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setting current season automatically #3

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31 changes: 21 additions & 10 deletions DataTreatment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -209,10 +209,19 @@ def change_regname(x):
return "Valle d'Aosta"
else: return x

YEAR_MIN, YEAR_MAX = 2023, 2024
## set current season

dates = datetime.date.today()

## define current season
if dates.month<5:
current_season = str(int(dates.year)-1)+'-'+str(int(dates.year))
elif dates.month>=11:
current_season = str(int(dates.year))+'-'+str(int(dates.year)+1)
dates, current_season

YEAR_MIN, YEAR_MAX = int(current_season.split('-')[0]), int(current_season.split('-')[1])

last_week = lastweek(dates)

my_yearweek = last_week
Expand Down Expand Up @@ -402,7 +411,7 @@ def transf_date(x):
weekly_complete.insert(5, "season", weekly_complete.submission_date.map(date_season))

#keep only current season
weekly_complete = weekly_complete[weekly_complete.season =='2023-2024']
weekly_complete = weekly_complete[weekly_complete.season == current_season]

# remove duplicates within the same week, keeping the last one
weekly = weekly_complete.drop_duplicates(['participantID','submission_week'], keep='last', inplace=False)
Expand Down Expand Up @@ -461,7 +470,7 @@ def transf_date(x):
data_ILI = data_ILI[ data_ILI.season.isin(seasons) ] #get only weeks in seasons

ILI_weeks=set(data_ILI.submission_week)
submission_weeks=[x for x in list(week_season.keys()) if x<='2024-18'] #ILI_weeks
submission_weeks=[x for x in list(week_season.keys())] #ILI_weeks

data_ILI['symptoms'] = data_ILI['weekly.Q1.0'].apply(lambda x: False if x==True else True)

Expand Down Expand Up @@ -542,10 +551,12 @@ def transf_date(x):
pd.Series(wau).to_frame('active users').to_csv(os.path.join(output_dir, 'active_users.csv'), header=True)

#save participants values
intake['gender'].value_counts().to_csv(os.path.join(output_dir, 'gender.csv'), header=True)
intake['edu'].value_counts().to_csv(os.path.join(output_dir, 'education.csv'), header=True)
intake['occupation'].value_counts().to_csv(os.path.join(output_dir, 'occupation.csv'), header=True)
intake['age_class'].value_counts().to_csv(os.path.join(output_dir, 'age.csv'), header=True)
participants = data.drop_duplicates('participantID')

participants['gender'].value_counts().to_csv(os.path.join(output_dir, 'gender.csv'), header=True)
participants['edu'].value_counts().to_csv(os.path.join(output_dir, 'education.csv'), header=True)
participants['occupation'].value_counts().to_csv(os.path.join(output_dir, 'occupation.csv'), header=True)
participants['age_class'].value_counts().to_csv(os.path.join(output_dir, 'age.csv'), header=True)


# ## Mappa
Expand All @@ -555,10 +566,10 @@ def transf_date(x):
regioni = gpd.read_file('Limiti01012024_g-2/Reg01012024_g/Reg01012024_g_WGS84.shp')
regioni = regioni[['DEN_REG','geometry']].set_index('DEN_REG')

partecipanti_reg = data_ILI.reg.value_counts().squeeze().reset_index().set_index('reg')
partecipanti_reg = data_ILI.reg.value_counts().reindex(list(regioni.index)).fillna(0).squeeze().reset_index().set_index('reg')

part_reg = intake.reg.value_counts().squeeze()/pop_reg * 100000
part_reg = part_reg.reindex(list(regioni.index))
part_reg = participants.reg.value_counts().squeeze()/pop_reg * 100000
part_reg = part_reg.reindex(list(regioni.index)).fillna(0)
part_reg = part_reg.reset_index().set_index('index')

reg_map = regioni.join(part_reg).reset_index().rename(columns={0:'count'})
Expand Down
8 changes: 4 additions & 4 deletions Plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,7 +113,7 @@ def _demographic_composition_plot():
ax3[1,1].set_xlabel('')
ax3[1,1].spines[['right', 'top']].set_visible(False)

fig3.text(0.0, 0.6, _('Number of participants in the 2023-2024 season'), va='center', rotation='vertical')
fig3.text(0.0, 0.6, _('Number of participants in the current season'), va='center', rotation='vertical')

plt.tight_layout()
st.pyplot(fig3)
Expand All @@ -122,7 +122,7 @@ def _geo_plot():

st.title(_('Geographic aspects'))

st.write(_("The first map shows the cumulative incidence in the 2023-2024 season of probable cases of influenza-like illness (ILI) reported in each region by Influweb participants."),
st.write(_("The first map shows the cumulative incidence in the current season of probable cases of influenza-like illness (ILI) reported in each region by Influweb participants."),
_('The second map shows the regional coverage of participants in each region expressed as the number of participants per 100,000 inhabitants.'))


Expand All @@ -140,14 +140,14 @@ def _geo_plot():
with tab4:
fig4, ax4 = plt.subplots(figsize=(6,6))
gdf.plot(ax=ax4, cmap='Blues', column='ar', legend=True, edgecolor="w", linewidth=.3,
legend_kwds={"label": _('Attack rate per 100,000 inhabitants in the 2023-2024 season'), "orientation": "vertical","shrink":0.6})
legend_kwds={"label": _('Attack rate per 100,000 inhabitants in the current season'), "orientation": "vertical","shrink":0.6})
ax4.axis('off')
st.pyplot(fig4)

with tab5:
fig5, ax5 = plt.subplots(figsize=(6,6))
gdf.plot(ax=ax5, cmap='Reds', column='count', legend=True, edgecolor="w", linewidth=.3,
legend_kwds={"label": _('Participants per 100,000 inhabitants in the 2023-2024 season'), "orientation": "vertical","shrink":0.6})
legend_kwds={"label": _('Participants per 100,000 inhabitants in the current season'), "orientation": "vertical","shrink":0.6})
ax5.axis('off')
st.pyplot(fig5)

Expand Down