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read_stats.py
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read_stats.py
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# this script generates the general statistics about covid-19 for today
# run with python read_stats.py
# Using PyMuPDF
# https://github.com/pymupdf/PyMuPDF
# for Code Snippets : https://pymupdf.readthedocs.io/en/latest/tutorial.html
import fitz
import re
import datetime
from os import path
import utils
#TODO generate all today's data (including cases for each city) in json format
#TODO generate it for every day since september
"""
takes a string containing a number (whose digits may be separated by spaces, like "some texxt here 2 356 861")
and returns the number within it (in previous example returns 2356861)
"""
def process_number(string_with_number) :
list_strings = string_with_number.split()
final_string = ""
for s in list_strings :
if s.isdigit() :
final_string += s
return int(final_string)
"""
takes a string containing a percentage (like "some texxt here 1,7%", "some random txt here 37%")
and returns the percentage within it (in previous examples returns "1,7%", "37%")
"""
def process_percentage(string_with_percentage) :
list_percentages = re.findall(r"\d+(?:,*\d+)?%",string_with_percentage)
if len(list_percentages) == 1 :
return list_percentages[0]
"""
takes a string containing an incidence factor (like "some texxt here 150/100.000 ", "some random txt here 110,15/100.000 ")
and returns the incidence factor within it (in previous examples returns "150/100.000", "110,15/100.000")
"""
def process_incidence(string_with_incidence) :
list_incidences = re.findall(r"\d+(?:,*\d+)?/100\.000",string_with_incidence)
if len(list_incidences) == 1 :
return list_incidences[0]
def read_stats(pdf_filename) :
total_cases = 0
new_cases = 0
total_excluded_cases = 0
new_excluded_cases = 0
total_deaths = 0
new_deaths = 0
total_recovered = 0
new_recovered = 0
total_active_cases = 0
total_cumul_incidence_rate = ""
last_24h_incidence_rate = ""
case_fatality_rate = ""
recovery_rate = ""
total_severe_cases = 0
last_24h_severe_cases = 0
total_under_intubation = 0
total_non_invasive_ventilation = 0
covid_beds_occupation = ""
last_24h_tests = 0
total_tests = 0
scraping_folder = "D:/morocovid/pdfBulletins"
filename = path.join(scraping_folder,pdf_filename)
doc = fitz.open(filename)
for page in doc :
text = page.getText()
break
text = text.split("\n")
for i in range(len(text)) :
if 'Cas conf' in text[i] :
total_cases_str = text[i+1]
new_cases_str = text[i+2]
elif 'Cas exclus' in text[i] :
total_excluded_cases_str = text[i+1]
new_excluded_cases_str = text[i+2]
elif 'Décès' in text[i] :
total_deaths_str = text[i+1]
new_deaths_str = text[i+2]
elif 'Guéris' in text[i] :
total_recovered_str = text[i+1]
new_recovered_str = text[i+2]
elif 'Cas actifs' in text[i] :
total_active_cases_str = text[i+1]
elif 'Incidence cumul' in text[i] :
total_cumul_incidence_rate_str = text[i+1]
elif 'Incidence de 24H' in text[i] :
last_24h_incidence_rate_str = text[i+1]
elif 'Taux de létalité' in text[i] :
case_fatality_rate_str = text[i+1]
elif 'Taux de guérison' in text[i] :
recovery_rate_str = text[i+1]
elif 'Nombre total' in text[i] :
total_severe_cases_str = text[i+1]
elif 'Les nouveaux cas de 24 heures' in text[i] :
last_24h_severe_cases_str = text[i+1]
elif 'intubation' in text[i] :
total_under_intubation_str = text[i+1]
elif 'invasive' in text[i] :
total_non_invasive_ventilation_str = text[i+1]
elif 'réanimation dédiés' in text[i] :
covid_beds_occupation_str = text[i+1]
total_cases = process_number(total_cases_str)
new_cases = process_number(new_cases_str)
total_excluded_cases = process_number(total_excluded_cases_str)
new_excluded_cases = process_number(new_excluded_cases_str)
last_24h_tests = new_excluded_cases + new_cases
total_tests = total_excluded_cases + total_cases
total_deaths = process_number(total_deaths_str)
new_deaths = process_number(new_deaths_str)
total_recovered = process_number(total_recovered_str)
new_recovered = process_number(new_recovered_str)
total_active_cases = process_number(total_active_cases_str)
total_cumul_incidence_rate = process_incidence(total_cumul_incidence_rate_str)
last_24h_incidence_rate = process_incidence(last_24h_incidence_rate_str)
case_fatality_rate = process_percentage(case_fatality_rate_str)
recovery_rate = process_percentage(recovery_rate_str)
total_severe_cases = process_number(total_severe_cases_str)
last_24h_severe_cases = process_number(last_24h_severe_cases_str)
total_under_intubation = process_number(total_under_intubation_str)
total_non_invasive_ventilation = process_number(total_non_invasive_ventilation_str)
covid_beds_occupation = process_percentage(covid_beds_occupation_str)
dict_corona = {
"total_cases" : total_cases,
"new_cases" : new_cases,
"total_excluded_cases" : total_excluded_cases,
"new_excluded_cases" : new_excluded_cases,
"last_24h_tests" : last_24h_tests,
"total_tests: " : total_tests,
"total_deaths" : total_deaths,
"new_deaths" : new_deaths,
"total_recovered" : total_recovered,
"new_recovered" : new_recovered,
"total_active_cases" : total_active_cases,
"total_cumul_incidence_rate" : total_cumul_incidence_rate,
"last_24h_incidence_rate" : last_24h_incidence_rate,
"case_fatality_rate" : case_fatality_rate,
"recovery_rate" : recovery_rate,
"total_severe_cases" : total_severe_cases,
"last_24h_severe_cases" : last_24h_severe_cases,
"total_under_intubation" : total_under_intubation,
"total_non_invasive_ventilation" : total_non_invasive_ventilation,
"covid_beds_occupation" : covid_beds_occupation
}
return dict_corona
if __name__ == "__main__":
print(read_stats(utils.get_todays_fileName("pdf")))