def test_001_process(self):
"""Test ISO2 primary_geo; build a date day, month, year; no primary_date; feature qualifies another feature."""
# Define mixmasta inputs:
mp = f"inputs{sep}test1_input.json"
fp = f"inputs{sep}test1_input.csv"
geo = 'admin2'
outf = f"outputs{sep}unittests"
# Process:
df, dct = mixmasta.process(fp, mp, geo, outf)
# Load expected output:
output_df = pd.read_csv(f'outputs{sep}test1_output.csv', index_col=False)
output_df = mixmasta.optimize_df_types(output_df)
with open(f'outputs{sep}test1_dict.json') as f:
output_dict = json.loads(f.read())
# Sort both data frames and reindex for comparison,.
cols = ['timestamp','country','admin1','admin2','admin3','lat','lng','feature','value']
df.sort_values(by=cols, inplace=True)
output_df.sort_values(by=cols, inplace=True)
df.reset_index(drop=True, inplace=True)
output_df.reset_index(drop =True, inplace=True)
# Assertions
assert_frame_equal(df, output_df)
assert_dict_equal(dct, output_dict)
def test_006_process(self):
"""Test multi primary_geo, resolve_to_gadm"""
# Define mixmasta inputs:
mp = f'inputs{sep}test6_hoa_conflict_input.json'
fp = f'inputs{sep}test6_hoa_conflict_input.csv'
geo = 'admin2'
outf = f'outputs{sep}unittests'
# Process:
df, dct = mixmasta.process(fp, mp, geo, outf)
# Load expected output:
output_df = pd.read_csv(f'outputs{sep}test6_hoa_conflict_output.csv', index_col=False)
output_df = mixmasta.optimize_df_types(output_df)
with open(f'outputs{sep}test6_hoa_conflict_dict.json') as f:
output_dict = json.loads(f.read())
# Sort both data frames and reindex for comparison,.
cols = ['timestamp','country','admin1','admin2','admin3','lat','lng','feature','value']
df.sort_values(by=cols, inplace=True)
output_df.sort_values(by=cols, inplace=True)
df.reset_index(drop=True, inplace=True)
output_df.reset_index(drop =True, inplace=True)
# Make the datatypes the same for value/feature and qualifying columns.
df['value'] = df['value'].astype('str')
df['feature'] = df['feature'].astype('str')
output_df['value'] = output_df['value'].astype('str')
output_df['feature'] = output_df['feature'].astype('str')
# Assertions
assert_frame_equal(df, output_df, check_categorical = False)
assert_dict_equal(dct, output_dict)
def test_002_process(self):
"""
Test GeoTiff This tests that multi-band geotiff processing is the same. Uses the
asset_wealth tif which has 4 bands of different years representing a
measure of wealth.
"""
# Define mixmasta inputs:
mp = f'inputs{sep}test2_assetwealth_input.json'
fp = f'inputs{sep}test2_assetwealth_input.tif'
geo = 'admin2'
outf = f'outputs{sep}unittests'
# Process:
df, dct = mixmasta.process(fp, mp, geo, outf)
#categories = df.select_dtypes(include=['category']).columns.tolist()
df['value'] = df['value'].astype('str')
# Load expected output:
output_df = pd.read_csv(f'outputs{sep}test2_assetwealth_output.csv', index_col=False)
with open(f'outputs{sep}test2_assetwealth_dict.json') as f:
output_dict = json.loads(f.read())
# Sort both data frames and reindex for comparison,.
cols = ['timestamp','country','admin1','admin2','admin3','lat','lng','feature','value']
df = df[cols]
output_df = output_df[cols]
# Optimize datatypes for output_df.
floats = output_df.select_dtypes(include=['float64']).columns.tolist()
output_df[floats] = output_df[floats].apply(pd.to_numeric, downcast='float')
ints = output_df.select_dtypes(include=['int64']).columns.tolist()
output_df[ints] = output_df[ints].apply(pd.to_numeric, downcast='integer')
# Standardize value and feature columns to str for comparison.
df['value'] = df['value'].astype('str')
df['feature'] = df['feature'].astype('str')
output_df['value'] = output_df['value'].astype('str')
output_df['feature'] = output_df['feature'].astype('str')
# Sort and reindex.
df.sort_values(by=cols, inplace=True)
df.reset_index(drop=True, inplace=True)
output_df.sort_values(by=cols, inplace=True)
output_df.reset_index(drop =True, inplace=True)
# Assertions
assert_frame_equal(df, output_df, check_categorical = False)
assert_dict_equal(dct, output_dict)