def download_SIM(ano, estado):
'''
Download and saves on PySUS cache directory
:param ano:
:param estado:
:return:
'''
download(estado, ano)
def test_group_and_count(self):
df = download('sp',2010)
df = decoders.translate_variables_SIM(df)
variables = ['CODMUNRES','SEXO','IDADE_ANOS']
counts = decoders.group_and_count(df,variables)
sample = counts[counts['CONTAGEM'] != 0]['CONTAGEM'].sample(20,random_state=0).tolist()
assert_array_equal(sample, [1.0, 1.0, 2.0, 4.0, 9.0, 1.0, 1.0, 1.0, 3.0, 289.0, 1.0, 3.0, 3.0, 19.0, 9.0, 1.0, 2.0, 1.0, 1.0, 3.0])
def test_translate_variables(self):
df = download('sp',2010)
df = decoders.translate_variables_SIM(df)
sex_array = df["SEXO"].unique().tolist()
assert_array_equal(sex_array, ['Masculino', 'Feminino', 'nan'])
raca_array = df['RACACOR'].unique().tolist()
assert_array_equal(raca_array, ['Branca', 'Preta', 'Amarela', 'nan', 'Parda', 'Indígena'])
def test_group_and_count(self):
df = download("sp", 2010)
df = decoders.translate_variables_SIM(df)
variables = ["CODMUNRES", "SEXO", "IDADE_ANOS"]
counts = SIM.group_and_count(df, variables)
sample = (counts[counts["COUNTS"] != 0]["COUNTS"].sample(
20, random_state=0).tolist())
assert_array_equal(
sample,
[
1.0,
1.0,
2.0,
4.0,
9.0,
1.0,
1.0,
1.0,
3.0,
289.0,
1.0,
3.0,
3.0,
19.0,
9.0,
1.0,
2.0,
1.0,
1.0,
3.0,
],
)
def test_translate_variables(self):
df = download("sp", 2010)
df = decoders.translate_variables_SIM(df)
sex_array = df["SEXO"].unique().tolist()
assert_array_equal(sex_array, ["Masculino", "Feminino", "nan"])
raca_array = df["RACACOR"].unique().tolist()
assert_array_equal(
raca_array, ["Branca", "Preta", "Amarela", "nan", "Parda", "Indígena"]
)
def test_redistribute(self):
df = download('sp',2010)
df = decoders.translate_variables_SIM(df)
variables = ['CODMUNRES','SEXO','IDADE_ANOS']
counts = decoders.group_and_count(df,variables)
sum_original = counts["CONTAGEM"].sum()
counts = decoders.redistribute(counts,variables)
sum_redistributed = counts["CONTAGEM"].sum()
assert_equal(sum_original,sum_redistributed)
sample = counts[counts['CONTAGEM'] != 0]['CONTAGEM'].sample(20,random_state=0).tolist()
assert_array_almost_equal(sample, [1.0026605509150972, 3.0076529330337682, 10.0, 3.0, 1.0, 7.030611240693058, 2.0, 1.0, 1.0003988761766138, 1.0, 5.0, 1.0, 2.0, 1.0, 1.0011890475332716, 1.0007766913402458, 3.0, 3.0, 1.0, 1.0], decimal=5)
def test_redistribute_missing_partial(self):
df = download("sp", 2010)
df = decoders.translate_variables_SIM(df,
age_classes=True,
classify_cid10_chapters=True)
group_variables = ["CODMUNRES", "SEXO", "IDADE_ANOS", "CID10_CHAPTER"]
counts = SIM.group_and_count(df, group_variables)
counts["COUNTS_ORIGINAL"] = counts["COUNTS"]
sum_original = counts["COUNTS"].sum()
counts = SIM.redistribute_missing(counts, group_variables[:3])
sum_redistributed = counts["COUNTS"].sum()
assert_equal(sum_original, round(sum_redistributed))
def test_redistribute(self):
df = download('sp', 2010)
df = decoders.translate_variables_SIM(df,
age_classes=True,
classify_cid10_chapters=True)
variables = ['CODMUNRES', 'SEXO', 'IDADE_ANOS', 'CID10_CHAPTER']
df = df[variables]
counts = group_and_count(df, variables)
sum_original = counts["COUNTS"].sum()
counts = redistribute_missing(counts, variables)
sum_redistributed = counts["COUNTS"].sum()
assert_almost_equal(sum_original, sum_redistributed, 10)
sample = counts[counts['COUNTS'] != 0]['COUNTS'].sample(
20, random_state=0).tolist()
assert_array_almost_equal(sample, [
1.0, 1.0000216033775462, 4.0, 1.0057015548341106,
2.000363538647316, 3.0005453079709743, 1.0, 2.0093748859678917,
1.0, 1.0006631753413024, 1.0, 1.0155903470702614,
1.0006446228186379, 1.0007163086475952, 4.0016700388384105,
1.0003146522751405, 5.202681974105347, 1.0057015548341106,
1.0006806444217275, 1.0000656718488452
],
decimal=5)
counts = redistribute_cid_chapter(counts,
['CODMUNRES', 'SEXO', 'IDADE_ANOS'])
sum_redistributed = counts["COUNTS"].sum()
assert_almost_equal(sum_original, sum_redistributed, 10)
sample = counts[counts['COUNTS'] != 0]['COUNTS'].sample(
20, random_state=0).tolist()
assert_array_almost_equal(sample, [
1.089135695829918, 1.1471212205224637, 97.66379391566016,
1.0006806444217275, 1.0526404291598292, 1.0002258989870523,
1.0006438895125183, 1.0022096833374972, 1.004692969527825,
1.0098947488581271, 1.3848786564718214, 1.0358818448712763,
1.0477163671352119, 1.1041264089747516, 1.0002258989870523,
4.00889998546595, 1.0435326872735615, 4.000315617188721,
1.0007163086475952, 2.0118196033377975
],
decimal=5)
def test_redistribute_missing(self):
df = download("sp", 2010)
df = decoders.translate_variables_SIM(df)
variables = ["CODMUNRES", "SEXO", "IDADE_ANOS"]
counts = SIM.group_and_count(df, variables)
sum_original = counts["COUNTS"].sum()
counts = SIM.redistribute_missing(counts, variables)
sum_redistributed = counts["COUNTS"].sum()
assert_equal(sum_original, sum_redistributed)
sample = (counts[counts["COUNTS"] != 0]["COUNTS"].sample(
20, random_state=0).tolist())
assert_array_almost_equal(
sample,
[
1.0026605509150972,
3.0076529330337682,
10.0,
3.0,
1.0,
7.030611240693058,
2.0,
1.0,
1.0003988761766138,
1.0,
5.0,
1.0,
2.0,
1.0,
1.0011890475332716,
1.0007766913402458,
3.0,
3.0,
1.0,
1.0,
],
decimal=5,
)
def test_download_CID10(self):
df = download('ba', 2007)
self.assertIn('IDADEMAE', df.columns)
self.assertGreater(len(df), 0)
def test_download_CID9(self):
df = download('mg', 1987)
self.assertIn('NECROPSIA', df.columns)
self.assertGreater(len(df), 0)
def test_download_CID9(self):
df = download("mg", 1987)
self.assertIn("NECROPSIA", df.columns)
self.assertGreater(len(df), 0)
def test_download_CID10(self):
df = download("ba", 2007)
self.assertIn("IDADEMAE", df.columns)
self.assertGreater(len(df), 0)
