def loaddata(self, filename=None, folder=None):
''' Load data from a spreadsheet '''
self.data = sc.loadspreadsheet(filename=filename, folder=folder)
self.data.filtercols(self.colnames.values(), die=True)
self.filename = filename
self.parse()
return None
if check('savespreadsheet', ['loadspreadsheet', 'Spreadsheet', 'savezip']):
formats = {
'header':{'bold':True, 'bg_color':'#3c7d3e', 'color':'#ffffff'},
'plain': {},
'big': {'bg_color':'#ffcccc'}}
formatdata = pl.zeros((nrows+1, ncols), dtype=object) # Format data needs to be the same size
formatdata[1:,:] = 'plain' # Format data
formatdata[1:,:][testdata[1:,:]>0.7] = 'big' # Find "big" numbers and format them differently
formatdata[0,:] = 'header' # Format header
sc.savespreadsheet(filename=files.excel, data=testdata, formats=formats, formatdata=formatdata)
# Test loading
if check('loadspreadsheet'):
data = sc.loadspreadsheet(files.excel)
print(data)
if check('readcells'):
wb = sc.Spreadsheet(filename=filedir+'exampledata.xlsx') # Load a sample databook to try pulling cells from
celltest = wb.readcells(method='xlrd', sheetname='Baseline year population inputs', cells=[[46, 2], [47, 2]]) # Grab cells using xlrd
celltest2 = wb.readcells(method='openpyexcel', wbargs={'data_only': True}, sheetname='Baseline year population inputs', cells=[[46, 2], [47, 2]]) # Grab cells using openpyexcel. You have to set wbargs={'data_only': True} to pull out cached values instead of formula strings
print('xlrd output: %s' % celltest)
print('openpyxl output: %s' % celltest2)
if check('Blobject'):
blob = sc.Blobject(files.excel)
f = blob.tofile()
wb = openpyxl.load_workbook(f)
import pylab as pl
import sciris as sc
dosave = True
fig1 = 1
fig2 = 1
fig3 = 1
sc.heading('Loading data...')
country_data = sc.loadspreadsheet('country-data.xlsx')
interv_data = sc.loadspreadsheet('rapid_interventions.xlsx')
D = sc.loadobj('results/rapid_data.obj')
R = sc.loadobj('results/rapid_results.obj')
def dalys_fig(label='', region=None, income=None):
sc.heading('DALYs figure')
idata = sc.dcp(interv_data)
idata.sort(col='Short name')
category1 = idata['Category 1'].tolist()
category2 = idata['Category 2'].tolist()
interv_category = [c1 + ': ' + c2 for c1, c2 in zip(category1, category2)]
categories = sorted(set(interv_category))
ncategories = len(categories)
nspends, nintervs = R[0]['dalys'].shape
assert idata['Short name'].tolist() == R[0]['meta']['interv_names'].tolist(
)
mapping = []
for i_c in interv_category:
import hiptool as hp
sc.heading('Initializing...')
sc.tic()
dosave = True
missing_data = ['remove', 'assumption'][1] # Choose how to handle missing data
spendings = [0.1, 0.3, 1, 3, 10, 30, 100, 300, 1000]#, 3000, 10000]
nspendings = len(spendings)
colors = sc.vectocolor(len(spendings))
# Load input files
D = sc.odict() # Data
R = sc.odict() # Results
bod_data = sc.loadobj('gbd-data.dat')
country_data = sc.loadspreadsheet('country-data.xlsx')
baseline_factor = country_data.findrow('Zambia', asdict=True)['icer_multiplier'] # Zambia was used for this
# Create default
P = hp.Project()
P.loadburden(filename='rapid_BoD.xlsx')
P.loadinterventions(filename='rapid_interventions.xlsx')
ninterventions = P.intervsets[0].data.nrows
# Load data
missing_data_adjustment_factor = 2
sc.heading('Loading data...')
for c,country in enumerate(country_data['name'].tolist()):
print(f' Working on {country}...')
D[country] = sc.dcp(P)
import pylab as pl
import sciris as sc
dosave = True
fig1 = True
sc.heading('Loading data...')
interv_data = sc.loadspreadsheet('rapid_interventions.xlsx')
spend_data = sc.loadspreadsheet('rapid_spending.xlsx')
D = sc.loadobj('results/rapid_spend_data.obj')
R = sc.loadobj('results/rapid_spend_results.obj')
#%% Fig. 1 -- DALYs
if fig1:
sc.heading('Spending figure')
interv_category = interv_data['Category 1'].tolist()
categories = sorted(set(interv_category))
ncategories = len(categories)
nintervs = len(R[0]['alloc'])
mapping = []
for i_c in interv_category:
for c, cat in enumerate(categories):
if i_c == cat:
mapping.append(c)
allocdata = pl.zeros((2, ncategories))
for country in R.keys(): # tODO: different regions
opt_alloc = R[country]['alloc']
orig_alloc = spend_data[country]
for i in range(nintervs):
c = mapping[i]
def test_legacy():
'''
Preserved for completeness, but too fragile to be used in automated unit testing
due to reliance on openpyxl (which is not a required Sciris dependency).
'''
# Define filenames
filedir = 'files' + os.sep
files = sc.prettyobj()
files.excel = filedir + 'test.xlsx'
files.binary = filedir + 'test.obj'
files.text = filedir + 'text.txt'
files.zip = filedir + 'test.zip'
tidyup = True
# Define the test data
nrows = 15
ncols = 3
testdata = pl.zeros((nrows + 1, ncols),
dtype=object) # Includes header row
testdata[0, :] = ['A', 'B', 'C'] # Create header
testdata[1:, :] = pl.rand(nrows, ncols) # Create data
# Test spreadsheet writing, and create the file for later
formats = {
'header': {
'bold': True,
'bg_color': '#3c7d3e',
'color': '#ffffff'
},
'plain': {},
'big': {
'bg_color': '#ffcccc'
}
}
formatdata = pl.zeros(
(nrows + 1, ncols),
dtype=object) # Format data needs to be the same size
formatdata[1:, :] = 'plain' # Format data
formatdata[1:, :][testdata[
1:, :] > 0.7] = 'big' # Find "big" numbers and format them differently
formatdata[0, :] = 'header' # Format header
sc.savespreadsheet(filename=files.excel,
data=testdata,
formats=formats,
formatdata=formatdata)
# Test loading
sc.heading('Loading spreadsheet')
data = sc.loadspreadsheet(files.excel)
print(data)
excel_path = filedir + 'exampledata.xlsx'
if os.path.exists(excel_path):
sc.heading('Reading cells')
wb = sc.Spreadsheet(
filename=excel_path
) # Load a sample databook to try pulling cells from
celltest = wb.readcells(method='xlrd',
sheetname='Baseline year population inputs',
cells=[[46, 2], [47,
2]]) # Grab cells using xlrd
celltest2 = wb.readcells(
method='openpyexcel',
wbargs={'data_only': True},
sheetname='Baseline year population inputs',
cells=[[46, 2], [47, 2]]
) # Grab cells using openpyexcel. You have to set wbargs={'data_only': True} to pull out cached values instead of formula strings
print('xlrd output: %s' % celltest)
print('openpyxl output: %s' % celltest2)
else:
print(f'{excel_path} not found, skipping...')
sc.heading('Loading a blobject')
blob = sc.Blobject(files.excel)
f = blob.tofile()
wb = openpyexcel.load_workbook(f)
ws = wb.active
ws['B7'] = 'Hi! '
wb.save(f)
blob.load(f)
blob.tofile(output=False)
data = sc.loadspreadsheet(fileobj=blob.bytes)
print(blob)
sc.pp(data)
# Test spreadsheet saving
sc.heading('Using a Spreadsheet')
S = sc.Spreadsheet(files.excel)
S.writecells(cells=['A6', 'B7', 'C8', 'D9'],
vals=['This', 'is', 'a', 'test']) # Method 1
S.writecells(cells=[pl.array([7, 1]) + i for i in range(4)],
vals=['And', 'so', 'is', 'this']) # Method 2
newdata = (pl.rand(3, 3) * 100).round()
S.writecells(startrow=14, startcol=1, vals=newdata,
verbose=True) # Method 3
S.save()
data = S.readcells(header=False)
print(S)
sc.pp(data)
sc.heading('Saveobj/loadobj')
sc.saveobj(files.binary, testdata)
obj = sc.loadobj(files.binary)
print(obj)
sc.heading('Savetext/loadtext')
sc.savetext(files.text, testdata)
obj = sc.loadtext(files.text)
print(obj)
sc.heading('Get files')
print('Files in current folder:')
sc.pp(sc.getfilelist())
sc.heading('Save zip')
sc.savezip(files.zip, [files.text, files.excel])
'''
Check that loading an object with a non-existent class works. The file
deadclass.obj was created with:
deadclass.py:
-------------------------------------------------
class DeadClass():
def __init__(self, x):
self.x = x
-------------------------------------------------
then:
-------------------------------------------------
import deadclass as dc
import sciris as sc
deadclass = dc.DeadClass(238473)
sc.saveobj('deadclass.obj', deadclass)
-------------------------------------------------
'''
dead_path = filedir + 'deadclass.obj'
if os.path.exists(dead_path):
sc.heading('Intentionally loading corrupted file')
obj = sc.loadobj(dead_path)
print('Loading corrupted object succeeded, x=%s' % obj.x)
else:
print(f'{dead_path} not found, skipping...')
# Tidy up
if tidyup:
sc.blank()
sc.heading('Tidying up')
for fn in [files.excel, files.binary, files.text, files.zip]:
try:
os.remove(fn)
print('Removed %s' % fn)
except:
pass
print('Done, all fileio tests succeeded')
return S
import pylab as pl
import sciris as sc
import hiptool as hp
sc.heading('Initializing...')
sc.tic()
dosave = True
missing_data = ['remove', 'assumption'][1] # Choose how to handle missing data
# Load input files
D = sc.odict() # Data
R = sc.odict() # Results
bod_data = sc.loadobj('gbd-data.dat')
country_data = sc.loadspreadsheet('country-data.xlsx')
spend_data = sc.loadspreadsheet('rapid_spending.xlsx')
baseline_factor = country_data.findrow(
'Zambia', asdict=True)['icer_multiplier'] # Zambia was used for this
# Get country list
countries = sc.dcp(spend_data.cols)
countries.remove('Short name')
# Create default
P = hp.Project()
P.loadburden(filename='rapid_BoD.xlsx')
P.loadinterventions(filename='rapid_interventions.xlsx')
ninterventions = P.intervsets[0].data.nrows
# Load data
sc.heading('Loading data...')