def modo():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(8, 6))
ax = fig.add_subplot(1, 1, 1)
wedges, _, _ = ax.pie(data.modos_values,
autopct='%1.1f%%',
startangle=90,
wedgeprops=dict(width=0.95),
colors=[blue, red, yellow, green])
ax.set_title('Casos por modo de contagio', fontsize=20)
ax.legend(wedges,
data.modos_labels,
loc='lower center',
bbox_to_anchor=(0.9, 0, 0.5, 0.5))
FigureCanvasAgg(fig).print_png('modo.png')
apiurl = config.SERVER_URI + '/modo'
watermark_text('modo.png', 'modo.png', apiurl, (0, 0))
return send_file('modo.png')
def sexo():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(8, 6))
ax = fig.add_subplot(1, 1, 1)
wedges, _, _ = ax.pie([data.mujeres, data.hombres, data.non_sex],
autopct='%1.1f%%',
startangle=90,
wedgeprops=dict(width=0.95),
colors=[red, blue, yellow])
ax.set_title('Casos por sexo', fontsize=20)
ax.legend(wedges,
data.sex_labels,
loc='lower center',
bbox_to_anchor=(0.9, 0, 0.5, 0.5))
FigureCanvasAgg(fig).print_png('sexo.png')
apiurl = config.SERVER_URI + '/sexo'
watermark_text('sexo.png', 'sexo.png', apiurl, (0, 0))
return send_file('sexo.png')
def resume():
datamodel.updater(data) # Call to update
increment = data.diagnosticados[-1] - data.diagnosticados[-2]
if increment > 0:
increment = '馃敽{}'.format(str(increment))
else:
increment = '馃敾{}'.format(str(increment * -1))
return jsonify({
'Diagnosticados':
data.total_diagnosticados,
'DiagnosticadosDay':
str(data.diagnosticados[-1]) + ' ({})'.format(increment),
'Activos':
data.total_activos,
'Recuperados':
data.total_recuperados,
'Recuperacion':
round(data.total_recuperados * 100 / data.total_activos, 1),
'Evacuados':
data.total_evacuados,
'Muertes':
data.total_muertes,
'Ingresados':
data.total_ingresados,
'Mortalidad':
round(data.total_muertes * 100 / data.total_diagnosticados, 1),
'Updated':
data.fecha
})
def summary_graph2():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(9, 6))
ax = fig.add_subplot(1, 1, 1)
total = data.total_ingresados + data.total_activos
wedges, _, _ = ax.pie([data.total_ingresados, data.total_activos],
autopct='%1.1f%%',
startangle=90,
wedgeprops=dict(width=0.95))
ax.legend(wedges, [
'Ingresados ' + str(round(data.total_ingresados / total * 100, 1)) +
'%',
'Activos ' + str(round(data.total_activos / total * 100, 1)) + '%',
],
loc='lower center',
bbox_to_anchor=(0.9, 0, 0.5, 1))
ax.set_title('Activos | Ingresados', fontsize=20)
FigureCanvasAgg(fig).print_png('summary2.png')
return send_file('summary2.png')
def nacionalidad_text():
datamodel.updater(data) # Call to update
return jsonify({
'Cubanos': data.cubanos,
'Extranjeros': data.extranjeros,
})
def edad():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(8, 6))
ax = fig.add_subplot(1, 1, 1)
ax.bar([str(k) for k in data.edades.keys()],
[v for v in data.edades.values()],
color=red)
for x, y in enumerate(data.edades.values()):
label = "{}".format(y)
ax.annotate(label, (x, y),
textcoords='offset points',
xytext=(0, 4),
ha='center')
ax.set_title('Distribuci贸n por grupos etarios', fontsize=20)
FigureCanvasAgg(fig).print_png('edades.png')
apiurl = config.SERVER_URI + '/edad'
watermark_text('edades.png', 'edades.png', apiurl, (0, 0))
return send_file('edades.png')
def municipio():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(15, 6))
ax = fig.add_subplot(1, 1, 1)
ax.barh([str(l[0]) for l in data.municipios_top10],
[l[1] for l in data.municipios_top10],
color='orange')
for i, v in enumerate(data.municipios_top10):
label = "{} ({}%)".format(
v[1], round(v[1] * 100 / data.total_diagnosticados, 2))
ax.annotate(label, (v[1], i),
textcoords='offset points',
xytext=(2, -3),
ha='left')
ax.set_title('Casos detectados por municipios (Top 10)', fontsize=20)
FigureCanvasAgg(fig).print_png('municipios.png')
return send_file('municipios.png')
def modo_text():
datamodel.updater(data) # Call to update
return jsonify({
'labels': data.modos_labels,
'values': data.modos_values,
})
def evolution_fallecidos():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(12, 7))
ax = fig.add_subplot(1, 1, 1)
xss = [str(i) for i in range(1, len(data.muertes_acc) + 1)]
xs = range(0, len(data.muertes_acc))
ax.plot(xss, data.muertes_acc, 'o-', label='Casos acumulados')
ax.plot(xss, data.muertes, 'o-', label='Casos en el d铆a')
for x, y in zip(xs, data.muertes_acc):
label = "{}".format(y)
ax.annotate(label, (x, y),
textcoords='offset points',
xytext=(0, 10),
ha='center')
for x, y in zip(xs, data.muertes):
label = "{}".format(y)
ax.annotate(label, (x, y),
textcoords='offset points',
xytext=(0, -15),
ha='center')
ax.set_title('Evoluci贸n de casos por d铆as (Fallecidos)', fontsize=15)
fig.legend(frameon=True, fontsize=12)
FigureCanvasAgg(fig).print_png('fallecidos.png')
return send_file('fallecidos.png')
def summary_graph1():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(9, 6))
ax = fig.add_subplot(1, 1, 1)
total = data.total_diagnosticados + data.total_recuperados + data.total_evacuados + data.total_muertes
wedges, _, _ = ax.pie([
data.total_diagnosticados, data.total_recuperados,
data.total_evacuados, data.total_muertes
],
autopct='%1.1f%%',
startangle=90,
wedgeprops=dict(width=0.95))
ax.legend(wedges, [
'Diagnosticados ' +
str(round(data.total_diagnosticados / total * 100, 1)) + '%',
'Recuperados ' + str(round(data.total_recuperados / total * 100, 1)) +
'%',
'Evacuados ' + str(round(data.total_evacuados / total * 100, 1)) + '%',
'Fallecidos ' + str(round(data.total_muertes / total * 100, 1)) + '%',
],
loc='lower center',
bbox_to_anchor=(0.9, 0, 0.5, 1))
ax.set_title('Resumen', fontsize=20)
FigureCanvasAgg(fig).print_png('summary1.png')
return send_file('summary1.png')
def test():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(15, 6))
(ax1, ax2) = fig.subplots(1, 2)
# Test realizados
xss = [str(k) for k in range(12, data.cant_days + 1)]
xs = range(0, data.cant_days + 1 - 12)
ax1.bar(xss, data.cant_tests)
ax1.bar(xss, data.detected_acc)
for x, y in zip(xs, data.cant_tests):
label = "{}".format(y)
ax1.annotate(label, (x, y),
textcoords='offset points',
xytext=(0, 4),
ha='center')
ax1.set_title('Tests acumulados por d铆a')
# Proporci贸n entre casos confirmados y test realizados
ax2.bar([str(k) for k in range(12, data.cant_days + 1)],
data.prop_test_vs_detected)
ax2.set_title('Proporci贸n detectados/tests realizados')
FigureCanvasAgg(fig).print_png('tests.png')
return send_file('tests.png')
def test_text():
datamodel.updater(data) # Call to update
return jsonify({
'cant_tests': data.cant_tests,
'detected_acc': data.detected_acc,
'prop': data.prop_test_vs_detected,
})
def sexo_text():
datamodel.updater(data) # Call to update
return jsonify({
'mujeres': data.mujeres,
'hombres': data.hombres,
'no determinado': data.non_sex
})
def evolution_text():
datamodel.updater(data) # Call to update
return jsonify({
'diagnosticados': data.diagnosticados,
'diagnosticados_acc': data.diagnosticados_acc,
'fallecidos': data.muertes,
'fallecidos_acc': data.muertes_acc
})
def evolution():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1)
xss = [str(i) for i in range(1, len(data.diagnosticados_acc) + 1)]
xs = range(0, len(data.diagnosticados_acc))
ax.plot(xss,
data.diagnosticados_acc,
'o-',
label='Casos acumulados',
color=red,
alpha=0.5)
ax.plot(xss, data.activos_acc, 'o-', label='Casos activos', color=red)
ax.plot(xss,
data.diagnosticados,
'o-',
label='Casos en el d铆a',
color=cyan)
for x, y in zip(xs, data.diagnosticados_acc):
label = "{}".format(y)
ax.annotate(label, (x, y),
textcoords='offset points',
xytext=(0, 10),
ha='center')
for x, y in zip(xs, data.activos_acc):
label = "{}".format(y)
ax.annotate(label, (x, y),
textcoords='offset points',
xytext=(0, 10),
ha='center')
for x, y in zip(xs, data.diagnosticados):
label = "{}".format(y)
ax.annotate(label, (x, y),
textcoords='offset points',
xytext=(0, -17),
ha='center')
ax.set_title('Evoluci贸n de casos por d铆as', fontsize=20)
fig.legend(frameon=True, fontsize=12)
FigureCanvasAgg(fig).print_png('evolution.png')
apiurl = config.SERVER_URI + '/evolution'
watermark_text('evolution.png', 'evolution.png', apiurl, (0, 0))
return send_file('evolution.png')
def reload():
dat = request.get_json()
if dat['token'] != config.TOKEN:
return jsonify({'message': 'updated data'})
datamodel.updater(data)
token = {'token': config.TOKEN}
requests.post(config.BOT_URI, json=token)
return jsonify({'message': 'Updated Data'})
def casos_extranjeros():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(8, 6))
ax = fig.add_subplot(1, 1, 1)
ax.bar([str(k) for k in data.paises.keys()],
[v for v in data.paises.values()])
ax.set_title('Distribuci贸n por nacionalidad casos extranjeros',
fontsize=20)
FigureCanvasAgg(fig).print_png('paises.png')
return send_file('paises.png')
def evolution_fallecidos():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(12, 7))
ax = fig.add_subplot(1, 1, 1)
xss = [str(i) for i in range(1, len(data.muertes_acc) + 1)]
xs = range(0, len(data.muertes_acc))
ax.plot(xss,
data.muertes_acc,
'o-',
label='Muertes acumuladas',
color=black)
ax.plot(xss, data.muertes, 'o-', label='Muertes en el d铆a', color=cyan)
for x, y in zip(xs, data.muertes_acc):
label = "{}".format(y)
ax.annotate(label, (x, y),
textcoords='offset points',
xytext=(0, 10),
ha='center')
for x, y in zip(xs, data.muertes):
label = "{}".format(y)
ax.annotate(label, (x, y),
textcoords='offset points',
xytext=(0, -15),
ha='center')
ax.set_title('Evoluci贸n de muertes por d铆as', fontsize=15)
fig.legend(frameon=True, fontsize=12)
FigureCanvasAgg(fig).print_png('fallecidos.png')
apiurl = config.SERVER_URI + '/evolution_fallecidos'
watermark_text('fallecidos.png', 'fallecidos.png', apiurl, (0, 0))
return send_file('fallecidos.png')
def nacionalidad():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(8, 6))
ax = fig.add_subplot(1, 1, 1)
wedges, _, _ = ax.pie([data.cubanos, data.extranjeros],
autopct='%1.1f%%',
startangle=90,
wedgeprops=dict(width=0.95))
ax.legend(wedges, ['Cubanos', 'Extranjeros'],
loc='lower center',
bbox_to_anchor=(0.9, 0, 0.5, 1))
ax.set_title('Casos por nacionalidad', fontsize=20)
FigureCanvasAgg(fig).print_png('nacionalidad.png')
return send_file('nacionalidad.png')
def casos_extranjeros():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(8, 6))
ax = fig.add_subplot(1, 1, 1)
ax.bar([str(k) for k in data.paises.keys()],
[v for v in data.paises.values()],
color=red)
ax.set_title('Distribuci贸n por nacionalidad casos extranjeros',
fontsize=20)
FigureCanvasAgg(fig).print_png('paises.png')
apiurl = config.SERVER_URI + '/casos_extranjeros'
watermark_text('paises.png', 'paises.png', apiurl, (0, 0))
return send_file('paises.png')
def modo():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(8, 6))
ax = fig.add_subplot(1, 1, 1)
wedges, _, _ = ax.pie(data.modos_values,
autopct='%1.1f%%',
startangle=90,
wedgeprops=dict(width=0.95))
ax.set_title('Casos por modo de contagio', fontsize=20)
ax.legend(wedges,
data.modos_labels,
loc='lower center',
bbox_to_anchor=(0.9, 0, 0.5, 0.5))
FigureCanvasAgg(fig).print_png('modo.png')
return send_file('modo.png')
def provincia():
datamodel.updater(data) # Call to update
fig = Figure(figsize=(15, 6))
ax = fig.add_subplot(1, 1, 1)
provs = []
provs_values = []
for i, v in enumerate(data.locations):
if v[0] != None:
provs.append(str(v[0]))
else:
provs.append('Desconocido')
provs_values.append(v[1])
label = "{} ({}%)".format(
v[1], round(v[1] * 100 / data.total_diagnosticados, 2))
ax.annotate(label, (v[1], i),
textcoords='offset points',
xytext=(2, -3),
ha='left')
ax.barh(provs, provs_values, color=red)
ax.set_title('Casos detectados por provincias', fontsize=20)
FigureCanvasAgg(fig).print_png('provincias.png')
apiurl = config.SERVER_URI + '/provincias'
watermark_text('provincias.png', 'provincias.png', apiurl, (0, 0))
return send_file('provincias.png')
def casos_extranjeros_text():
datamodel.updater(data) # Call to update
return jsonify(dict(data.paises))
def municipio_text():
datamodel.updater(data) # Call to update
return jsonify({
'municipios': data.mlocations,
})
def provincia_text():
datamodel.updater(data) # Call to update
return jsonify({
'provincias': data.locations,
})
def edad_text():
datamodel.updater(data) # Call to update
return jsonify(data.edades)
