saliksyed

import random
import torchvision
import torch
import torch.nn as nn
import skimage
from torch.utils.data import Dataset
from typing import Callable
from collections import OrderedDict
from functools import partial
import torchvision.transforms as transforms

saliksyed

HEADER                                            01-JUN-22                     
TITLE     ALPHAFOLD MONOMER V2.0 PREDICTION FOR D(1C) DOPAMINE RECEPTOR         
TITLE    2 (P42291)                                                             
COMPND    MOL_ID: 1;                                                            
COMPND   2 MOLECULE: D(1C) DOPAMINE RECEPTOR;                                   
COMPND   3 CHAIN: A                                                             
SOURCE    MOL_ID: 1;                                                            
SOURCE   2 ORGANISM_SCIENTIFIC: XENOPUS LAEVIS;                                 
SOURCE   3 ORGANISM_TAXID: 8355                                                 
REMARK   1                                                                      

saliksyed

saliksyed

from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
import tensorflow as tf
import matplotlib.pyplot as plt

def add_prefix(model, prefix: str, custom_objects=None):
    config = model.get_config()
    old_to_new = {}

saliksyed

import pyvista
from PIL import Image
import shutil
import os
import numpy as np
import random

OUTPUT_DIR = 'extruded_polygons'
NUM_IMAGES = 10000

saliksyed

3.2.2 Run initial mongo shell

mongo admin

you should get a mongo shell like

saliksyed

3. Setup local MongoDB server as a Docker container

3.1. Download and launch local mongo docker image.

sudo docker run -p 27017:27017 --name some-mongo -d mongo --auth

This will download the official mongo Docker image and run it in detached mode with auth turned on.

saliksyed

function fibonacci(num){
  var a = 1, b = 0, temp;

  while (num >= 0){
    temp = a;
    a = a + b;
    b = temp;
    num--;
  }

saliksyed

"""
Downloaded from: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE118189
"""
with open('GSE118189_ATAC_counts.txt', 'r') as f:
	cell_files = None
	for line in f.readlines():
		line = line.rstrip()
		if not cell_files:
			cell_types = line.split('\t')
			cell_files = [open('./ImmuneAtlasBed/'  + cell + '.bed' , 'w') for cell in cell_types]

saliksyed

class Pathway:
  def __init__(self, api):
    self.api = api      

  def datasets(self):
    """ Returns the list of all pathway datasets available"""
    pass

  def getPathways(self, datasets=[Dataset.KEGG]):