keyword stringclasses 7
values | repo_name stringlengths 8 98 | file_path stringlengths 4 244 | file_extension stringclasses 29
values | file_size int64 0 84.1M | line_count int64 0 1.6M | content stringlengths 1 84.1M ⌀ | language stringclasses 14
values |
|---|---|---|---|---|---|---|---|
3D | aafkegros/MicroscopyNodes | microscopynodes/min_nodes/nodeScaleBox.py | .py | 10,077 | 231 | import bpy
import numpy as np
from .nodesBoolmultiplex import axes_demultiplexer_node_group
#initialize scalebox node group
# Arguably should be refactored to a rescaled primitive cube -> subdivide modifier -> set pos to max -> merge by distance
# Might be simpler than separate mesh grids, and have less redundancy
de... | Python |
3D | aafkegros/MicroscopyNodes | microscopynodes/min_nodes/nodesBoolmultiplex.py | .py | 4,415 | 103 | import bpy
def axes_multiplexer_node_group():
node_group = bpy.data.node_groups.get("multiplex_axes")
if node_group:
return node_group
node_group= bpy.data.node_groups.new(type = 'GeometryNodeTree', name = "multiplex_axes")
links = node_group.links
interface = node_group.interface
... | Python |
3D | aafkegros/MicroscopyNodes | microscopynodes/min_nodes/shader_nodes/nodeRemapObjectID.py | .py | 2,706 | 63 | import bpy
def remap_oid_node():
node_group = bpy.data.node_groups.get("Labelmask Remap Switch")
if node_group:
return node_group
node_group= bpy.data.node_groups.new(type = 'ShaderNodeTree', name = "Labelmask Remap Switch")
links = node_group.links
interface = node_group.interface
int... | Python |
3D | aafkegros/MicroscopyNodes | microscopynodes/min_nodes/shader_nodes/cmap_menus.py | .py | 3,149 | 76 | import bpy
import numpy as np
import cmap
CMAP_CATEGORIES = {
"sequential": "IPO_LINEAR",
"diverging": "LINCURVE",
"cyclic" : "MESH_CIRCLE",
"qualitative":"OUTLINER_DATA_POINTCLOUD",
"miscellaneous":"ADD",
}
def cmap_submenu_class(op, opname, category, namespace=None):
def draw(self... | Python |
3D | aafkegros/MicroscopyNodes | microscopynodes/min_nodes/shader_nodes/__init__.py | .py | 1,397 | 37 | from . import cmap_menus
from .nodeVolumeAlpha import volume_alpha_node
from .handle_cmap import set_color_ramp_from_ch, get_lut
from .nodeRemapObjectID import remap_oid_node
from . import ops
import bpy
class MIN_MT_CMAP_ADD(bpy.types.Menu):
bl_idname = "MIN_MT_CMAP_ADD"
bl_label = "Add LUT"
def draw(se... | Python |
3D | aafkegros/MicroscopyNodes | microscopynodes/min_nodes/shader_nodes/ops.py | .py | 4,179 | 113 | import bpy
from bpy.types import Context, Operator
from .handle_cmap import get_lut, set_color_ramp
from bpy.props import (StringProperty, FloatProperty,
PointerProperty, IntProperty,
BoolProperty, EnumProperty
)
# from .nodeCmap import cmap_node
c... | Python |
3D | aafkegros/MicroscopyNodes | microscopynodes/min_nodes/shader_nodes/handle_cmap.py | .py | 1,376 | 37 | import bpy
import cmap
def set_color_ramp_from_ch(ch, ramp_node):
lut, linear = get_lut(ch['cmap'], ch['single_color'])
set_color_ramp(ramp_node, lut, linear, ch['cmap'])
return
def set_color_ramp(ramp_node, lut, linear, name):
from ...ui.preferences import addon_preferences
if addon_preferences(... | Python |
3D | aafkegros/MicroscopyNodes | microscopynodes/min_nodes/shader_nodes/nodeVolumeAlpha.py | .py | 3,266 | 71 | import bpy
from .nodeIgnoreExtremes import ignore_extremes_node_group
import cmap
def volume_alpha_node():
node_group = bpy.data.node_groups.get("Volume Transparency")
if node_group:
return node_group
node_group= bpy.data.node_groups.new(type = 'ShaderNodeTree', name = "Volume Transparency")
l... | Python |
3D | aafkegros/MicroscopyNodes | microscopynodes/min_nodes/shader_nodes/nodeIgnoreExtremes.py | .py | 2,504 | 69 | import bpy
def ignore_extremes_node_group():
node_group = bpy.data.node_groups.get("Ignore Extremes")
if node_group:
return node_group
node_group= bpy.data.node_groups.new(type = 'ShaderNodeTree', name = "Ignore Extremes")
links = node_group.links
interface = node_group.interface
... | Python |
3D | aafkegros/MicroscopyNodes | docs/faq.md | .md | 440 | 8 | # Help and Contact
The main venue for **Usage questions** is the
{: style="height:15px"} [image.sc forum](https://forum.image.sc/tag/microscopy-nodes) and you can also search here for previous questions.
If you've found a **bug** (or suspect something even a little bit of b... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/outdated.md | .md | 1,045 | 24 | # Installing and using Microscopy Nodes with Blender < 4.2
## Install
- Download an appropriate microscopynodes/tif2blender zip file from the [releases page](https://github.com/oanegros/microscopynodes/releases). Please note the Blender version number.
Start blender.
Install the `microscopynodes` Add-On:
- In Blend... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/index.md | .md | 165 | 4 | <meta http-equiv="refresh" content="0; url=./tutorials/1_start/" />
If you are not redirected, [click here to start with the first tutorial](tutorials/1_start.md).
| Markdown |
3D | aafkegros/MicroscopyNodes | docs/overview.md | .md | 2,021 | 39 | # Microscopy in Blender
`Microscopy Nodes` is a Blender add-on that incorporates bioimage support for the open-source software blender. {{ svg('microscopy_nodes') }} Microscopy Nodes simplifies loading bioimage (tif/zarr) files as volumetric objects in Blender.
Please make some pretty figures with this add-on!
For... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/clean_svg.py | .py | 1,084 | 32 | import xml.etree.ElementTree as ET
import re
from svgpathtools import parse_path, Path
import sys
def clean_and_scale_svg(input_path, output_path, scale=0.01):
ET.register_namespace('', "http://www.w3.org/2000/svg")
tree = ET.parse(input_path)
root = tree.getroot()
for elem in root.findall(".//*"):
... | Python |
3D | aafkegros/MicroscopyNodes | docs/tutorials/preferences.md | .md | 1,219 | 24 | # Preferences / Customization
The {{ svg("microscopy_nodes") }} Microscopy Nodes addon has {{ svg("preferences") }} **Preferences** to allow for a custom experience and defaults.
You can find these under `Edit > Preferences > Add-ons > Microscopy Nodes`.
Her... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/tutorials/large_data.md | .md | 4,016 | 74 | # Large data
Microscopy data is often very large, {{ svg("microscopy_nodes") }} Microscopy Nodes has some strategies to deal with this. These depend on the size of the data, the shape of the data, and your computational resources (and skills). The key of this is working at a smaller scale, and then **reloading** to la... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/tutorials/1_start.md | .md | 6,347 | 111 | # 1. First Use
## **Installing Microscopy Nodes**
{{ youtube("BFMX0Dk5rIw", 360, 200) }}
1. Open Blender.
2. Navigate to `Edit > Preferences`.
3. In the Add-ons tab, search for `Microscopy Nodes`.
4. Click **Install** to download and enable the add-on.
## **Blender Interface Overview**
The Blender interface is ver... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/tutorials/rendering.md | .md | 4,343 | 75 | # Rendering
There are a lot of extra parameters that can be adjusted to optimize rendering in Blender. All of these are explained in the {{ svg("blender") }} [Blender manual](https://docs.blender.org/manual/en/latest/render/cycles/render_settings/index.html). Some, however, are especially useful to know for microscopy... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/tutorials/2_loading_data.md | .md | 6,250 | 136 | # Loading microscopy data
## Video tutorials
The **Fluorescence** tutorial shows how to load *emissive* data, and the **EM** tutorial shows how to load *scattering data*, these settings can be good to interchange!
{{ youtube("lroStEHiPV8", 280, 158) }}
{{ youtube("Rwq7Tu8Avss", 280, 158) }}
The **labelmask/surface... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/tutorials/ome_zarr_troubleshooting.md | .md | 1,750 | 26 | # OME-Zarr troubleshooting
[OME-Zarr](https://ngff.openmicroscopy.org/about/index.html) is a developing standard and is very flexible, which sometimes makes it hard to read and write, and no software supports all features.
{{ svg("microscopy_nodes") }} Microscopy Nodes supports OME-Zarr **up to version 0.5**, to loa... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/tutorials/5_creating_ouput.md | .md | 3,948 | 68 | # Creating output
Creating output from a scene in Blender is done by adding a {{ svg("view_camera") }} camera and pressing `Render > Render Image` or `Render > Render Animation` for the full animation. This writes images or movies to your [output folder](./rendering.md#output-location-and-format).
{{ youtube("jcERgoB... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/tutorials/3_objects.md | .md | 5,223 | 115 | # 3. Objects
Microscopy Nodes loads your microscopy data as different types of **objects**, depending on how you loaded each channel.
Each type of object is placed in a {{ svg("outliner_ob_empty") }} **holder** collection. The **Axes** and **Slice Cube** are alw... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/tutorials/surface_smoothing.md | .md | 1,060 | 26 | # Surface modification
After loading a {{ svg("outliner_data_pointcloud") }} labelmask or {{ svg("outliner_data_surface") }} surface, the geometry is often still quite jagged.
This can be edited through two techniques:
- changing the **mesh density** in the [preferences](./preferences.md) and reloading
- adding smo... | Markdown |
3D | aafkegros/MicroscopyNodes | docs/tutorials/4_shading.md | .md | 9,255 | 150 | # Shading
**Shading** encompasses the visualization of Blender's objects. The shading options can be found in two places:
- in the {{ svg("workspace") }} Shader Nodes workspace, find this in the {{ svg("topbar") }} topbar.
- in the {{ svg("material") }} material tab of the {{ svg("properties") }} properties.
These t... | Markdown |
3D | aafkegros/MicroscopyNodes | tests/test_load_types.py | .py | 648 | 24 | from .utils import *
import pytest
loadable = [['volume'],['surface'],['labelmask'], [], ['volume', 'surface'], 'mixed']
@pytest.mark.parametrize('load_as', loadable)
@pytest.mark.parametrize('arrtype', ['5D_5cube', '2D_5x10', '5D_nonrect'])
def test_loading_types(arrtype, load_as):
prep_load(arrtype)
for ch... | Python |
3D | aafkegros/MicroscopyNodes | tests/__init__.py | .py | 0 | 0 | null | Python |
3D | aafkegros/MicroscopyNodes | tests/utils.py | .py | 5,266 | 158 | import os
os.environ["MIN_TEST"] = "1"
import bpy
import yaml
from microscopynodes.handle_blender_structs import *
from microscopynodes.file_to_array import *
from microscopynodes.load_components import *
import microscopynodes
import numpy as np
import pytest
import tifffile
import platform
import imageio.v3 as iio
... | Python |
3D | aafkegros/MicroscopyNodes | tests/conftest.py | .py | 1,168 | 42 | import pytest
import bpy
import microscopynodes
import shutil, os
import gc, time
microscopynodes._test_register()
@pytest.hookimpl(trylast=True)
def pytest_sessionfinish(session, exitstatus):
import microscopynodes
# regrettably necessary, pytest segfaults if properties
# with callback functions stay ali... | Python |
3D | aafkegros/MicroscopyNodes | tests/test_zarr_reload.py | .py | 2,055 | 56 | from .utils import *
import pytest
@pytest.mark.parametrize('level', [None, 0, 1, 2])
def test_zarr(level):
prep_load()
bpy.context.scene.MiN_input_file = str(Path(test_folder).parent / 'test_data' / '5D_5cube.zarr')
if not level is None:
bpy.context.scene.MiN_selected_array_option = str(bpy.... | Python |
3D | ZhangLingMing1/TSGCNet | train.py | .py | 4,585 | 115 | from dataloader import plydataset
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
import time
import numpy as np
import os
from tensorboardX import SummaryWriter
from torch.autograd import Variable
from pathlib import Path
import torch.nn.functional as F
import datetime
import logging
from ut... | Python |
3D | ZhangLingMing1/TSGCNet | TSGCNet.py | .py | 10,340 | 293 | import os
import sys
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
from torch.autograd import Variable
def knn(x, k):
inner = -2 * torch.matmul(x.transpose(2, 1), x)
xx = torch.sum(x ** 2, dim=1, keepdim=True)
pairwise_distance = -... | Python |
3D | ZhangLingMing1/TSGCNet | utils.py | .py | 4,415 | 118 | # *_*coding:utf-8 *_*
import os
import numpy as np
import torch
from torch.autograd import Variable
from tqdm import tqdm
from collections import defaultdict
import pandas as pd
from dataloader import generate_plyfile, plydataset
def compute_cat_iou(pred,target,iou_tabel): # pred [B,N,C] target [B,N]
iou_list... | Python |
3D | ZhangLingMing1/TSGCNet | dataloader.py | .py | 7,188 | 164 | from plyfile import PlyData
import numpy as np
from torch.utils.data import DataLoader,Dataset,random_split
import os
import pandas as pd
labels = ((255, 255, 255), (255, 0, 0), (255, 125, 0),(255, 255, 0), (0, 255, 0), (0, 255, 255),
(0, 0, 255), (255, 0, 255))
def get_data(path=""):
labels = ([255,... | Python |
3D | llien30/point_cloud_anomaly_detection | train.py | .py | 4,094 | 156 | import argparse
import os
import random
import numpy as np
import torch
import wandb
import yaml
from addict import Dict
# from emd.emd_module import emdModule
from libs.checkpoint import save_checkpoint
from libs.dataset import ShapeNeth5pyDataset
from libs.foldingnet import SkipValiationalFoldingNet
from libs.helpe... | Python |
3D | llien30/point_cloud_anomaly_detection | test.py | .py | 10,046 | 321 | import argparse
import os
import random
from typing import List
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import yaml
from addict import Dict
from sklearn.metrics import auc, roc_curve
from torch.utils.data import DataLoader
from libs.checkpoint import resume
from libs.datase... | Python |
3D | llien30/point_cloud_anomaly_detection | libs/loss.py | .py | 1,506 | 45 | # from collections import Counter, defaultdict
import torch
import torch.nn as nn
# from ortools.linear_solver import pywraplp
class ChamferLoss(nn.Module):
def __init__(self):
super(ChamferLoss, self).__init__()
self.use_cuda = torch.cuda.is_available()
def batch_pairwise_dist(self, x, y):... | Python |
3D | llien30/point_cloud_anomaly_detection | libs/dataset.py | .py | 12,035 | 374 | import copy
import glob
import json
import os
import random
# from .visualize import vis_points_3d
from typing import Tuple
import h5py
import numpy as np
import pandas as pd
import torch
from torch.utils import data
from .load_obj import loadOBJ
from .sampling import fartherst_point_sampling
class ShapeNetDataset... | Python |
3D | llien30/point_cloud_anomaly_detection | libs/load_obj.py | .py | 309 | 16 | def loadOBJ(filepath: str) -> list:
file = open(filepath, "r")
vertices = []
for line in file:
vals = line.split()
if len(vals) == 0:
continue
if vals[0] == "v":
v = list(map(float, vals[1:4]))
vertices.append(v)
return vertices
| Python |
3D | llien30/point_cloud_anomaly_detection | libs/vis_histogram.py | .py | 839 | 29 | import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def vis_histgram(csv_file: str, save_dir: str) -> None:
df = pd.read_csv(csv_file, index_col=0)
label = df["1"]
result = df["2"]
normal_result = []
abnormal_result = []
for lbl, r in zip(label, result):
... | Python |
3D | llien30/point_cloud_anomaly_detection | libs/visualize.py | .py | 818 | 39 | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def vis_points_3d(points, save_name):
# print(pos_events.shape)
points = points.to("cpu").detach().numpy()
fig = plt.figure()
ax = Axes3D(fig)
# fig = fig.add_subplot(111, projection="3d")
ax.plot(
points[:, 2],
... | Python |
3D | llien30/point_cloud_anomaly_detection | libs/meter.py | .py | 597 | 24 | class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=":f"):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def updat... | Python |
3D | llien30/point_cloud_anomaly_detection | libs/__init__.py | .py | 96 | 5 | from .dataset import *
from .foldingnet import *
from .helper import *
from .visualize import *
| Python |
3D | llien30/point_cloud_anomaly_detection | libs/checkpoint.py | .py | 1,073 | 44 | import os
from typing import Tuple
import torch
import torch.nn as nn
import torch.optim as optim
def save_checkpoint(
result_path: str,
epoch: int,
model: nn.Module,
optimizer: optim.Optimizer,
) -> None:
save_states = {
"epoch": epoch,
"model_state_dict": model.state_dict(),
... | Python |
3D | llien30/point_cloud_anomaly_detection | libs/sampling.py | .py | 1,372 | 44 | import numpy as np
def l2_norm(x, y):
"""Calculate l2 norm (distance) of `x` and `y`.
Args:
x (numpy.ndarray or cupy): (batch_size, num_point, coord_dim)
y (numpy.ndarray): (batch_size, num_point, coord_dim)
Returns (numpy.ndarray): (batch_size, num_point)
"""
return ((x - y) ** 2)... | Python |
3D | llien30/point_cloud_anomaly_detection | libs/helper.py | .py | 15,778 | 477 | import os
import time
from typing import Any, List, Tuple
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import wandb
from torch import optim
from torch.distributions import Categorical
from torch.utils.data import DataLoader
from .emd.emd_m... | Python |
3D | llien30/point_cloud_anomaly_detection | libs/foldingnet.py | .py | 13,746 | 418 | import itertools
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .visualize import vis_points_3d
def knn(x: torch.tensor, k: int) -> int:
batch_size = x.size(0)
num_points = x.size(2)
inner = -2 * torch.matmul(x.transpose(2, 1), x)
xx = torch.sum(x ** 2, d... | Python |
3D | llien30/point_cloud_anomaly_detection | utils/make_sphere.py | .py | 494 | 27 | import random
from math import cos, pi, sin
import numpy as np
def make_sphere(N: int) -> None:
points = []
for i in range(N):
theta = 2 * pi * random.random()
phi = 2 * pi * random.random()
x = sin(phi) * cos(theta)
y = sin(phi) * sin(theta)
z = cos(phi)
point... | Python |
3D | kuangxh9/SuperWater | organize_pdb_dataset.py | .py | 3,204 | 72 | import os
import shutil
from collections import defaultdict
from tqdm import tqdm
from argparse import ArgumentParser
parser = ArgumentParser(description="Process PDB files and organize dataset.")
parser.add_argument("--raw_data", type=str, required=True, help="Name of the dataset folder containing PDB files.")
parser... | Python |
3D | kuangxh9/SuperWater | train.py | .py | 6,739 | 157 | import copy
import math
import os
from functools import partial
import numpy as np
import random
import wandb
import torch
torch.multiprocessing.set_sharing_strategy('file_system')
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (64000, rlimit[1]))
import... | Python |
3D | kuangxh9/SuperWater | inference_water_pos.py | .py | 11,335 | 265 | import gc
import math
import os
import numpy as np
import random
import shutil
import torch.nn as nn
from argparse import Namespace, ArgumentParser, FileType
import torch.nn.functional as F
from functools import partial
import wandb
import torch
import time
from sklearn.metrics import roc_auc_score
from torch_geometric... | Python |
3D | kuangxh9/SuperWater | check_gpu.py | .py | 532 | 17 | import torch
def check_cuda_devices():
if not torch.cuda.is_available():
print("CUDA is not available.")
return
num_gpus = torch.cuda.device_count()
print(f"Number of CUDA devices available: {num_gpus}")
for i in range(num_gpus):
device_name = torch.cuda.get_device_name(i)
... | Python |
3D | kuangxh9/SuperWater | utils/training.py | .py | 6,382 | 156 | import copy
import numpy as np
from torch_geometric.loader import DataLoader
from tqdm import tqdm
from confidence.dataset import ListDataset
from utils import so3, torus
from utils.sampling import sampling, randomize_position_multiple
import torch
from utils.diffusion_utils import get_t_schedule
from utils.min_dist ... | Python |
3D | kuangxh9/SuperWater | utils/so3.py | .py | 3,660 | 97 | import os
import numpy as np
import torch
from scipy.spatial.transform import Rotation
MIN_EPS, MAX_EPS, N_EPS = 0.01, 2, 1000
X_N = 2000
"""
Preprocessing for the SO(3) sampling and score computations, truncated infinite series are computed and then
cached to memory, therefore the precomputation is only run ... | Python |
3D | kuangxh9/SuperWater | utils/visualise.py | .py | 2,190 | 53 | from rdkit.Chem.rdmolfiles import MolToPDBBlock, MolToPDBFile
import rdkit.Chem
from rdkit import Geometry
from collections import defaultdict
import copy
import numpy as np
import torch
from rdkit import Chem
from rdkit.Chem import rdmolfiles
class PDBFile:
def __init__(self, mol):
self.parts = defau... | Python |
3D | kuangxh9/SuperWater | utils/diffusion_utils.py | .py | 2,797 | 74 | import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from scipy.stats import beta
from utils.geometry import axis_angle_to_matrix, rigid_transform_Kabsch_3D_torch
from utils.torsion import modify_conformer_torsion_angles
def t_to_sigma(t_tr, args):
tr_sigma = args.tr_s... | Python |
3D | kuangxh9/SuperWater | utils/geometry.py | .py | 4,021 | 124 | import math
import torch
def quaternion_to_matrix(quaternions):
"""
From https://pytorch3d.readthedocs.io/en/latest/_modules/pytorch3d/transforms/rotation_conversions.html
Convert rotations given as quaternions to rotation matrices.
Args:
quaternions: quaternions with real part first,
... | Python |
3D | kuangxh9/SuperWater | utils/find_water_pos.py | .py | 1,267 | 37 | import numpy as np
import warnings
from Bio.PDB import PDBParser
from openbabel import openbabel as ob
def find_real_water_pos(file_path, model_index=0):
file_extension = file_path.split('.')[-1].lower()
if file_extension == 'pdb':
warnings.simplefilter('ignore')
parser = PDBPars... | Python |
3D | kuangxh9/SuperWater | utils/nearest_point_dist.py | .py | 325 | 9 | import torch
def get_nearest_point_distances(set1, set2):
points1 = torch.tensor(set1, dtype=torch.float)
points2 = torch.tensor(set2, dtype=torch.float)
dists = torch.cdist(points1, points2)
min_dists, indices = torch.min(dists, dim=1)
min_dists_np = min_dists.numpy()
return min_dists_np, in... | Python |
3D | kuangxh9/SuperWater | utils/inference_utils.py | .py | 10,690 | 274 | import os
import torch
from Bio.PDB import PDBParser
from esm import FastaBatchedDataset, pretrained
from rdkit.Chem import AddHs, MolFromSmiles
from torch_geometric.data import Dataset, HeteroData
import esm
from datasets.process_mols import parse_pdb_from_path, generate_conformer, read_molecule, get_lig_graph_with_... | Python |
3D | kuangxh9/SuperWater | utils/cluster_centroid.py | .py | 2,794 | 81 | import numpy as np
from scipy.spatial import distance_matrix, cKDTree
from scipy.spatial.distance import pdist
def find_centroids(pred_coords, coords_prob, threshold=0.5,
cluster_distance=1.52, use_weighted_avg=True, clash_distance=2.2,
dedupe_decimals=6, tol=1e-8):
"""
R... | Python |
3D | kuangxh9/SuperWater | utils/min_dist.py | .py | 379 | 12 | import torch
from scipy.optimize import linear_sum_assignment
from scipy.spatial.distance import cdist
import numpy as np
def match_points_and_get_distances(data1, data2):
dist_matrix = cdist(data1, data2)
row_ind, col_ind = linear_sum_assignment(dist_matrix)
min_distances = np.array([dist_matrix[i, j] for... | Python |
3D | kuangxh9/SuperWater | utils/seed.py | .py | 5,511 | 132 | import logging
import os
import random
from random import getstate as python_get_rng_state
from random import setstate as python_set_rng_state
from typing import Any, Dict, Optional
import numpy as np
import torch
from lightning_fabric.utilities.rank_zero import _get_rank, rank_prefixed_message, rank_zero_only, rank_... | Python |
3D | kuangxh9/SuperWater | utils/torus.py | .py | 2,609 | 84 | import numpy as np
import tqdm
import os
"""
Preprocessing for the SO(2)/torus sampling and score computations, truncated infinite series are computed and then
cached to memory, therefore the precomputation is only run the first time the repository is run on a machine
"""
def p(x, sigma, N=10):
p_ = 0
... | Python |
3D | kuangxh9/SuperWater | utils/utils.py | .py | 9,726 | 245 | import os
import subprocess
import warnings
from datetime import datetime
import signal
from contextlib import contextmanager
import numpy as np
import torch
import yaml
from rdkit import Chem
from rdkit.Chem import RemoveHs, MolToPDBFile
from torch_geometric.nn.data_parallel import DataParallel
from torch.nn.parallel ... | Python |
3D | kuangxh9/SuperWater | utils/sampling.py | .py | 4,588 | 92 | import numpy as np
import torch
from torch_geometric.loader import DataLoader
from utils.diffusion_utils import modify_conformer, set_time
from utils.torsion import modify_conformer_torsion_angles
from scipy.spatial.transform import Rotation as R
# from utils.visualise import save_water_to_pdb_file
import os
def rand... | Python |
3D | kuangxh9/SuperWater | utils/torsion.py | .py | 3,606 | 94 | import networkx as nx
import numpy as np
import torch, copy
from scipy.spatial.transform import Rotation as R
from torch_geometric.utils import to_networkx
from torch_geometric.data import Data
"""
Preprocessing and computation for torsional updates to conformers
"""
def get_transformation_mask(pyg_data):
G ... | Python |
3D | kuangxh9/SuperWater | utils/parsing.py | .py | 23,272 | 267 |
from argparse import ArgumentParser,FileType
def parse_train_args():
# General arguments
parser = ArgumentParser()
parser.add_argument('--config', type=FileType(mode='r'), default=None)
parser.add_argument('--log_dir', type=str, default='workdir', help='Folder in which to save model and logs')
par... | Python |
3D | kuangxh9/SuperWater | confidence/dataset.py | .py | 15,205 | 287 | import itertools
import math
import os
import pickle
import random
from argparse import Namespace
from functools import partial
import copy
from scipy.spatial import cKDTree
import time
import numpy as np
import pandas as pd
import torch
import yaml
from torch_geometric.data import Dataset, Data
from torch_geometric.l... | Python |
3D | kuangxh9/SuperWater | confidence/confidence_train.py | .py | 15,068 | 301 | import gc
import math
import os
import shutil
from argparse import Namespace, ArgumentParser, FileType
import torch.nn.functional as F
from functools import partial
import wandb
import torch
from sklearn.metrics import roc_auc_score
from torch_geometric.loader import DataListLoader, DataLoader
from tqdm import tqdm
f... | Python |
3D | kuangxh9/SuperWater | webapp/app.py | .py | 11,818 | 402 | import os
import shutil
import subprocess
from flask import (
Flask,
render_template,
request,
redirect,
url_for,
send_file,
session,
Response,
)
import traceback
app = Flask(__name__, static_folder="static", template_folder="templates")
app.secret_key = "SESSION_DUMMY_KEY"
@app.route(... | Python |
3D | kuangxh9/SuperWater | models/all_atom_score_model.py | .py | 23,325 | 401 | from e3nn import o3
import torch
from torch import nn
from torch.nn import functional as F
from torch_cluster import radius, radius_graph
from torch_scatter import scatter_mean
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
from models.score_model import AtomEncoder, ... | Python |
3D | kuangxh9/SuperWater | models/score_model.py | .py | 19,780 | 391 | import math
from e3nn import o3
import torch
from torch import nn
from torch.nn import functional as F
from torch_cluster import radius, radius_graph
from torch_scatter import scatter, scatter_mean
import numpy as np
from e3nn.nn import BatchNorm
from utils import so3, torus
from datasets.process_mols import lig_feat... | Python |
3D | kuangxh9/SuperWater | datasets/esm_embeddings_to_pt.py | .py | 558 | 17 |
import os
from argparse import ArgumentParser
import torch
from tqdm import tqdm
parser = ArgumentParser()
parser.add_argument('--esm_embeddings_path', type=str, default='data/embeddings_output', help='')
parser.add_argument('--output_path', type=str, default='data/esm2_3billion_embeddings.pt', help='')
args = pars... | Python |
3D | kuangxh9/SuperWater | datasets/conformer_matching.py | .py | 7,071 | 197 | import copy, time
import numpy as np
from collections import defaultdict
from rdkit import Chem, RDLogger
from rdkit.Chem import AllChem, rdMolTransforms
from rdkit import Geometry
import networkx as nx
from scipy.optimize import differential_evolution
RDLogger.DisableLog('rdApp.*')
"""
Conformer matching routine... | Python |
3D | kuangxh9/SuperWater | datasets/process_mols.py | .py | 26,058 | 581 | import copy
import os
import warnings
import numpy as np
import scipy.spatial as spa
import torch
from Bio.PDB import PDBParser
from Bio.PDB.PDBExceptions import PDBConstructionWarning
from rdkit import Chem
from rdkit.Chem.rdchem import BondType as BT
from rdkit.Chem import AllChem, GetPeriodicTable, RemoveHs
from rd... | Python |
3D | kuangxh9/SuperWater | datasets/pdbbind.py | .py | 13,359 | 262 | import binascii
import glob
import hashlib
import os
import pickle
from collections import defaultdict
from multiprocessing import Pool
import random
import copy
import re
import numpy as np
import torch
from rdkit.Chem import MolToSmiles, MolFromSmiles, AddHs
from torch_geometric.data import Dataset, HeteroData
from t... | Python |
3D | kuangxh9/SuperWater | datasets/esm_embedding_preparation_water.py | .py | 2,850 | 98 | import os
from argparse import FileType, ArgumentParser
import numpy as np
import pandas as pd
from Bio.PDB import PDBParser
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from tqdm import tqdm
from Bio import SeqIO
parser = ArgumentParser()
parser.add_argument('--out_file', type=str, default="data/prep... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/__init__.py | .py | 0 | 0 | null | Python |
3D | lvqiujie/Mol2Context-vec | tasks/BACE/get_bace_data.py | .py | 4,785 | 161 | import pandas as pd
from sklearn.externals import joblib
import numpy as np
import os
# step 1
filepath="bace/bace.csv"
df = pd.read_csv(filepath, header=0, encoding="gbk")
w_file = open("bace/bace.smi", mode='w', encoding="utf-8")
all_label = []
all_smi = []
for line in df.values:
# aa = np.array(line[:17], dtyp... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/BACE/bace_train.py | .py | 12,821 | 282 | # from rdkit import Chem
# from rdkit.Chem import AllChem
import random
from tasks.utils.model import *
from sklearn.externals import joblib
import numpy as np
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
import matplotlib.pyplot as plt
import seaborn as sns
device = torch.device("cud... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/hiv/hiv_train.py | .py | 16,832 | 408 | import sys
sys.path.append('./')
import os
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals import joblib
import numpy as np
import math
import random
fro... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/hiv/get_hiv_data.py | .py | 4,809 | 163 | import sys
sys.path.append('./')
import pandas as pd
from sklearn.externals import joblib
import numpy as np
import os
# step 1
filepath="hiv/hiv.csv"
df = pd.read_csv(filepath, header=0, encoding="gbk")
w_file = open("hiv/hiv.smi", mode='w', encoding="utf-8")
all_label = []
all_smi = []
for line in df.values:
sm... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/utils/model.py | .py | 4,092 | 111 | import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import math
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LSTM(nn.Module):
"""搭建rnn网络"""
def __init__(self, ... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/utils/__init__.py | .py | 0 | 0 | null | Python |
3D | lvqiujie/Mol2Context-vec | tasks/utils/util.py | .py | 6,345 | 135 | import sys
sys.path.append('./')
import numpy as np
from sklearn.model_selection import KFold
from sklearn.externals import joblib
def split_data(x, y, all_smi, k_fold, name):
y = np.array(y)
all_smi = np.array(all_smi)
# save_path = 'hiv/'+str(k_fold)+'-fold-index.pkl'
# if os.path.isfile(save_path):... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/FreeSolv/train2.py | .py | 35,119 | 793 | from rdkit import Chem
import torch
import os
import torch.nn as nn
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.ext... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/FreeSolv/train.py | .py | 14,327 | 386 | from rdkit import Chem
import torch
import torch.nn as nn
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals imp... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/FreeSolv/test.py | .py | 1,531 | 40 | import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from tasks.FreeSolv.train import LSTM, MyDataset
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals import joblib
import numpy as np
# 设置超参数
input_size = 5... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/clintox/clintox_train.py | .py | 12,871 | 262 | import os
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals import joblib
import numpy as np
import random
from sklearn import metrics
from sklearn.metrics... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/clintox/get_clintox_data.py | .py | 5,581 | 181 | import pandas as pd
import numpy as np
from rdkit import Chem
import os
from rdkit.Chem import Descriptors
from sklearn.externals import joblib
# step 1
filepath="clintox/clintox.csv"
df = pd.read_csv(filepath, header=0, encoding="gbk")
w_file = open("clintox/clintox.smi", mode='w', encoding="utf-8")
all_label = []
a... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/toxcast/toxcast_train.py | .py | 20,961 | 442 | import sys, os
sys.path.append('./')
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals import joblib
from sklearn.metrics import precision_recall_curve
imp... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/toxcast/get_toxcast_data.py | .py | 4,829 | 161 | import sys
sys.path.append('./')
import pandas as pd
from sklearn.externals import joblib
import numpy as np
import os
# step 1
filepath="toxcast/toxcast_data.csv"
df = pd.read_csv(filepath, header=0, encoding="gbk")
all_label = []
all_smi = []
w_file = open("toxcast/toxcast.smi", mode='w',encoding="utf-8")
for line i... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/esol/esol_train.py | .py | 13,443 | 360 | from rdkit import Chem
import torch
import torch.nn as nn
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals imp... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/esol/get_data.py | .py | 5,780 | 185 | import pandas as pd
from sklearn.externals import joblib
import numpy as np
from rdkit import Chem
from rdkit.Chem import Descriptors
import os
# step 1
filepath="esol/delaney-processed3.csv"
df = pd.read_csv(filepath, header=0, encoding="gbk")
w_file = open("esol/esol.smi", mode='w',encoding="utf-8")
all_label = []
... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/esol/esol_train2.py | .py | 33,347 | 778 | from rdkit import Chem
import torch
import os
import torch.nn as nn
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.ext... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/tox21/tox_train.py | .py | 21,491 | 462 | import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals import joblib
from sklearn.metrics import precision_recall_curve
import numpy as np
import math
import ra... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/tox21/get_tox_data.py | .py | 5,169 | 179 | import sys
sys.path.append('./')
import pandas as pd
from sklearn.externals import joblib
import numpy as np
import os
# NR-AR NR-AR-LBD NR-AhR NR-Aromatase
# NR-ER NR-ER-LBD NR-PPAR-gamma SR-ARE
# SR-ATAD5 SR-HSE SR-MMP SR-p53
dict_label = {"NR-AR":0,
"NR-AR-LBD":1,
"NR-AhR":2,
... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/lipop/train2.py | .py | 33,437 | 779 | from rdkit import Chem
import torch
import os
import torch.nn as nn
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.ext... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/lipop/train.py | .py | 14,921 | 386 | from rdkit import Chem
import torch
import torch.nn as nn
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals imp... | Python |
3D | lvqiujie/Mol2Context-vec | tasks/lipop/get_data.py | .py | 5,704 | 184 | import pandas as pd
from sklearn.externals import joblib
import numpy as np
from rdkit import Chem
from rdkit.Chem import Descriptors
import os
# step 1
filepath="lipop/Lipophilicity.csv"
df = pd.read_csv(filepath, header=0, encoding="gbk")
w_file = open("lipop/lipop.smi", mode='w',encoding="utf-8")
all_label = []
al... | Python |
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