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 | Autodesk/molecular-design-toolkit | deployment/pull-cache.sh | .sh | 1,125 | 43 | #!/usr/bin/env bash
if [ -z ${CI_BRANCH} ]; then
echo "\$CI_BRANCH" var not set.
exit 10
fi
function echocmd() {
echo "> $@"
$@
}
function run-pull(){
# pull an image. If successful, retags the image with the "cache" tag
img=$1
tag=$2
imgpath="${REPO}${img}-${tag}"
echocmd docker pull ... | Shell |
3D | Autodesk/molecular-design-toolkit | deployment/run-ci-tests.sh | .sh | 3,072 | 110 | #!/usr/bin/env bash
# Drives tests for our CI system. This looks for the following environment variables:
# Defined by codeship
# - CI_BRANCH
# - CI_COMMIT_MESSAGE
# - PROJECT_ID
# Defined in ../codeship-services.yml
# - TESTENV
# - PYVERSION
set -e # fail immediately if any command fails
if [ -z "${CI_BRANCH}" ]; t... | Shell |
3D | Autodesk/molecular-design-toolkit | deployment/publish.sh | .sh | 987 | 33 | #!/bin/bash
# Publish a new release (triggered by a git tag that conforms to a PEP440 release)
# Exit 1 if there's a mismatch between the git tag and the package's version
#
# Expects to run in base directory of the repository
# fail immediately if any command fails:
set -e
echo "Now deploying moldesign-${CI_BRANCH}... | Shell |
3D | Autodesk/molecular-design-toolkit | deployment/send-test-status.py | .py | 1,615 | 58 | #!/usr/bin/env python
"""
This script accesses the github API to send a custom status message
about test results
"""
import os
import sys
import github
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('exitcode', type=str)
parser.add_argument('msg', type=str)
parser.add_argument('--deployed', a... | Python |
3D | Autodesk/molecular-design-toolkit | deployment/pull-chemdocker.sh | .sh | 329 | 11 | #!/usr/bin/env bash
chemdocker_tag=$(cat /opt/molecular-design-toolkit/moldesign/compute/CHEMDOCKER_TAG)
for image in nwchem-6.6 \
ambertools-16 \
ambertools-17 \
opsin-2.1.0; do
docker pull chemdocker/${image}:${chemdocker_tag} | tee -a pull.log | egrep -i 'pull|alread... | Shell |
3D | Autodesk/molecular-design-toolkit | deployment/print-environment.sh | .sh | 538 | 19 | #!/usr/bin/env bash
echo
echo " ==== ${TESTENV} Test Environment for Python ${PYVERSION} ==== "
echo
echo " * Python interpreter: $(python -c "import sys; print(sys.version)")"
echo
echo " * Conda version: $(conda --version 2>/dev/null || echo 'not installed')"
echo
# This will print logging messages about optional i... | Shell |
3D | Autodesk/molecular-design-toolkit | deployment/push-and-tag.sh | .sh | 668 | 30 | #!/bin/bash
set -e
if [ -z ${CI_BRANCH} ]; then
echo "\$CI_BRANCH" var not set.
exit 1
fi
if [ -z ${DOCKERHUB_USER} ] || [ -z ${DOCKERHUB_PASSWORD} ]; then
echo "Dockerhub credentials not provided. Skipping push ..."
exit 0
fi
function echocmd() {
echo "> $@"
$@
}
docker login -u ${DOCKERHUB_USER... | Shell |
3D | rhyan10/G-SchNetOE62 | PCA_functions.py | .py | 32,943 | 714 | import os
import struct
import numpy as np
import ase.db as adb
from sklearn.decomposition import PCA, IncrementalPCA
from dscribe.descriptors import Descriptor, SOAP, MBTR
# Object type imports
from typing import Generator, Tuple, Union, Any, Optional
from numpy.typing import ArrayLike
from ase.db.core import Databas... | Python |
3D | rhyan10/G-SchNetOE62 | qm9_data.py | .py | 10,151 | 252 | import logging
import os
import re
import shutil
import tarfile
import tempfile
from urllib import request as request
from urllib.error import HTTPError, URLError
from base64 import b64encode, b64decode
import numpy as np
import torch
from ase.db import connect
from ase.io.extxyz import read_xyz
from ase.units import ... | Python |
3D | rhyan10/G-SchNetOE62 | loopLUMO.py | .py | 6,231 | 115 | import numpy as np
import statistics
import ase.io
import ase
import ase.io.xyz
import argparse
import subprocess
import ase.io
import pickle
import sys
import shutil
import time
sys.path.append('../G-SchNetOE62')
import utility_functions
from utility_functions import print_atom_bond_ring_stats
from ase import neighbor... | Python |
3D | rhyan10/G-SchNetOE62 | template_filter_generated_working.py | .py | 16,663 | 347 |
import numpy as np
import pickle
import os
import argparse
import time
from scipy.spatial.distance import pdist
from schnetpack import Properties
from utility_classes import Molecule, ConnectivityCompressor
from utility_functions import update_dict
from ase import Atoms
from ase.db import connect
def get_parser():
... | Python |
3D | rhyan10/G-SchNetOE62 | display_molecules.py | .py | 19,675 | 383 | import argparse
import sys
import os
import subprocess
import numpy as np
import tempfile
from ase.db import connect
from ase.io import write
from utility_classes import IndexProvider
def get_parser():
""" Setup parser for command line arguments """
main_parser = argparse.ArgumentParser()
main_parser.add... | Python |
3D | rhyan10/G-SchNetOE62 | analysis.py | .py | 9,202 | 235 | import ase.io
import pickle
import sys
import time
sys.path.append('./GSchNetOE62')
from ase import neighborlist
from utility_classes import Molecule
import numpy as np
class MoleculeAnalysis():
@staticmethod
def get_neighbours(geoms,element):
dist=[]
nneighbours = []
ntype=[]
... | Python |
3D | rhyan10/G-SchNetOE62 | dbanalysis.py | .py | 23,022 | 545 | import ase.io
import ase.db
import sys
import os
import re
import numpy as np
import argparse
sys.path.append('./GSchNetOE62')
from utility_classes import Molecule
import math, sys, random, os
import rdkit.Chem as Chem
import rdkit.Chem.AllChem as AllChem
import json
import six
import sys
project_root = "/storage/chem/... | Python |
3D | rhyan10/G-SchNetOE62 | template_data_bias.py | .py | 7,774 | 163 | import logging
from pathlib import Path
import numpy as np
import torch
from ase.db import connect
from schnetpack import Properties
from schnetpack.datasets import AtomsData
from utility_classes import ConnectivityCompressor
from template_preprocess_dataset import preprocess_dataset
class TemplateData(AtomsData):
... | Python |
3D | rhyan10/G-SchNetOE62 | utility_classes_ob3.py | .py | 36,255 | 876 | '''
Functionally identical to the regular utility_classes.py, except updated to use
OpenBabel 3 rather than OpenBabel 2 (tested on ver. 3.1.1)
'''
import operator
import re
import numpy as np
from openbabel import openbabel as ob
from openbabel import pybel
from multiprocessing import Process
from rdkit import Chem
f... | Python |
3D | rhyan10/G-SchNetOE62 | radial_calculation.py | .py | 1,692 | 40 | import ase.io
import ase
import numpy as np
import ase.neighborlist
db = ase.io.read("OE62.db", ":")
alldists = []
maxdists = []
neighbordists=[]
valencedict = {}
# make a dict that has an entry for each atom type
available_atom_types = [1, 3, 5, 6, 7, 8, 9, 14, 15, 16, 17, 33, 34, 35, 52, 53]
for i in available_atom_t... | Python |
3D | rhyan10/G-SchNetOE62 | radical_filtering.py | .py | 1,469 | 46 | import ase.io
import pickle
import sys
import time
import numpy as np
#geoms = ase.io.read("./data/OE62.db")
#geoms = ase.io.read("generated_molecules.db",":100")
geoms=ase.io.read("./models/model1/generated/generated_molecules_11.db",":100")
sys.path.append('./GSchNetOE62')
from GSchNetOE62 import utility_functions
fr... | Python |
3D | rhyan10/G-SchNetOE62 | template_preprocess_dataset.py | .py | 12,931 | 272 | import collections
import argparse
import sys
import time
import numpy as np
import logging
from ase.db import connect
from scipy.spatial.distance import pdist, squareform
from utility_classes import ConnectivityCompressor, Molecule
from multiprocessing import Process, Queue
from pathlib import Path
# list names of co... | Python |
3D | rhyan10/G-SchNetOE62 | utility_classes.py | .py | 39,030 | 956 | import operator
import re
import numpy as np
import openbabel as ob
import pybel
from multiprocessing import Process
from rdkit import Chem
from scipy.spatial.distance import squareform
class Molecule:
'''
Molecule class that allows to get statistics such as the connectivity matrix,
molecular fingerprint,... | Python |
3D | rhyan10/G-SchNetOE62 | template_filter_generated.py | .py | 29,634 | 620 |
import numpy as np
import pickle
import os
import argparse
import time
import collections
from scipy.spatial.distance import pdist
from schnetpack import Properties
from utility_classes import Molecule, ConnectivityCompressor
from utility_functions import update_dict
from ase import Atoms
from ase.db import connect
i... | Python |
3D | rhyan10/G-SchNetOE62 | nn_classes.py | .py | 12,412 | 334 | import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
from collections import Iterable
import schnetpack as spk
from schnetpack.nn import MLP
from schnetpack.metrics import Metric
### OUTPUT MODULE ###
class AtomwiseWithProcessing(nn.Module):
r"""
Atom-wise dense layers that a... | Python |
3D | rhyan10/G-SchNetOE62 | loopHL.py | .py | 5,941 | 113 | import numpy as np
import statistics
import ase.io
import ase
import ase.io.xyz
import argparse
import subprocess
import ase.io
import pickle
import sys
import shutil
import time
sys.path.append('../G-SchNetOE62')
import utility_functions
from utility_functions import print_atom_bond_ring_stats
from ase import neighbor... | Python |
3D | rhyan10/G-SchNetOE62 | loopHOMO.py | .py | 5,974 | 113 | import numpy as np
import statistics
import ase.io
import ase
import ase.io.xyz
import argparse
import subprocess
import ase.io
import pickle
import sys
import shutil
import time
sys.path.append('../G-SchNetOE62')
import utility_functions
from utility_functions import print_atom_bond_ring_stats
from ase import neighbor... | Python |
3D | rhyan10/G-SchNetOE62 | qm9_preprocess_dataset.py | .py | 22,236 | 498 | import collections
import argparse
import sys
import time
import numpy as np
import logging
from ase.db import connect
from scipy.spatial.distance import pdist
from utility_classes import ConnectivityCompressor, Molecule
from multiprocessing import Process, Queue
from pathlib import Path
def get_parser():
""" Set... | Python |
3D | rhyan10/G-SchNetOE62 | qm9_filter_generated.py | .py | 59,408 | 1,291 | import numpy as np
import collections
import pickle
import os
import argparse
import openbabel as ob
import pybel
import time
import json
from schnetpack import Properties
from utility_classes import Molecule, ConnectivityCompressor
from utility_functions import run_threaded, print_atom_bond_ring_stats, update_dict
fr... | Python |
3D | rhyan10/G-SchNetOE62 | gschnet_script.py | .py | 36,197 | 772 | import argparse
import logging
import os
import pickle
import time
from shutil import copyfile, rmtree
import numpy as np
import torch
import torch.nn as nn
from torch.optim import Adam
from torch.utils.data.sampler import RandomSampler
from ase import Atoms
import ase.visualize as asv
import schnetpack as spk
from s... | Python |
3D | rhyan10/G-SchNetOE62 | utility_functions.py | .py | 52,621 | 1,143 | import torch
import os
import json
import numpy as np
import torch.nn.functional as F
from multiprocessing import Queue
from scipy.spatial.distance import pdist, squareform
from torch.autograd import Variable
from schnetpack import Properties
from utility_classes import ProcessQ, IndexProvider
def boolean_string(s)... | Python |
3D | rhyan10/G-SchNetOE62 | template_data.py | .py | 7,760 | 163 | import logging
from pathlib import Path
import numpy as np
import torch
from ase.db import connect
from schnetpack import Properties
from schnetpack.datasets import AtomsData
from utility_classes import ConnectivityCompressor
from template_preprocess_dataset import preprocess_dataset
class TemplateData(AtomsData):
... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-03/model_prediction_no_reg_val_dset.py | .py | 2,734 | 88 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
hist_path = os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro/vox_fluoro_img_no_l1_l2_loss'... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-03/model_prediction_no_reg.py | .py | 2,729 | 88 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
hist_path = os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro/vox_fluoro_img_no_l1_l2_loss'... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-03/model_prediction.py | .py | 2,707 | 88 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
hist_path = os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro/vox_fluoro_img_stnd_loss')
hi... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-03/model_prediction_L1_0-1_L2_0-1.py | .py | 2,727 | 88 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
hist_path = os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro/vox_fluoro_img_stnd_loss')
hi... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-17/model_prediction_vox_fluoro_norm_mse.py | .py | 3,082 | 94 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
file_base_name = 'vox_fluoro_norm_mse'
hist_path = os.path.join(os.path.expanduser('~/fluor... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-17/model_prediction_vox_fluoro_no_bn.py | .py | 4,670 | 132 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
file_base_name = 'vox_fluoro_no_bn'
hist_path = os.path.join(os.path.expanduser('~/fluoro/c... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-17/model_prediction_vox_fluoro_norm.py | .py | 3,078 | 94 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
file_base_name = 'vox_fluoro_norm'
hist_path = os.path.join(os.path.expanduser('~/fluoro/co... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-17/model_prediction_vox_fluoro_no_bn_mae.py | .py | 4,674 | 132 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
file_base_name = 'vox_fluoro_no_bn_mae'
hist_path = os.path.join(os.path.expanduser('~/fluo... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-17/model_prediction_vox_fluoro_norm_nadam_lr_0-01_mse.py | .py | 3,096 | 94 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
file_base_name = 'vox_fluoro_norm_nadam_lr_0-01_mse'
hist_path = os.path.join(os.path.expan... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-17/model_prediction_vox_fluoro_min_max_1.py | .py | 4,674 | 132 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
file_base_name = 'vox_fluoro_min_max_1'
hist_path = os.path.join(os.path.expanduser('~/fluo... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-17/model_prediction_vox_fluoro_res_update_mae.py | .py | 3,665 | 101 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
file_base_name = 'vox_fluoro_res_update_mae'
hist_path = os.path.join(os.path.expanduser('~... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-17/model_prediction_vox_fluoro_std.py | .py | 3,077 | 94 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
file_base_name = 'vox_fluoro_std'
hist_path = os.path.join(os.path.expanduser('~/fluoro/cod... | Python |
3D | john-drago/fluoro | code/jupyt/update_2019-Sep-17/model_prediction_vox_fluoro_res.py | .py | 3,641 | 101 | '''
This module will attempt to predict model parameters by using a trained model.
'''
import tensorflow as tf
import os
import h5py
import numpy as np
import pickle
base_dir = os.path.expanduser('~/fluoro/data/compilation')
file_base_name = 'vox_fluoro_res'
hist_path = os.path.join(os.path.expanduser('~/fluoro/cod... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_talos_conv_2.py | .py | 13,103 | 297 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import keras
import talos
from sklearn.model_selection import train_test_split
import pickle
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/just_fluoro'), exp... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_talos_dense_1.py | .py | 13,190 | 298 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import keras
import talos
from sklearn.model_selection import train_test_split
import pickle
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/just_fluoro'), exp... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_talos_trial1.py | .py | 12,674 | 283 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import keras
import talos
from sklearn.model_selection import train_test_split
import pickle
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter'), expr_name))
os.... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_hyperparameter_talos.py | .py | 12,867 | 282 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import keras
import talos
from sklearn.model_selection import train_test_split
import pickle
save_dir = os.path.abspath(os.path.expanduser('~/fluoro/code/hyperparameter/talos_1'))
os.makedirs(save_dir,exist_ok=True)
expr_name = 'just_fluoro_ta... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_hyperparameter_hyperas.py | .py | 9,433 | 174 |
import numpy as np
import h5py
import tensorflow as tf
import keras
import os
import graphviz
import sys
from sklearn.model_selection import train_test_split
import json
import csv
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
save_dir = os.pat... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_talos_conv_3.py | .py | 13,191 | 298 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import keras
import talos
from sklearn.model_selection import train_test_split
import pickle
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/just_fluoro'), exp... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_talos_l1_l2_reg.py | .py | 12,997 | 296 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import keras
import talos
from sklearn.model_selection import train_test_split
import pickle
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/just_fluoro'), exp... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/temp_model.py | .py | 10,852 | 240 | #coding=utf-8
try:
import numpy as np
except:
pass
try:
import h5py
except:
pass
try:
import tensorflow as tf
except:
pass
try:
import keras
except:
pass
try:
import os
except:
pass
try:
import graphviz
except:
pass
try:
import sys
except:
pass
try:
im... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_talos_reg_act_kern.py | .py | 12,931 | 293 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import keras
import talos
from sklearn.model_selection import train_test_split
import pickle
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/just_fluoro'), exp... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_talos_conv_1.py | .py | 13,097 | 297 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import keras
import talos
from sklearn.model_selection import train_test_split
import pickle
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/just_fluoro'), exp... | Python |
3D | john-drago/fluoro | code/hyperparameter/just_fluoro/just_fluoro_talos_testdeploy.py | .py | 13,223 | 304 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import keras
import talos
from sklearn.model_selection import train_test_split
import pickle
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/just_fluoro'), exp... | Python |
3D | john-drago/fluoro | code/hyperparameter/vox_fluoro/vox_fluoro_res_talos_2/vox_fluoro_res_talos_2.py | .py | 61,219 | 1,147 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
import talos
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/vox_fluoro'), e... | Python |
3D | john-drago/fluoro | code/hyperparameter/vox_fluoro/vox_fluoro_img_stnd_hyperas/vox_fluoro_img_stnd_hyperas.py | .py | 14,888 | 293 | import numpy as np
import h5py
import tensorflow as tf
import keras
import os
import json
import csv
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperpara... | Python |
3D | john-drago/fluoro | code/hyperparameter/vox_fluoro/vox_fluoro_img_stnd_hyperas/vox_fluoro_img_stnd_hyperas_test.py | .py | 15,239 | 304 | import numpy as np
import h5py
import tensorflow as tf
import keras
import os
import sys
import pickle
import json
import csv
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser... | Python |
3D | john-drago/fluoro | code/hyperparameter/vox_fluoro/vox_fluoro_res_talos_test/vox_fluoro_res_talos_test.py | .py | 60,564 | 1,137 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
import talos
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/vox_fluoro'), e... | Python |
3D | john-drago/fluoro | code/hyperparameter/vox_fluoro/vox_fluoro_res_talos/vox_fluoro_res_talos.py | .py | 60,792 | 1,137 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
import talos
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperparameter/vox_fluoro'), e... | Python |
3D | john-drago/fluoro | code/hyperparameter/vox_fluoro/vox_fluoro_res_hyperas/vox_fluoro_res_hyperas.py | .py | 42,378 | 704 | import numpy as np
import h5py
import tensorflow as tf
import keras
import os
import json
import csv
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/hyperpar... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_no_bn/vox_fluoro_no_bn.py | .py | 64,042 | 1,255 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-20
# We are continuing the usage of the architecture based on the residual nets
# In this file, we are goign to continue normalizing the calibration inputs between -1 and 1, but we will only run the min... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_deeper_bn/vox_fluoro_deeper_bn.py | .py | 32,332 | 606 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating how a deeper conv net, which paradoxically has fewer parameters would fair
# No regularization
expr_name = sys.argv[0][:-3]
e... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_norm_nadam_lr_0-01_mse/vox_fluoro_norm_nadam_lr_0-01_mse.py | .py | 62,610 | 1,229 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-19
# We are continuing the usage of the architecture based on the residual nets
# In this file, we are going to normalize the calibration inputs from -1 to 1.
# We likewise are going to normalize the lab... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_res_test/vox_fluoro_res_v1.py | .py | 20,486 | 433 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro'), expr_name))
print(save_... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_res_test/vox_fluoro_res_v2.py | .py | 43,363 | 828 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro'), expr_name))
print(save_... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_res_test/vox_fluoro_res_test.py | .py | 49,047 | 935 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro'), expr_name))
print(save_... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_norm_nadam_elu_act_final/vox_fluoro_norm_nadam_elu_act_final.py | .py | 62,527 | 1,229 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-18
# We are continuing the usage of the architecture based on the residual nets
# In this file, we are going to normalize the calibration inputs from -1 to 1.
# We likewise are going to normalize the lab... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_min_max_1/vox_fluoro_min_max_1.py | .py | 63,401 | 1,256 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-20
# We are continuing the usage of the architecture based on the residual nets
# In this file, we are goign to continue normalizing the calibration inputs between -1 and 1, but we will only run the mi... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_res_update/vox_fluoro_res_update.py | .py | 60,680 | 1,171 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro'), expr_name))
os.makedirs... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_norm_nadam_lr_0-01_mae/vox_fluoro_norm_nadam_lr_0-01_mae.py | .py | 62,546 | 1,229 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-18
# We are continuing the usage of the architecture based on the residual nets
# In this file, we are going to normalize the calibration inputs from -1 to 1.
# We likewise are going to normalize the lab... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_base/vox_fluoro_base.py | .py | 18,787 | 373 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
# import sys
import pickle
from sklearn.model_selection import train_test_split
# sys.path.append(os.path.abspath(os.path.expanduser('~/fluoro/code')))
# import datacomp.h5py_multidimensional_array as h5py_multidimensional_array
# from da... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_img_stnd_loss/vox_fluoro_img_stnd_loss.py | .py | 22,149 | 457 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating changing the l1 and l2 regularization
expr_name = sys.argv[0][:-3]
expr_no = '2'
save_dir = os.path.abspath(os.path.join(os.pat... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_img_stnd_100_loss/vox_fluoro_img_stnd_100_loss.py | .py | 20,842 | 457 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# sys.path.append(os.path.abspath(os.path.expanduser('~/fluoro/code')))
# import datacomp.h5py_multidimensional_array as h5py_multidimensional_array
# from data... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_res/vox_fluoro_res.py | .py | 57,192 | 1,112 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro'), expr_name))
print(save_... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_norm_mse/vox_fluoro_norm_mse.py | .py | 62,529 | 1,228 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-18
# We are continuing the usage of the architecture based on the residual nets
# In this file, we are going to normalize the calibration inputs from -1 to 1.
# We likewise are going to normalize the lab... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_res_mae/vox_fluoro_res_mae.py | .py | 57,754 | 1,132 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro'), expr_name))
print(save_... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_base_2_overfit/vox_fluoro_base_2_overfit.py | .py | 27,364 | 506 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-23
# We are going to go back to earlier architecture to see if we can overfit the training set.
# We are going to also do per image normalization between -1 and 1.
# We are not going to normalize the l... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_mean_scale_abs/vox_fluoro_mean_scale_abs.py | .py | 61,791 | 1,213 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-23
# We are continuing the usage of the architecture based on the residual nets.
# In this file, we are going to normalize the calibration inputs from -1 to 1.
# We are going to also do per image normal... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_res_update_mae/vox_fluoro_res_update_mae.py | .py | 60,658 | 1,171 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro'), expr_name))
os.makedirs... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_norm/vox_fluoro_norm.py | .py | 62,334 | 1,225 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-18
# We are continuing the usage of the architecture based on the residual nets
# In this file, we are going to normalize the calibration inputs from -1 to 1.
# We likewise are going to normalize the lab... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_img_stnd/vox_fluoro_img_stnd.py | .py | 22,149 | 457 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating changing the l1 and l2 regularization
expr_name = sys.argv[0][:-3]
expr_no = '2'
save_dir = os.path.abspath(os.path.join(os.pat... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_l1_0-1_l2_0-1_var_loss/vox_fluoro_l1_0-1_l2_0-1_var_loss.py | .py | 22,262 | 458 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating changing the l1 and l2 regularization to 0.1 and 0.1 respectively
# This experiment is also going to evaluate the var loss as op... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_base_2/vox_fluoro_base_2.py | .py | 26,684 | 484 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-23
# We are going to go back to earlier architecture to see if we can overfit the training set.
# We are going to also do per image normalization between -1 and 1.
# We are not going to normalize the l... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_deeper_reg/vox_fluoro_deeper_reg.py | .py | 31,567 | 590 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating how a deeper conv net, which paradoxically has fewer parameters would fair
# No regularization
expr_name = sys.argv[0][:-3]
e... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_res_rms_prop/vox_fluoro_res_rms_prop.py | .py | 57,195 | 1,112 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro'), expr_name))
print(save_... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_overfit/vox_fluoro_overfit.py | .py | 64,037 | 1,257 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-21
# We are continuing the usage of the architecture based on the residual nets
# The main purpose of this test is to see if we can overfit the data. We are only going to try to train on 12 samples to t... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_l1_l2/vox_fluoro_img_stnd.py | .py | 22,260 | 460 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# sys.path.append(os.path.abspath(os.path.expanduser('~/fluoro/code')))
# import datacomp.h5py_multidimensional_array as h5py_multidimensional_array
# from data... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_small_norm/vox_fluoro_small_norm.py | .py | 30,589 | 631 | import numpy as np
import h5py
import tensorflow as tf
import os
import sys
import pickle
import datetime
# 2019-09-30
# In this file we are going to complete unit testing to see where the current model goes wrong.
# We are not initially going to use batch normalization or dropout.
expr_name = sys.argv[0][:-3]
sav... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_no_bn_mae/vox_fluoro_no_bn_mae.py | .py | 64,042 | 1,255 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-20
# We are continuing the usage of the architecture based on the residual nets
# In this file, we are goign to continue normalizing the calibration inputs between -1 and 1, but we will only run the min... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_std/vox_fluoro_std.py | .py | 62,625 | 1,230 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# 2019-09-18
# We are continuing the usage of the architecture based on the residual nets
# In this file, we are going to standardize the calibration inputs with mean 0 and std 1
# We likewise are going to standar... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_two_lin_no_reg/vox_fluoro_two_lin_no_reg.py | .py | 22,143 | 458 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating changing the l1 and l2 regularization to 0 and 0 respectively
# This experiment is also going to evaluate the var loss as oppose... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_res_batch_10/vox_fluoro_res_batch_10.py | .py | 57,193 | 1,112 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
# We are going to try to do some residual netowrks
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.path.expanduser('~/fluoro/code/jupyt/vox_fluoro'), expr_name))
print(save_... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_deeper/vox_fluoro_deeper.py | .py | 30,584 | 575 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating how a deeper conv net, which paradoxically has fewer parameters would fair
# No regularization
expr_name = sys.argv[0][:-3]
e... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_no_reg_var_loss/vox_fluoro_no_reg_var_loss.py | .py | 22,141 | 458 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating changing the l1 and l2 regularization to 0 and 0 respectively
# This experiment is also going to evaluate the var loss as oppose... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_two_lin_l1_0-005_l2_0-005/vox_fluoro_two_lin_l1_0-005_l2_0-005.py | .py | 22,253 | 458 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating changing the l1 and l2 regularization to 0 and 0 respectively
# This experiment is also going to evaluate the var loss as oppose... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_img_no_l1_l2_loss/vox_fluoro_img_no_l1_l2_loss.py | .py | 22,032 | 457 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating changing the l1 and l2 regularization
expr_name = sys.argv[0][:-3]
expr_no = '1'
save_dir = os.path.abspath(os.path.join(os.pat... | Python |
3D | john-drago/fluoro | code/vox_fluoro/history/vox_fluoro_deeper_bn_nadam/vox_fluoro_deeper_bn_nadam.py | .py | 32,330 | 606 | import numpy as np
import h5py
import tensorflow as tf
# import keras
import os
import sys
import pickle
from sklearn.model_selection import train_test_split
# This experiment is evaluating how a deeper conv net, which paradoxically has fewer parameters would fair
# No regularization
expr_name = sys.argv[0][:-3]
e... | Python |
3D | john-drago/fluoro | code/datacomp/norm_and_std_dsets.py | .py | 9,248 | 238 | '''
This module will generate the necessary data points for all of the datasets (for training fluoroscopic neural net) in order to perform normalization and standardization.
'''
import numpy as np
import h5py
import os
import time
# -----------------------------------------------------------------
load_dir = '/Volum... | Python |
3D | john-drago/fluoro | code/datacomp/save_to_mat.py | .py | 5,151 | 168 | '''
This module will devise some functions that can create a .mat file in the expected format of the .mat files that will determine the position and rotation of the bone for each frame.
'''
import numpy as np
import scipy.io as sio
import os
from coord_change import Angles2Basis
def generate_save_mat(dict_data, fil... | Python |
3D | john-drago/fluoro | code/datacomp/data_augmentation_test.py | .py | 53,534 | 1,376 | '''
This function will perform data augmentation on our current data set. Basically, we will do small translations and rotations on our voxel dataset to increase the number of instances we are currently training with.
'''
import os
import scipy.io as sio
import skimage
import numpy as np
import trimesh
import pandas a... | Python |
3D | john-drago/fluoro | code/datacomp/data_organization.py | .py | 31,986 | 875 | '''
This file will organize the data in the 'fluoro/data' folder. It will organize the stl files and the photos into large matrices to allow for training.
'''
import os
from coord_change import Global2Local_Coord, Basis2Angles
import scipy.io as sio
import skimage
import numpy as np
import trimesh
import pandas as pd
... | Python |
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