def get_summary_data(row):
woreda_hc = Dataset.merged_woreda_hc_mb()
woreda_hc = woreda_hc[woreda_hc['woreda'] == row['woreda']].sum()
woreda_hp = Dataset.merged_woreda_hp_mb()
woreda_hp = woreda_hp[woreda_hp['woreda'] == row['woreda']].sum()
return [[
Utils.p('II. Baseline Malaria Burden, 2008 EFY',
10,
True,
'#0085B7',
align=Align.LEFT), '', '', ''
],
[
Utils.p('Malaria cases reported by',
8,
True,
align=Align.LEFT),
Utils.p('Health Centers', 7, True, '#5A5A5C'),
Utils.p('Health Posts', 7, True, '#5A5A5C'),
Utils.p('Total', 7, True, '#5A5A5C')
],
[
'',
Utils.p(woreda_hc['mb_2008_y']),
Utils.p(woreda_hp['mb_2008_y']),
Utils.p(woreda_hc['mb_2008_y'] + woreda_hp['mb_2008_y'])
]]
def get_dx_hc_data(row):
data = [[
Utils.p('Malaria diagnosis method used by HCs',
8,
True,
align=Align.LEFT), '', '', ''
],
[
'',
Utils.p('Light Microscopy', 7, True, '#5A5A5C'),
Utils.p('RDT', 7, True, '#5A5A5C'),
Utils.p('Both', 7, True, '#5A5A5C')
]]
woreda_hc = Dataset.merged_woreda_hc()
for index, r in woreda_hc[woreda_hc['woreda'] ==
row['woreda']].iterrows():
data.append([
Utils.p(Utils.short(r['hfname']), 7, align=Align.LEFT),
Utils.p(r['dxmic'], 7),
Utils.p(r['dxrdt'], 7),
Utils.p(r['dxboth'], 7)
])
return data
def get_data(row):
data = [[
Utils.p('IV. Finance', 10, True, '#0085B7', align=Align.LEFT), '',
'', '', ''
],
[
Utils.p('Woreda', 8, True, align=Align.LEFT), '', '',
Utils.p('Health center', 8, True, align=Align.LEFT), ''
],
[
Utils.p('Woreda allocate funds for malaria activities',
7,
align=Align.LEFT),
Utils.p(row['bgt'], 7, align=Align.LEFT), '', '',
Utils.p('Participate in health insurance', 7, True,
'#5A5A5C')
]]
woreda_hc = Dataset.merged_woreda_hc()
for index, r in woreda_hc[woreda_hc['woreda'] ==
row['woreda']].iterrows():
data.append([
'', '', '',
Utils.p(Utils.short(r['hfname']), 7, align=Align.LEFT),
Utils.p('Yes' if r['insu'] == 1 else 'No', 7)
])
return data
def get_thc_data(row):
data = [[
' Health center level', 'HCs', 'Vector control', 'Diagnosis',
'Treatment', 'Severe case mngt.', 'IRT', 'M&E', 'ICCM',
'Supply Management', 'SBCC'
]]
woreda_hc = Dataset.merged_woreda_hc()
for index, r in woreda_hc[woreda_hc['woreda'] ==
row['woreda']].iterrows():
data.append([
'',
Utils.short(r['hfname']),
Utils.n(r['vect_y']),
Utils.n(r['dx_y']),
Utils.n(r['rx_y']),
Utils.n(r['mxsevr']),
Utils.n(r['IRT']),
Utils.n(r['sme_y']),
Utils.n(r['iccm_y']),
Utils.n(r['scm_y']),
Utils.n(r['sbcc_y'])
])
return data
def get_data(row):
data = [[Utils.p('V. Health Information System', 10, True, '#0085B7', align=Align.LEFT), '', '', '', '', '', ''],
[Utils.p('Woreda', 8, True, align=Align.LEFT), '', '', Utils.p(
'Health center', 8, True, align=Align.LEFT), '', '', ''],
[Utils.p('Have malaria epidemic monitoring charts', 7, align=Align.LEFT),
Utils.p(row['chart'], 7), '', '', Utils.p('Have epidemic chart', 7, True, '#5A5A5C', align=Align.LEFT), Utils.p('Calculate API', 7, True, '#5A5A5C', align=Align.LEFT), Utils.p('Calculate TPR', 7, True, '#5A5A5C', align=Align.LEFT)],
[Utils.p('Calculate Total Positive Rate(TPR)', 7, align=Align.LEFT),
Utils.p(row['tpr'], 7), '', '', '', '', ''],
[Utils.p('Calculate Annual Parasitic Incidence(API)', 7, align=Align.LEFT),
Utils.p(row['api'], 7), '', '', '', '', ''],
[Utils.p('Include NGO and private Health Facilities in PHEM report', 7, align=Align.LEFT),
Utils.p(row['priv'], 7), '', '', '', '', '']
]
woreda_hc = Dataset.merged_woreda_hc()
i = 0
for index, r in woreda_hc[woreda_hc['woreda'] == row['woreda']].iterrows():
if i < 3:
data[i+3][3] = Utils.p(Utils.short(r['hfname']),
7, align=Align.LEFT)
data[i+3][4] = Utils.p(r['chart_y'], 7)
data[i+3][5] = Utils.p(r['api_y'], 7)
data[i+3][6] = Utils.p(r['tpr_y'], 7)
else:
data.append(['', '', '', Utils.p(Utils.short(r['hfname']), 7, align=Align.LEFT), Utils.p(
r['chart_y'], 7), Utils.p(r['api_y'], 7), Utils.p(r['tpr_y'], 7)])
i += 1
return data
def get_data(row):
data = [[
Utils.p('VII. Health Service Delivery',
10,
True,
'#0085B7',
align=Align.LEFT), '', '', '', '', '', '', '', ''
],
[
Utils.p('Availability of Manuals and Guideline at',
8,
True,
align=Align.LEFT), '', '', '', '', '', '', '', ''
],
[
'',
Utils.p('Laboraotory diagnosis of malaria', 7, True,
'#5A5A5C'),
Utils.p('External qauallity assessment', 7, True,
'#5A5A5C'),
Utils.p('Bloold sample collection', 7, True, '#5A5A5C'),
Utils.p('Giemsa preparation', 7, True, '#5A5A5C'),
Utils.p('Blood film prepartion', 7, True, '#5A5A5C'),
Utils.p('Blood film staining', 7, True, '#5A5A5C'),
Utils.p('Microscopy job aid', 7, True, '#5A5A5C'),
Utils.p('Malaria treatment', 7, True, '#5A5A5C')
],
[
Utils.p('Woreda', 7, align=Align.LEFT),
Utils.em(row['labdxbk'], False),
Utils.em(row['eqabk'], False),
Utils.em(row['smplcolbk'], False),
Utils.em(row['gmsbk'], False),
Utils.em(row['bfbk'], False),
Utils.em(row['bfsbk'], False),
Utils.em(row['micjbk'], False),
Utils.em(row['malrxbk'], False)
]]
woreda_hc = Dataset.merged_woreda_hc()
for index, r in woreda_hc[woreda_hc['woreda'] ==
row['woreda']].iterrows():
data.append([
Utils.p(Utils.short(r['hfname']), 7, align=Align.LEFT),
Utils.em(r['labdxbk_y'], False),
Utils.em(r['eqabk_y'], False),
Utils.em(r['splcolbk'], False),
Utils.em(r['gmsbk_y'], False),
Utils.em(r['bfbk_y'], False),
Utils.em(r['bfsbk_y'], False),
Utils.em(r['micbk_y'], False),
Utils.em(r['malrxbk_y'], False)
])
return data
def generate_hew_bar_chart(row):
woreda_hp = Dataset.merged_woreda_hp()
woreda_hp = woreda_hp[woreda_hp['woreda'] == row['woreda']]
sorted_data = woreda_hp.sort_values(by=['total_extension'])
category = sorted_data['hpname'].apply(lambda x: Utils.short(str(x)))
data = sorted_data['total_extension']
return Utils.generate_bar_chart(category, data, ylabel='# of HEWs')
def generate_pie_chart(row):
woreda_hp = Dataset.merged_woreda_hp_mb()
categories, data = Utils.rearrange(
woreda_hp[(woreda_hp['woreda'] == row['woreda'])
& (woreda_hp['mb_2008_y'] > 0)], 'name_hp', 'mb_2008_y')
# shorten the name of each health post
categories = categories.apply(lambda x: Utils.short(x))
return Utils.generate_pie_chart(categories, data)
def generate_bar_chart(row):
woreda_hc = Dataset.merged_woreda_hc_mb()
woreda_hc = woreda_hc[woreda_hc['woreda'] == row['woreda']]
sorted_data = woreda_hc.sort_values(by=['mb_2008_y'])
category = sorted_data['name_hc'].apply(lambda x: Utils.short(x))
data = sorted_data['mb_2008_y']
return Utils.generate_bar_chart(category,
data,
ylabel='# of malaria cases')
def get_hew_tree_map(row):
woreda_hp = Dataset.merged_woreda_hp()
sizes = []
labels = []
for index, r in woreda_hp[(woreda_hp['woreda'] == row['woreda']) & (
woreda_hp['total_extension'] > 0)].iterrows():
sizes.append(r['total_extension'])
labels.append('{}\n{:.0f}'.format(Utils.short(r['hpname']),
r['total_extension']))
return Utils.generate_tree_map(labels, sizes)
def get_data(row):
data = [[Utils.p('VI. Supply Chain Management', 10, True, '#0085B7', align=Align.LEFT), '', '', '', ''],
[Utils.p('Woreda', 8, True, align=Align.LEFT), '', '', Utils.p(
'Health center', 8, True, align=Align.LEFT), ''],
[Utils.p('Uses Integrated Pharmaceutical Logistics System (IPLS)', 7, align=Align.LEFT), Utils.p(
row['ipls'], 7), '', '', Utils.p('IPLS use', 7, True, '#5A5A5C')],
]
woreda_hc = Dataset.merged_woreda_hc()
for index, r in woreda_hc[woreda_hc['woreda'] == row['woreda']].iterrows():
data.append(['', '', '', Utils.p(Utils.short(
r['hfname']), 7, align=Align.LEFT), Utils.p(r['ipls_y'], 7)])
return data
def get_stock_out_data(row):
data = [
[Utils.p('Stocked out of commodities in the last 6 months at',
8, True, align=Align.LEFT), '', '', '', '', '', '', ''],
['', Utils.p('Giemsa', 7, True, '#5A5A5C'), Utils.p('RDT', 7, True, '#5A5A5C'), Utils.p('Coartum', 7, True, '#5A5A5C'), Utils.p('Chloroquine tablet', 7, True, '#5A5A5C'), Utils.p(
'Chloroquine syrups', 7, True, '#5A5A5C'), Utils.p('Artesunate Injection', 7, True, '#5A5A5C'), Utils.p('Artesunate suppository', 7, True, '#5A5A5C')],
[Utils.p('Woreda', 7, align=Align.LEFT), Utils.em(row['gmsa']), Utils.em(row['rdt']), Utils.em(row['crtm']), Utils.em(
row['chlrqntb']), Utils.em(row['chlrqnsyp']), Utils.em(row['artsntinj']), Utils.em(row['artsntsp'])]
]
woreda_hc = Dataset.merged_woreda_hc()
for index, r in woreda_hc[woreda_hc['woreda'] == row['woreda']].iterrows():
data.append([Utils.p(Utils.short(r['hfname']), 7, align=Align.LEFT), Utils.em(r['gmsa_y']), Utils.em(r['rdt_y']), Utils.em(r['crtm_y']), Utils.em(
r['chlrqntb_y']), Utils.em(r['chlrqnsyp_y']), Utils.em(r['artsntinj_y']), Utils.em(r['artsntsp_y'])])
return data
def get_stacked_bar_chart(row):
woreda_hc = Dataset.merged_woreda_hc()
woreda_hc = woreda_hc[woreda_hc['woreda'] == row['woreda']]
categories = np.array(
woreda_hc['hfname'].apply(lambda x: Utils.short(x)))
clinical = np.array(woreda_hc['clncstaff'])
lab = np.array(woreda_hc['labstaff'])
pharmacy = np.array(woreda_hc['pharm'])
phem = np.array(woreda_hc['phem_y'])
hmis = np.array(woreda_hc['hmis_y'])
values = [clinical, lab, pharmacy, phem, hmis]
color_label = [('#009076', 'Clinical'), ('#00dca6', 'Laboratory'),
('#ff483a', 'Pharmacy'), ('#18a1cd', 'PHEM'),
('#15607a', 'HMIS')]
return Utils.generate_stacked_bar_chart(categories,
values,
color_label,
ylabel='# of staff')
def __generate_malaria_burden(self, row):
data = MalariaBurden.get_summary_data(row)
t = Table(data, colWidths='*', spaceBefore=0)
t.setStyle(MalariaBurden.get_table_style())
self.__story.append(t)
woreda_hp = Dataset.merged_woreda_hp_mb()
woreda_hp = woreda_hp[(woreda_hp['woreda'] == row['woreda'])]
data = MalariaBurden.get_chart_data(row, woreda_hp)
if woreda_hp.shape[0] > MAX_HF:
t = Table(
data,
colWidths=[None, None, 12 * mm, None, 12 * mm, None, 12 * mm],
spaceBefore=2)
else:
t = Table(data, colWidths='*', spaceBefore=2)
t.setStyle(
MalariaBurden.get_chart_table_style(woreda_hp.shape[0],
data.__len__()))
self.__story.append(t)