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reintroduce cache
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@francesconazzaro
francesconazzaro committed Apr 16, 2021
1 parent 05d18fe commit 80a8839
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Showing 2 changed files with 24 additions and 24 deletions.
2 changes: 1 addition & 1 deletion import_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -215,7 +215,7 @@ def get_mobility_country(country):
# self.italy_path = os.path.join(base_path, 'COVID-19/dati-andamento-nazionale/dpc-covid19-ita-andamento-nazionale.csv')


# @st.cache(show_spinner = False, ttl=60*60)
@st.cache(show_spinner = False, ttl=60*60)
def covid19():
popolazione = population()
terapie_intensive = intensive_care()
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46 changes: 23 additions & 23 deletions plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -168,7 +168,7 @@ def get_matplotlib_cmap(cmap_name, bins, alpha=1):
return contour_colour_list


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def test_positivity_rate(data_in, country, rule):
data = data_in[country]
PALETTE = get_default_palette() #itertools.cycle(plotly.colors.qualitative.Plotly) #get_matplotlib_cmap('tab10', bins=8))
Expand Down Expand Up @@ -217,7 +217,7 @@ def test_positivity_rate(data_in, country, rule):
return fig


# @st.cache(show_spinner=False)
@st.cache(show_spinner=False)
def normalisation(data, population, rule):
if RULE_MAP[rule] == 'percentage':
new_data = data / population * UNITA
Expand Down Expand Up @@ -283,7 +283,7 @@ def plot_average(plot_data, palette, fig, name, palette_alpha, secondary_y=True,
# )


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_selection(data_in, country, rule, start_positivi, start_ti, start_ricoveri, stop_positivi, stop_ti, stop_ricoveri, start_deceduti, stop_deceduti, log=True, secondary_y=True):

PALETTE = get_default_palette() # itertools.cycle(get_matplotlib_cmap('tab10', bins=8))
Expand Down Expand Up @@ -421,7 +421,7 @@ def vaccines_summary_plot_data(region, what):
title = "Dosi consegnate per 100 mila abitanti"
return plot_data, title

# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def vaccines_summary(vaccines, what):
titles = [title for title in np.unique(vaccines.administration.area) if title not in ['Italia', 'P.A. Bolzano', 'P.A. Trento']]
titles = sorted(['Trentino Alto Adige'] + titles)
Expand Down Expand Up @@ -492,7 +492,7 @@ def summary_plot_data(region, what):
return plot_data, title


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def summary(data, what):
titles = [title for title in data if title not in ['P.A. Bolzano', 'P.A. Trento', 'Italia']]
titles += ['Trentino Alto Adige']
Expand Down Expand Up @@ -541,7 +541,7 @@ def translate(data, days_delay=0):
return data_t


# @st.cache(show_spinner=False)
@st.cache(show_spinner=False)
def mortality(data, offset=-7):
italy = data['Italia']
fig = make_subplots(1, 1, subplot_titles=['Mortalità apparente'])
Expand All @@ -564,7 +564,7 @@ def mortality(data, offset=-7):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def comparison(data, offset=7):
deceduti = data.deceduti.diff().rolling(7).mean()
positivi_shift = translate(data.nuovi_positivi.rolling(7).mean(), offset) / 100 * 1.3
Expand Down Expand Up @@ -602,7 +602,7 @@ def comparison(data, offset=7):
return fig


# @st.cache(show_spinner=False)
@st.cache(show_spinner=False)
def mobility_data(mobility_plot_data, variable, variables):
fig = make_subplots(1, subplot_titles=[variable], specs=[[{"secondary_y": True}]])
PALETTE = itertools.cycle(plotly.colors.qualitative.Plotly) #get_matplotlib_cmap('tab10', bins=8))
Expand Down Expand Up @@ -637,7 +637,7 @@ def mobility_data(mobility_plot_data, variable, variables):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_vaccines(vaccines, area=None, unita=UNITA, subplot_title='Dosi somministrate per 100 mila abitanti', fill=None, height=500):
fig = make_subplots(1, subplot_titles=[subplot_title])
maxs = []
Expand Down Expand Up @@ -698,7 +698,7 @@ def plot_vaccines(vaccines, area=None, unita=UNITA, subplot_title='Dosi somminis
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_deliveries(deliveries, area):
fig = make_subplots(1, subplot_titles=[f"Dosi di vaccino consegnate per 100 mila abitanti"])
plot_data = deliveries[deliveries.area == area]
Expand Down Expand Up @@ -737,7 +737,7 @@ def plot_deliveries(deliveries, area):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def categories_timeseries(vaccines, area, cumulate=False):
data_area = vaccines[vaccines.area == area]
population = data_area.popolazione
Expand Down Expand Up @@ -766,7 +766,7 @@ def sum_doses(data):
return data.prima_dose + data.seconda_dose


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def fornitori_timeseries(vaccines, area, cumulate=False):
plot_data = {}
if area == 'Italia':
Expand Down Expand Up @@ -852,7 +852,7 @@ def cum(data):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def ages_timeseries(vaccines, area, cumulate=False):
if area == 'Italia':
child = vaccines[vaccines.fascia_anagrafica == '16-19'].groupby('data_somministrazione').sum()
Expand Down Expand Up @@ -897,7 +897,7 @@ def ages_timeseries(vaccines, area, cumulate=False):
return plot_fill(data_list, names, subplot_title=f"{area}: Somministrazioni vaccino per fascia d'età", cumulate=cumulate, legend=legend)


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_ages(vaccines, area):
if area == 'Italia':
url = "https://raw.githubusercontent.com/italia/covid19-opendata-vaccini/master/dati/anagrafica-vaccini-summary-latest.csv"
Expand All @@ -924,7 +924,7 @@ def plot_ages(vaccines, area):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_second_dose_percentage(vaccines, area):
plot_data = vaccines[vaccines.area == area].sum()
not_prima_dose = plot_data.prima_dose - plot_data.seconda_dose
Expand All @@ -946,7 +946,7 @@ def plot_second_dose_percentage(vaccines, area):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_category(vaccines, area):
plot_data = vaccines[vaccines.area == area].sum()
pie = go.Pie(
Expand All @@ -967,7 +967,7 @@ def plot_category(vaccines, area):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_fornitore(vaccines, area):
plot_data = vaccines[vaccines.area == area].groupby('fornitore').sum()
pie = go.Pie(
Expand All @@ -988,7 +988,7 @@ def plot_fornitore(vaccines, area):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_vaccine_popolation(vaccines, area):
vaccines_area = vaccines[vaccines.area == area]
vaccines_pop = vaccines_area.seconda_dose.cumsum().iloc[-1]
Expand Down Expand Up @@ -1016,7 +1016,7 @@ def plot_vaccine_popolation(vaccines, area):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_percentage(vaccines, deliveries, area):
vaccines_area = vaccines[vaccines.area == area]
tot_vaccines = (vaccines_area.sesso_maschile + vaccines_area.sesso_femminile).cumsum().iloc[-1]
Expand Down Expand Up @@ -1045,7 +1045,7 @@ def plot_percentage(vaccines, deliveries, area):
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_vaccines_prediction(vaccines, area, npoints=7, p0=(np.datetime64("2021-01-01", "s"), np.timedelta64(48 * 60 * 60, "s"))):
plot_data = vaccines[vaccines.area == area]
popolazione = plot_data.popolazione
Expand Down Expand Up @@ -1147,7 +1147,7 @@ def plot_fill(data_list, names, population_list=None, unita=100000, cumulate=Tru
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_deliveries(deliveries, population, unita=100, subplot_title='', start=None, height=500):
PALETTE_1 = get_default_palette() # itertools.cycle(get_matplotlib_cmap('tab10', bins=8))
PALETTE_2 = get_default_palette() # itertools.cycle(get_matplotlib_cmap('tab10', bins=8))
Expand Down Expand Up @@ -1227,7 +1227,7 @@ def plot_deliveries(deliveries, population, unita=100, subplot_title='', start=N
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def cumulate_and_not(data_list, names, population_list, unita=100, subplot_title='', start=None, height=500):
PALETTE = itertools.cycle(get_matplotlib_cmap('tab10', bins=8))
PALETTE_ALPHA = itertools.cycle(get_matplotlib_cmap('tab10', bins=8, alpha=1))
Expand Down Expand Up @@ -1304,7 +1304,7 @@ def cumulate_and_not(data_list, names, population_list, unita=100, subplot_title
return fig


# @st.cache(allow_output_mutation=True, show_spinner=False)
@st.cache(allow_output_mutation=True, show_spinner=False)
def plot_variables(data_list, names, rule=None, popolazione=None, title='', yaxis_title='', xaxes_range=None, secondary_y=True, nrows=1):
PALETTE = itertools.cycle(get_matplotlib_cmap('tab10', bins=8))
PALETTE_ALPHA = itertools.cycle(get_matplotlib_cmap('tab10', bins=8, alpha=.3))
Expand Down

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