def test_as_table_columns__with_pandas_DataFrame():
df = pd.DataFrame({
'foobar': ("foo", "bar", "baz", "qux", "asdf"),
'x': tuple(math.pi*i for i in range(5)),
'n': (101, 202, 303, 404, 505),
'really': (False, True, False, True, False),
'size': ('small', 'large', 'medium', 'medium', 'large')},
columns=['foobar', 'x', 'n', 'really', 'size'])
cols = as_table_columns(df)
expected_columns = [
{'defaultValue': '',
'columnType': 'STRING',
'name': 'foobar',
'maximumSize': 30,
'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'},
{'columnType': 'DOUBLE',
'name': 'x',
u'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'},
{'columnType': 'INTEGER',
'name': 'n',
'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'},
{'columnType': 'BOOLEAN',
'name': 'really',
'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'},
{'defaultValue': '',
'columnType': 'STRING',
'name': 'size',
'maximumSize': 30,
'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'}
]
assert expected_columns == cols
def test_as_table_columns__with_pandas_DataFrame():
df = pd.DataFrame({
'foobar': ("foo", "bar", "baz", "qux", "asdf"),
'x': tuple(math.pi*i for i in range(5)),
'n': (101, 202, 303, 404, 505),
'really': (False, True, False, True, False),
'size': ('small', 'large', 'medium', 'medium', 'large')},
columns=['foobar', 'x', 'n', 'really', 'size'])
cols = as_table_columns(df)
expected_columns = [
{'defaultValue': '',
'columnType': 'STRING',
'name': 'foobar',
'maximumSize': 30,
'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'},
{'columnType': 'DOUBLE',
'name': 'x',
u'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'},
{'columnType': 'INTEGER',
'name': 'n',
'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'},
{'columnType': 'BOOLEAN',
'name': 'really',
'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'},
{'defaultValue': '',
'columnType': 'STRING',
'name': 'size',
'maximumSize': 30,
'concreteType': 'org.sagebionetworks.repo.model.table.ColumnModel'}
]
assert_equals(expected_columns, cols)
def test_tables_pandas():
try:
## check if we have pandas
import pandas as pd
## create a pandas DataFrame
df = pd.DataFrame({
'A' : ("foo", "bar", "baz", "qux", "asdf"),
'B' : tuple(math.pi*i for i in range(5)),
'C' : (101, 202, 303, 404, 505),
'D' : (False, True, False, True, False)})
cols = as_table_columns(df)
cols[0].maximumSize = 20
schema = Schema(name="Nifty Table", columns=cols, parent=project)
## store in Synapse
table = syn.store(Table(schema, df))
## retrieve the table and verify
results = syn.tableQuery('select * from %s'%table.schema.id)
df2 = results.asDataFrame()
## simulate rowId-version rownames for comparison
df.index = ['%s_0'%i for i in range(5)]
assert all(df2 == df)
except ImportError as e1:
sys.stderr.write('Pandas is apparently not installed, skipping test_tables_pandas.\n\n')
def test_as_table_columns():
try:
import pandas as pd
df = pd.DataFrame({
'foobar': ("foo", "bar", "baz", "qux", "asdf"),
'x':
tuple(math.pi * i for i in range(5)),
'n': (101, 202, 303, 404, 505),
'really': (False, True, False, True, False),
'size': ('small', 'large', 'medium', 'medium', 'large')
})
cols = as_table_columns(df)
cols[0]['name'] == 'foobar'
cols[0]['columnType'] == 'STRING'
cols[1]['name'] == 'x'
cols[1]['columnType'] == 'DOUBLE'
cols[1]['name'] == 'n'
cols[1]['columnType'] == 'INTEGER'
cols[1]['name'] == 'really'
cols[1]['columnType'] == 'BOOLEAN'
cols[1]['name'] == 'size'
# TODO: support Categorical when fully supported in Pandas Data Frames
cols[1]['columnType'] == 'STRING'
except ImportError as e1:
sys.stderr.write(
'Pandas is apparently not installed, skipping test_as_table_columns.\n\n'
)
def test_as_table_columns():
try:
import pandas as pd
df = pd.DataFrame({
'foobar' : ("foo", "bar", "baz", "qux", "asdf"),
'x' : tuple(math.pi*i for i in range(5)),
'n' : (101, 202, 303, 404, 505),
'really' : (False, True, False, True, False),
'size' : ('small', 'large', 'medium', 'medium', 'large')})
cols = as_table_columns(df)
cols[0]['name'] == 'foobar'
cols[0]['columnType'] == 'STRING'
cols[1]['name'] == 'x'
cols[1]['columnType'] == 'DOUBLE'
cols[1]['name'] == 'n'
cols[1]['columnType'] == 'INTEGER'
cols[1]['name'] == 'really'
cols[1]['columnType'] == 'BOOLEAN'
cols[1]['name'] == 'size'
# TODO: support Categorical when fully supported in Pandas Data Frames
cols[1]['columnType'] == 'STRING'
except ImportError as e1:
sys.stderr.write('Pandas is apparently not installed, skipping test_as_table_columns.\n\n')
def test_tables_pandas():
try:
## check if we have pandas
import pandas as pd
## create a pandas DataFrame
df = pd.DataFrame({
'A': ("foo", "bar", "baz", "qux", "asdf"),
'B': tuple(math.pi * i for i in range(5)),
'C': (101, 202, 303, 404, 505),
'D': (False, True, False, True, False)
})
cols = as_table_columns(df)
cols[0].maximumSize = 20
schema = Schema(name="Nifty Table", columns=cols, parent=project)
## store in Synapse
table = syn.store(Table(schema, df))
## retrieve the table and verify
results = syn.tableQuery('select * from %s' % table.schema.id)
df2 = results.asDataFrame()
## simulate rowId-version rownames for comparison
df.index = ['%s_0' % i for i in range(5)]
assert all(df2 == df)
except ImportError as e1:
sys.stderr.write(
'Pandas is apparently not installed, skipping test_tables_pandas.\n\n'
)
def test_pandas_to_table():
pd = _try_import_pandas('test_pandas_to_table')
df = pd.DataFrame(dict(a=[1, 2, 3], b=["c", "d", "e"]))
schema = Schema(name="Baz",
parent="syn12345",
columns=as_table_columns(df))
print("\n", df, "\n\n")
## A dataframe with no row id and version
table = Table(schema, df)
for i, row in enumerate(table):
print(row)
assert row[0] == (i + 1)
assert row[1] == ["c", "d", "e"][i]
assert len(table) == 3
## If includeRowIdAndRowVersion=True, include empty row id an versions
## ROW_ID,ROW_VERSION,a,b
## ,,1,c
## ,,2,d
## ,,3,e
table = Table(schema, df, includeRowIdAndRowVersion=True)
for i, row in enumerate(table):
print(row)
assert row[0] is None
assert row[1] is None
assert row[2] == (i + 1)
## A dataframe with no row id and version
df = pd.DataFrame(index=["1_7", "2_7", "3_8"],
data=dict(a=[100, 200, 300], b=["c", "d", "e"]))
print("\n", df, "\n\n")
table = Table(schema, df)
for i, row in enumerate(table):
print(row)
assert row[0] == ["1", "2", "3"][i]
assert row[1] == ["7", "7", "8"][i]
assert row[2] == (i + 1) * 100
assert row[3] == ["c", "d", "e"][i]
## A dataframe with row id and version in columns
df = pd.DataFrame(
dict(ROW_ID=["0", "1", "2"],
ROW_VERSION=["8", "9", "9"],
a=[100, 200, 300],
b=["c", "d", "e"]))
print("\n", df, "\n\n")
table = Table(schema, df)
for i, row in enumerate(table):
print(row)
assert row[0] == ["0", "1", "2"][i]
assert row[1] == ["8", "9", "9"][i]
assert row[2] == (i + 1) * 100
assert row[3] == ["c", "d", "e"][i]
def test_pandas_to_table():
try:
import pandas as pd
df = pd.DataFrame(dict(a=[1,2,3], b=["c", "d", "e"]))
schema = Schema(name="Baz", parent="syn12345", columns=as_table_columns(df))
print("\n", df, "\n\n")
## A dataframe with no row id and version
table = Table(schema, df)
for i, row in enumerate(table):
print(row)
assert row[0]==(i+1)
assert row[1]==["c", "d", "e"][i]
assert len(table)==3
## If includeRowIdAndRowVersion=True, include empty row id an versions
## ROW_ID,ROW_VERSION,a,b
## ,,1,c
## ,,2,d
## ,,3,e
table = Table(schema, df, includeRowIdAndRowVersion=True)
for i, row in enumerate(table):
print(row)
assert row[0] is None
assert row[1] is None
assert row[2]==(i+1)
## A dataframe with no row id and version
df = pd.DataFrame(index=["1_7","2_7","3_8"], data=dict(a=[100,200,300], b=["c", "d", "e"]))
print("\n", df, "\n\n")
table = Table(schema, df)
for i, row in enumerate(table):
print(row)
assert row[0]==["1","2","3"][i]
assert row[1]==["7","7","8"][i]
assert row[2]==(i+1)*100
assert row[3]==["c", "d", "e"][i]
## A dataframe with row id and version in columns
df = pd.DataFrame(dict(ROW_ID=["0","1","2"], ROW_VERSION=["8","9","9"], a=[100,200,300], b=["c", "d", "e"]))
print("\n", df, "\n\n")
table = Table(schema, df)
for i, row in enumerate(table):
print(row)
assert row[0]==["0","1","2"][i]
assert row[1]==["8","9","9"][i]
assert row[2]==(i+1)*100
assert row[3]==["c", "d", "e"][i]
except ImportError as e1:
sys.stderr.write('Pandas is apparently not installed, skipping test_pandas_to_table.\n\n')
def write_synapse_table(table_data,
synapse_project_id,
table_name='',
username='',
password=''):
"""
Write data to a Synapse table.
Parameters
----------
table_data : Pandas DataFrame
Synapse table contents
synapse_project_id : string
Synapse ID for project within which table is to be written
table_name : string
schema name of table
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Examples
--------
>>> from mhealthx.io_data import read_synapse_table_files, write_synapse_table
>>> in_synapse_table_id = 'syn4590865'
>>> synapse_project_id = 'syn4899451'
>>> column_names = []
>>> download_limit = None
>>> out_path = '.'
>>> username = ''
>>> password = ''
>>> table_data, files = read_synapse_table_files(in_synapse_table_id, column_names, download_limit, out_path, username, password)
>>> table_name = 'Contents of ' + in_synapse_table_id
>>> write_synapse_table(table_data, synapse_project_id, table_name, username, password)
"""
import synapseclient
from synapseclient import Schema, Table, as_table_columns
syn = synapseclient.Synapse()
# Log in to Synapse:
if username and password:
syn.login(username, password)
else:
syn.login()
table_data.index = range(table_data.shape[0])
schema = Schema(name=table_name,
columns=as_table_columns(table_data),
parent=synapse_project_id,
includeRowIdAndRowVersion=False)
syn.store(Table(schema, table_data))
def test_tables_pandas():
try:
## check if we have pandas
import pandas as pd
#import numpy for datatypes
import numpy as np
## create a pandas DataFrame
df = pd.DataFrame({
'A': ("foo", "bar", "baz", "qux", "asdf"),
'B':
tuple(0.42 * i for i in range(5)),
'C': (101, 202, 303, 404, 505),
'D': (False, True, False, True, False),
# additional data types supported since SYNPY-347
'int64':
tuple(np.int64(range(5))),
'datetime64':
tuple(
np.datetime64(d) for d in [
'2005-02-01', '2005-02-02', '2005-02-03', '2005-02-04',
'2005-02-05'
]),
'string_':
tuple(
np.string_(s)
for s in ['urgot', 'has', 'dark', 'mysterious', 'past'])
})
cols = as_table_columns(df)
cols[0].maximumSize = 20
schema = Schema(name="Nifty Table", columns=cols, parent=project)
## store in Synapse
table = syn.store(Table(schema, df))
## retrieve the table and verify
results = syn.tableQuery('select * from %s' % table.schema.id,
resultsAs='csv')
df2 = results.asDataFrame(convert_to_datetime=True)
## simulate rowId-version rownames for comparison
df.index = ['%s_0' % i for i in range(5)]
#for python3 we need to convert from numpy.bytes_ to str or the equivalence comparision fails
if six.PY3: df['string_'] = df['string_'].transform(str)
# df2 == df gives Dataframe of boolean values; first .all() gives a Series object of ANDed booleans of each column; second .all() gives a bool that is ANDed value of that Series
assert (df2 == df).all().all()
except ImportError as e1:
sys.stderr.write(
'Pandas is apparently not installed, skipping test_tables_pandas.\n\n'
)
def test_iter_with_table_row_metadata(self):
# csv file has row metadata, self.headers does not
data = "ROW_ID,ROW_VERSION,col\n" \
"1,2,\"I like trains\"\n" \
"5,1,\"weeeeeeeeeeee\"\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
expected_rows = [["I like trains"], ["weeeeeeeeeeee"]]
for expected_row, table_row in zip(expected_rows, table):
assert_equals(expected_row, table_row)
def test_iter_no_row_metadata(self):
# both csv headers and self.headers do not contains row metadata
data = "col1,col2\n" \
"1,2\n" \
"2,1\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
expected_rows = [[1, 2], [2, 1]]
for expected_row, table_row in zip(expected_rows, table):
assert_equals(expected_row, table_row)
def test_iter_no_row_metadata(self):
# both csv headers and self.headers do not contains row metadata
data = "col1,col2\n" \
"1,2\n" \
"2,1\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
expected_rows = [[1, 2], [2, 1]]
for expected_row, table_row in zip(expected_rows, table):
assert expected_row == table_row
def test_iter_with_table_row_metadata(self):
# csv file has row metadata, self.headers does not
data = "ROW_ID,ROW_VERSION,col\n" \
"1,2,\"I like trains\"\n" \
"5,1,\"weeeeeeeeeeee\"\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
expected_rows = [["I like trains"], ["weeeeeeeeeeee"]]
for expected_row, table_row in zip(expected_rows, table):
assert expected_row == table_row
def test_dict_to_table():
d = dict(a=[1, 2, 3], b=["c", "d", "e"])
df = pd.DataFrame(d)
schema = Schema(name="Baz", parent="syn12345", columns=as_table_columns(df))
with patch.object(CsvFileTable, "from_data_frame") as mocked_from_data_frame:
Table(schema, d)
# call_agrs is a tuple with values and name
agrs_list = mocked_from_data_frame.call_args[0]
# getting the second argument
df_agr = agrs_list[1]
assert df_agr.equals(df)
def test_iter_with_mismatch_row_metadata(self):
# self.headers and csv file headers contains mismatch row metadata
data = "ROW_ID,ROW_VERSION,ROW_ETAG,col\n" \
"1,2,etag1,\"I like trains\"\n" \
"5,1,etag2,\"weeeeeeeeeeee\"\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn(name="ROW_ID", columnType="STRING"),
SelectColumn(name="ROW_VERSION", columnType="STRING")] + \
[SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
iter = table.__iter__()
pytest.raises(ValueError, next, iter)
def test_dict_to_table():
d = dict(a=[1, 2, 3], b=["c", "d", "e"])
df = pd.DataFrame(d)
schema = Schema(name="Baz", parent="syn12345", columns=as_table_columns(df))
with patch.object(CsvFileTable, "from_data_frame") as mocked_from_data_frame:
Table(schema, d)
# call_agrs is a tuple with values and name
agrs_list = mocked_from_data_frame.call_args[0]
# getting the second argument
df_agr = agrs_list[1]
assert_true(df_agr.equals(df))
def test_iter_with_mismatch_row_metadata(self):
# self.headers and csv file headers contains mismatch row metadata
data = "ROW_ID,ROW_VERSION,ROW_ETAG,col\n" \
"1,2,etag1,\"I like trains\"\n" \
"5,1,etag2,\"weeeeeeeeeeee\"\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn(name="ROW_ID", columnType="STRING"),
SelectColumn(name="ROW_VERSION", columnType="STRING")] + \
[SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
iter = table.__iter__()
assert_raises(ValueError, next, iter)
def test_iter_row_metadata_mismatch_in_headers(self):
# csv file does not contain row metadata, self.headers does
data = "col1,col2\n" \
"1,2\n" \
"2,1\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn(name="ROW_ID", columnType="STRING"),
SelectColumn(name="ROW_VERSION", columnType="STRING")] + \
[SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
iter = table.__iter__()
pytest.raises(ValueError, next, iter)
def test_iter_row_metadata_mismatch_in_headers(self):
# csv file does not contain row metadata, self.headers does
data = "col1,col2\n" \
"1,2\n" \
"2,1\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn(name="ROW_ID", columnType="STRING"),
SelectColumn(name="ROW_VERSION", columnType="STRING")] + \
[SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
iter = table.__iter__()
assert_raises(ValueError, next, iter)
def write_synapse_table(table_data, synapse_project_id, table_name='',
username='', password=''):
"""
Write data to a Synapse table.
Parameters
----------
table_data : Pandas DataFrame
Synapse table contents
synapse_project_id : string
Synapse ID for project within which table is to be written
table_name : string
schema name of table
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Examples
--------
>>> from mhealthx.xio import read_files_from_synapse_row
>>> from mhealthx.xtra import write_synapse_table
>>> synapse_table = 'syn4590865'
>>> row =
>>> column_name = ''
>>> out_path = '.'
>>> username = ''
>>> password = ''
>>> table_data, files = read_files_from_synapse_row(synapse_table, row, column_name, out_path, username, password)
>>> synapse_project_id = 'syn4899451'
>>> table_name = 'Contents of ' + synapse_table
>>> write_synapse_table(table_data, synapse_project_id, table_name, username, password)
"""
import synapseclient
from synapseclient import Schema, Table, as_table_columns
syn = synapseclient.Synapse()
# Log in to Synapse:
if username and password:
syn.login(username, password)
else:
syn.login()
table_data.index = range(table_data.shape[0])
schema = Schema(name=table_name, columns=as_table_columns(table_data),
parent=synapse_project_id, includeRowIdAndRowVersion=False)
syn.store(Table(schema, table_data))
def test_tables_pandas():
# create a pandas DataFrame
df = pd.DataFrame({
'A': ("foo", "bar", "baz", "qux", "asdf"),
'B':
tuple(0.42 * i for i in range(5)),
'C': (101, 202, 303, 404, 505),
'D': (False, True, False, True, False),
# additional data types supported since SYNPY-347
'int64':
tuple(np.int64(range(5))),
'datetime64':
tuple(
np.datetime64(d) for d in [
'2005-02-01', '2005-02-02', '2005-02-03', '2005-02-04',
'2005-02-05'
]),
'string_':
tuple(
np.string_(s)
for s in ['urgot', 'has', 'dark', 'mysterious', 'past'])
})
cols = as_table_columns(df)
cols[0].maximumSize = 20
schema = Schema(name="Nifty Table", columns=cols, parent=project)
# store in Synapse
table = syn.store(Table(schema, df))
# retrieve the table and verify
results = syn.tableQuery('select * from %s' % table.schema.id,
resultsAs='csv')
df2 = results.asDataFrame(convert_to_datetime=True)
# simulate rowId-version rownames for comparison
df.index = ['%s_0' % i for i in range(5)]
# for python3 we need to convert from numpy.bytes_ to str or the equivalence comparision fails
if six.PY3:
df['string_'] = df['string_'].transform(str)
# SYNPY-717
df['datetime64'] = df['datetime64'].apply(
lambda x: pd.Timestamp(x).tz_localize('UTC'))
# df2 == df gives Dataframe of boolean values; first .all() gives a Series object of ANDed booleans of each column;
# second .all() gives a bool that is ANDed value of that Series
assert_frame_equal(df2, df)
def test_pandas_to_table():
df = pd.DataFrame(dict(a=[1, 2, 3], b=["c", "d", "e"]))
schema = Schema(name="Baz",
parent="syn12345",
columns=as_table_columns(df))
# A dataframe with no row id and version
table = Table(schema, df)
for i, row in enumerate(table):
assert_equals(row[0], (i + 1))
assert_equals(row[1], ["c", "d", "e"][i])
assert_equals(len(table), 3)
# If includeRowIdAndRowVersion=True, include empty row id an versions
# ROW_ID,ROW_VERSION,a,b
# ,,1,c
# ,,2,d
# ,,3,e
table = Table(schema, df, includeRowIdAndRowVersion=True)
for i, row in enumerate(table):
assert_is_none(row[0])
assert_is_none(row[1])
assert_equals(row[2], (i + 1))
# A dataframe with no row id and version
df = pd.DataFrame(index=["1_7", "2_7", "3_8"],
data=dict(a=[100, 200, 300], b=["c", "d", "e"]))
table = Table(schema, df)
for i, row in enumerate(table):
assert_equals(row[0], ["1", "2", "3"][i])
assert_equals(row[1], ["7", "7", "8"][i])
assert_equals(row[2], (i + 1) * 100)
assert_equals(row[3], ["c", "d", "e"][i])
# A dataframe with row id and version in columns
df = pd.DataFrame(
dict(ROW_ID=["0", "1", "2"],
ROW_VERSION=["8", "9", "9"],
a=[100, 200, 300],
b=["c", "d", "e"]))
table = Table(schema, df)
for i, row in enumerate(table):
assert_equals(row[0], ["0", "1", "2"][i])
assert_equals(row[1], ["8", "9", "9"][i])
assert_equals(row[2], (i + 1) * 100)
assert_equals(row[3], ["c", "d", "e"][i])
def test_as_table_columns__with_csv_file():
string_io = StringIOContextManager(
'ROW_ID,ROW_VERSION,Name,Born,Hipness,Living\n'
'"1", "1", "John Coltrane", 1926, 8.65, False\n'
'"2", "1", "Miles Davis", 1926, 9.87, False')
cols = as_table_columns(string_io)
assert_equals(cols[0]['name'], 'Name')
assert_equals(cols[0]['columnType'], 'STRING')
assert_equals(cols[1]['name'], 'Born')
assert_equals(cols[1]['columnType'], 'INTEGER')
assert_equals(cols[2]['name'], 'Hipness')
assert_equals(cols[2]['columnType'], 'DOUBLE')
assert_equals(cols[3]['name'], 'Living')
assert_equals(cols[3]['columnType'], 'STRING')
def test_iter_with_file_view_row_metadata(self):
# csv file and self.headers contain matching row metadata
data = "ROW_ID,ROW_VERSION,ROW_ETAG,col\n" \
"1,2,etag1,\"I like trains\"\n" \
"5,1,etag2,\"weeeeeeeeeeee\"\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn(name="ROW_ID", columnType="STRING"),
SelectColumn(name="ROW_VERSION", columnType="STRING"),
SelectColumn(name="ROW_ETAG", columnType="STRING")] + \
[SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
expected_rows = [['1', '2', "etag1", "I like trains"],
['5', '1', "etag2", "weeeeeeeeeeee"]]
for expected_row, table_row in zip(expected_rows, table):
assert expected_row == table_row
def test_iter_with_file_view_row_metadata(self):
# csv file and self.headers contain matching row metadata
data = "ROW_ID,ROW_VERSION,ROW_ETAG,col\n" \
"1,2,etag1,\"I like trains\"\n" \
"5,1,etag2,\"weeeeeeeeeeee\"\n"
cols = as_table_columns(StringIOContextManager(data))
headers = [SelectColumn(name="ROW_ID", columnType="STRING"),
SelectColumn(name="ROW_VERSION", columnType="STRING"),
SelectColumn(name="ROW_ETAG", columnType="STRING")] + \
[SelectColumn.from_column(col) for col in cols]
with patch.object(io, "open", return_value=StringIOContextManager(data)):
table = CsvFileTable("syn123", "/fake/file/path", headers=headers)
expected_rows = [['1', '2', "etag1", "I like trains"],
['5', '1', "etag2", "weeeeeeeeeeee"]]
for expected_row, table_row in zip(expected_rows, table):
assert_equals(expected_row, table_row)
def test_as_table_columns__with_csv_file():
string_io = StringIOContextManager(
'ROW_ID,ROW_VERSION,Name,Born,Hipness,Living\n'
'"1", "1", "John Coltrane", 1926, 8.65, False\n'
'"2", "1", "Miles Davis", 1926, 9.87, False'
)
cols = as_table_columns(string_io)
assert_equals(cols[0]['name'], 'Name')
assert_equals(cols[0]['columnType'], 'STRING')
assert_equals(cols[1]['name'], 'Born')
assert_equals(cols[1]['columnType'], 'INTEGER')
assert_equals(cols[2]['name'], 'Hipness')
assert_equals(cols[2]['columnType'], 'DOUBLE')
assert_equals(cols[3]['name'], 'Living')
assert_equals(cols[3]['columnType'], 'STRING')
def test_pandas_to_table():
df = pd.DataFrame(dict(a=[1, 2, 3], b=["c", "d", "e"]))
schema = Schema(name="Baz", parent="syn12345", columns=as_table_columns(df))
# A dataframe with no row id and version
table = Table(schema, df)
for i, row in enumerate(table):
assert_equals(row[0], (i+1))
assert_equals(row[1], ["c", "d", "e"][i])
assert_equals(len(table), 3)
# If includeRowIdAndRowVersion=True, include empty row id an versions
# ROW_ID,ROW_VERSION,a,b
# ,,1,c
# ,,2,d
# ,,3,e
table = Table(schema, df, includeRowIdAndRowVersion=True)
for i, row in enumerate(table):
assert_is_none(row[0])
assert_is_none(row[1])
assert_equals(row[2], (i+1))
# A dataframe with no row id and version
df = pd.DataFrame(index=["1_7", "2_7", "3_8"], data=dict(a=[100, 200, 300], b=["c", "d", "e"]))
table = Table(schema, df)
for i, row in enumerate(table):
assert_equals(row[0], ["1", "2", "3"][i])
assert_equals(row[1], ["7", "7", "8"][i])
assert_equals(row[2], (i+1)*100)
assert_equals(row[3], ["c", "d", "e"][i])
# A dataframe with row id and version in columns
df = pd.DataFrame(dict(ROW_ID=["0", "1", "2"], ROW_VERSION=["8", "9", "9"], a=[100, 200, 300], b=["c", "d", "e"]))
table = Table(schema, df)
for i, row in enumerate(table):
assert_equals(row[0], ["0", "1", "2"][i])
assert_equals(row[1], ["8", "9", "9"][i])
assert_equals(row[2], (i+1)*100)
assert_equals(row[3], ["c", "d", "e"][i])
def concatenate_tables_to_synapse_table(frames,
synapse_project_id,
table_name,
username='',
password=''):
"""
Concatenate multiple dataframes and store as a Synapse table.
Reuse the indices from the original DataFrame,
increasing number of columns.
Parameters
----------
frames : list of pandas DataFrames
paths to files to upload to Synapse
synapse_project_id : string
Synapse ID for project to which table is to be written
table_name : string
schema name of table
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
table_data : Pandas DataFrame
output table
synapse_project_id : string
Synapse ID for project
Examples
--------
>>> import pandas as pd
>>> from mhealthx.io_data import concatenate_tables_to_synapse_table
>>> df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
>>> 'B': ['B0', 'B1', 'B2', 'B3'],
>>> 'C': ['C0', 'C1', 'C2', 'C3'],
>>> 'D': ['D0', 'D1', 'D2', 'D3']},
>>> index=[0, 1, 2, 3])
>>> df2 = pd.DataFrame({'E': ['A4', 'A5', 'A6', 'A7'],
>>> 'F': ['B4', 'B5', 'B6', 'B7'],
>>> 'G': ['C4', 'C5', 'C6', 'C7'],
>>> 'H': ['D4', 'D5', 'D6', 'D7']},
>>> index=[0, 1, 2, 3])
>>> frames = [df1, df2]
>>> synapse_project_id = 'syn4899451'
>>> table_name = 'Test to join tables'
>>> username = ''
>>> password = ''
>>> table_data, synapse_project_id = concatenate_tables_to_synapse_table(frames, synapse_project_id, table_name, username, password)
"""
import pandas as pd
import synapseclient
from synapseclient import Schema, Table, as_table_columns
syn = synapseclient.Synapse()
# Log in to Synapse:
if username and password:
syn.login(username, password)
else:
syn.login()
# Concatenate dataframes: reuse the indices from the original DataFrame,
# increasing number of columns:
table_data = pd.concat(frames, axis=1) #, join_axes=[frames[0].index])
# Create table schema:
schema = Schema(name=table_name,
columns=as_table_columns(table_data),
parent=synapse_project_id)
# Store as Synapse table:
table = syn.store(Table(schema, table_data))
return table_data, synapse_project_id
def copy_synapse_table(synapse_table_id, synapse_project_id, table_name='',
remove_columns=[], username='', password=''):
"""
Copy Synapse table to another Synapse project.
Parameters
----------
synapse_table_id : string
Synapse ID for table to copy
synapse_project_id : string
copy table to project with this Synapse ID
table_name : string
schema name of table
remove_columns : list of strings
column headers for columns to be removed
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
table_data : Pandas DataFrame
Synapse table contents
table_name : string
schema name of table
synapse_project_id : string
Synapse ID for project within which table is to be written
Examples
--------
>>> from mhealthx.io_data import copy_synapse_table
>>> synapse_table_id = 'syn4590865'
>>> synapse_project_id = 'syn4899451'
>>> table_name = 'Copy of ' + synapse_table_id
>>> remove_columns = ['audio_audio.m4a', 'audio_countdown.m4a']
>>> username = ''
>>> password = ''
>>> table_data, table_name, synapse_project_id = copy_synapse_table(synapse_table_id, synapse_project_id, table_name, remove_columns, username, password)
"""
import synapseclient
from synapseclient import Schema
from synapseclient.table import Table, as_table_columns
syn = synapseclient.Synapse()
# Log in to Synapse:
if username and password:
syn.login(username, password)
else:
syn.login()
# Download Synapse table as a dataframe:
results = syn.tableQuery("select * from {0}".format(synapse_table_id))
table_data = results.asDataFrame()
# Remove specified columns:
if remove_columns:
for remove_column in remove_columns:
del table_data[remove_column]
# Upload to Synapse table:
table_data.index = range(table_data.shape[0])
schema = Schema(name=table_name, columns=as_table_columns(table_data),
parent=synapse_project_id,
includeRowIdAndRowVersion=False)
table = syn.store(Table(schema, table_data))
return table_data, table_name, synapse_project_id
def copy_synapse_table(synapse_table_id,
synapse_project_id,
table_name='',
remove_columns=[],
username='',
password=''):
"""
Copy Synapse table to another Synapse project.
Parameters
----------
synapse_table_id : string
Synapse ID for table to copy
synapse_project_id : string
copy table to project with this Synapse ID
table_name : string
schema name of table
remove_columns : list of strings
column headers for columns to be removed
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
table_data : Pandas DataFrame
Synapse table contents
table_name : string
schema name of table
synapse_project_id : string
Synapse ID for project within which table is to be written
Examples
--------
>>> from mhealthx.io_data import copy_synapse_table
>>> synapse_table_id = 'syn4590865'
>>> synapse_project_id = 'syn4899451'
>>> table_name = 'Copy of ' + synapse_table_id
>>> remove_columns = ['audio_audio.m4a', 'audio_countdown.m4a']
>>> username = ''
>>> password = ''
>>> table_data, table_name, synapse_project_id = copy_synapse_table(synapse_table_id, synapse_project_id, table_name, remove_columns, username, password)
"""
import synapseclient
from synapseclient import Schema
from synapseclient.table import Table, as_table_columns
syn = synapseclient.Synapse()
# Log in to Synapse:
if username and password:
syn.login(username, password)
else:
syn.login()
# Download Synapse table as a dataframe:
results = syn.tableQuery("select * from {0}".format(synapse_table_id))
table_data = results.asDataFrame()
# Remove specified columns:
if remove_columns:
for remove_column in remove_columns:
del table_data[remove_column]
# Upload to Synapse table:
table_data.index = range(table_data.shape[0])
schema = Schema(name=table_name,
columns=as_table_columns(table_data),
parent=synapse_project_id,
includeRowIdAndRowVersion=False)
table = syn.store(Table(schema, table_data))
return table_data, table_name, synapse_project_id
def concatenate_tables_to_synapse_table(frames, synapse_project_id,
table_name, username='', password=''):
"""
Concatenate multiple dataframes and store as a Synapse table.
Reuse the indices from the original DataFrame,
increasing number of columns.
Parameters
----------
frames : list of pandas DataFrames
paths to files to upload to Synapse
synapse_project_id : string
Synapse ID for project to which table is to be written
table_name : string
schema name of table
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
table_data : Pandas DataFrame
output table
synapse_project_id : string
Synapse ID for project
Examples
--------
>>> import pandas as pd
>>> from mhealthx.io_data import concatenate_tables_to_synapse_table
>>> df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
>>> 'B': ['B0', 'B1', 'B2', 'B3'],
>>> 'C': ['C0', 'C1', 'C2', 'C3'],
>>> 'D': ['D0', 'D1', 'D2', 'D3']},
>>> index=[0, 1, 2, 3])
>>> df2 = pd.DataFrame({'E': ['A4', 'A5', 'A6', 'A7'],
>>> 'F': ['B4', 'B5', 'B6', 'B7'],
>>> 'G': ['C4', 'C5', 'C6', 'C7'],
>>> 'H': ['D4', 'D5', 'D6', 'D7']},
>>> index=[0, 1, 2, 3])
>>> frames = [df1, df2]
>>> synapse_project_id = 'syn4899451'
>>> table_name = 'Test to join tables'
>>> username = ''
>>> password = ''
>>> table_data, synapse_project_id = concatenate_tables_to_synapse_table(frames, synapse_project_id, table_name, username, password)
"""
import pandas as pd
import synapseclient
from synapseclient import Schema, Table, as_table_columns
syn = synapseclient.Synapse()
# Log in to Synapse:
if username and password:
syn.login(username, password)
else:
syn.login()
# Concatenate dataframes: reuse the indices from the original DataFrame,
# increasing number of columns:
table_data = pd.concat(frames, axis=1) #, join_axes=[frames[0].index])
# Create table schema:
schema = Schema(name=table_name, columns=as_table_columns(table_data),
parent=synapse_project_id)
# Store as Synapse table:
table = syn.store(Table(schema, table_data))
return table_data, synapse_project_id
