def test_normalize_col_name():
vma_r = VMAReader(wb)
assert vma_r.normalize_col_name(
'Conedition calculation') == 'Conversion calculation'
assert vma_r.normalize_col_name(
'Manually Exclude Data?') == 'Exclude Data?'
assert vma_r.normalize_col_name('Weight by: Production') == 'Weight'
def test_read_xls_num_dfs():
""" Check we produce the right amount of tables + discard empty ones """
vma_r = VMAReader(wb)
df_dict = vma_r.read_xls()
assert len(df_dict) == 24
num_empty_tables = len([x for (x, _, _) in df_dict.values() if x is None])
assert num_empty_tables == 13
def test_single_table():
vma_r = VMAReader(wb)
result, _, _ = vma_r.read_single_table('C48',
sheetname='Variable Meta-analysis',
fixed_summary=False)
expected = pd.read_csv(thisdir.joinpath('silvopasture_vma1.csv'))
expected['Year / Date'] = expected['Year / Date'].astype('object')
pd.testing.assert_frame_equal(expected, result)
def test_rrs():
wb = xlrd.open_workbook(thisdir.joinpath('solarpvutil_vma.xlsm'))
vma_r = VMAReader(wb)
df_dict = vma_r.read_xls(alt_vma=True)
table = df_dict['Current Adoption'][0]
assert table.at[
0,
'SOURCE ID: Author/Org, Date, Info'] == 'IRENA (2016)_Historical data for solar PV'
assert table.at[
0,
'Assumptions'] == 'Assuming that PV utility represents around 60% of total Solar PV'
assert table.at[0, 'Exclude Data?'] == True
def test_read_xls():
""" Check some specifc values from Silvopasture """
vma_r = VMAReader(wb)
df_dict = vma_r.read_xls()
table = df_dict[
'SOLUTION Net Profit Margin per Functional Unit per Annum'][0]
assert table.at[5, 'Raw Data Input'] == 416
table = df_dict['Sequestration Rates'][0]
assert len(table) == 28
(table, exclude, summary) = df_dict['SOLUTION Energy Efficiency Factor']
assert table is None
assert exclude == False
assert len(summary) == 3
assert pd.isna(summary[0])
assert pd.isna(summary[1])
assert pd.isna(summary[2])
def _read_xls(self, filename, title):
"""
Read a properly formatted xlsx/xlsm file (with a Variable
Meta-analysis sheet) to instantiate this VMA.
Arguments:
filename: pathlib.Path to an Excel file
title: string matching VMA name in the Variable Meta-analysis sheet
Populates self.source_data, self.df, and self.fixed_summary if the
required values are present.
"""
workbook = xlrd.open_workbook(filename=filename)
vma_reader = VMAReader(workbook)
if 'Variable Meta-analysis-DD' in workbook.sheet_names():
alt_vma = True
else:
alt_vma = False
# Pull the desired table from this workbook
try:
(xl_df, use_weight, summary) = \
vma_reader.xls_df_dict(alt_vma=alt_vma, title=title)[title]
except KeyError:
# The title wasn't available in the given workbook. Read all titles
# to give the user a hint.
full_dict = vma_reader.xls_df_dict(alt_vma=alt_vma)
raise ValueError(
f"Title {title!r} not available in {filename}\nOptions\n\t" + \
"\n\t".join(full_dict.keys())
)
self._convert_from_human_readable(xl_df, filename)
# Populate the self.fixed_summary field if the values are valid
fixed_summary = check_fixed_summary(*summary)
if fixed_summary is not None:
self.fixed_summary = fixed_summary
def test_read_xls_additional_var():
""" Check the additional var from Silvopasture """
vma_r = VMAReader(wb)
df_dict = vma_r.read_xls()
assert 'Percent silvopasture area to the total grassland area (including potential)' in df_dict
def test_single_table_use_weight():
vma_r = VMAReader(wb)
_, uw, _ = vma_r.read_single_table('C48',
sheetname='Variable Meta-analysis',
fixed_summary=False)
assert not uw