dmitrysarov

dmitrysarov

#iteratively fill fold with balancing probobilities of next item respectivly it size
#df - dataframe of subjects CT slices, different subjects can have different number of slices
#folds splits can't have same subject in it
value_count = df['subject'].value_counts() #count how much each subject have slices
number_of_folds = 5
np.random.seed(42)
for num, chunk in enumerate(np.array_split(value_count, range(number_of_folds, len(value_count), number_of_folds))):
# chunk - is sorted by count
    if num == 0:
        #initialize folds

dmitrysarov

def gray2hot(graymask, num_classes=2, include_background=True):                           
    '''Convert grayscale image of mask to multichannel mask                               
    if include_background=True number of channels in output will be             
    num_classes + 1                                                             
    '''                                                                         
    graymask = graymask.astype(np.int32)                                                  
    onehot_mask = np.zeros((graymask.shape[0], graymask.shape[1], num_classes+include_background), dtype=np.int32) # +1 relate to background                                  
    if include_background:                                                                
        onehot_mask[:, :, 0] = 1 # initialize all as background at first                  
    if np.all(graymask == 0):                                                             

dmitrysarov

from itertools import combinations
styles = map(lambda x: x+'-', list('.^o<>1234sp*hH+xDd|_'))
for num, line_style in enumerate(styles, 1):
    plt.plot(range(10), [num]*10, line_style, label=line_style)
plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
plt.show()

dmitrysarov

import  ipywidgets as widgets
from IPython.display import display, clear_output
from fxd import FxdInputFile
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from PIL import Image
import pickle
import numpy as np
from copy import deepcopy
%config InlineBackend.close_figures=False

dmitrysarov

snakeviz - tool helping to visualize data of after you script profile.
to install: python3 -m pip install snakeviz
to profile create profile log file do: python3 -m cProfile -o profile.log you_script.py
to run snakeviz (not in browser but run server): python3 -m snakeviz -s -H 0.0.0.0 -p 9022 profile.log

dmitrysarov

from multiprocessing import Process, Queue                                         
from time import sleep                                                             
                                                                                   
def loader(init_q, cache_q):                                                       
    while not init_q.empty():                                                      
        if cache_q.full():                                                         
            continue                                                               
        cache_q.put(init_q.get())                                                  
def predicter(init_q, cache_q):                                                    
    while True:                                                                    

dmitrysarov

first cosulution found was https://askubuntu.com/questions/1032633/18-04-screen-remains-blank-after-wake-up-from-suspend
but it switch off second screen

second i decide to install nvidia driver, and switch off Nouveau driver (open source nvidia river) http://us.download.nvidia.com/XFree86/Linux-x86_64/378.13/README/commonproblems.html#nouveau
https://medium.com/@antonioszeto/how-to-install-nvidia-driver-on-ubuntu-18-04-7b464bab43e6

dmitrysarov

# %matplotlib inline
%matplotlib notebook

from ipywidgets import interact, widgets, Layout
import matplotlib.pyplot as plt
from matplotlib.image import AxesImage
from IPython.display import display
from numpy.fft import fftshift, fft2, ifft2, ifftshift
import time
from skimage import transform

dmitrysarov

import numpy as np
import matplotlib.pyplot as plt

class Iterative_hist(object):
    '''
    Collect distribution information in iterative manner. 
    Useful when whole data not fit memory.
    Usage:
        if min and max data value is not known, call set_min_max() method, iterative, on hall dataset
        call add_data() method, for data addition