g-leech

# You can get your own copy of the MCMC trace: https://github.com/g-leech/masks_v_mandates#run
# but I've included the quantiles in this script for reproducibility. 

import numpy as np
sns.set_style("whitegrid")

def exp_reduction(a, x):
    reductions = 1 - np.exp((-1.0) * a * x)
    return reductions.mean()

g-leech

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

# ## Excess COVID-19 mortality vs reported deaths over time
#  
# split bar?
#  
# y-axis: deaths 
# x-axis: time
# 

g-leech

def handle_utterance_str(text) :
    if text[0] != "'" and text[0] != '"' :
        text = f'"{text}"'
    
    text = text.replace('"', '\"')
    text = text.replace("'", '\"')
    return "handle_utterance(1,{},Output)".format(text)

def escape_and_call_prolexa(text) :
    libPrefix = "prolexa:"

g-leech

#%tensorflow_version 2.x
import pandas as pd
import numpy as np
import re
from nltk import word_tokenize          
from nltk.stem import WordNetLemmatizer 
from scipy.sparse import hstack


from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

g-leech

import pandas as pd 

# From https://www.notion.so/977d5e5be0434bf996704ec361ad621d?v=fe54f89ca9e04ac799af42b39e1efc4b
path = "COVID 19 Containment measures data.csv"
df = pd.read_csv(path)

withoutUS = df[~df["Country"].str.contains("US:")]
withoutUS = withoutUS[~withoutUS["Country"].str.contains("United States")]
numCountries = withoutUS.Country.unique().shape[0]

g-leech

import math

# assumes bivariate normal, dichotomised groups
def dichotomy_r_to_d(r) :
    d = 2*r / (math.sqrt(1 - r**2))
    return d

# Equation 9
# https://sci-hub.tw/10.1037/1082-989X.11.4.386
def r_to_d(r, n1, n2) :

g-leech

from scipy.spatial.distance import cdist
import numpy as np
import matplotlib.pyplot as plt

# First write a covariance function. e.g. rbf
def radial_basis_kernel(x1, x2, varSigma, lengthScale):
    if x2 is None:
        d = cdist(x1, x1)
    else:
        d = cdist(x1, x2)

g-leech

import numpy as np

probability = 1/10000
wealth = 120000
houseValue = 100000

bound = probability * np.log(wealth - houseValue) \
		+ (1 - probability) * np.log(wealth)
maxPremium = wealth - np.exp(bound)

g-leech

# https://help.open.ac.uk/documents/policies/working-out-your-class-of-honours/files/50/honours-class-working-out.pdf
# Only 2nd and 3rd year courses count.

# Course, credits, grade, year
courses = [
        ["M343", 30, "Distinction", 3],
        ["M249", 30, "Distinction", 2],
        ["M248", 30, "Distinction", 2],
        ["MST210", 60, "Grade 2 Pass", 2]
    ]

g-leech

##################################
## DAY 1

# Evaluate 1 + 1 and then 1 + "one". Is Io strongly typed or weakly typed? 
1 + 1
1+"one" 
# > Exception: argument 0 to method '+' must be a Number, not a 'Sequence'

# Io is Strong: it does not perform implicit casts between types.