#! /usr/bin/python3

# usage: mksummary.py [-h] [-v] [-o {pdf png}]

# outputs summary data

# for now, PDF is single file (uncomment 'with' statements to split)

# png is one file per table

# By Nenad Rijavec

# Feel free to use, share or modify as you see fit.

import json

import sys, getopt, argparse

import os

import math

import datetime

import matplotlib

import matplotlib.pyplot as plt

from matplotlib.backends.backend_pdf import PdfPages

from parse_data import parse_latest

import process

import plot_modules

from reportlab.pdfgen import canvas

from reportlab.lib import colors

from reportlab.lib import enums

from reportlab.lib.pagesizes import letter

from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image

from reportlab.platypus import Table, TableStyle, PageBreak

from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle

from reportlab.lib.units import inch

# sorting functions for state trend table

# though output mostly as ints, values are floats, so no need to worry

# about ties

def sort_pos(state):

return state['positives'][state['n_samples']-1]

def sort_active(state):

return state['active'][state['n_samples']-1]

def sort_d1(state):

return state['pos_d1'][state['n_samples']-1]

def sort_d2(state):

return state['pos_d2'][state['n_samples']-1]

def sort_d3(state):

return state['pos_d3'][state['n_samples']-1]

def sort_dpos(state):

return val

def sort_dd1(state):

return val

def sort_dmodel(state):

return val

def sort_amodel(state):

return val

def sort_ddpos(state):

return state['days_doubled']['pos']

def sort_ddact(state):

return state['days_doubled']['act']

def sort_death(state):

return state['eff_death']

def sort_death7(state):

return state['death7'][state['n_samples']-1]

def sort_mort_pos(state):

return state['eff_death']/state['eff_positives']

def sort_mort_newpos(state):

return state['death7'][state['n_samples']-1]/state['pos_d1'][state['n_samples']-1]

def sort_mort_pop(state):

return state['eff_death']/10000

def single_trend_table( sorted, styles, elements, title, footnote ) :

elements.append(PageBreak())

def make_trend_report(states, fname ):

doc.build(elements)

def single_mortality_table( sorted, styles, elements, title, footnote ) :

elements.append(PageBreak())

def make_mortality_report(states, fname ):

doc.build(elements)

parser = argparse.ArgumentParser(description='analysis of number of cases')

parser.add_argument('-o', \

choices=['pdf', 'png'], \

default='pdf', \

nargs='?', \

help='output type, pdf or png, default is pdf')

parser.add_argument('-v', \

action='store_true' ,\

help='verbose')

args = parser.parse_args()

verbose = args.v

if args.o == 'pdf':

matplotlib.use('pdf')

if verbose:

print( 'generating PDF' )

else:

matplotlib.use('agg')

if verbose:

print( 'generating PNG' )

states = parse_latest()

# generate the numbers

summary = process.analyze(states)

datestr = str(datetime.date.today()-datetime.timedelta(days=1))

# trend table for all states, sorted on each column

make_trend_report( states, 'trends-' + datestr + '.pdf' )

# mortality table for all states, sorted on each column

make_mortality_report( states, 'mortality-' + datestr + '.pdf' )

# antibody analysis

sum_tot = 0

sum_pos = 0

n_have = 0

sum_pop = 0

sum_vpos = 0

sum_vtot = 0

for state in states :

n_samp = state['n_samples']

tta = state['data'][0]['totalTestsAntibody']

pta = state['data'][0]['positiveTestsAntibody']

nta = state['data'][0]['negativeTestsAntibody']

ttpa = state['data'][0]['totalTestsPeopleAntibody']

ptpa = state['data'][0]['positiveTestsPeopleAntibody']

ntpa = state['data'][0]['negativeTestsPeopleAntibody']

if tta != None and tta > 1000 and pta != None and pta > 0 :

pop = state['pop']

pos = state['positives'][n_samp-1] * pop / 1000000

tot = pos + state['negatives'][n_samp-1] * pop / 1000000

print( state['name'], ":", pop,\

'viral population: ', "{:,.2f}".format(100*pos/pop)+'%', \

'test:', "{:,.2f}".format(100*pos/tot)+'%', \

' antibody :', "{:,.2f}".format(100*pta/tta)+'%' )

n_have += 1

sum_tot += tta

sum_pos += pta

sum_vpos += pos

sum_vtot += tot

sum_pop += pop

print( n_have, "states, total population:", sum_pop )

print( "viral:", sum_vpos, \

"tested:", "{:,.2f}".format(100*sum_vpos / sum_vtot)+'%', \

"population:", "{:,.2f}".format(100*sum_vpos / sum_pop )+'%' )

print( "antibody:",

"{:,.2f}".format(100*sum_pos / sum_tot )+'%' )

# state trend analysis

n_d2_pos = 0

n_d2_neg = 0

n_d2_inc = 0

n_d2_dec = 0

d2_max = 0

d2_max_state = ''

d2_min = 0

d2_min_state = ''

death_max = 0

death_max_state = ''

for state in states:

n_samples = state['n_samples']

pos = state['positives'][n_samples-1]

d1 = state['pos_d1'][n_samples-1]

d2 = state['pos_d2'][n_samples-1]

double_d = state['days_to_double']

if d2 > d2_max :

d2_max = d2

d2_max_state = state['name']

if d2 < d2_min :

d2_min = d2

d2_min_state = state['name']

if d2 > 0:

n_d2_pos += 1

else:

n_d2_neg +=1

delta = d2 > state['pos_d2'][n_samples-3]

if delta :

n_d2_inc += 1

else:

n_d2_dec +=1

dd1 = state['death7'][n_samples-1]

if dd1 > death_max:

death_max = dd1

death_max_state = state['name']

print( 'max number of new cases: ', int(summary['max_d1']), ' on ', \

summary['max_d1_date'], ' in ', summary['max_d1_state'] )

print( 'max tests per million: ', int(summary['max_tpm']), ' on ', \

summary['max_tpm_date'], ' in ', summary['max_tpm_state'] )

print( 'd2 min = ',"{:,.2f}".format(d2_min),' in ',d2_min_state)

print( 'd2 max = ',"{:,.2f}".format(d2_max),' in ',d2_max_state)

print( 'max death average over last 7 days = ',"{:,.2f}".format(death_max), \

' in ',death_max_state)

print( n_d2_pos, ' states with increasing rate' )

print( n_d2_neg, ' states with decreasing rate' )

print( n_d2_inc, ' states with accelerating rate' )

print( n_d2_dec, ' states with deccelerating rate' )

sys.exit(0)