def assemble(kL, rL, conf):
#ufmt.pprint(conf)
#sys.exit()
# labels are in the first column
labels = generate_labels(kL, conf)
# these are the rows, values converted to str
pL = []
for vL in rL:
pL.append([str(n) for n in vL])
# next add dates as the first row
if not conf['no_dates']:
dL = get_dates(conf)
if conf['rate']:
dL.append('stat')
pL = [dL] + pL
# add a space to the first col, first row
labels = [''] + labels
if conf['total']:
# add a space to the first col, last row
labels.append('total')
# conf['totals'] contains the actual values
tL = conf['totals_line']
if conf['rate']:
tL.append(round(umath.stat(tL), 3))
# convert the last row to str as well
last = [str(v) for v in tL]
pL += [last]
# add an extra 2 spaces to each value in the last column
if conf['rate']:
for vL in pL:
vL[-1] = ' ' + vL[-1]
return labels, pL
import sys, os
base = os.environ.get('covid_base')
sys.path.insert(0, base)
sys.path.insert(1, base + '/myutil')
from ustrings import us_states
from umath import stat
from all_states import rL, labels
#------
us_states['DC'] = 'DC'
states = [us_states[e] for e in labels]
values = []
for vL in rL:
st = stat(vL[-15:])
values.append(st)
#---------------------
import plotly.express as px
fig = px.choropleth(locations=states,
locationmode="USA-states",
color=values,
scope="usa")
fig.show()
def calc(kL, rL, conf):
if conf['verbose']:
pprint(conf, 'fmt')
# here is where we trim according to -n
rL = trim_columns(rL, conf)
conf['totals_line'] = umath.totals(rL)
# if normalizing
# also adjust totals to total population
if conf['pop']:
kL, nL, total_pop = do_pop_normalization(kL, rL)
tL = conf['totals_line']
conf['totals_line'] = normalize(total_pop, tL)
rL = nL
if conf['average']:
rL = compute_average(rL, conf)
pL = []
# special case b/c eu key not in keylist
if 'eu' in conf['names']:
if kL[0] == ';;;Austria':
if conf['only']:
tL = [str(n) for n in conf['totals_line']]
return [';;;eu'], [conf['totals_line']]
# compute stats based on the last 7 days
# unless we trimmed above to fewer than 7 values
i = 7
# sort only
if not conf['rate']:
if conf['sort']:
pL = []
for kL, vL in zip(kL, rL):
pL.append([kL, vL])
# sort on the latest value
pL.sort(key=lambda t: t[1][-1], reverse=True)
kL = [t[0] for t in pL]
rL = [t[1] for t in pL]
# stats (rate) +/- sort
elif conf['rate']:
# keep each stat as float until after sort
stats = [round(umath.stat(vL[-i:]), 3) for vL in rL]
for kL, vL, st in zip(kL, rL, stats):
pL.append([kL, vL, st])
if conf['sort']:
# sort on the statistic
pL.sort(key=lambda t: t[2], reverse=True)
kL = []
rL = []
for t in pL:
kL.append(t[0])
t[1] += [str(t[2]).ljust(5)]
rL.append(t[1])
# trim the rows at this time
# totals are calculated but not yet attached
if conf['N']:
N = conf['N']
rL = rL[:N]
kL = kL[:N]
return kL, rL
# we do not need to filter:
# sL = counties['features']
# sL = [e for e in sL if e[u'properties'][u'STATE'] == fips]
#--------------------
# we need to construct a pandas data frame with
# fips stats
kL = []
for state in states:
skL = ukeys.key_list_for_search_term(D, state, mode='state')
kL.extend(skL)
rL = [D[k]['cases'][-10:] for k in kL]
sL = [umath.stat(vL) for vL in rL]
sL = [umath.quintiles(n) for n in sL]
#--------------------
fips = [ukeys.fips_for_key(k) for k in kL]
#--------------------
import pandas as pd
df = pd.DataFrame({'fips': fips, 'stat': sL})
#print(df)
#--------------------
# we need to construct a pandas data frame with
# fips stats
sys.path.insert(1, base + '/analysis')
from all_states import states, rL
# dict from 'Alabama' to 'AL'
from ustates import states
from ustates import state_to_abbrev as stD
abbrev = [stD[state] for state in states]
st = [umath.stat(vL[-7:]) for vL in rL]
#--------------------
import pandas as pd
df = pd.DataFrame(data={'state': abbrev, 'value': st})
#--------------------
import plotly.express as px
from ucolors import cL
fig = px.choropleth(df,
locations=abbrev,