edraizen

# Pablo Gainza Cirauqui 2016 LPDI IBI STI EPFL
# This pymol plugin for Masif just enables the load ply functions.

import os, sys
import math, re

from pymol import cmd, stored
import sys

from pymol import cmd, stored

edraizen

import torch
import numpy as np
import sparseconvnet as scn
from more_itertools import pairwise
from torch.nn.parallel._functions import Scatter

class Batch(object):
    def __init__(self, indices, data, truth, chunk_sizes=None, dim=3):
        assert isinstance(indices, torch.Tensor), "indices must be tensor"
        assert isinstance(data, torch.Tensor), "data must be tensor"

edraizen

DEBUG:toil.batchSystems.mesos.batchSystem:Preparing to launch Mesos task 0 with 1.00 cores, 2048.00 MiB memory, and 2048.00 MiB disk using offer 5b6aaf7d-99c1-410a-85f3-dc9602313361-O20 ...
DEBUG:toil.batchSystems.mesos.batchSystem:Launched Mesos task 0.
WARNING:toil.batchSystems.mesos.batchSystem:Executor 'toil-28072' reported lost with status '32512'.
WARNING:toil.batchSystems.mesos.batchSystem:Handling failure of executor 'toil-28072' on agent '5b6aaf7d-99c1-410a-85f3-dc9602313361-S2'.
DEBUG:toil.batchSystems.mesos.batchSystem:Available files: dict_keys(['/slave/log', '/var/lib/mesos/slaves/5b6aaf7d-99c1-410a-85f3-dc9602313361-S2/frameworks/5b6aaf7d-99c1-410a-85f3-dc9602313361-0032/executors/toil-27424/runs/f56c948d-a6b4-4ea5-9639-7518518f6ace', '/var/lib/mesos/slaves/5b6aaf7d-99c1-410a-85f3-dc9602313361-S2/frameworks/5b6aaf7d-99c1-410a-85f3-dc9602313361-0033/executors/toil-28072/runs/807e0102-ecc0-406c-b9b8-f60a845ec65f'])
WARNING:toil.batchSystems.mesos.batchSystem:Attempting to retrieve executor error l

edraizen

import os  
import pandas as pd
import dask.dataframe as dd
from dask.multiprocessing import get

num_workers = 20 #or use int(os.environ["SLURM_JOB_CPUS_PER_NODE"])

def apply_row(row):
  return pd.Series({
    "newColA": row["A"]+row["B"]),

edraizen

SELECT
	i.mol_sdi_id,
	i.nbr_sdi_id,
	i.nbr_score,
	i.nbr_taxid,
	i.nbr_superfam_id,
	i.int_superfam_id,
	i.nbr_obs_int_id,
	i.face_seq as resn,
	f.seqloc as resi

edraizen

import torch.nn as nn
import sparseconvnet as scn
import torch.nn.functional as F

class Dropout(nn.Module):
    def __init__(self, p=0.5, inplace=False):
        super(Dropout, self).__init__()
        if p < 0 or p > 1:
            raise ValueError("dropout probability has to be between 0 and 1, "
                             "but got {}".format(p))

edraizen

import torch
import torch.nn as nn
import sparseconvnet as scn

class UNet3D(nn.Module):
    """Sparse 3D Unet for voxel level prediction.
    
    Modified from shiba24/3d-unet and ellisdg/3DUnetCNN

    Parameters

edraizen

from itertools import groupby
from torch.nn.modules.loss import _Loss

class DiceLoss(_Loss):
    def __init__(self, size_average=True, smooth=1.):
        super(DiceLoss, self).__init__(size_average)
        self.smooth = smooth

    def forward(self, input, target, locations, weights=None):
        if self.size_average:

edraizen

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

edraizen

#Standard libraries
import os, sys
import logging

#Requried Libraries
from joblib import Parallel, delayed
try:
    #Import libraries to use use cluster
    from cluster_helper.cluster import cluster_view