bakirillov

import dash
import dash_table
import pandas as pd
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, State, Output

external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

bakirillov

http://rspb.royalsocietypublishing.org/content/283/1833/20160600 - How humans drive speciation as well as extinction
https://www.sciencedirect.com/science/article/pii/S2530064417300500 - Humans as niche constructors: Revisiting the concept of chronic anthropogenic disturbances in ecology
https://link.springer.com/article/10.1007/BF02390892 - A new approach to the classification of anthropogenic plant communities
https://www.nature.com/articles/s41559-016-0065 - Human behaviour as a long-term ecological driver of non-human evolution
https://www.frontiersin.org/articles/10.3389/feart.2018.00151/full - Human Fire Legacies on Ecological Landscapes
http://www.pnas.org/content/98/10/5433 - Human-caused environmental change: Impacts on plant diversity and evolution

bakirillov

Предобработка данных секвенирования
http://arxiv.org/abs/1603.09195

Интерпретация показаний нейронных сетей
https://arxiv.org/pdf/1704.02685.pdf
https://arxiv.org/abs/1605.01133

Генеративное моделирование и драг-дизайн
https://arxiv.org/abs/1704.07555
https://www.ncbi.nlm.nih.gov/pubmed/28703000

bakirillov

import argparse
from moviepy.editor import *

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Cut parts of video clip")
    parser.add_argument(
        "source",
        metavar="Source",
        help="Input filename"
    )

bakirillov

import numpy as np
from tqdm import tqdm

class GenAlgWithoutPopulationRestriction():
    
    def __init__(
        self, N_init=10, target_string="ESCHO SPORIT SO MNOY BUDESH LOLKA SASAII",
        temperature=1, pm=0.5, pmating=0.6
    ):
        self.alphabet = [a for a in "ABCDEFGHIJKLMNOPQRSTUVWXYZ "]

bakirillov

guide,target
TTTAGCTCCTACTCAGACTGTTACTCTG,TTTAttTCCTgCTCAGACTGTgtCTCTG
TTTAGCTCCTACTCAGACTGTTACTCTG,TTTcaaTCCTtCTCAGAaTGTTcCTCTG
TTTAGCTCCTACTCAGACTGTTACTCTG,TTTttCTttTtCTgAGACTGTTACTCTG
TTTAGCTCCTACTCAGACTGTTACTCTG,TTTAcCatCTACTCAGAaTGTTcCTCTG
TTTAGCTCCTACTCAGACTGTTACTCTG,TTTAGCTgCTACTtAGtCTtTTACTCTt
TTTAGCTCCTACTCAGACTGTTACTCTG,TTTgtCaCCTACTCAcACacTTACTCTG
TTTAGCTCCTACTCAGACTGTTACTCTG,TTTctCTCCTACTaAtcCTGTTACTgTG
TTTAGCTCCTACTCAGACTGTTACTCTG,TTTtaCTgCTACTCAcACatTTACTCTG
TTTAGCTCCTACTCAGACTGTTACTCTG,TTTAGCagtTcCTCAGACTGgTtCTCTG

bakirillov

import os
import json
import telebot
from flask import Flask

ih = open("config.json", "r")
config = json.load(ih)
ih.close()

TOKEN = config["bot_token"]

bakirillov

import io
import os
import dash
import base64
import dash_table
import pandas as pd
import pickle as pkl
import os.path as op
import face_recognition as fr
import dash_core_components as dcc

bakirillov

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import torch
import numpy as np
import torch.nn as nn
from tqdm import tqdm

bakirillov

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import torch
import numpy as np
import torch.nn as nn
from tqdm import tqdm