'''
Create the calibrated sim for the King County results. Note: this script relies
on kc_synthpops.ppl, which is created by cache_sp_pop.py, as well as several
CSV files.
'''
# Standard packages
import os
import numpy as np
import pandas as pd
import sciris as sc
import covasim as cv
cv.check_save_version('1.5.0', die=True)
# Define the input files
inputs = 'inputs'
epi_data_file = f'{inputs}/20200719trim5_KingCounty_Covasim.csv'
age_data_file = f'{inputs}/20200719trim5_KingCounty_AgeHist.csv'
safegraph_file = f'{inputs}/KC_weeklyinteractions_071520.csv'
popfile_stem = f'{inputs}/kc_synthpops_normal_withstaff_seed'
def make_safegraph(sim):
''' Create interventions representing SafeGraph data '''
# Load data.values
fn = safegraph_file
df = pd.read_csv(fn)
week = df['week']
w = c = df['p.tot'].valueshiop
'''
Create the calibrated sim for the King County results. Note: this script relies
on kc_synthpops_with_ltcf_seed, which is created by create_sp_pop.py, as well as several
CSV files.
'''
# Standard packages
import os
import numpy as np
import pandas as pd
import sciris as sc
import covasim as cv
cv.check_save_version('1.7.2', die=False) # was 1.5.0
# Define the input files
inputs = 'inputs'
epi_data_file = f'{inputs}/20200628chop5_KingCounty_Covasim.csv'
age_data_file = f'{inputs}/20200628chop5_KingCounty_AgeHist.csv'
safegraph_file = f'{inputs}/KC_weeklyinteractions_070120.csv'
popfile_stem = f'{inputs}/kc_synthpops_normal_withstaff_seed'
popfile_stem_change = f'{inputs}/kc_synthpops_clustered_withstaff_seed'
def make_safegraph(sim, mobility_file):
''' Create interventions representing SafeGraph data '''
# Load data.values
if mobility_file is None:
fn = safegraph_file
else:
'''
Benchmark the intervention
'''
import os
import sciris as sc
import covasim as cv
import synthpops as sp
import covasim_schools as cvsch
from testing_in_schools import create_sim as cs
from testing_in_schools.testing_scenarios import generate_scenarios, generate_testing
from testing_in_schools.calibrate_model import evaluate_sim
cv.check_save_version('1.7.2', folder='gitinfo', comments={'SynthPops':sc.gitinfo(sp.__file__)})
debug = False
use_intervention = True
# NOTE: The following may be bypassed below by hard-coded pop_size and folder
folder = '../testing_in_schools/v20201015_225k'
pop_size = 2.25e5 # 1e5 2.25e4 2.25e5
calibfile = os.path.join(folder, 'pars_cases_begin=75_cases_end=75_re=1.0_prevalence=0.002_yield=0.024_tests=225_pop_size=225000.json')
def scenario(es, ms, hs):
return {
'pk': None,
'es': sc.dcp(es),
'ms': sc.dcp(ms),
'hs': sc.dcp(hs),
'uv': None,
}
def test_misc():
sc.heading('Testing miscellaneous functions')
sim_path = 'test_misc.sim'
json_path = 'test_misc.json'
gitinfo_path = 'test_misc.gitinfo'
fig_path = 'test_misc.png'
fig_comments = 'Test comment'
# Data loading
cv.load_data(csv_file)
cv.load_data(xlsx_file)
with pytest.raises(NotImplementedError):
cv.load_data('example_data.unsupported_extension')
with pytest.raises(ValueError):
cv.load_data(xlsx_file, columns=['missing_column'])
# Dates
d1 = cv.date('2020-04-04')
d2 = cv.date(sc.readdate('2020-04-04'))
ds = cv.date('2020-04-04', d2)
assert d1 == d2
assert d2 == ds[0]
with pytest.raises(ValueError):
cv.date([(2020, 4, 4)]) # Raises a TypeError which raises a ValueError
with pytest.raises(ValueError):
cv.date('Not a date')
cv.daydiff('2020-04-04')
# Run sim for more investigations
sim = cv.Sim(pop_size=500, verbose=0)
sim.run()
sim.plot(do_show=False)
# Saving and loading
cv.savefig(fig_path, comments=fig_comments)
cv.save(filename=sim_path, obj=sim)
cv.load(filename=sim_path)
# Version checks
cv.check_version('0.0.0') # Nonsense version
print('↑ Should complain about version')
with pytest.raises(ValueError):
cv.check_version('0.0.0', die=True)
# Git checks
cv.git_info(json_path)
cv.git_info(json_path, check=True)
# Poisson tests
c1 = 5
c2 = 8
for alternative in ['two-sided', 'larger', 'smaller']:
cv.poisson_test(c1, c2, alternative=alternative)
for method in ['score', 'wald', 'sqrt', 'exact-cond']:
cv.poisson_test(c1, c2, method=method)
with pytest.raises(ValueError):
cv.poisson_test(c1, c2, method='not a method')
# Test locations
for location in [None, 'viet-nam']:
cv.data.show_locations(location)
# Test versions
with pytest.raises(ValueError):
cv.check_save_version('1.3.2', die=True)
cv.check_save_version(cv.__version__,
filename=gitinfo_path,
comments='Test')
# Test PNG
try:
metadata = cv.get_png_metadata(fig_path, output=True)
assert metadata['Covasim version'] == cv.__version__
assert metadata['Covasim comments'] == fig_comments
except ImportError as E:
print(
f'Cannot test PNG function since pillow not installed ({str(E)}), skipping'
)
# Tidy up
remove_files(sim_path, json_path, fig_path, gitinfo_path)
return
'''Show optimized runs'''
# %Imports
import os
import sciris as sc
#from SPS_analysis import create_sim as cs
import create_sim as cs
import covasim as cv
import numpy as np
from covasim import utils as cvu
import pandas as pd
import random
cv.check_save_version('1.7.2',
die=False) # Initial analysis conducted using v1.5.0
def school_dict(sim, school_dicts):
school_results = dict()
label = sim.base_sim.label
school_results[label] = dict()
if len(sim.sims) > 1: # if running multiple seeds, print all of them
school_results[label]['num_teachers'] = []
school_results[label]['num_teachers_tested'] = []
school_results[label]['num_teachers_test_pos'] = []
school_results[label]['num_teachers_screen_pos'] = []
school_results[label]['num_teacher_cases'] = []
school_results[label]['num_staff'] = []
school_results[label]['num_staff_tested'] = []
school_results[label]['num_staff_test_pos'] = []
'''Show optimized runs'''
import sciris as sc
from reopening_scenarios import create_sim as cs
from calibration import plot_calibration as pltcal
import covasim as cv
cv.check_save_version('1.4.7', die=True)
if __name__ == "__main__":
rerun = True
do_save = True
do_plot = True
n_reps = 20
date = '2020-06-17'
if rerun:
indices = range(n_reps)
jsonfile = 'optimization_v12_safegraph_061720.json'
json = sc.loadjson(jsonfile)
all_sims = []
for index in indices:
entry = json[index]
pars = entry['pars']
pars['rand_seed'] = int(entry['index'])
all_sims.append(cs.create_sim(pars=pars))
msim = cv.MultiSim(sims=all_sims)
msim.run(reseed=False,
'''
Trajetories from the prediction
'''
import sciris as sc
import covasim as cv
import pylab as pl
import matplotlib as mplt
# pl.switch_backend('agg')
# Check version
cv.check_save_version('2.0.0', filename='covasim_version.json', die=True)
mplt.rcParams['font.family'] = 'Roboto'
do_plot = 1
do_save = 0
do_show = 1
verbose = 1
seed = 1
scenario = ['2ndwave', '1stwave'
][0] # Set a number to pick a scenario from the available options
tti_scen = ['current'][0] # Ditt0
version = 'v1'
date = '2020nov29'
folder = f'results_FINAL_{date}'
file_path = f'{folder}/phase_{version}' # Completed below
data_path = 'UK_Covid_cases_october15.xlsx' # 'UK_Covid_cases_december6.xlsx'
fig_path = f'{file_path}_{scenario}.png'
