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

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

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

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

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

#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