Lauler

import numpy as np
import os
import pandas as pd
import pysrt
import argparse
from tqdm import tqdm

parser = argparse.ArgumentParser()

parser.add_argument(

Lauler

import os
import numpy as np
import scipy as sp
# from tensorflow.contrib.saved_model import load_keras_model
# from tensorflow import keras
from tensorflow.keras.models import load_model
import tensorflow.keras as keras
import tensorflow.keras.backend as K
from tensorflow.keras import backend
from tensorflow.keras.preprocessing.image import ImageDataGenerator

Lauler

import os
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
from efficientnet.tfkeras import EfficientNetB5, EfficientNetB3, preprocess_input
from tensorflow.keras.applications import DenseNet201
from tensorflow.keras import backend as K
from tensorflow.keras import optimizers
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau

Lauler

library(jsonlite)

faton <- fromJSON("https://api.opendota.com/api/players/36933/matches") # My matches and basic stats
fatonmatch <- fromJSON("https://api.opendota.com/api/matches/2863026979") # one of my parsed matches

# Need to make SQL-call to 
# https://api.opendota.com/api/explorer in order to get a full list of professional matches
promatches <- fromJSON("https://api.opendota.com/api/proMatches") 

faton$match_id # matchid of all my matches

Lauler

stocks <- read.table("returns.csv")
head(stocks,200)

stocks_quantiles <- data.frame(NA, NA, NA, NA, NA, NA)
names(stocks_quantiles) <- c("year", "five", "twofive", "median", "sevenfive", "ninefive")
stocks_quantiles$year <- 1:40

for(i in 1:40){
  for(j in 2:6){
    stocks_quantiles[i, j] <- quantile(stocks$multip[stocks$year == i], 

Lauler

cstrike <- read.csv("CSrandom2.csv")
csgo <- read.csv("CSgopolar.csv")

p <- ggplot(data = cstrike, aes(x = x, y = y)) + 
  geom_point(alpha = 0.05, colour = "Blue") +
  theme(axis.title.y = element_text(angle = 0, hjust = 0)) +
  theme_set(theme_bw(base_size = 20)) +
  ggtitle("Spread/Inaccuracy in CS 1.6 (100 000 shots)")

p

Lauler

cstrike <- read.csv("CSrandom2.csv")
csgo <- read.csv("CSgopolar.csv")

p <- ggplot(data = cstrike, aes(x = x, y = y)) + 
  stat_binhex(aes(fill = ..density.. * 100), bins = 8) +
  scale_fill_gradientn(colours=c("Gray97","Firebrick3"), 
                       name = "Percentage of shots") +
  theme_set(theme_bw(base_size = 20)) +
  theme(axis.title.y = element_text(angle = 0, hjust = 0)) +
  ggtitle("Distribution of shots in CS 1.6's spread box")

Lauler

import random
import math

def cs16():    
    with open('CS16spread.csv', 'w') as file:
        i = 0
        file.write("x" + "," + "y" + "\n")
        
        
        while i < 100000:

Lauler

import math

tickrate = 100
firerate = 0.1 # ak47 firerate (seconds)
seconds = 2 # nr of seconds to run spraying simulation

def inacc():
    
    with open('CSinaccuracypre2.csv', 'w') as file: