galenseilis

from scipy import stats
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-1, 1, num=150)

y = 10*x + np.random.normal(size=x.size)

y[11:15] += 10
y[-5:] -= 7

galenseilis

import numpy as np

def scale(x, a=0, b=1):
    if a > b:
        a, b = b, a
    min_x = np.min(x)
    result = (x - min_x) / (np.max(x) - min_x)
    result *= (b - a)
    result += a
    return result

galenseilis

import numpy as np

def scale(x, a=0, b=1):
    if a > b:
        a, b = b, a
    min_x = np.min(x)
    xout = (x - min_x) / (np.max(x) - min_x)
    xout *= (b - a)
    xout += a
    sigma_in = np.std(x, ddof=1)

galenseilis

import numpy as np

def convert_range(value, r1, r2 ):
    return (value - r1[0]) * (r2[1] - r2[0]) / (r1[1] - r1[0]) + r2[0]

if __name__ == '__main__':
    print(convert_range(328.17,[300.77,559.22 ],[1,10]))

galenseilis

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import describe

if __name__ == '__main__':
    x = np.random.normal(2, 3, size=10**4)
    z = x + x
    q = 2 * x
    plt.hist(x, bins=100, alpha=0.1)
    plt.hist(z, bins=100, alpha=0.1)

galenseilis

class ParameterTensor(tf.keras.models.Model):

    def __init__(self, shape):
        super().__init__()
        self.w = tf.random.normal(shape=shape)
        self.w = tf.Variable(self.w, trainable=True)

    def call(self, x=None):
        return self.w

galenseilis

import tensorflow as tf

class ChiCorr(tf.keras.losses.Loss):

    def chilogl(self, x, k=2):
        ll = k * tf.math.log(x) / 2
        ll = ll - x / 2
        ll = ll - k * tf.math.log(2.0) / 2
        ll = ll - tf.math.lgamma(k / 2)
        return -ll

galenseilis

import matplotlib.pyplot as plt
import numpy as np
from scipy import stats


z1 = np.random.chisquare(10, 10**4)
z2 = np.random.chisquare(10, 10**4)
c = 10
y = (z1 + c) / (z1 - z2)
sort_y = np.sort(y)

galenseilis

from astropy.stats import jackknife_stats
import numpy as np
from scipy.spatial.distance import pdist

data = np.random.normal(size=10**3)

def test_statistic(x):
    x = np.unique(x)
    x = x.reshape(-1,1)
    return np.min(pdist(x, metric='cityblock'))

galenseilis

from dateutil import parser
import os
import requests
import zipfile

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

pd.set_option('display.max_rows', 500)