I hereby claim:
- I am collinarnett on github.
- I am collinarnett (https://keybase.io/collinarnett) on keybase.
- I have a public key ASDpLh5BV42nb3PqkA5T4p2neVWsovqb3cmx9H_9OraQJQo
To claim this, I am signing this object:
I hereby claim:
To claim this, I am signing this object:
{ | |
inputs = { | |
flake-utils.url = "github:numtide/flake-utils"; | |
nickel.url = "github:tweag/nickel"; | |
}; | |
outputs = { self, nixpkgs, flake-utils, nickel }@inputs: | |
flake-utils.lib.eachDefaultSystem (system: | |
let pkgs = nixpkgs.legacyPackages.${system}; | |
in rec { | |
devShell = |
Professors will often instruct students to use zeus without explain much. This is not unusual since their objective is not to teach you about the command line interface (CLI) but rather the content of the course. This guide is meant to simplify the process of using Zeus while also giving a brief glimse into the motivation behind using the CLI.
Currently the process of logging into Zeus is as follows:
import io | |
import picamera | |
import logging | |
import socketserver | |
from threading import Condition | |
from http import server | |
PAGE="""\ | |
<html> | |
<head> |
# 64x64 | |
class Generator64(nn.Module): | |
def __init__(self, ngpu): | |
super(Generator64, self).__init__() | |
self.ngpu = ngpu | |
self.main = nn.Sequential( | |
nn.ConvTranspose2d(100, 512, kernel_size=4, stride=1, padding=0), | |
nn.BatchNorm2d(512), | |
nn.LeakyReLU(0.2), | |
nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1), |
def calc_dist_matrix(residues): | |
"""Returns a matrix of distances between residues of the same chain.""" | |
size = len(residues) | |
answer = np.zeros((size, size), np.float) | |
for row, residue_one in enumerate(residues): | |
for col, residue_two in enumerate(residues): | |
answer[row, col] = residue_one["CA"] - residue_two["CA"] | |
return answer |
from pathlib import Path | |
from urllib import request | |
from tqdm import tqdm | |
test_folder = Path("test") | |
train_folder = Path("train") | |
test_list = Path('test_ids.txt').read_text().splitlines() | |
train_list = Path('train_ids.txt').read_text().splitlines() | |
test_folder.mkdir(exist_ok=True) |