def test_single_dim_order(self):
spec = Spec(name='test',
dims=['technology_type'],
coords={
'technology_type':
['water_meter', 'electricity_meter', 'other', 'aaa']
},
dtype='float')
df = pd.DataFrame([
{
'technology_type': 'water_meter',
'test': 5
},
{
'technology_type': 'electricity_meter',
'test': 6
},
{
'technology_type': 'other',
'test': 7
},
{
'technology_type': 'aaa',
'test': 8
},
])
da = DataArray(spec, numpy.array([5., 6., 7., 8.]))
da_from_df = DataArray.from_df(spec, df)
da_from_df_2 = DataArray.from_df(spec, df)
assert da == da_from_df
assert da == da_from_df_2
def test_multi_dim_order(self):
spec = Spec(name='test',
coords={
'lad': ['c', 'a', 'b'],
'interval': [4, 2]
},
dims=['lad', 'interval'],
dtype='float')
data = numpy.array(
[
# 4 2
[1, 2], # c
[5, 6], # a
[9, 0] # b
],
dtype='float')
da = DataArray(spec, data)
df = pd.DataFrame([
{
'test': 6.0,
'lad': 'a',
'interval': 2
},
{
'test': 0.0,
'lad': 'b',
'interval': 2
},
{
'test': 2.0,
'lad': 'c',
'interval': 2
},
{
'test': 5.0,
'lad': 'a',
'interval': 4
},
{
'test': 9.0,
'lad': 'b',
'interval': 4
},
{
'test': 1.0,
'lad': 'c',
'interval': 4
},
]).set_index(['lad', 'interval'])
da_from_df = DataArray.from_df(spec, df)
assert da_from_df == da
da_to_df = da.as_df().sort_index()
df = df.sort_index()
pd.testing.assert_frame_equal(da_to_df, df)
def test_from_multiindex(self):
spec = Spec(name='test',
dims=['multi'],
coords={'multi': ['b', 'a', 'c']},
dtype='float')
index = pd.MultiIndex.from_product([['b', 'a', 'c']], names=['multi'])
df = pd.DataFrame({'test': [1, 2, 3]}, index=index)
da_from_df = DataArray.from_df(spec, df)
da = DataArray(spec, numpy.array([1, 2, 3]))
assert da == da_from_df
def test_error_duplicate_rows_multi_index(self):
spec = Spec(name='test',
dims=['a', 'b'],
coords={
'a': [1, 2],
'b': [3, 4]
},
dtype='int')
df = pd.DataFrame([
{
'a': 1,
'b': 3,
'test': 0
},
{
'a': 2,
'b': 3,
'test': 1
},
{
'a': 1,
'b': 4,
'test': 2
},
{
'a': 2,
'b': 4,
'test': 3
},
{
'a': 2,
'b': 4,
'test': 4
},
])
with raises(SmifDataMismatchError) as ex:
DataArray.from_df(spec, df)
msg = "Data for 'test' contains duplicate values at [{'a': 2, 'b': 4}]"
msg_alt = "Data for 'test' contains duplicate values at [{'b': 4, 'a': 2}]"
assert msg in str(ex.value) or msg_alt in str(ex.value)
def test_scalar(self):
# should handle zero-dimensional case (numpy array as scalar)
data = numpy.array(2.0)
spec = Spec(name='test', dims=[], coords={}, dtype='float')
da = DataArray(spec, data)
df = pd.DataFrame([{'test': 2.0}])
da_from_df = DataArray.from_df(spec, df)
assert da_from_df == da
df_from_da = da.as_df()
pd.testing.assert_frame_equal(df_from_da, df)
def test_df_round_trip_2d(self):
spec = Spec.from_dict({
'name': 'two_d',
'dims': ['a', 'z'],
'coords': {
'a': ['q', 'p'],
'z': ['a', 'c', 'b'],
},
'dtype': 'float'
})
da = DataArray(spec, numpy.array([
[5., 6., 7.],
[8., 9., 0.],
]))
df = pd.DataFrame([
{
'z': 'a',
'a': 'p',
'two_d': 8.
},
{
'z': 'c',
'a': 'q',
'two_d': 6.
},
{
'z': 'a',
'a': 'q',
'two_d': 5.
},
{
'z': 'b',
'a': 'q',
'two_d': 7.
},
{
'z': 'b',
'a': 'p',
'two_d': 0.
},
{
'z': 'c',
'a': 'p',
'two_d': 9.
},
])
df = df.set_index(spec.dims)
df_from_da = da.as_df()
da_from_df = DataArray.from_df(spec, df_from_da)
assert_array_equal(da.data, da_from_df.data)
def dataframe_to_data_array(dataframe, spec, path):
if spec.dims:
data_array = DataArray.from_df(spec, dataframe)
else:
# zero-dimensional case (scalar)
data = dataframe[spec.name]
if data.shape != (1, ):
msg = "Data for '{}' should contain a single value, instead got {} while " + \
"reading from {}"
raise SmifDataMismatchError(
msg.format(spec.name, len(data), path))
data_array = DataArray(spec, data.iloc[0])
return data_array
def test_from_df_partial(self, spec):
"""Should create a DataArray that can handle missing data, returning nan/null
"""
df = pd.DataFrame({
'a': ['a1'],
'b': ['b1'],
'c': ['c2'],
'test_data': [1]
}).set_index(['a', 'b', 'c'])
expected_data = numpy.full(spec.shape, numpy.nan)
expected_data[0, 0, 1] = 1.0
expected = DataArray(spec, expected_data)
actual = DataArray.from_df(spec, df)
assert_array_equal(actual.data, expected.data)
assert actual == expected
def test_match_metadata(self):
spec = Spec(name='test',
dims=['region'],
coords={'region': ['oxford']},
dtype='int64')
# must have a column named the same as the spec.name
df = pd.DataFrame([{
'region': 'oxford',
'other': 'else'
}]).set_index(['region'])
msg = "Data for 'test' expected a data column called 'test' and index names " + \
"['region'], instead got data columns ['other'] and index names ['region']"
with raises(SmifDataMismatchError) as ex:
DataArray.from_df(spec, df)
assert msg in str(ex.value)
# may not be indexed, if columns are otherwise all okay
df = pd.DataFrame([{'region': 'oxford', 'test': 1}])
DataArray.from_df(spec, df)
# must have an index level for each spec dimension
df = pd.DataFrame([{'test': 3.14}])
msg = "Data for 'test' expected a data column called 'test' and index names " + \
"['region'], instead got data columns ['test'] and index names [None]"
with raises(SmifDataMismatchError) as ex:
DataArray.from_df(spec, df)
assert msg in str(ex.value)
# must not have dimension labels outside of the spec dimension
df = pd.DataFrame([{
'test': 3.14,
'region': 'oxford'
}, {
'test': 3.14,
'region': 'extra'
}]).set_index(['region'])
msg = "Data for 'test' contained unexpected values in the set of coordinates for " + \
"dimension 'region': ['extra']"
with raises(SmifDataMismatchError) as ex:
DataArray.from_df(spec, df)
assert msg in str(ex.value)
def test_to_from_df(self):
df = pd.DataFrame([{
'test': 3,
'region': 'oxford',
'interval': 1
}]).set_index(['region', 'interval'])
spec = Spec(name='test',
dims=['region', 'interval'],
coords={
'region': ['oxford'],
'interval': [1]
},
dtype='int64')
da = DataArray(spec, numpy.array([[3.]], dtype='int64'))
da_from_df = DataArray.from_df(spec, df)
assert da_from_df == da
da_to_df = da.as_df()
pd.testing.assert_frame_equal(da_to_df, df)
def test_df_round_trip(self):
spec = Spec.from_dict({
'name': 'multi_savings',
'description': 'The savings from various technologies',
'dims': ['technology_type'],
'coords': {
'technology_type':
['water_meter', 'electricity_meter', 'other', 'aaa']
},
'dtype': 'float',
'abs_range': (0, 100),
'exp_range': (3, 10),
'unit': '%'
})
da = DataArray(spec, numpy.array([5., 6., 7., 8.]))
df = pd.DataFrame([
{
'technology_type': 'water_meter',
'multi_savings': 5.
},
{
'technology_type': 'electricity_meter',
'multi_savings': 6.
},
{
'technology_type': 'other',
'multi_savings': 7.
},
{
'technology_type': 'aaa',
'multi_savings': 8.
},
])
df = df.set_index(spec.dims)
df_from_da = da.as_df()
da_from_df = DataArray.from_df(spec, df_from_da)
assert_array_equal(da.data, da_from_df.data)
def get_multidimensional_param():
spec = Spec.from_dict({
'name': 'ss_t_base_heating',
'description': 'Industrial base temperature',
'default': '../energy_demand/parameters/ss_t_base_heating.csv',
'unit': '',
'dims': ['interpolation_params', 'end_yr'],
'coords': {
'interpolation_params': ['diffusion_choice', 'value_ey'],
'end_yr': [2030, 2050]
},
'dtype': 'float'
})
dataframe = pd.DataFrame([
{
'interpolation_params': 'diffusion_choice',
'end_yr': 2030,
'ss_t_base_heating': 0
},
{
'interpolation_params': 'diffusion_choice',
'end_yr': 2050,
'ss_t_base_heating': 0
},
{
'interpolation_params': 'value_ey',
'end_yr': 2030,
'ss_t_base_heating': 15.5
},
{
'interpolation_params': 'value_ey',
'end_yr': 2050,
'ss_t_base_heating': 15.5
},
]).set_index(['interpolation_params', 'end_yr'])
return DataArray.from_df(spec, dataframe)
def _df_to_ndarray(self, output_name, dataframe):
spec = self.outputs[output_name]
dataframe.set_index(spec.dims, inplace=True)
dataarray = DataArray.from_df(spec, dataframe)
return dataarray.data
def test_from_df(self, small_da, small_da_df):
"""Should create a DataArray from a pandas.DataFrame
"""
actual = DataArray.from_df(small_da.spec, small_da_df)
assert actual == small_da
def main():
# Read connection details
if 'NISMOD_API_USER' in os.environ and 'NISMOD_API_PASSWORD' in os.environ:
username = os.environ['NISMOD_API_USER']
password = os.environ['NISMOD_API_PASSWORD']
else:
parser = configparser.ConfigParser()
parser.read(os.path.join(os.path.dirname(__file__), 'dbconfig.ini'))
username = parser['nismod-api']['user']
password = parser['nismod-api']['password']
auth = (username, password)
try:
os.mkdir(CACHE_PATH)
except FileExistsError:
pass
# Population
get_population(auth)
process_oa_population()
process_oa_to_lad_population()
# Read
oa_pop = pandas.read_csv(os.path.join(CACHE_PATH, 'oa_population.csv'))
lad_pop = pandas.read_csv(os.path.join(CACHE_PATH, 'lad_population.csv'))
# to DataArray
years = list(range(POPULATION_MIN_YEAR, POPULATION_MAX_YEAR + 1))
oas = list(oa_pop.oa.unique())
oa_spec = Spec(name='population',
dims=['timestep', 'oa'],
coords={
'oa': oas,
'timestep': years
},
dtype='int')
lads = list(lad_pop.lad_gb_2016.unique())
lad_spec = Spec(name='population',
dims=['timestep', 'lad_gb_2016'],
coords={
'lad_gb_2016': lads,
'timestep': years
},
dtype='int')
# Write to CSV and Parquet stores
text_store = CSVDataStore(STORE_BASE_PATH)
binary_store = ParquetDataStore(STORE_BASE_PATH)
lad_key = 'population_nismod_db.v5_lad16'
lad_pop_da = DataArray.from_df(lad_spec, lad_pop.set_index(lad_spec.dims))
text_store.write_scenario_variant_data('{}.csv'.format(lad_key),
lad_pop_da)
binary_store.write_scenario_variant_data('{}.parquet'.format(lad_key),
lad_pop_da)
oa_key = 'population_nismod_db.v5_oa'
oa_pop_da = DataArray.from_df(oa_spec, oa_pop.set_index(oa_spec.dims))
text_store.write_scenario_variant_data('{}.csv'.format(oa_key), oa_pop_da)
binary_store.write_scenario_variant_data('{}.parquet'.format(oa_key),
oa_pop_da)
