def plot_year_on_year_fold_change_by_site(df):
df = aggregate_counts(df)
fold_changes = calculate_change_by_month(df)
palette = load_color_palette()
fig = plt.figure()
ax = fig.gca()
for i, village in enumerate(df['Study Sites'].unique()):
if village in intervention_villages:
linestyle = '-o'
arm = 'int'
else:
linestyle = '--o'
arm = 'con'
sdf = fold_changes[fold_changes[village] != -1]
ax.plot(sdf['month'],
sdf[village] * 100,
linestyle,
color=palette[i],
label='%s %s' % (village, arm))
ax.set_ylabel('year on year pct change in vector count')
ax.set_xticklabels(['', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
ax.legend()
plt.savefig(
os.path.join(plotdir, 'HLC_year_on_year_pct_change_by_site.png'))
plt.show()
def weekly_deaths():
df = pd.read_csv(
os.path.join(box_data_path, 'Cleaned Data',
'%s_jg_aggregated_covidregion.csv' % LL_date))
df['date'] = pd.to_datetime(df['date'])
df = df[df['date'] >= date(2020, 3, 1)]
df = df[df['deaths'] > 0]
firstday = np.min(df['date'])
df['week'] = df['date'].apply(lambda x: int((x - firstday).days / 7))
df = df.groupby('week')[['deaths']].agg(np.sum).reset_index()
df = df.sort_values(by='week')
df['date'] = df['week'].apply(lambda x: firstday + timedelta(days=7 * x))
df2 = copy.copy(df[['week', 'deaths']])
df2['date'] = df2['week'].apply(
lambda x: firstday + timedelta(days=7 * x + 6))
df = pd.concat([df, df2])
df = df.sort_values(by='date')
palette = load_color_palette('wes')
formatter = mdates.DateFormatter("%m-%d")
sns.set_style('whitegrid', {'axes.linewidth': 0.5})
fig = plt.figure()
ax = fig.gca()
ax.plot()
ax.plot(df['date'], df['deaths'], '-', color=palette[0])
ax.set_ylabel('deaths')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
fig.savefig(os.path.join(plot_path, 'IL_weekly_deaths_LL%s.png' % LL_date))
fig.savefig(os.path.join(plot_path, 'IL_weekly_deaths_LL%s.pdf' % LL_date),
format='PDF')
def plot_by_intervention(adf, plotname) :
num_years = len(adf['year'].unique())
num_targets = len(adf['target'].unique())
ann_prev_channel = 'annual mean Infected'
day_prev_channel = 'year_end_Infected'
sweep_variables = ["x_Temporary_Larval_Habitat",
'dirus_Anthropophily',
]
sns.set_style('whitegrid', {'axes.linewidth' : 0.5})
palette = load_color_palette('wes')
for (x_temp, anth), df in adf.groupby(sweep_variables) :
fig1 = plt.figure('%s Anth%.2f LH%.1f prevalence' % (plotname, anth, x_temp), figsize=(10,6))
fig1.subplots_adjust(left=0.07, right=0.95)
axes1 = [fig1.add_subplot(num_targets, num_years, i + 1) for i in range(num_years * num_targets)]
fig2 = plt.figure('%s Anth%.2f LH%.1f fraction eliminated' % (plotname, anth, x_temp), figsize=(10,6))
fig2.subplots_adjust(left=0.07, right=0.95)
axes2 = [fig2.add_subplot(num_targets, num_years, i + 1) for i in range(num_years * num_targets)]
for i, ((target, year), gdf) in enumerate(df.groupby(['target', 'year'])) :
ax1 = axes1[i]
ax2 = axes2[i]
for j, ((intervention, ivm_duration), ddf) in enumerate(gdf.groupby(['intervention','IVM duration'])) :
if 'drug' in intervention :
if 'ivermectin' not in intervention :
label = 'DP'
else :
label = 'DP + End%d' % ivm_duration
elif 'ivermectin' in intervention :
label = 'End%d' % ivm_duration
else :
label = ''
sdf = ddf.groupby('coverage')[ann_prev_channel].agg(np.mean).reset_index()
ax1.plot(sdf['coverage'], sdf[ann_prev_channel], color=palette[j], label=label)
sdf = ddf.groupby('coverage')[day_prev_channel].agg(count_zero).reset_index()
ax2.plot(sdf['coverage'], sdf[day_prev_channel], color=palette[j], label=label)
for ax in [ax1, ax2] :
ax.set_xlim(0,1)
if i%num_years > 0 :
ax.set_yticklabels([])
else :
ax.set_ylabel(target)
if i < num_years :
ax.set_title('year %d' % year)
if i < len(axes1)-num_years :
ax.set_xticklabels([])
else :
ax.set_xlabel('coverage')
ax1.set_ylim(0, 0.2)
ax2.set_ylim(0, 1)
axes1[-1].legend()
axes2[-1].legend()
fig2.savefig(os.path.join(plotdir, '%s Anth%.2f LH%.1f fraction eliminated.png' % (plotname, anth, x_temp)))
fig2.savefig(os.path.join(plotdir, '%s Anth%.2f LH%.1f fraction eliminated.pdf' % (plotname, anth, x_temp)),
format='PDF')
def plot_county_line_by_region(region_key) :
df = assign_counties_restore_region()
df = df.sort_values(by=['update_date', region_key, 'County'])
regions = pd.DataFrame( { 'region' : df[region_key].unique(),
'cindex' : range(len(df[region_key].unique()))})
regions = regions.set_index('region')
fig_cases = setup_fig('cases')
fig_tpr = setup_fig('TPR')
palette = load_color_palette('wes')
ci = 0
for ri, (reg, rdf) in enumerate(df.groupby(region_key)) :
for county, cdf in rdf.groupby('County') :
cdf['daily_pos'] = np.insert(np.diff(cdf['Positive_Cases']), 0, 0)
cdf['daily_test'] = np.insert(np.diff(cdf['Tested']), 0, 0)
cdf.loc[cdf['daily_test'] == 0, 'daily_test'] = 1
cdf['daily_tpr'] = cdf['daily_pos']/cdf['daily_test']
ax = fig_cases.add_subplot(9,12,ci+1)
colorbin = regions.at[reg, 'cindex']%len(palette)
if len(cdf) < 10 :
ax.bar(cdf['update_date'].values[1:], np.diff(cdf['Positive_Cases']),
align='center', color=palette[colorbin], linewidth=0, alpha=0.3)
max_pos = np.max(cdf['Positive_Cases'])
else :
cdf['moving_ave'] = cdf['daily_pos'].rolling(window=7, center=False).mean()
max_pos = np.max(cdf['moving_ave'])
ax.plot(cdf['update_date'], cdf['moving_ave'], '-', color=palette[colorbin])
ax.fill_between(cdf['update_date'].values, [0]*len(cdf['moving_ave']), cdf['moving_ave'],
linewidth=0, color=palette[colorbin], alpha=0.3)
format_axis(ax, ci, df, county, max_pos, 'cases')
ax = fig_tpr.add_subplot(9,12,ci+1)
cdf = cdf[cdf['Positive_Cases'] <= cdf['Tested']]
if len(cdf) < 10 :
ax.plot(cdf['update_date'], cdf['daily_tpr'], '-', color=palette[colorbin])
ax.fill_between(cdf['update_date'].values, [0] * len(cdf['daily_tpr']), cdf['daily_tpr'],
linewidth=0, color=palette[colorbin], alpha=0.3)
max_pos = np.max(cdf['daily_tpr'])
else :
cdf['moving_ave'] = cdf['daily_tpr'].rolling(window=7, center=False).mean()
ax.plot(cdf['update_date'], cdf['moving_ave'], '-', color=palette[colorbin])
ax.fill_between(cdf['update_date'].values, [0]*len(cdf['moving_ave']), cdf['moving_ave'],
linewidth=0, color=palette[colorbin], alpha=0.3)
max_pos = np.max(cdf['moving_ave'])
format_axis(ax, ci, df, county, max_pos, 'TPR')
ci += 1
fname = 'region' if region_key == 'restore_region' else 'covid_region'
fig_cases.savefig(os.path.join(plot_dir, 'idph_public_county_%s_cases.pdf' % fname), format='PDF')
fig_tpr.savefig(os.path.join(plot_dir, 'idph_public_county_%s_tpr.pdf' % fname), format='PDF')
plt.close(fig_cases)
plt.close(fig_tpr)
def plot_IL_cases() :
IL_fname = os.path.join(datapath, 'Corona virus reports', 'illinois_public.csv')
df = pd.read_csv(IL_fname, parse_dates=['update_date'])
df = df.rename(columns={'update_date' : 'date'})
df = df.sort_values(by='date')
df = df.fillna(0)
palette = load_color_palette('wes')
formatter = mdates.DateFormatter("%m-%d")
sns.set_style('whitegrid', {'axes.linewidth' : 0.5})
fig = plt.figure(figsize=(8,8))
fig.subplots_adjust(left=0.1, right=0.97, bottom=0.05, top=0.97)
ax = fig.add_subplot(4,1,1)
df['daily_pos'] = np.insert(np.diff(df['tests_pos']), 0, 0)
ax.bar(df['date'].values[1:], np.diff(df['tests_pos']),
align='center', color=palette[0], linewidth=0, alpha=0.5)
df['moving_ave'] = df['daily_pos'].rolling(window=7, center=False).mean()
ax.plot(df['date'], df['moving_ave'], '-', color=palette[0])
ax.set_ylabel('positives')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax = fig.add_subplot(4,1,2)
ax.bar(df['date'].values[1:], df['new_tests'][1:],
align='center', color=palette[1], linewidth=0, alpha=0.5)
df['moving_ave'] = df['new_tests'].rolling(window=7, center=False).mean()
ax.plot(df['date'], df['moving_ave'], '-', color=palette[1])
ax.set_ylabel('tests')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax = fig.add_subplot(4,1,3)
df['daily_tpr'] = df['daily_pos']/df['new_tests']
ax.bar(df['date'].values[1:], df['daily_tpr'][1:],
align='center', color=palette[2], linewidth=0, alpha=0.5)
df['moving_ave'] = df['daily_tpr'].rolling(window=7, center=False).mean()
ax.plot(df['date'], df['moving_ave'], '-', color=palette[2])
ax.set_ylabel('TPR')
ax.set_ylim(0, 0.3)
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
print(df[['date', 'daily_tpr']].tail(20))
ax = fig.add_subplot(4,1,4)
ax.bar(df['date'].values[1:], np.diff(df['deaths']),
align='center', color=palette[3], linewidth=0, alpha=0.5)
df['daily_death'] = np.insert(np.diff(df['deaths']), 0, 0)
df['moving_ave'] = df['daily_death'].rolling(window=7, center=False).mean()
ax.plot(df['date'], df['moving_ave'], '-', color=palette[3])
ax.set_ylabel('deaths')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
fig.savefig(os.path.join(plot_dir, 'idph_public_cases_and_deaths.pdf'), format='PDF')
plt.close(fig)
def plot_by_event_count(adf, plotname) :
num_years = len(adf['year'].unique())
num_targets = len(adf['target'].unique())
day_prev_channel = 'year_end_Infected'
palette = load_color_palette('wes')
sns.set_style('whitegrid', {'axes.linewidth' : 0.5})
fig = plt.figure(plotname, figsize=(10, 6))
fig.subplots_adjust(left=0.07, right=0.95)
axes = [fig.add_subplot(num_targets, num_years, i + 1) for i in range(num_years * num_targets)]
for i, ((target, year), gdf) in enumerate(adf.groupby(['target', 'year'])):
ax = axes[i]
for j, ((intervention, ivm_duration), ddf) in enumerate(gdf.groupby(['intervention', 'IVM duration'])):
if 'drug' in intervention:
if 'ivermectin' not in intervention:
label = 'DP'
else:
label = 'DP + End%d' % ivm_duration
elif 'ivermectin' in intervention:
label = 'End%d' % ivm_duration
else:
label = ''
cdf = adf[(adf['target'] == target) & (adf['intervention'] == intervention) &
(adf['IVM duration'] == ivm_duration) & (adf['year'] <= year)]
num_seeds = np.max(ddf['Run_Number'])
zeroes = ddf.groupby('coverage')[day_prev_channel].agg(count_zero).reset_index()
events = cdf.groupby('coverage')['Event_Count'].agg(np.sum).reset_index()
ax.plot(events['Event_Count']/num_seeds, zeroes[day_prev_channel], color=palette[j], label=label)
if i % num_years > 0:
ax.set_yticklabels([])
else:
ax.set_ylabel(target)
if i < num_years:
ax.set_title('year %d' % year)
if i < len(axes) - num_years:
ax.set_xticklabels([])
else:
ax.set_xlabel('doses distributed')
ax.set_ylim(0, 1)
ax.set_xlim(0, 1700)
axes[-1].legend()
fig.savefig(os.path.join(plotdir, '%s fraction eliminated v doses.png' % plotname))
fig.savefig(os.path.join(plotdir, '%s fraction eliminated v doses.pdf' % plotname), format='PDF')
def plot_number_tested_timeseries(adf):
cols = ['num_delivered', 'num_tested']
df = adf.groupby('delivery_date')[cols].agg(np.sum).reset_index()
df = df.sort_values(by='delivery_date')
df['excess_delivered'] = df['num_delivered'] - df['num_tested']
sdf = df[(df['delivery_date'] < date(2020, 8, 10))]
print('avg daily deliveries during pilot', np.mean(sdf['num_delivered']))
sdf = df[(df['delivery_date'] >= date(2020, 8, 10))
& (df['delivery_date'] < date(2021, 1, 10))]
print('avg daily deliveries during scale up',
np.mean(sdf['num_delivered']))
sns.set_style('whitegrid', {'axes.linewidth': 0.5})
fig = plt.figure(figsize=(8, 4))
ax = fig.gca()
palette = [load_color_palette('wes')[x] for x in [3, 4]]
formatter = mdates.DateFormatter("%m-%d")
ax.bar(df['delivery_date'],
df['num_tested'],
color=palette[1],
align='center',
linewidth=0,
alpha=0.5)
ax.bar(df['delivery_date'],
df['excess_delivered'],
bottom=df['num_tested'],
color=palette[0],
align='center',
linewidth=0,
alpha=0.5)
for c, col in enumerate(cols):
df['moving_ave'] = df[col].rolling(window=7, center=True).mean()
ax.plot(df['delivery_date'],
df['moving_ave'],
'-',
color=palette[c],
label=col.split('_')[-1])
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.legend()
ax.set_ylabel('number of individuals')
ax.set_xlabel('date of delivery')
fig.savefig(os.path.join(plotdir, 'daily_number_tested.png'))
fig.savefig(os.path.join(plotdir, 'daily_number_tested.pdf'), format='PDF')
def plot_hlc_by_site():
df = load_df()
palette = load_color_palette()
fig = plt.figure('HLC vector counts', figsize=(16, 10))
axes = [fig.add_subplot(4, 4, x + 1) for x in range(14)]
fig.subplots_adjust(left=0.05, right=0.97, top=0.95, bottom=0.05)
linestyles = ['-o', '--o']
for locindex, (location, ldf) in enumerate(df.groupby('Position')):
grouped = ldf.groupby(['Study Sites', 'date'])
sdf = grouped['mosquito ID'].agg(len).reset_index()
sdf.rename(columns={'mosquito ID': 'vector count'}, inplace=True)
sdf.sort_values(by=['Study Sites', 'date'], inplace=True)
for s, (site, ssdf) in enumerate(sdf.groupby('Study Sites')):
ax = axes[s]
ax.plot(ssdf['date'],
ssdf['vector count'],
linestyles[locindex],
color=palette[s],
label=location if s == 0 else '')
ax.set_title(site)
ax.set_xlim(np.min(df['date']), np.max(df['date']))
if s < 10:
ax.set_xticklabels([])
else:
ax.set_xticklabels([
'Jun \'16', '', '', 'Dec \'16', '', '', 'Jun \'17', '', '',
'Dec \'17', ''
])
if s == 0 and locindex > 0:
ax.legend()
if s % 4 == 0:
ax.set_ylabel('mosquito count')
if locindex == 1 and site in intervention_villages:
ymin, ymax = ax.get_ylim()
ax.fill_between([atsb_start, np.max(df['date'])], [ymin, ymin],
[ymax, ymax],
color='k',
alpha=0.25,
linewidth=0)
ax.set_ylim(ymin, ymax)
plt.savefig(os.path.join(plotdir, 'HLC_by_site.png'))
plt.savefig(os.path.join(plotdir, 'HLC_by_site.pdf'), format='PDF')
def plot_by_anth(adf, plotname) :
num_years = len(adf['year'].unique())
num_targets = len(adf['target'].unique())
day_prev_channel = 'year_end_Infected'
palette = load_color_palette('wes')
sns.set_style('whitegrid', {'axes.linewidth' : 0.5})
fig = plt.figure(plotname, figsize=(10, 6))
fig.subplots_adjust(left=0.07, right=0.95)
axes = [fig.add_subplot(num_targets, num_years, i + 1) for i in range(num_years * num_targets)]
for i, ((target, year), gdf) in enumerate(adf.groupby(['target', 'year'])):
ax = axes[i]
for j, ((intervention, ivm_duration), ddf) in enumerate(gdf.groupby(['intervention', 'IVM duration'])):
if 'drug' in intervention:
if 'ivermectin' not in intervention:
label = 'DP'
else:
label = 'DP + End%d' % ivm_duration
elif 'ivermectin' in intervention:
label = 'End%d' % ivm_duration
else:
label = ''
sdf = ddf.groupby('dirus_Anthropophily')[day_prev_channel].agg(count_zero).reset_index()
ax.plot(sdf['dirus_Anthropophily'], sdf[day_prev_channel], color=palette[j], label=label)
if i % num_years > 0:
ax.set_yticklabels([])
else:
ax.set_ylabel(target)
if i < num_years:
ax.set_title('year %d' % year)
if i < len(axes) - num_years:
ax.set_xticklabels([])
else:
ax.set_xlabel('dirus anthropophily')
ax.set_ylim(0, 1)
ax.set_xlim(0, 1)
axes[-1].legend()
fig.savefig(os.path.join(plotdir, '%s fraction eliminated.png' % plotname))
fig.savefig(os.path.join(plotdir, '%s fraction eliminated.pdf' % plotname), format='PDF')
def plot_LL_all_IL():
df = pd.read_csv(
os.path.join(box_data_path, 'Cleaned Data',
'%s_jg_aggregated_covidregion.csv' % LL_date))
df = df.groupby('date')[['cases', 'deaths',
'admissions']].agg(np.sum).reset_index()
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values(by='date')
df = df[df['date'] >= date(2020, 3, 15)]
palette = load_color_palette('wes')
formatter = mdates.DateFormatter("%m-%d")
sns.set_style('whitegrid', {'axes.linewidth': 0.5})
fig = plt.figure(figsize=(8, 6))
fig.subplots_adjust(left=0.1, right=0.97, bottom=0.05, top=0.97)
def plot_data(adf, ax, col, color):
ax.bar(adf['date'].values,
adf[col],
align='center',
color=color,
linewidth=0,
alpha=0.5)
adf['moving_ave'] = adf[col].rolling(window=7, center=True).mean()
ax.plot(adf['date'], adf['moving_ave'], '-', color=color)
ax.set_ylabel('positives')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.set_ylabel(col)
ax = fig.add_subplot(3, 1, 1)
plot_data(df, ax, 'cases', palette[0])
ax = fig.add_subplot(3, 1, 2)
plot_data(df, ax, 'admissions', palette[4])
ax = fig.add_subplot(3, 1, 3)
plot_data(df, ax, 'deaths', palette[3])
fig.savefig(os.path.join(plot_path, 'IL_cases_deaths_LL%s.png' % LL_date))
fig.savefig(os.path.join(plot_path, 'IL_cases_deaths_LL%s.pdf' % LL_date),
format='PDF')
def plot_by_ivm_duration(df, plotname) :
num_years = len(df['year'].unique())
num_targets = len(df['target'].unique())
min_ivm_duration = np.min(df[df['IVM duration'] > 0]['IVM duration'])
max_ivm_duration = np.max(df['IVM duration'])
day_prev_channel = 'year_end_Infected'
sns.set_style('whitegrid', {'axes.linewidth' : 0.5})
palette = load_color_palette('wes')
fig = plt.figure(plotname, figsize=(10,6))
fig.subplots_adjust(left=0.07, right=0.95)
axes = [fig.add_subplot(num_targets, num_years, i + 1) for i in range(num_years * num_targets)]
for i, ((target, year), gdf) in enumerate(df.groupby(['target', 'year'])) :
ax = axes[i]
for j, (intervention, ddf) in enumerate(gdf.groupby(['intervention'])) :
sdf = ddf.groupby('IVM duration')[day_prev_channel].agg(count_zero).reset_index()
if 'ivermectin' not in intervention :
ax.plot([min_ivm_duration, max_ivm_duration], [sdf[day_prev_channel], sdf[day_prev_channel]],
color=palette[j], label=intervention)
else :
ax.plot(sdf['IVM duration'], sdf[day_prev_channel], color=palette[j], label=intervention)
if i%num_years > 0 :
ax.set_yticklabels([])
else :
ax.set_ylabel(target)
if i < num_years :
ax.set_title('year %d' % year)
if i < len(axes)-num_years :
ax.set_xticklabels([])
else :
ax.set_xlabel('IVM duration')
ax.set_ylim(0, 1)
axes[-1].legend()
fig.savefig(os.path.join(plotdir, '%s fraction eliminated v IVM duration.png' % plotname))
fig.savefig(os.path.join(plotdir, '%s fraction eliminated v IVM duration.pdf' % plotname), format='PDF')
def finalize(self, all_data):
selected = [data for sim, data in all_data.items()]
if len(selected) == 0:
print("No data have been returned... Exiting...")
return
df = pd.concat(selected).reset_index(drop=True)
num_nodes = len(df['node'].unique())
num_channels = len(self.spatial_channels)
sns.set_style('white', {'axes.linewidth': 0.5})
palette = load_color_palette()
fig = plt.figure('Spatial Outputs')
for c, channel in enumerate(self.spatial_channels):
gdf = df.groupby(['node',
'time'])[channel].agg([np.mean, top95,
bot5]).reset_index()
for n, (node, ndf) in enumerate(gdf.groupby('node')):
ax = fig.add_subplot(num_channels, num_nodes,
c * num_nodes + n + 1)
ax.plot(ndf['time'], ndf['mean'], color=palette[n], label=node)
ax.fill_between(ndf['time'],
ndf['bot5'],
ndf['top95'],
color=palette[n],
alpha=0.3,
linewidth=0)
if n == 0:
ax.set_ylabel(channel)
if c == 0:
ax.set_title('node %d' % node)
if c == num_channels - 1:
ax.set_xlabel('day')
plt.show()
def plot_emresource(scale=''):
ems_regions = {
'northcentral': [1, 2],
'northeast': [7, 8, 9, 10, 11],
'central': [3, 6],
'southern': [4, 5]
}
ref_df = pd.read_csv(
os.path.join(datapath, 'covid_IDPH', 'Corona virus reports',
'emresource_by_region.csv'))
sxmin = '2020-03-24'
xmin = datetime.strptime(sxmin, '%Y-%m-%d')
xmax = datetime.today()
datetoday = xmax.strftime('%y%m%d')
ref_df['suspected_and_confirmed_covid_icu'] = ref_df[
'suspected_covid_icu'] + ref_df['confirmed_covid_icu']
ref_df['date'] = pd.to_datetime(ref_df['date_of_extract'])
first_day = datetime.strptime('2020-03-24', '%Y-%m-%d')
ref_df = ref_df.rename(
columns={
'confirmed_covid_deaths_prev_24h': 'deaths',
'confirmed_covid_icu': 'ICU conf',
'confirmed_covid_on_vents': 'vents conf',
'suspected_and_confirmed_covid_icu': 'ICU conf+susp',
'covid_non_icu': 'non ICU'
})
channels = ['ICU conf+susp', 'ICU conf', 'vents conf', 'deaths', 'non ICU']
ref_df = ref_df[['date', 'covid_region'] + channels]
palette = load_color_palette('wes')
formatter = mdates.DateFormatter("%m-%d")
fig_all = plt.figure(figsize=(10, 8))
fig = plt.figure(figsize=(14, 10))
fig.subplots_adjust(left=0.07,
right=0.97,
top=0.95,
bottom=0.05,
hspace=0.25)
def format_plot(ax):
ax.set_xlim(xmin, )
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
if scale == 'log':
ax.set_yscale('log')
for ri, (restore_region, ems_list) in enumerate(ems_regions.items()):
ax_all = fig_all.add_subplot(2, 2, ri + 1)
ax = fig.add_subplot(4, 6, 6 * ri + 1)
pdf = ref_df[ref_df['covid_region'].isin(ems_list)].groupby(
'date').agg(np.sum).reset_index()
for (c, name) in enumerate(channels):
if name == 'non ICU':
df = pdf[pdf['date'] >= date(2020, 5, 6)]
else:
df = pdf
df['moving_ave'] = df[name].rolling(window=7, center=True).mean()
ax_all.plot(df['date'].values,
df['moving_ave'],
color=palette[c],
label=name)
ax_all.scatter(df['date'].values,
df[name],
s=10,
linewidth=0,
color=palette[c],
alpha=0.3,
label='')
ax.plot(df['date'].values,
df['moving_ave'],
color=palette[c],
label=name)
ax.scatter(df['date'].values,
df[name],
s=10,
linewidth=0,
color=palette[c],
alpha=0.3,
label='')
ax_all.set_title(restore_region)
format_plot(ax_all)
if ri == 1:
ax_all.legend()
format_plot(ax)
ax.set_ylabel(restore_region)
ax.set_title('total')
for ei, ems in enumerate(ems_list):
ax = fig.add_subplot(4, 6, 6 * ri + 1 + ei + 1)
df = ref_df[ref_df['covid_region'] == ems]
for (c, name) in enumerate(channels):
df['moving_ave'] = df[name].rolling(window=7,
center=True).mean()
ax.plot(df['date'].values,
df['moving_ave'],
color=palette[c],
label=name)
ax.scatter(df['date'].values,
df[name],
s=10,
linewidth=0,
color=palette[c],
alpha=0.3,
label='')
ax.set_title('covid region %d' % ems)
format_plot(ax)
if ems == 2:
ax.legend(bbox_to_anchor=(1.5, 1))
fig_all.savefig(
os.path.join(plotdir, 'EMResource_by_restore_region_%s.png' % scale))
fig_all.savefig(os.path.join(
plotdir, 'EMResource_by_restore_region_%s.pdf' % scale),
format='PDF')
fig.savefig(
os.path.join(plotdir, 'EMResource_by_covid_region_%s.png' % scale))
fig.savefig(os.path.join(plotdir,
'EMResource_by_covid_region_%s.pdf' % scale),
format='PDF')
def finalize(self, all_data):
selected = [data for sim, data in all_data.items()]
if len(selected) == 0:
print("No data have been returned... Exiting...")
return
adf = pd.concat(selected).reset_index(drop=True)
num_migration_types = len(adf['MigrationType'].unique())
sns.set_style('white', {'axes.linewidth': 0.5})
palette = load_color_palette()
fig = plt.figure('Migration', figsize=(16, 7))
fig.subplots_adjust(right=0.98, left=0.05, wspace=0.25)
for m, (mig, mdf) in enumerate(adf.groupby('MigrationType')):
ax = fig.add_subplot(num_migration_types, 4, m * 4 + 1)
num_events = mdf.groupby('Run_Number')['Event'].agg(len).values
sns.distplot(num_events, ax=ax, color=palette[m], label=mig)
ax.set_xlabel('number of trips')
ax.set_ylabel('frac of sims')
ax.legend()
ax = fig.add_subplot(num_migration_types, 4, m * 4 + 2)
for r, rdf in mdf.groupby('Run_Number'):
sns.kdeplot(rdf['AgeYears'],
ax=ax,
color=palette[m],
linewidth=0.5,
alpha=0.5,
label='')
sns.kdeplot(mdf['AgeYears'], ax=ax, color=palette[m], label='')
ax.set_xlabel('Traveler age')
ax.set_ylabel('fraction of trips')
ax.set_xlim(-5, 205)
ax = fig.add_subplot(num_migration_types, 4, m * 4 + 3)
for r, rdf in mdf.groupby('Run_Number'):
trip_durations = []
grouped = rdf.groupby('IndividualID')
for gn, gdf in grouped:
t = gdf['Time'].values
d = [
t[2 * x + 1] - t[2 * x] for x in range(int(len(t) / 2))
]
trip_durations.extend(d)
sns.kdeplot(trip_durations,
ax=ax,
linewidth=0.5,
color=palette[m])
ax.set_xlabel('days at destination')
ax.set_ylabel('fraction of trips')
ax = fig.add_subplot(num_migration_types, 4, m * 4 + 4)
if mig == 'intervention':
for r, rdf in mdf.groupby('Run_Number'):
sns.kdeplot(
rdf[rdf['To_NodeID'] == self.forest_nodeid]['Time'],
ax=ax,
color=palette[2],
label='',
linewidth=0.5,
alpha=0.5)
sns.kdeplot(
mdf[mdf['To_NodeID'] == self.forest_nodeid]['Time'],
ax=ax,
color=palette[2],
label='to forest')
for r, rdf in mdf.groupby('Run_Number'):
sns.kdeplot(
rdf[rdf['From_NodeID'] == self.forest_nodeid]['Time'],
ax=ax,
color=palette[3],
label='',
linewidth=0.5,
alpha=0.5)
sns.kdeplot(
mdf[mdf['From_NodeID'] == self.forest_nodeid]['Time'],
ax=ax,
color=palette[3],
label='from forest')
else:
for r, rdf in mdf.groupby('Run_Number'):
sns.kdeplot(rdf['Time'],
ax=ax,
color=palette[3],
label='',
linewidth=0.5,
alpha=0.5)
sns.kdeplot(mdf['Time'], ax=ax, label='', color=palette[3])
ax.set_xlim(-5, 370)
ax.set_xlabel('travel day')
ax.set_ylabel('fraction of trips')
plt.show()
def plot_emresource(scale='') :
ems_regions = {
'northcentral' : [1, 2],
'northeast' : [7, 8, 9, 10, 11],
'central' : [3, 6],
'southern' : [4, 5]
}
ref_df = pd.read_csv(os.path.join(datapath, 'covid_IDPH', 'Corona virus reports',
'emresource_by_region.csv'))
sxmin = '2020-03-24'
xmin = datetime.strptime(sxmin, '%Y-%m-%d')
xmax = datetime.today()
datetoday = xmax.strftime('%y%m%d')
ref_df['suspected_and_confirmed_covid_icu'] = ref_df['suspected_covid_icu'] + ref_df['confirmed_covid_icu']
ref_df['date'] = pd.to_datetime(ref_df['date_of_extract'])
first_day = datetime.strptime('2020-03-24', '%Y-%m-%d')
ref_df = ref_df.rename(columns={
'confirmed_covid_deaths_prev_24h' : 'deaths',
'confirmed_covid_icu' : 'ICU conf',
'confirmed_covid_on_vents' : 'vents conf',
'suspected_and_confirmed_covid_icu' : 'ICU conf+susp',
'covid_non_icu' : 'non ICU'
})
# channels = ['ICU conf+susp', 'ICU conf', 'vents conf', 'deaths', 'non ICU']
channels = ['ICU conf', 'non ICU']
ref_df = ref_df[['date', 'covid_region'] + channels]
palette = load_color_palette('wes')
formatter = mdates.DateFormatter("%m-%d")
sns.set_style('whitegrid', {'axes.linewidth' : 0.5})
fig = plt.figure(figsize=(14,10))
fig.subplots_adjust(left=0.07, right=0.97, top=0.95, bottom=0.05, hspace=0.25)
# ref_df = ref_df[(ref_df['date'] >= date(2020, 3, 1)) & (ref_df['date'] < date(2021, 1, 1))]
def format_plot(ax) :
ax.set_xlim(xmin, )
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
if scale == 'log' :
ax.set_yscale('log')
for ri, (covid_region, pdf) in enumerate(ref_df.groupby('covid_region')) :
ax = fig.add_subplot(4,3,ri+1)
for (c,name) in enumerate(channels):
if name == 'non ICU' :
df = pdf[pdf['date'] >= date(2020,5,6)]
else :
df = pdf
df['moving_ave'] = df[name].rolling(window = 7, center=True).mean()
ax.plot(df['date'].values, df['moving_ave'], color=palette[c], label=name)
ax.scatter(df['date'].values, df[name], s=10, linewidth=0, color=palette[c], alpha=0.3, label='')
if name == 'non ICU' :
ax.set_ylim(0,)
# ax.set_xlim(date(2020,3,25), date(2021,1,1))
ax.set_title('covid region %d' % covid_region)
format_plot(ax)
il_df = ref_df.groupby('date')[channels].agg(np.sum).reset_index()
# print(il_df.tail(10))
ax = fig.add_subplot(4, 3, 12)
for (c, name) in enumerate(channels):
if name == 'non ICU':
df = il_df[il_df['date'] >= date(2020, 5, 6)]
else:
df = il_df
df['moving_ave'] = df[name].rolling(window=7, center=True).mean()
ax.plot(df['date'].values, df['moving_ave'], color=palette[c], label=name)
ax.scatter(df['date'].values, df[name], s=10, linewidth=0, color=palette[c], alpha=0.3, label='')
ax.set_title('Illinois')
format_plot(ax)
ax.legend()
# ax.legend(bbox_to_anchor=(1.5, 1))
fig.savefig(os.path.join(plotdir, 'EMResource_by_covid_region_%s.png' % scale))
fig.savefig(os.path.join(plotdir, 'EMResource_by_covid_region_%s.pdf' % scale), format='PDF')
def plot_tpr(adf):
cols = ['num_positive', 'num_tested']
df = adf[adf['covid_region'] == 11].groupby('delivery_date')[cols].agg(
np.sum).reset_index()
df = df.sort_values(by='delivery_date')
df = df[df['num_tested'] > 0]
df['fraction_positive'] = df['num_positive'] / df['num_tested']
sns.set_style('whitegrid', {'axes.linewidth': 0.5})
fig = plt.figure(figsize=(8, 4))
ax = fig.add_subplot(2, 1, 1)
palette = [load_color_palette('wes')[x] for x in [5, 4]]
formatter = mdates.DateFormatter("%m-%d")
# ax.scatter(df['delivery_date'].values, df['fraction_positive'].values, df['num_tested'], color=palette[0],
# label='daily')
df['moving_ave_frac'] = df['fraction_positive'].rolling(
window=7, center=True).mean()
ax.plot(df['delivery_date'],
df['moving_ave_frac'],
'-',
color=palette[0],
label='moving average')
df['moving_ave_test'] = df['num_tested'].rolling(window=7,
center=True).sum()
df['moving_ave_pos'] = df['num_positive'].rolling(window=7,
center=True).sum()
lows, highs = [], []
for r, row in df.iterrows():
low, high = proportion_confint(row['moving_ave_pos'],
row['moving_ave_test'])
lows.append(low)
highs.append(high)
ax.fill_between(df['delivery_date'].values,
lows,
highs,
color=palette[0],
linewidth=0,
alpha=0.3)
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.set_ylabel('percent positive')
ax.set_title('Region 11')
ax.set_xlim(date(2020, 6, 10), date(2021, 1, 15))
ax = fig.add_subplot(2, 1, 2)
cdf = pd.read_csv(
os.path.join(datapath, 'Corona virus reports', 'CLI_admissions.csv'))
cdf = cdf[cdf['region'] == 'Chicago']
cdf['date'] = pd.to_datetime(cdf['date'])
cdf = cdf[(cdf['date'] >= np.min(df['delivery_date']) - timedelta(days=3))
&
(cdf['date'] <= np.max(df['delivery_date'] + timedelta(days=3)))]
cdf = cdf.groupby('date')['inpatient'].agg(np.sum).reset_index()
cdf = cdf.sort_values(by='date')
cdf['moving_ave_cli'] = cdf['inpatient'].rolling(window=7,
center=True).mean()
ax.plot(cdf['date'], cdf['moving_ave_cli'], '-', color=palette[1])
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.set_ylabel('CLI admissions')
ax.set_xlabel('date')
ax.set_ylim(0, )
ax.set_xlim(date(2020, 6, 10), date(2021, 1, 15))
fig.savefig(os.path.join(plotdir, 'TPR_and_CLI.png'))
fig.savefig(os.path.join(plotdir, 'TPR_and_CLI.pdf'), format='PDF')
def plot_data(df,
tag,
to_compare=None,
ymin=None,
ymax=None,
savename='None',
save=1):
fig = plt.figure(tag, figsize=(5, 5))
fig.subplots_adjust(left=0.05, right=0.98)
palette = load_color_palette()
for i, ia in enumerate(['Total']):
ax = fig.add_subplot(1, 1, i + 1)
plt.grid()
for iarm, (comp, compdf) in enumerate(df.groupby(to_compare)):
if comp == 'SMC15':
ax.plot(compdf['Coverage'],
compdf[ia + '_mean'],
'-',
color=palette[iarm],
label=comp)
elif comp == 'SMC10':
ax.plot(compdf['Coverage'],
compdf[ia + '_mean'],
'-',
color=palette[iarm],
label=comp)
else:
ax.plot(compdf['Coverage'],
compdf[ia + '_mean'],
'-',
color=palette[iarm],
label=comp.split('+')[2])
ax.fill_between(compdf['Coverage'],
compdf[ia + '_mean'] - compdf[ia + '_std'],
compdf[ia + '_mean'] + compdf[ia + '_std'],
color=palette[iarm],
linewidth=0,
alpha=0.3)
if ia == 'Annual EIR':
ax.set_title('%s' % ia)
else:
ax.set_title('clinical cases')
ax.set_ylim(ymin, ymax)
ax.set_xlim(0.0, 1.0)
if i == 0:
ax.legend(bbox_to_anchor=(1.05, 0.5), )
fig.suptitle('%s' % savename)
fig.text(0.5, -0.01, 'Coverage', ha='center', va='center')
fig.text(0.0,
0.5,
'% reduction',
ha='center',
va='center',
rotation='vertical')
if save:
save_double(fig, savename=savename)
return fig
'20210205_IL_locale_uniform_range_mr_bvariant_infectivity_0.10_bvariant',
'20210204_IL_locale_uniform_range_ae_bvariant_infectivity_0.25_bvariant',
'20210204_IL_locale_uniform_range_ae_bvariant_infectivity_0.5_bvariant',
'20210204_IL_locale_uniform_range_ae_bvariant_infectivity_0.75_bvariant'
]
labels = ['baseline', '10%', '25%', '50%', '75%']
col = 'deaths'
plot_first_day = date(2021, 2, 1)
plot_last_day = date(2021, 5, 1)
fig = plt.figure(figsize=(6, 4))
fig.subplots_adjust(left=0.2)
ax = fig.gca()
p = load_color_palette('wes')
palette = [p[x] for x in [8, 4, 2, 1, 3]]
for s, scen in enumerate(scen_dirs):
simpath = os.path.join(projectpath, 'cms_sim', 'simulation_output',
scen)
fname = 'nu_%s_All.csv' % scen.split('_')[0]
df = pd.read_csv(os.path.join(simpath, fname))
df['date'] = pd.to_datetime(df['date'])
df = df[(df['date'] >= plot_first_day) & (df['date'] <= plot_last_day)]
ax.bar([s],
np.sum(df['%s_median' % col]),
align='center',
color=palette[s],
label=labels[s])
ax.plot([s, s],
