def benchmod(module=None, filename=None, output=sys.stdout, format="text", profile=False):
if module is None:
if filename is None:
module = sys.modules.get("__main__")
else:
filename = str(Path(filename).resolve())
if not filename.endswith(".py"):
error = "{0!r} is not a Python file"
raise ValueError(error.format(filename))
sys.insert(0, filename.dirname())
try:
module = __import__(filename.namebase)
except ImportError:
error = "unable to import {0!r}"
raise ValueError(error.format(filename))
finally:
del sys.path[0]
results = benchmark(module)
if profile:
dir = Path("profiles")
if dir.exists():
if not dir.is_dir():
raise OSError("'profiles' is not a directory")
else:
dir.mkdir(parents=True, exist_ok=True)
import docbench
docbench.profile(module, output_dir=str(dir))
if format == "text":
content = table(results)
elif format == "json":
content = json.dumps(results)
else:
raise ValueError("unknown format {0!r}".format(format))
if output is not None:
output.write(content)
try:
output.flush()
except AttributeError:
pass
return results
import sys
sys.insert(0, 'monodepth_files/')
from utils.functions import *
from monodepth_model import *
import tensorflow as tf
import numpy as np
import argparse
import os
import time
import math
import utils.losses as losses
#import pickle
def get_net(params, checkpoint_file, batch_size):
size = [256, 512]
input_image = tf.placeholder(shape=[batch_size, size[0], size[1], 3],
dtype='float32',
name='input_image')
# initializing adversarial image
adv_image = tf.Variable(tf.random_uniform([1, size[0], size[1], 3],
minval=-10 / 256.0,
maxval=10 / 256.0),
name='noise_image',
dtype='float32')
# clipping for imperceptibility constraint
adv_image = tf.clip_by_value(adv_image, -10 / 256.0, 10 / 256.0)
input_batch = tf.add(input_image, adv_image)
model = MonodepthModel(params, input_batch)
import sys from os import path widget_path = path.join(path.dirname(path.abspath(__file__)), '../') sys.insert(0, widget_path)
from __future__ import print_function
import sys
sys.insert(0, '.')
import os
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.nn.functional as F
import torchvision
from torchvision import datasets, models, transforms
import torchvision.models as models
from vgg import VGG
learning_rate = 1e-3
batch_size = 16
epoches = 30
data_dir = 'dataset'
save_path = 'final.pth'
train = 'train'
test = 'val'
def trainval(dataloders, model, optimizer, scheduler, criterion, dataset_sizes, phase='train'):
for epoch in range(epoches):
print('Epoch {}/{}'.format(epoch, epoches - 1))
print('-' * 10)
import sys
import numpy as np
import matplotlib.pyplot as plt
sys.insert('/path/to/caffe/python')
import caffe
caffe.set_device(0)
caffe.set_mode_gpu()
#!/usr/bin/python #-*- coding:utf-8 -*- ################################################ #File Name: FindPromoterMutationFromExome.py #Author: C.J. Liu #Mail: [email protected] #Created Time: Mon 31 Oct 2016 01:13:37 PM CDT ################################################ import os, sys import argparse sys.insert( 0, '/home/cliu18/liucj/projects/1.Mutation_calling_in_non-condig_region_through_EXOME/0.scripts' ) import callVariantsOfNoncodingRegion import IntegrativeRecurrencyProcessing def usage(): description = '''Task: Find Non-coding region mutation by exome sequencing, and find recurency of mutation in one tumor. Exome sequencing protocol is to captures all exon regions of human genome, besides, it can capture some other parts of genome such as 5'utr, 3'utr, non coding regions et al. I got all regulatory feature region of human from Ensembl, and use GATK -L paramter to call mutation only on these region. Annotation of mutation site is provided. The final result of scripts are the recurent mutation site. ''' usage = """%(prog)s -i <bam_dir> -o <output_dir> -n <number_of_threads""" parser = argparse.ArgumentParser(description=description, usage=usage) parser.add_argument( "-i", dest="input", type=str,
import sys
sys.insert(0, './lib')
import tkinter as tk
from tkinter import commondialog
from tkinter.commondialog import Dialog
from tkinter import Canvas
from tkinter import *
from tkinter.ttk import *
# class Game(tk.Frame):
# def __init__(self):
# tk.Frame.__init__(self)
# self.grid()
# self.master.title('My game of Chance')
main = tk.Tk()
canvas = tk.Canvas(main, height= 700, width=700, bg='black')
canvas.pack()
frame = tk.Frame(main, bg='red')
frame.place(relheight=0.5, relwidth=0.5 )
restart_message = tk.messagebox.askquestion(main, title=start_game, message="choice" command=restart)
restart_message.pack()
main.mainloop()