_yticks([0,10,20,30])
-
-ax.set_xlabel('x',fontsize=12)
-ax.set_ylabel('y',fontsize=12)
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-
-../_images/output_15_0.png -
features = tsdart_model.transform(data,return_type='hypersphere_embs')
-state_centers = tsdart_estimator.fit(data).state_centers
-
-
-
fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(1)
-ax.set_aspect('equal')
-
-c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('ood scores',fontsize=12)
-
-ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--')
-ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--')
-ax.plot([0,state_centers[2,0]],[0,state_centers[2,1]],linewidth=2,color='black',linestyle='--')
-
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-
-ax.set_xlim(-1.1,1.1)
-ax.set_ylim(-1.1,1.1)
-
-ax.set_xticks([-1,0,1])
-ax.set_yticks([-1,0,1])
-
-ax.set_xlabel('z1',fontsize=12)
-ax.set_ylabel('z2',fontsize=12)
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-
-../_images/output_17_0.png -
-
-

4 states model

-
set_random_seed(1)
-
-val = int(len(dataset)*0.10)
-train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val])
-
-loader_train = DataLoader(train_data, batch_size=1000, shuffle=True)
-loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False)
-
-lobe = TSDARTLayer([2,20,20,20,10,3],n_states=4)
-lobe = lobe.to(device=device)
-
-tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=3, n_states=4, pretrain=10)
-tsdart_model = tsdart.fit(loader_train, n_epochs=20, validation_loader=loader_val).fetch_model()
-
-
-
tsdart_estimator = TSDARTEstimator(tsdart_model)
-ood_scores = tsdart_estimator.fit(data).ood_scores
-
-
-
# quadruple-well potential
-V = lambda x,y: 1/8*(np.cos(x/6)-3*np.sin(x/3)+5)*(np.cos(y/6)-np.sin(y/3)+3)
-
-fig,ax = plt.subplots(1,1,figsize=(4,3))
-for axis in ['top','bottom','left','right']:
-    ax.spines[axis].set_linewidth(1)
-ax.set_aspect('equal')
-
-x = np.arange(0,30+0.1,0.1)
-y = np.arange(0,30+0.1,0.1)
-xx,yy = np.meshgrid(x,y)
-z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1)
-z = z - z.min()
-z = z*1/0.4 # temperature is 0.4.
-z = np.ma.masked_greater(z,10)
-
-c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1)
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=2.5,width=1.5)
-cb.set_label('ood scores',fontsize=12)
-
-ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1)
-
-ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5)
-
-ax.set_xlim(0,30)
-ax.set_ylim(0,30)
-
-ax.set_xticks([0,10,20,30])
-ax.set_yticks([0,10,20,30])
-
-ax.set_xlabel('x',fontsize=12)
-ax.set_ylabel('y',fontsize=12)
-
-r=0.1
-g=0.1
-b=0.2
-ax.patch.set_facecolor((r,g,b,.15))
-
-
-../_images/output_21_0.png -
features = tsdart_model.transform(data,return_type='hypersphere_embs')
-state_centers = tsdart_estimator.fit(data).state_centers
-
-
-
r = 1
-pi = np.pi
-cos = np.cos
-sin = np.sin
-phi, theta = np.mgrid[0.0:pi:100j, 0.0:2.0*pi:100j]
-x = r*sin(phi)*cos(theta)
-y = r*sin(phi)*sin(theta)
-z = r*cos(phi)
-
-plt.rcParams['figure.figsize'] = (5,4)
-fig = plt.figure()
-ax = fig.add_subplot(111, projection='3d')
-
-ax.plot_surface(
-   x, y, z,  rstride=2, cstride=2, color='c', alpha=0.1, linewidth=100,antialiased=False)
-
-ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],[0,state_centers[0,2]],linewidth=2,color='black',linestyle='--')
-ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],[0,state_centers[1,2]],linewidth=2,color='black',linestyle='--')
-ax.plot([0,state_centers[2,0]],[0,state_centers[2,1]],[0,state_centers[2,2]],linewidth=2,color='black',linestyle='--')
-ax.plot([0,state_centers[3,0]],[0,state_centers[3,1]],[0,state_centers[3,2]],linewidth=2,color='black',linestyle='--')
-
-c = ax.scatter(features[:,0],features[:,1],features[:,2],c=ood_scores[:],s=1,alpha=1,cmap='coolwarm')
-
-cb = fig.colorbar(c)
-cb.ax.tick_params(labelsize=10,length=3,width=1.5)
-cb.set_label('ood scores',fontsize=10)
-
-ax.set_xlim([-1,1])
-ax.set_ylim([-1,1])
-ax.set_zlim([-1,1])
-ax.set_xticks([-1,-0.5,0,0.5,1])
-ax.set_yticks([-1,-0.5,0,0.5,1])
-ax.set_zticks([-1,-0.5,0,0.5,1],[-1,-0.5,0,0.5,1])
-ax.set_aspect("equal")
-ax.tick_params(axis="both",labelsize=10,direction='out',length=7.5,width=2.5)
-
-ax.set_xlabel('z1',fontsize=12)
-ax.set_ylabel('z2',fontsize=12)
-ax.set_zlabel('z3',fontsize=12)
-
-ax.view_init(elev=15., azim=105)
-
-
-../_images/output_23_0.png -
- - -
-
- -
-
-
-
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- import numpy as np - import torch - import torch.nn as nn - from matplotlib import pyplot as plt - - from torch.utils.data.dataloader import DataLoader - from torch.utils.data import random_split - - from tsdart.utils import set_random_seed - from tsdart.loss import Prototypes - from tsdart.model import TSDART, TSDARTLayer, TSDARTEstimator - from tsdart.dataprocessing import Preprocessing - -.. code:: ipython3 - - if torch.cuda.is_available(): - device = torch.device('cuda') - print('cuda is available') - else: - device = torch.device('cpu') - print('cpu') - - -.. parsed-literal:: - - cpu - - -Muller potential -~~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - A = np.array([-10,-5,-17/2,0.75]) - a = np.array([-1,-1,-6.5,0.7]) - b = np.array([0,0,11,0.6]) - c = np.array([-10,-10,-6.5,0.7]) - xbar = np.array([1,0,-0.5,-1]) - ybar = np.array([0,0.5,1.5,1]) - - def V(x,y): - s = 0. - for i in range(4): - s += A[i]*np.exp(a[i]*(x-xbar[i])**2+b[i]*(x-xbar[i])*(y-ybar[i])+c[i]*(y-ybar[i])**2) - return s - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(2) - ax.set_aspect('equal') - x = np.arange(-1.7,1.2+0.01,0.01) - y = np.arange(-0.35,2.1+0.01,0.01) - xx,yy = np.meshgrid(x,y) - z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1) - z = z - z.min() - z = z*1/0.9 # temperature is 0.4. - z = np.ma.masked_greater(z, 10) - c = ax.contourf(x,y,z,cmap='rainbow',levels=20,zorder=1) - ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=0.2) - cb = fig.colorbar(c) - #ax.grid(True) - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('free energy/kT',fontsize=12) - - ax.set_xlim(-1.5,1.15) - ax.set_ylim(-0.3,2.1) - - ax.set_yticks([0,1,2]) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - ax.set_xlabel('x1',fontsize=12) - ax.set_ylabel('x2',fontsize=12) - - - - -.. parsed-literal:: - - Text(0, 0.5, 'x2') - - - - -.. image:: output_3_1.png - - -Create dataset -~~~~~~~~~~~~~~ - -.. code:: ipython3 - - data = np.load('../data/muller/muller.npy') - - pre = Preprocessing(dtype=np.float32) - dataset = pre.create_dataset(lag_time=1,data=data) - -2 states model -~~~~~~~~~~~~~~ - -.. code:: ipython3 - - set_random_seed(1) - - val = int(len(dataset)*0.10) - train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val]) - - loader_train = DataLoader(train_data, batch_size=1000, shuffle=True) - loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False) - - lobe = TSDARTLayer([2,20,20,20,10,2],n_states=2) - lobe = lobe.to(device=device) - ### 50 epochs for fully optimization - tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=2, pretrain=50) - tsdart_model = tsdart.fit(loader_train, n_epochs=100, validation_loader=loader_val).fetch_model() - - -.. parsed-literal:: - - - -.. code:: ipython3 - - tsdart_estimator = TSDARTEstimator(tsdart_model) - ood_scores = tsdart_estimator.fit(data).ood_scores - -.. code:: ipython3 - - A = np.array([-10,-5,-17/2,0.75]) - a = np.array([-1,-1,-6.5,0.7]) - b = np.array([0,0,11,0.6]) - c = np.array([-10,-10,-6.5,0.7]) - xbar = np.array([1,0,-0.5,-1]) - ybar = np.array([0,0.5,1.5,1]) - - def V(x,y): - s = 0. - for i in range(4): - s += A[i]*np.exp(a[i]*(x-xbar[i])**2+b[i]*(x-xbar[i])*(y-ybar[i])+c[i]*(y-ybar[i])**2) - return s - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(2) - ax.set_aspect('equal') - x = np.arange(-1.7,1.2+0.01,0.01) - y = np.arange(-0.35,2.1+0.01,0.01) - xx,yy = np.meshgrid(x,y) - z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1) - z = z - z.min() - z = z*1/0.9 # temperature is 0.4. - z = np.ma.masked_greater(z, 10) - - c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1) - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('ood scores',fontsize=12) - - ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1) - - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - - ax.set_xlim(-1.5,1.15) - ax.set_ylim(-0.3,2.1) - - ax.set_yticks([0,1,2]) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - ax.set_xlabel('x1',fontsize=12) - ax.set_ylabel('x2',fontsize=12) - - - - -.. parsed-literal:: - - Text(0, 0.5, 'x2') - - - - -.. image:: output_9_1.png - - -.. code:: ipython3 - - features = tsdart_model.transform(data,return_type='hypersphere_embs') - state_centers = tsdart_estimator.fit(data).state_centers - -.. code:: ipython3 - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(1) - ax.set_aspect('equal') - - c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1) - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('ood scores',fontsize=12) - - ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--') - ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--') - - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - - ax.set_xlim(-1.1,1.1) - ax.set_ylim(-1.1,1.1) - - ax.set_xticks([-1,0,1]) - ax.set_yticks([-1,0,1]) - - ax.set_xlabel('z1',fontsize=12) - ax.set_ylabel('z2',fontsize=12) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - - -.. image:: output_11_0.png - - -3 states model -~~~~~~~~~~~~~~ - -.. code:: ipython3 - - set_random_seed(1) - - val = int(len(dataset)*0.10) - train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val]) - - loader_train = DataLoader(train_data, batch_size=1000, shuffle=True) - loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False) - - lobe = TSDARTLayer([2,20,20,20,10,2],n_states=3) - lobe = lobe.to(device=device) - ### 50 epochs for fully optimization - tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=3, pretrain=50) - tsdart_model = tsdart.fit(loader_train, n_epochs=100, validation_loader=loader_val).fetch_model() - - -.. parsed-literal:: - - - -.. code:: ipython3 - - tsdart_estimator = TSDARTEstimator(tsdart_model) - ood_scores = tsdart_estimator.fit(data).ood_scores - -.. code:: ipython3 - - A = np.array([-10,-5,-17/2,0.75]) - a = np.array([-1,-1,-6.5,0.7]) - b = np.array([0,0,11,0.6]) - c = np.array([-10,-10,-6.5,0.7]) - xbar = np.array([1,0,-0.5,-1]) - ybar = np.array([0,0.5,1.5,1]) - - def V(x,y): - s = 0. - for i in range(4): - s += A[i]*np.exp(a[i]*(x-xbar[i])**2+b[i]*(x-xbar[i])*(y-ybar[i])+c[i]*(y-ybar[i])**2) - return s - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(2) - ax.set_aspect('equal') - x = np.arange(-1.7,1.2+0.01,0.01) - y = np.arange(-0.35,2.1+0.01,0.01) - xx,yy = np.meshgrid(x,y) - z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1) - z = z - z.min() - z = z*1/0.9 # temperature is 0.4. - z = np.ma.masked_greater(z, 10) - - c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1) - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('ood scores',fontsize=12) - - ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1) - - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - - ax.set_xlim(-1.5,1.15) - ax.set_ylim(-0.3,2.1) - - ax.set_yticks([0,1,2]) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - ax.set_xlabel('x1',fontsize=12) - ax.set_ylabel('x2',fontsize=12) - - - - -.. parsed-literal:: - - Text(0, 0.5, 'x2') - - - - -.. image:: output_15_1.png - - -.. code:: ipython3 - - features = tsdart_model.transform(data,return_type='hypersphere_embs') - state_centers = tsdart_estimator.fit(data).state_centers - -.. code:: ipython3 - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(1) - ax.set_aspect('equal') - - c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1) - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('ood scores',fontsize=12) - - ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--') - ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--') - ax.plot([0,state_centers[2,0]],[0,state_centers[2,1]],linewidth=2,color='black',linestyle='--') - - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - - ax.set_xlim(-1.1,1.1) - ax.set_ylim(-1.1,1.1) - - ax.set_xticks([-1,0,1]) - ax.set_yticks([-1,0,1]) - - ax.set_xlabel('z1',fontsize=12) - ax.set_ylabel('z2',fontsize=12) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - - -.. image:: output_17_0.png - diff --git a/docs/_build/html/_sources/_example/quadruple-well-example.rst.txt b/docs/_build/html/_sources/_example/quadruple-well-example.rst.txt deleted file mode 100644 index 785b478..0000000 --- a/docs/_build/html/_sources/_example/quadruple-well-example.rst.txt +++ /dev/null @@ -1,455 +0,0 @@ -.. code:: ipython3 - - import numpy as np - import torch - import torch.nn as nn - from matplotlib import pyplot as plt - - from torch.utils.data.dataloader import DataLoader - from torch.utils.data import random_split - - from tsdart.utils import set_random_seed - from tsdart.loss import Prototypes - from tsdart.model import TSDART, TSDARTLayer, TSDARTEstimator - from tsdart.dataprocessing import Preprocessing - -.. code:: ipython3 - - if torch.cuda.is_available(): - device = torch.device('cuda') - print('cuda is available') - else: - device = torch.device('cpu') - print('cpu') - - -.. parsed-literal:: - - cpu - - -Quadruple-well potential -~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - # quadruple-well potential - # See "RPnet: a reverse-projection-based neural network for coarse-graining metastable conformational states for protein dynamics" for simulation details. - # The temperature is specified as 0.4 - - V = lambda x,y: 1/8*(np.cos(x/6)-3*np.sin(x/3)+5)*(np.cos(y/6)-np.sin(y/3)+3) - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(1) - ax.set_aspect('equal') - - x = np.arange(0,30+0.1,0.1) - y = np.arange(0,30+0.1,0.1) - xx,yy = np.meshgrid(x,y) - z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1) - z = z - z.min() - z = z*1/0.4 # temperature is 0.4. - z = np.ma.masked_greater(z,10) - - c = ax.contourf(x,y,z,cmap='rainbow',levels=20,zorder=1) - ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=0.2) - cb = fig.colorbar(c) - #ax.grid(True) - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('free energy/kT',fontsize=12) - - ax.set_xlim(0,30) - ax.set_ylim(0,30) - - ax.set_xticks([0,10,20,30]) - ax.set_yticks([0,10,20,30]) - - ax.set_xlabel('x',fontsize=12) - ax.set_ylabel('y',fontsize=12) - - - - -.. parsed-literal:: - - Text(0, 0.5, 'y') - - - - -.. image:: output_3_1.png - - -Create dataset -~~~~~~~~~~~~~~ - -.. code:: ipython3 - - data = np.load('../data/quadruple-well/quadruple-well.npy') - - pre = Preprocessing(dtype=np.float32) - dataset = pre.create_dataset(lag_time=10,data=data) - -2 states model -~~~~~~~~~~~~~~ - -.. code:: ipython3 - - set_random_seed(1) - - val = int(len(dataset)*0.10) - train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val]) - - loader_train = DataLoader(train_data, batch_size=1000, shuffle=True) - loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False) - - lobe = TSDARTLayer([2,20,20,20,10,2],n_states=2) - lobe = lobe.to(device=device) - - tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=2, pretrain=10) - tsdart_model = tsdart.fit(loader_train, n_epochs=20, validation_loader=loader_val).fetch_model() - - -.. parsed-literal:: - - - -.. code:: ipython3 - - tsdart_estimator = TSDARTEstimator(tsdart_model) - ood_scores = tsdart_estimator.fit(data).ood_scores - -.. code:: ipython3 - - # quadruple-well potential - V = lambda x,y: 1/8*(np.cos(x/6)-3*np.sin(x/3)+5)*(np.cos(y/6)-np.sin(y/3)+3) - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(1) - ax.set_aspect('equal') - - x = np.arange(0,30+0.1,0.1) - y = np.arange(0,30+0.1,0.1) - xx,yy = np.meshgrid(x,y) - z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1) - z = z - z.min() - z = z*1/0.4 # temperature is 0.4. - z = np.ma.masked_greater(z,10) - - c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1) - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('ood scores',fontsize=12) - - ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1) - - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - - ax.set_xlim(0,30) - ax.set_ylim(0,30) - - ax.set_xticks([0,10,20,30]) - ax.set_yticks([0,10,20,30]) - - ax.set_xlabel('x',fontsize=12) - ax.set_ylabel('y',fontsize=12) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - - -.. image:: output_9_0.png - - -.. code:: ipython3 - - features = tsdart_model.transform(data,return_type='hypersphere_embs') - state_centers = tsdart_estimator.fit(data).state_centers - -.. code:: ipython3 - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(1) - ax.set_aspect('equal') - - c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1) - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('ood scores',fontsize=12) - - ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--') - ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--') - - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - - ax.set_xlim(-1.1,1.1) - ax.set_ylim(-1.1,1.1) - - ax.set_xticks([-1,0,1]) - ax.set_yticks([-1,0,1]) - - ax.set_xlabel('z1',fontsize=12) - ax.set_ylabel('z2',fontsize=12) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - - -.. image:: output_11_0.png - - -3 states model -~~~~~~~~~~~~~~ - -.. code:: ipython3 - - set_random_seed(1) - - val = int(len(dataset)*0.10) - train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val]) - - loader_train = DataLoader(train_data, batch_size=1000, shuffle=True) - loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False) - - lobe = TSDARTLayer([2,20,20,20,10,2],n_states=3) - lobe = lobe.to(device=device) - - tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=2, n_states=3, pretrain=10) - tsdart_model = tsdart.fit(loader_train, n_epochs=20, validation_loader=loader_val).fetch_model() - - -.. parsed-literal:: - - - -.. code:: ipython3 - - tsdart_estimator = TSDARTEstimator(tsdart_model) - ood_scores = tsdart_estimator.fit(data).ood_scores - -.. code:: ipython3 - - # quadruple-well potential - V = lambda x,y: 1/8*(np.cos(x/6)-3*np.sin(x/3)+5)*(np.cos(y/6)-np.sin(y/3)+3) - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(1) - ax.set_aspect('equal') - - x = np.arange(0,30+0.1,0.1) - y = np.arange(0,30+0.1,0.1) - xx,yy = np.meshgrid(x,y) - z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1) - z = z - z.min() - z = z*1/0.4 # temperature is 0.4. - z = np.ma.masked_greater(z,10) - - c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1) - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('ood scores',fontsize=12) - - ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1) - - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - - ax.set_xlim(0,30) - ax.set_ylim(0,30) - - ax.set_xticks([0,10,20,30]) - ax.set_yticks([0,10,20,30]) - - ax.set_xlabel('x',fontsize=12) - ax.set_ylabel('y',fontsize=12) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - - -.. image:: output_15_0.png - - -.. code:: ipython3 - - features = tsdart_model.transform(data,return_type='hypersphere_embs') - state_centers = tsdart_estimator.fit(data).state_centers - -.. code:: ipython3 - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(1) - ax.set_aspect('equal') - - c = ax.scatter(features[:,0],features[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1) - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('ood scores',fontsize=12) - - ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],linewidth=2,color='black',linestyle='--') - ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],linewidth=2,color='black',linestyle='--') - ax.plot([0,state_centers[2,0]],[0,state_centers[2,1]],linewidth=2,color='black',linestyle='--') - - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - - ax.set_xlim(-1.1,1.1) - ax.set_ylim(-1.1,1.1) - - ax.set_xticks([-1,0,1]) - ax.set_yticks([-1,0,1]) - - ax.set_xlabel('z1',fontsize=12) - ax.set_ylabel('z2',fontsize=12) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - - -.. image:: output_17_0.png - - -4 states model -~~~~~~~~~~~~~~ - -.. code:: ipython3 - - set_random_seed(1) - - val = int(len(dataset)*0.10) - train_data, val_data = torch.utils.data.random_split(dataset, [len(dataset)-val, val]) - - loader_train = DataLoader(train_data, batch_size=1000, shuffle=True) - loader_val = DataLoader(val_data, batch_size=len(val_data), shuffle=False) - - lobe = TSDARTLayer([2,20,20,20,10,3],n_states=4) - lobe = lobe.to(device=device) - - tsdart = TSDART(lobe = lobe, learning_rate = 1e-3, device = device, mode = 'regularize', beta=0.01, feat_dim=3, n_states=4, pretrain=10) - tsdart_model = tsdart.fit(loader_train, n_epochs=20, validation_loader=loader_val).fetch_model() - - -.. parsed-literal:: - - - -.. code:: ipython3 - - tsdart_estimator = TSDARTEstimator(tsdart_model) - ood_scores = tsdart_estimator.fit(data).ood_scores - -.. code:: ipython3 - - # quadruple-well potential - V = lambda x,y: 1/8*(np.cos(x/6)-3*np.sin(x/3)+5)*(np.cos(y/6)-np.sin(y/3)+3) - - fig,ax = plt.subplots(1,1,figsize=(4,3)) - for axis in ['top','bottom','left','right']: - ax.spines[axis].set_linewidth(1) - ax.set_aspect('equal') - - x = np.arange(0,30+0.1,0.1) - y = np.arange(0,30+0.1,0.1) - xx,yy = np.meshgrid(x,y) - z = V(xx.ravel(),yy.ravel()).reshape(len(y),-1) - z = z - z.min() - z = z*1/0.4 # temperature is 0.4. - z = np.ma.masked_greater(z,10) - - c = ax.scatter(data[:,0],data[:,1],c=ood_scores,cmap='coolwarm',s=1,alpha=1) - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=2.5,width=1.5) - cb.set_label('ood scores',fontsize=12) - - ax.contour(x,y,z,levels=20,zorder=1,colors='black',alpha=1) - - ax.tick_params(axis="both",labelsize=12,direction='out',length=3.5,width=1.5) - - ax.set_xlim(0,30) - ax.set_ylim(0,30) - - ax.set_xticks([0,10,20,30]) - ax.set_yticks([0,10,20,30]) - - ax.set_xlabel('x',fontsize=12) - ax.set_ylabel('y',fontsize=12) - - r=0.1 - g=0.1 - b=0.2 - ax.patch.set_facecolor((r,g,b,.15)) - - - -.. image:: output_21_0.png - - -.. code:: ipython3 - - features = tsdart_model.transform(data,return_type='hypersphere_embs') - state_centers = tsdart_estimator.fit(data).state_centers - -.. code:: ipython3 - - r = 1 - pi = np.pi - cos = np.cos - sin = np.sin - phi, theta = np.mgrid[0.0:pi:100j, 0.0:2.0*pi:100j] - x = r*sin(phi)*cos(theta) - y = r*sin(phi)*sin(theta) - z = r*cos(phi) - - plt.rcParams['figure.figsize'] = (5,4) - fig = plt.figure() - ax = fig.add_subplot(111, projection='3d') - - ax.plot_surface( - x, y, z, rstride=2, cstride=2, color='c', alpha=0.1, linewidth=100,antialiased=False) - - ax.plot([0,state_centers[0,0]],[0,state_centers[0,1]],[0,state_centers[0,2]],linewidth=2,color='black',linestyle='--') - ax.plot([0,state_centers[1,0]],[0,state_centers[1,1]],[0,state_centers[1,2]],linewidth=2,color='black',linestyle='--') - ax.plot([0,state_centers[2,0]],[0,state_centers[2,1]],[0,state_centers[2,2]],linewidth=2,color='black',linestyle='--') - ax.plot([0,state_centers[3,0]],[0,state_centers[3,1]],[0,state_centers[3,2]],linewidth=2,color='black',linestyle='--') - - c = ax.scatter(features[:,0],features[:,1],features[:,2],c=ood_scores[:],s=1,alpha=1,cmap='coolwarm') - - cb = fig.colorbar(c) - cb.ax.tick_params(labelsize=10,length=3,width=1.5) - cb.set_label('ood scores',fontsize=10) - - ax.set_xlim([-1,1]) - ax.set_ylim([-1,1]) - ax.set_zlim([-1,1]) - ax.set_xticks([-1,-0.5,0,0.5,1]) - ax.set_yticks([-1,-0.5,0,0.5,1]) - ax.set_zticks([-1,-0.5,0,0.5,1],[-1,-0.5,0,0.5,1]) - ax.set_aspect("equal") - ax.tick_params(axis="both",labelsize=10,direction='out',length=7.5,width=2.5) - - ax.set_xlabel('z1',fontsize=12) - ax.set_ylabel('z2',fontsize=12) - ax.set_zlabel('z3',fontsize=12) - - ax.view_init(elev=15., azim=105) - - - -.. image:: output_23_0.png - diff --git a/docs/_build/html/_sources/chapters/installation.rst.txt b/docs/_build/html/_sources/chapters/installation.rst.txt deleted file mode 100644 index d4bb164..0000000 --- a/docs/_build/html/_sources/chapters/installation.rst.txt +++ /dev/null @@ -1,42 +0,0 @@ -Installation ------------- - -System requires -*************** -The software package can be installed and runned on Linux, Windows, and MacOS - -Dependency of Python and Python packages: - - -.. code-block:: bash - - python == 3.9 - numpy == 1.26.1 - scipy == 1.11.4 - torch == 1.13.1 - tqdm == 4.66.1 - -.. note:: - 1. Versions that has been previously tested on are also listed below, other versions should work the sameersions that has been previously tested on are listed above, other versions should work the same. - - 2. The required python packages with the latest versions will be automatically installed if these python packages are not already present in your local Python environment. - - -Installation for source -*********************** -1. Download and install the latest `Anaconda `_ distribution: - -.. code-block:: bash - - wget https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Linux-x86_64.sh - ./Anaconda3-2024.06-1-Linux-x86_64.sh - - -2. Create a new ``conda`` virtual environment and install the ts-dart source code locally: - -.. code-block:: bash - - conda create -n ts-dart python=3.9 - conda activate ts-dart - git clone https://github.com/xuhuihuang/ts-dart.git - python -m pip install ./ts-dart diff --git a/docs/_build/html/_sources/chapters/intro.rst.txt b/docs/_build/html/_sources/chapters/intro.rst.txt deleted file mode 100644 index dc6714e..0000000 --- a/docs/_build/html/_sources/chapters/intro.rst.txt +++ /dev/null @@ -1,14 +0,0 @@ -Brief intro ------------- - -**TS-DART: Transition State identification via Dispersion and vAriational principle Regularized neural neTworks** - -Abstract -******** - -Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous fundamental biological processes. Markov state models (MSMs) constructed from Molecular Dynamics (MD) simulations have demonstrated considerable success in studying protein conformational changes, which are often associated with rare events transiting over free energy barriers. However, it remains challenging for MSMs to identify the transition states, as they group MD conformations into discrete metastable states and do not provide information on transition states lying at the top of free energy barriers between metastable states. Inspired by recent advances in trustworthy artificial intelligence (AI) for detecting out-of-distribution (OOD) data, we present Transition State identification via Dispersion and vAriational principle Regularized neural neTworks (TS-DART). This deep learning approach effectively detects the transition states from MD simulations using hyperspherical embeddings in the latent space. The key insight of TS-DART is to treat the transition state structures as OOD data, recognizing that the transition states are less populated and exhibit a distributional shift from metastable states. Our TS-DART method offers an end-to-end pipeline for identifying transition states from MD simulations. By introducing a dispersion loss function to regularize the hyperspherical latent space, TS-DART can discern transition state conformations that separate multiple metastable states in an MSM. Furthermore, TS-DART provides hyperspherical latent representations that preserve all relevant kinetic geometries of the original dynamics. We demonstrate the power of TS-DART by applying it to a 2D-potential, alanine dipeptide and the translocation of a DNA motor protein on DNA. In all these systems, TS-DART outperforms previous methods in identifying transition states. As TS-DART integrates the dimensionality reduction, state decomposition, and transition state identification in a unified framework, we anticipate that it will be applicable for studying transition states of protein conformational changes. - -Illustration -************ - -.. image:: ../figs/fig1.png diff --git a/docs/_build/html/_sources/chapters/tutorials.rst.txt b/docs/_build/html/_sources/chapters/tutorials.rst.txt deleted file mode 100644 index c00d547..0000000 --- a/docs/_build/html/_sources/chapters/tutorials.rst.txt +++ /dev/null @@ -1,57 +0,0 @@ -Tutorials -=========== - -Jupyter notebook examples -************************* - -.. raw:: html - - - - - - -
- - Example on muller potential - Thumbnail -
- - 		- - Example on quadruple-well potential - Thumbnail -
- -
- - -Start with python script -************************ - -.. code-block:: bash - - python ./ts-dart/scripts/train_tsdart.py \ - --seed 1 \ - --device 'cpu' \ - --lag_time 10 \ - --encoder_sizes 2 20 20 20 10 2 \ - --feat_dim 2 \ - --n_states 2 \ - --beta 0.01 \ - --gamma 1 \ - --proto_update_factor 0.5 \ - --scaling_temperature 0.1 \ - --learning_rate 0.001 \ - --pretrain 10 \ - --n_epochs 20 \ - --train_split 0.9 \ - --train_batch_size 1000 \ - --data_directory ./ts-dart/data/quadruple-well \ - --saving_directory . - -Or - -.. code-block:: bash - - sh ./ts-dart/scripts/train_tsdart.sh diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt deleted file mode 100644 index 6e10334..0000000 --- a/docs/_build/html/_sources/index.rst.txt +++ /dev/null @@ -1,19 +0,0 @@ -.. TS-DART documentation master file, created by - sphinx-quickstart on Thu Sep 26 10:46:28 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -TS-DART documentation -===================== - -TS-DART identifies transition states of protein conformational changes from MD simulations via out-of-distribution detection (OOD) in the hyperspherical latent space. - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - chapters/intro - chapters/installation - chapters/tutorials - source/tsdart - diff --git a/docs/_build/html/_sources/source/modules.rst.txt b/docs/_build/html/_sources/source/modules.rst.txt deleted file mode 100644 index c6bea6f..0000000 --- a/docs/_build/html/_sources/source/modules.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -tsdart -====== - -.. toctree:: - :maxdepth: 4 - - tsdart diff --git a/docs/_build/html/_sources/source/tsdart.rst.txt b/docs/_build/html/_sources/source/tsdart.rst.txt deleted file mode 100644 index 3473a48..0000000 --- a/docs/_build/html/_sources/source/tsdart.rst.txt +++ /dev/null @@ -1,34 +0,0 @@ -tsdart package -============== - -tsdart.dataprocessing module ----------------------------- - -.. automodule:: tsdart.dataprocessing - :members: - :undoc-members: - :show-inheritance: - -tsdart.loss module ------------------- - -.. automodule:: tsdart.loss - :members: - :undoc-members: - :show-inheritance: - -tsdart.model module -------------------- - -.. automodule:: tsdart.model - :members: - :undoc-members: - :show-inheritance: - -tsdart.utils module -------------------- - -.. automodule:: tsdart.utils - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js deleted file mode 100644 index 8141580..0000000 --- a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js +++ /dev/null @@ -1,123 +0,0 @@ -/* Compatability shim for jQuery and underscores.js. - * - * Copyright Sphinx contributors - * Released under the two clause BSD licence - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. 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- -/** - * Simple result scoring code. - */ -if (typeof Scorer === "undefined") { - var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [docname, title, anchor, descr, score, filename] - // and returns the new score. - /* - score: result => { - const [docname, title, anchor, descr, score, filename] = result - return score - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: { - 0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5, // used to be unimportantResults - }, - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - partialTitle: 7, - // query found in terms - term: 5, - partialTerm: 2, - }; -} - -const _removeChildren = (element) => { - while (element && element.lastChild) element.removeChild(element.lastChild); 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- linkUrl = requestUrl; - } else { - // normal html builders - requestUrl = contentRoot + docName + docFileSuffix; - linkUrl = docName + docLinkSuffix; - } - let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + anchor; - linkEl.dataset.score = score; - linkEl.innerHTML = title; - if (descr) { - listItem.appendChild(document.createElement("span")).innerHTML = - " (" + descr + ")"; - // highlight search terms in the description - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - } - else if (showSearchSummary) - fetch(requestUrl) - .then((responseData) => responseData.text()) - .then((data) => { - if (data) - listItem.appendChild( - Search.makeSearchSummary(data, searchTerms, anchor) - ); - // highlight search terms in the summary - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - }); - Search.output.appendChild(listItem); -}; -const _finishSearch = (resultCount) => { - Search.stopPulse(); - Search.title.innerText = _("Search Results"); - if (!resultCount) - Search.status.innerText = Documentation.gettext( - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = _( - "Search finished, found ${resultCount} page(s) matching the search query." - ).replace('${resultCount}', resultCount); -}; -const _displayNextItem = ( - results, - resultCount, - searchTerms, - highlightTerms, -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), searchTerms, highlightTerms); - setTimeout( - () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; -// Helper function used by query() to order search results. -// Each input is an array of [docname, title, anchor, descr, score, filename]. -// Order the results by score (in opposite order of appearance, since the -// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. -const _orderResultsByScoreThenName = (a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings -} - -/** - * Search Module - */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, - - htmlToText: (htmlString, anchor) => { - const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - for (const removalQuery of [".headerlink", "script", "style"]) { - htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); - } - if (anchor) { - const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); - if (anchorContent) return anchorContent.textContent; - - console.warn( - `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` - ); - } - - // if anchor not specified or not found, fall back to main content - const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent) return docContent.textContent; - - console.warn( - "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." - ); - return ""; - }, - - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); - }, - - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), - - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); - } - }, - - hasIndex: () => Search._index !== null, - - deferQuery: (query) => (Search._queued_query = query), - - stopPulse: () => (Search._pulse_status = -1), - - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { - Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch: (query) => { - // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.classList.add("search"); - - const out = document.getElementById("search-results"); - Search.title = out.appendChild(searchText); - Search.dots = Search.title.appendChild(document.createElement("span")); - Search.status = out.appendChild(searchSummary); - Search.output = out.appendChild(searchList); - - const searchProgress = document.getElementById("search-progress"); - // Some themes don't use the search progress node - if (searchProgress) { - searchProgress.innerText = _("Preparing search..."); - } - Search.startPulse(); - - // index already loaded, the browser was quick! - if (Search.hasIndex()) Search.query(query); - else Search.deferQuery(query); - }, - - _parseQuery: (query) => { - // stem the search terms and add them to the correct list - const stemmer = new Stemmer(); - const searchTerms = new Set(); - const excludedTerms = new Set(); - const highlightTerms = new Set(); - const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); - splitQuery(query.trim()).forEach((queryTerm) => { - const queryTermLower = queryTerm.toLowerCase(); - - // maybe skip this "word" - // stopwords array is from language_data.js - if ( - stopwords.indexOf(queryTermLower) !== -1 || - queryTerm.match(/^\d+$/) - ) - return; - - // stem the word - let word = stemmer.stemWord(queryTermLower); - // select the correct list - if (word[0] === "-") excludedTerms.add(word.substr(1)); - else { - searchTerms.add(word); - highlightTerms.add(queryTermLower); - } - }); - - if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js - localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) - } - - // console.debug("SEARCH: searching for:"); - // console.info("required: ", [...searchTerms]); - // console.info("excluded: ", [...excludedTerms]); - - return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; - }, - - /** - * execute search (requires search index to be loaded) - */ - _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // Collect multiple result groups to be sorted separately and then ordered. - // Each is an array of [docname, title, anchor, descr, score, filename]. - const normalResults = []; - const nonMainIndexResults = []; - - _removeChildren(document.getElementById("search-progress")); - - const queryLower = query.toLowerCase().trim(); - for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { - for (const [file, id] of foundTitles) { - const score = Math.round(Scorer.title * queryLower.length / title.length); - const boost = titles[file] === title ? 1 : 0; // add a boost for document titles - normalResults.push([ - docNames[file], - titles[file] !== title ? `${titles[file]} > ${title}` : title, - id !== null ? "#" + id : "", - null, - score + boost, - filenames[file], - ]); - } - } - } - - // search for explicit entries in index directives - for (const [entry, foundEntries] of Object.entries(indexEntries)) { - if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id, isMain] of foundEntries) { - const score = Math.round(100 * queryLower.length / entry.length); - const result = [ - docNames[file], - titles[file], - id ? "#" + id : "", - null, - score, - filenames[file], - ]; - if (isMain) { - normalResults.push(result); - } else { - nonMainIndexResults.push(result); - } - } - } - } - - // lookup as object - objectTerms.forEach((term) => - normalResults.push(...Search.performObjectSearch(term, objectTerms)) - ); - - // lookup as search terms in fulltext - normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); - - // let the scorer override scores with a custom scoring function - if (Scorer.score) { - normalResults.forEach((item) => (item[4] = Scorer.score(item))); - nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); - } - - // Sort each group of results by score and then alphabetically by name. - normalResults.sort(_orderResultsByScoreThenName); - nonMainIndexResults.sort(_orderResultsByScoreThenName); - - // Combine the result groups in (reverse) order. - // Non-main index entries are typically arbitrary cross-references, - // so display them after other results. - let results = [...nonMainIndexResults, ...normalResults]; - - // remove duplicate search results - // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept - let seen = new Set(); - results = results.reverse().reduce((acc, result) => { - let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); - if (!seen.has(resultStr)) { - acc.push(result); - seen.add(resultStr); - } - return acc; - }, []); - - return results.reverse(); - }, - - query: (query) => { - const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); - const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); - - // for debugging - //Search.lastresults = results.slice(); // a copy - // console.info("search results:", Search.lastresults); - - // print the results - _displayNextItem(results, results.length, searchTerms, highlightTerms); - }, - - /** - * search for object names - */ - performObjectSearch: (object, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const objects = Search._index.objects; - const objNames = Search._index.objnames; - const titles = Search._index.titles; - - const results = []; - - const objectSearchCallback = (prefix, match) => { - const name = match[4] - const fullname = (prefix ? prefix + "." : "") + name; - const fullnameLower = fullname.toLowerCase(); - if (fullnameLower.indexOf(object) < 0) return; - - let score = 0; - const parts = fullnameLower.split("."); - - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower === object || parts.slice(-1)[0] === object) - score += Scorer.objNameMatch; - else if (parts.slice(-1)[0].indexOf(object) > -1) - score += Scorer.objPartialMatch; // matches in last name - - const objName = objNames[match[1]][2]; - const title = titles[match[0]]; - - // If more than one term searched for, we require other words to be - // found in the name/title/description - const otherTerms = new Set(objectTerms); - otherTerms.delete(object); - if (otherTerms.size > 0) { - const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); - if ( - [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) - ) - return; - } - - let anchor = match[3]; - if (anchor === "") anchor = fullname; - else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; - - const descr = objName + _(", in ") + title; - - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) - score += Scorer.objPrio[match[2]]; - else score += Scorer.objPrioDefault; - - results.push([ - docNames[match[0]], - fullname, - "#" + anchor, - descr, - score, - filenames[match[0]], - ]); - }; - Object.keys(objects).forEach((prefix) => - objects[prefix].forEach((array) => - objectSearchCallback(prefix, array) - ) - ); - return results; - }, - - /** - * search for full-text terms in the index - */ - performTermsSearch: (searchTerms, excludedTerms) => { - // prepare search - const terms = Search._index.terms; - const titleTerms = Search._index.titleterms; - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - - const scoreMap = new Map(); - const fileMap = new Map(); - - // perform the search on the required terms - searchTerms.forEach((word) => { - const files = []; - const arr = [ - { files: terms[word], score: Scorer.term }, - { files: titleTerms[word], score: Scorer.title }, - ]; - // add support for partial matches - if (word.length > 2) { - const escapedWord = _escapeRegExp(word); - if (!terms.hasOwnProperty(word)) { - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - } - if (!titleTerms.hasOwnProperty(word)) { - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); - }); - } - } - - // no match but word was a required one - if (arr.every((record) => record.files === undefined)) return; - - // found search word in contents - arr.forEach((record) => { - if (record.files === undefined) return; - - let recordFiles = record.files; - if (recordFiles.length === undefined) recordFiles = [recordFiles]; - files.push(...recordFiles); - - // set score for the word in each file - recordFiles.forEach((file) => { - if (!scoreMap.has(file)) scoreMap.set(file, {}); - scoreMap.get(file)[word] = record.score; - }); - }); - - // create the mapping - files.forEach((file) => { - if (!fileMap.has(file)) fileMap.set(file, [word]); - else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); - }); - }); - - // now check if the files don't contain excluded terms - const results = []; - for (const [file, wordList] of fileMap) { - // check if all requirements are matched - - // as search terms with length < 3 are discarded - const filteredTermCount = [...searchTerms].filter( - (term) => term.length > 2 - ).length; - if ( - wordList.length !== searchTerms.size && - wordList.length !== filteredTermCount - ) - continue; - - // ensure that none of the excluded terms is in the search result - if ( - [...excludedTerms].some( - (term) => - terms[term] === file || - titleTerms[term] === file || - (terms[term] || []).includes(file) || - (titleTerms[term] || []).includes(file) - ) - ) - break; - - // select one (max) score for the file. - const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); - // add result to the result list - results.push([ - docNames[file], - titles[file], - "", - null, - score, - filenames[file], - ]); - } - return results; - }, - - /** - * helper function to return a node containing the - * search summary for a given text. keywords is a list - * of stemmed words. - */ - makeSearchSummary: (htmlText, keywords, anchor) => { - const text = Search.htmlToText(htmlText, anchor); - if (text === "") return null; - - const textLower = text.toLowerCase(); - const actualStartPosition = [...keywords] - .map((k) => textLower.indexOf(k.toLowerCase())) - .filter((i) => i > -1) - .slice(-1)[0]; - const startWithContext = Math.max(actualStartPosition - 120, 0); - - const top = startWithContext === 0 ? "" : "..."; - const tail = startWithContext + 240 < text.length ? "..." : ""; - - let summary = document.createElement("p"); - summary.classList.add("context"); - summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - - return summary; - }, -}; - -_ready(Search.init); diff --git a/docs/_build/html/_static/sphinx_highlight.js b/docs/_build/html/_static/sphinx_highlight.js deleted file mode 100644 index 8a96c69..0000000 --- a/docs/_build/html/_static/sphinx_highlight.js +++ /dev/null @@ -1,154 +0,0 @@ -/* Highlighting utilities for Sphinx HTML documentation. */ -"use strict"; - -const SPHINX_HIGHLIGHT_ENABLED = true - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - const rest = document.createTextNode(val.substr(pos + text.length)); - parent.insertBefore( - span, - parent.insertBefore( - rest, - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - /* There may be more occurrences of search term in this node. So call this - * function recursively on the remaining fragment. - */ - _highlight(rest, addItems, text, className); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - -/** - * Small JavaScript module for the documentation. - */ -const SphinxHighlight = { - - /** - * highlight the search words provided in localstorage in the text - */ - highlightSearchWords: () => { - if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight - - // get and clear terms from localstorage - const url = new URL(window.location); - const highlight = - localStorage.getItem("sphinx_highlight_terms") - || url.searchParams.get("highlight") - || ""; - localStorage.removeItem("sphinx_highlight_terms") - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - - // get individual terms from highlight string - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - _("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(() => { - /* Do not call highlightSearchWords() when we are on the search page. - * It will highlight words from the *previous* search query. - */ - if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); - SphinxHighlight.initEscapeListener(); -}); diff --git a/docs/_build/html/chapters/installation.html b/docs/_build/html/chapters/installation.html deleted file mode 100644 index 858426e..0000000 --- a/docs/_build/html/chapters/installation.html +++ /dev/null @@ -1,156 +0,0 @@ - - - - - - - Installation — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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- -
-

Installation

-
-

System requires

-

The software package can be installed and runned on Linux, Windows, and MacOS

-

Dependency of Python and Python packages:

-
python == 3.9
-numpy == 1.26.1
-scipy == 1.11.4
-torch == 1.13.1
-tqdm == 4.66.1
-
-
-
-

Note

-
    -

  1. Versions that has been previously tested on are also listed below, other versions should work the sameersions that has been previously tested on are listed above, other versions should work the same.

    2. -

  2. The required python packages with the latest versions will be automatically installed if these python packages are not already present in your local Python environment.

    3. -
-
-
-
-

Installation for source

-
    -

  1. Download and install the latest Anaconda distribution:

    2. -
-
wget https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Linux-x86_64.sh
-./Anaconda3-2024.06-1-Linux-x86_64.sh
-
-
-
    -

  1. Create a new conda virtual environment and install the ts-dart source code locally:

    2. -
-
conda create -n ts-dart python=3.9
-conda activate ts-dart
-git clone https://github.com/xuhuihuang/ts-dart.git
-python -m pip install ./ts-dart
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-
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- - - - \ No newline at end of file diff --git a/docs/_build/html/chapters/intro.html b/docs/_build/html/chapters/intro.html deleted file mode 100644 index a7560c3..0000000 --- a/docs/_build/html/chapters/intro.html +++ /dev/null @@ -1,127 +0,0 @@ - - - - - - - Brief intro — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - - - -
- - -
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Brief intro

-

TS-DART: Transition State identification via Dispersion and vAriational principle Regularized neural neTworks

-
-

Abstract

-

Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous fundamental biological processes. Markov state models (MSMs) constructed from Molecular Dynamics (MD) simulations have demonstrated considerable success in studying protein conformational changes, which are often associated with rare events transiting over free energy barriers. However, it remains challenging for MSMs to identify the transition states, as they group MD conformations into discrete metastable states and do not provide information on transition states lying at the top of free energy barriers between metastable states. Inspired by recent advances in trustworthy artificial intelligence (AI) for detecting out-of-distribution (OOD) data, we present Transition State identification via Dispersion and vAriational principle Regularized neural neTworks (TS-DART). This deep learning approach effectively detects the transition states from MD simulations using hyperspherical embeddings in the latent space. The key insight of TS-DART is to treat the transition state structures as OOD data, recognizing that the transition states are less populated and exhibit a distributional shift from metastable states. Our TS-DART method offers an end-to-end pipeline for identifying transition states from MD simulations. By introducing a dispersion loss function to regularize the hyperspherical latent space, TS-DART can discern transition state conformations that separate multiple metastable states in an MSM. Furthermore, TS-DART provides hyperspherical latent representations that preserve all relevant kinetic geometries of the original dynamics. We demonstrate the power of TS-DART by applying it to a 2D-potential, alanine dipeptide and the translocation of a DNA motor protein on DNA. In all these systems, TS-DART outperforms previous methods in identifying transition states. As TS-DART integrates the dimensionality reduction, state decomposition, and transition state identification in a unified framework, we anticipate that it will be applicable for studying transition states of protein conformational changes.

-
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Illustration

-../_images/fig1.png -
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- - - - \ No newline at end of file diff --git a/docs/_build/html/chapters/tutorials.html b/docs/_build/html/chapters/tutorials.html deleted file mode 100644 index a50fdb9..0000000 --- a/docs/_build/html/chapters/tutorials.html +++ /dev/null @@ -1,165 +0,0 @@ - - - - - - - Tutorials — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - - - -
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Tutorials

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Jupyter notebook examples

- - - - - -
- - Example on muller potential - Thumbnail -
- - 		- - Example on quadruple-well potential - Thumbnail -
- -
-
-

Start with python script

-
python ./ts-dart/scripts/train_tsdart.py \
-    --seed 1 \
-    --device 'cpu' \
-    --lag_time 10 \
-    --encoder_sizes 2 20 20 20 10 2 \
-    --feat_dim 2 \
-    --n_states 2 \
-    --beta 0.01 \
-    --gamma 1 \
-    --proto_update_factor 0.5 \
-    --scaling_temperature 0.1 \
-    --learning_rate 0.001 \
-    --pretrain 10 \
-    --n_epochs 20 \
-    --train_split 0.9 \
-    --train_batch_size 1000 \
-    --data_directory ./ts-dart/data/quadruple-well \
-    --saving_directory .
-
-
-

Or

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sh ./ts-dart/scripts/train_tsdart.sh
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- - - - \ No newline at end of file diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html deleted file mode 100644 index 79f210d..0000000 --- a/docs/_build/html/genindex.html +++ /dev/null @@ -1,340 +0,0 @@ - - - - - - Index — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
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    • -
  • Index
    • -
  • -
    • -
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- - -

Index

- -
- C - | D - | E - | F - | L - | M - | O - | P - | S - | T - | V - -
-

C

- - - -
- -

D

- - -
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E

- - - -
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F

- - - -
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L

- - -
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M

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O

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P

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S

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- - - - \ No newline at end of file diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html deleted file mode 100644 index 73c3261..0000000 --- a/docs/_build/html/index.html +++ /dev/null @@ -1,140 +0,0 @@ - - - - - - - TS-DART documentation — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

TS-DART documentation

-

TS-DART identifies transition states of protein conformational changes from MD simulations via out-of-distribution detection (OOD) in the hyperspherical latent space.

-
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Contents:

- -
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- - -
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-
- - - - \ No newline at end of file diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv deleted file mode 100644 index 9394c2a..0000000 Binary files a/docs/_build/html/objects.inv and /dev/null differ diff --git a/docs/_build/html/py-modindex.html b/docs/_build/html/py-modindex.html deleted file mode 100644 index d72adae..0000000 --- a/docs/_build/html/py-modindex.html +++ /dev/null @@ -1,145 +0,0 @@ - - - - - - Python Module Index — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- - - - \ No newline at end of file diff --git a/docs/_build/html/search.html b/docs/_build/html/search.html deleted file mode 100644 index 562d31b..0000000 --- a/docs/_build/html/search.html +++ /dev/null @@ -1,125 +0,0 @@ - - - - - - Search — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - -
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- - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js deleted file mode 100644 index 4508d22..0000000 --- a/docs/_build/html/searchindex.js +++ /dev/null @@ -1 +0,0 @@ -Search.setIndex({"alltitles": {"2 states model": [[0, "states-model"], [1, "states-model"]], "3 states model": [[0, "id1"], [1, "id1"]], "4 states model": [[1, "id2"]], "Abstract": [[3, "abstract"]], "Brief intro": [[3, null]], "Contents:": [[5, null]], "Create dataset": [[0, "create-dataset"], [1, "create-dataset"]], "Illustration": [[3, "illustration"]], "Installation": [[2, null]], "Installation for source": [[2, "installation-for-source"]], "Jupyter notebook examples": [[4, "jupyter-notebook-examples"]], "Muller potential": [[0, null]], "Parameters": [[7, "parameters"], [7, "id1"], [7, "id2"], [7, "id4"], [7, "id5"], [7, "id7"], [7, "id8"], [7, "id10"], [7, "id11"], [7, "id13"], [7, "id14"], [7, "id16"], [7, "id17"], [7, "id19"], [7, "id20"], [7, 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b/docs/_build/html/source/modules.html deleted file mode 100644 index 78522fb..0000000 --- a/docs/_build/html/source/modules.html +++ /dev/null @@ -1,187 +0,0 @@ - - - - - - - tsdart — TS-DART 1.0.0 documentation - - - - - - - - - - - - - - - - - -
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tsdart

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tsdart package

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tsdart.dataprocessing module

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-class tsdart.dataprocessing.Preprocessing(dtype=<class 'numpy.float32'>)
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Preprocess the original trajectories to create datasets for training.

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dtype : dtype, default = np.float32

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-create_dataset(data, lag_time)
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Create the dataset as the input to TS-DART.

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Parameters

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datalist or ndarray

The original trajectories.

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lag_timeint

The lag_time used to create the dataset consisting of time-instant and time-lagged data.

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Returns

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datasetlist

List of tuples: the length of the list represents the number of data. -Each tuple has two elements: one is the instantaneous data frame, the other is the corresponding time-lagged data frame.

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-transform2pw(data)
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Transform xyz coordinates data to pairwise distances data.

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Parameters

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datalist or ndarray

xyz coordinates data, shape of each traj [num_frames,num_atoms,3].

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Returns

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pw_datalist or ndarray

Pairwise distances data.

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tsdart.loss module

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-class tsdart.loss.DisLoss(feat_dim, n_states, device, proto_update_factor=0.5, scaling_temperature=0.1)
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Bases: Module

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Compute dispersion loss.

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feat_dimint

The dimension of the euclidean space where the latent hypersphere is embedded. -The dimension of latent hypersphere is (feat_dim-1).

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n_statesint

Number of metastable states to be specified.

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devicetorch.device

The device on which the torch modules are executed.

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proto_update_factorfloat, default = 0.5

The state center update factor.

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scaling_temperaturefloat, default = 0.1

The scaling hyperparameter to compute dispersion loss.

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-forward(features, labels)
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Compute dispersion loss.

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featurestorch.Tensor

Hyperspherical embeddings of a batch of data.

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labelstorch.Tensor

Metastable states of a batch of data.

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-
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-

Returns

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losstorch.Tensor

Dispersion loss

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-
-
-
- -
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-output_mean_score()
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- -
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-save()
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- -
-
- -
-
-class tsdart.loss.Prototypes(n_states, device, scaling_temperature=0.1)
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Bases: Module

-

Compute the prototypes (state center vectors). Used for evaluating validation data.

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-

Parameters

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n_statesint

Number of metastable states to be specified.

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devicetorch.device

The device on which the torch modules are executed.

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scaling_temperaturefloat, default = 0.1

The scaling hyperparameter to compute dispersion loss.

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-
-clear()
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- -
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-forward(features, labels)
-

Compute dispersion loss.

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-

Parameters

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featurestorch.Tensor

Hyperspherical embeddings of a batch of data.

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labelstorch.Tensor

Metastable states of a batch of data.

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-
-
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-

Returns

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prototypestorch.Tensor

State center vectors of shape [n_states, feat_dim].

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-
-
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- -
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-output_mean_disloss()
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- -
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-output_mean_prototypes()
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- -
-
- -
-
-class tsdart.loss.VAMPLoss(epsilon=1e-06, mode='regularize', symmetrized=False)
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Bases: Module

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Compute VAMP2 loss.

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Parameters

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epsilonfloat, default = 1e-6

The regularization/trunction parameters for eigenvalues.

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modestr, default = ‘regularize’

‘regularize’: regularize the eigenvalues by adding epsilon. -‘trunc’: truncate the eigenvalues by filtering out the eigenvalues below epsilon.

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symmetrizedboolean, default = False

Whether to symmetrize time-correlation matrices or not.

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-
-clear()
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- -
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-forward(data)
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Compute VAMP2 loss.

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-

Parameters

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datatuple

Softmax probabilities of batch of transition pairs.

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-
-
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Returns

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VAMP2 loss

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-
- -
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-output_mean_score()
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- -
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-save()
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- -
-
- -
-
-

tsdart.model module

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-class tsdart.model.TSDART(lobe, optimizer='Adam', device=None, learning_rate=0.001, epsilon=1e-06, mode='regularize', symmetrized=False, dtype=<class 'numpy.float32'>, feat_dim=2, n_states=4, proto_update_factor=0.5, scaling_temperature=0.1, beta=0.01, save_model_interval=None, pretrain=0, print=False)
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Bases: object

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The method used to train TS-DART.

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-

Parameters

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datalist or ndarray

The original trajectories.

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optimizerstr, default = ‘Adam’

The type of optimizer used for training.

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devicetorch.device, default = None

The device on which the torch modules are executed.

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learning_ratefloat, default = 1e-3

The learning rate of the optimizer.

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epsilonfloat, default = 1e-6

The strength of the regularization/truncation under which matrices are inverted.

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modestr, default = ‘regularize’

‘regularize’: regularize the eigenvalues by adding epsilon. -‘trunc’: truncate the eigenvalues by filtering out the eigenvalues below epsilon.

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symmetrizedboolean, default = False

Whether to symmetrize time-correlation matrices or not.

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dtypedtype, default = np.float32

The data type of the input data and the parameters of the model.

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feat_dimint, default = 2

The dimension of the euclidean space where the latent hypersphere is embedded. -The dimension of latent hypersphere is (feat_dim-1).

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n_statesint, default = 4

Number of metastable states to be specified.

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proto_update_factorfloat, default = 0.5

The state center update factor.

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scaling_temperaturefloat, default = 0.1

The scaling hyperparameter to compute dispersion loss.

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betafloat, default = 0.01

The weight of dispersion loss.

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save_model_intervalint, default = None

Saving the model every ‘save_model_interval’ epochs.

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pretrainint, default = 0

The number of epochs of the pretraining with pure VAMP2 loss.

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printboolean, default = False

Whether to print the validation loss every epoch during the training.

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-fetch_model()
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- -
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-fit(train_loader, n_epochs=1, validation_loader=None, progress=<class 'tqdm.std.tqdm'>)
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Performs fit on data.

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Parameters

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train_loadertorch.utils.data.DataLoader

Yield a tuple of batches representing instantaneous and time-lagged samples for training.

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n_epochsint, default=1

The number of epochs to use for training. -Note that n_epochs should be larger than pretrain.

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validation_loadertorch.utils.data.DataLoader, optional, default=None

Yield a tuple of batches representing instantaneous and time-lagged samples for validation.

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-

progress : context manager, default=tqdm

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-
-

Returns

-

self : TSDART

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-
- -
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-partial_fit(data)
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- -
-
-property training_dis
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- -
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-property training_vamp
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- -
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-validate(val_data)
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- -
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-property validation_dis
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- -
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-property validation_prototypes
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- -
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-property validation_vamp
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- -
-
- -
-
-class tsdart.model.TSDARTEstimator(tsdart_model: TSDARTModel)
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Bases: object

-

The TS-DART estimator the generate the state center vectors and ood scores of original trajectories.

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-

Parameters

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tsdart_modelTSDARTModel

The trained TS-DART model.

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-fit(data)
-

Fit the TS-DART model with original trajectories to compute OOD scores and state center vectors.

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-

Parameters

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datalist or ndarray

The original trajectories.

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-

Returns

-

self : TSDARTEstimator

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- -
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-property ood_scores
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- -
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-property state_centers
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- -
-
- -
-
-class tsdart.model.TSDARTLayer(layer_sizes: list, n_states: int, scale=1)
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Bases: Module

-

Create TS-DART lobe.

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-

Parameters

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layer_sizeslist

The size of each layer of the encoder. -The last component should represent the dimension of the euclidean space where the latent hypersphere is embedded.

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n_statesint

Number of metastable states to be specified.

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scaleint, default = 1

The radius of the hypersphere.

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-
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-forward(x)
-

Defines the computation performed at every call.

-

Should be overridden by all subclasses.

-
-

Note

-

Although the recipe for forward pass needs to be defined within -this function, one should call the Module instance afterwards -instead of this since the former takes care of running the -registered hooks while the latter silently ignores them.

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-
- -
-
- -
-
-class tsdart.model.TSDARTModel(lobe, device=None, dtype=<class 'numpy.float32'>)
-

Bases: object

-

The TS-DART model from TS-DART.

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-

Parameters

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lobetorch.nn.Module

TS-DART lobe.

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devicetorch device, default = None

The device on which the torch modules are executed.

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dtypedtype, default = np.float32

The data type of the input data and the parameters of the model.

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-
-
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-property lobe
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- -
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-transform(data, return_type='probs')
-

Transform the original trajectores to different outputs after training.

-
-

Parameters

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-
datalist or ndarray

The original trajectories.

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return_typestring

‘probs’: the softmax probabilties to assign each conformation to a metastable state. -‘states’: the metastable state assignments of each conformation. -‘hypersphere_embs’: the hyperspherical embeddings of each conformation.

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-
-
-
- -
-
- -
-
-

tsdart.utils module

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-tsdart.utils.calculate_inverse(matrix, epsilon=1e-06, return_sqrt=False, mode='regularize')
-

This method can be applied to compute the inverse or the square-root of the inverse of the matrix, -this method will be further used to estimate koopman matrix.

-
-

Parameters

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matrixtorch.Tensor

The matrix to be inverted.

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-
epsilonfloat, default = 1e-6

The regularization/trunction parameters for eigenvalues.

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return_sqrtboolean, optional, default = False

If True, the square root of the inverse matrix is returned instead.

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modestr, default = ‘regularize’

‘regularize’: regularize the eigenvalues by adding epsilon. -‘trunc’: truncate the eigenvalues by filtering out the eigenvalues below epsilon.

-
-
-
-
-

Returns

-
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inversetorch.Tensor

Inverse of the matrix.

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-
-
-
- -
-
-tsdart.utils.compute_covariance_matrix(x: Tensor, y: Tensor, remove_mean=True)
-

This method can be applied to compute the covariance matrix from two batches of data.

-
-

Parameters

-
-
xtorch.Tensor

The first batch of data of shape [batch_size, num_basis].

-
-
ytorch.Tensor

The second batch of data of shape [batch_size, num_basis].

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-
remove_meanboolean, optional, default = True

Whether to remove mean of the data.

-
-
-
-
-

Returns

-
-
(cov_00, cov_01, cov11)Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

Instantaneous covariance matrix of x, time-lagged covariance matrix of x and y, -and instantaneous covariance matrix of y.

-
-
-
-
- -
-
-tsdart.utils.eig_decomposition(matrix, epsilon=1e-06, mode='regularize')
-

This method can be applied to do the eig-decomposition for a rank deficient hermetian matrix, -this method will be further used to estimate koopman matrix.

-
-

Parameters

-
-
matrixtorch.Tensor

The hermitian matrix: specifically, the covariance matrix.

-
-
epsilonfloat, default = 1e-6

The regularization/trunction parameters for eigenvalues.

-
-
modestr, default = ‘regularize’

‘regularize’: regularize the eigenvalues by adding epsilon. -‘trunc’: truncate the eigenvalues by filtering out the eigenvalues below epsilon.

-
-
-
-
-

Returns

-
-
(eigval, eigvec)Tuple[torch.Tensor, torch.Tensor]

Eigenvalues and eigenvectors.

-
-
-
-
- -
-
-tsdart.utils.estimate_koopman_matrix(data: Tensor, data_lagged: Tensor, epsilon=1e-06, mode='regularize', symmetrized=False)
-

This method can be applied to compute the koopman matrix from time-instant and time-lagged data.

-
-

Parameters

-
-
datatorch.Tensor

The time-instant data of shape [batch_size, num_basis].

-
-
data_laggedtorch.Tensor

The time-lagged data of shape [batch_size, num_basis].

-
-
epsilonfloat, default = 1e-6

The regularization/trunction parameters for eigenvalues.

-
-
modestr, default = ‘regularize’

‘regularize’: regularize the eigenvalues by adding epsilon. -‘trunc’: truncate the eigenvalues by filtering out the eigenvalues below epsilon.

-
-
symmetrizedboolean, default = False

Whether to symmetrize time-correlation matrices or not.

-
-
-
-
-

Returns

-
-
koopman_matrixtorch.Tensor

The koopman matrix of shape [num_basis, num_basis].

-
-
-
-
- -
-
-tsdart.utils.map_data(data, device=None, dtype=<class 'numpy.float32'>)
-

This function is used to yield the torch.Tensor type data from multiple trajectories.

-
-

Parameters

-
-
datalist or tuple or ndarray

The trajectories of data.

-
-
devicetorch device, default = None

The device on which the torch modules are executed.

-
-
dtypedtype, default = np.float32

The data type of the input data and the parameters of the model.

-
-
-
-
-

Yields

-
-
xtorch.Tensor

The mapped data.

-
-
-
-
- -
-
-tsdart.utils.set_random_seed(seed)
-
- -
-
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-
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