def test_iter_invalid_extra_cols_handled():
# Test a schema-invalid extra column in one row
source = [
['key', 'value'],
['one', 1, 'unexpected'],
['two', 2],
]
table = Table(source, schema=SCHEMA_MIN)
errors = []
def handler(exc, row_number, row_data, error_data):
errors.append((exc, row_number, row_data, error_data))
actual = list(table.iter(exc_handler=handler))
expect = [
['one', 1],
['two', 2],
]
assert actual == expect
assert len(errors) == 1
expect_row_data = OrderedDict([('key', 'one'), ('value', 1),
('tableschema-cast-error-extra-col-3',
'unexpected')])
_check_error(errors[0],
expect_exc_class=exceptions.CastError,
expect_exc_str='Row length',
expect_row_number=2,
expect_row_data=expect_row_data,
expect_error_data=expect_row_data)
def test_iter_with_headers_field_names_mismatch_stream_closed():
table = Table('data/data_headers_field_names_mismatch.csv',
schema=SCHEMA_CSV)
with pytest.raises(exceptions.CastError) as excinfo:
for _ in table.iter():
pass
assert table._Table__stream.closed
def test_iter_unique_primary_key_violation_handled():
# Test exception handler option to switch off fail-fast data validation
# behaviour
schema = deepcopy(SCHEMA_CSV)
schema['primaryKey'] = 'id'
source = [
['id', 'age', 'name'],
[1, 39, 'Paul'],
[1, 36, 'Jane'],
]
table = Table(source, schema=schema)
errors = []
def handler(exc, row_number, row_data, error_data):
errors.append((exc, row_number, row_data, error_data))
expect = [
[1, 39, 'Paul'],
[1, 36, 'Jane'],
]
actual = list(table.iter(exc_handler=handler))
assert actual == expect
assert len(errors) == 1
exc, row_number, row_data, error_data = errors[0]
assert isinstance(exc, exceptions.UniqueKeyError)
assert row_number == 3 # actual row number including header line
assert row_data == OrderedDict([('id', 1), ('age', 36), ('name', 'Jane')])
assert error_data == OrderedDict([('id', 1)])
assert 'duplicates' in str(exc)
def test_iter_missing_cols_handled():
source = [
['key', 'value'],
[
'one',
],
['two', 2],
]
table = Table(source, schema=SCHEMA_MIN)
errors = []
def handler(exc, row_number, row_data, error_data):
errors.append((exc, row_number, row_data, error_data))
actual = list(table.iter(exc_handler=handler))
expect = [
['one', None],
['two', 2],
]
assert actual == expect
expect_row_data = OrderedDict([('key', 'one'), ('value', None)])
_check_error(errors[0],
expect_exc_class=exceptions.CastError,
expect_exc_str='Row length',
expect_row_number=2,
expect_row_data=expect_row_data,
expect_error_data=expect_row_data)
def test_iter_with_headers_field_names_mismatch_handled():
source = [
['id', 'bad', 'name'],
[1, 39, 'Paul'],
[2, 42, 'Peter'],
]
table = Table(source, schema=SCHEMA_CSV)
errors = []
def handler(exc, row_number, row_data, error_data):
errors.append((exc, row_number, row_data, error_data))
expect = []
actual = list(table.iter(exc_handler=handler))
assert actual == expect
assert len(errors) == 2
for i, error in enumerate(errors):
expect_keyed_row_data = OrderedDict(zip(source[0], source[i + 1]))
exc, row_number, row_data, error_data = error
assert isinstance(exc, exceptions.CastError)
assert row_number == i + 2 # actual row number including header line
assert row_data == expect_keyed_row_data
assert error_data == expect_keyed_row_data
assert 'match schema field names' in str(exc)
def test_iter_single_field_foreign_key_invalid_handled():
relations = deepcopy(FK_RELATIONS)
relations['people'][2]['firstname'] = 'Max'
table = Table(FK_SOURCE, schema=FK_SCHEMA)
errors = []
def handler(exc, row_number, row_data, error_data):
errors.append((exc, row_number, row_data, error_data))
expect = [
['1', {
'firstname': 'Alex',
'surname': 'Martin'
}, 'Martin'],
['2', {
'firstname': 'John',
'surname': 'Dockins'
}, 'Dockins'],
['3', {}, 'White'],
]
actual = list(table.iter(relations=relations, exc_handler=handler))
assert actual == expect
assert len(errors) == 1
exc, row_number, row_data, error_data = errors[0]
assert row_number == 4
expect_keyed_row_data = OrderedDict(zip(FK_SOURCE[0], FK_SOURCE[3]))
assert row_data == expect_keyed_row_data
assert error_data == OrderedDict([('name', 'Walter')])
assert isinstance(exc, exceptions.UnresolvedFKError)
assert 'Foreign key' in str(exc)
def test_iter_invalid_col_value_handled():
# Test a schema-invalid column value in one row, handled
source = [
['key', 'value'],
['one', 'not_an_int'],
['two', 2],
]
table = Table(source, schema=SCHEMA_MIN)
errors = []
def handler(exc, row_number, row_data, error_data):
errors.append((exc, row_number, row_data, error_data))
actual = list(table.iter(exc_handler=handler))
expect = [
['one', 'not_an_int'],
['two', 2],
]
assert actual == expect
assert isinstance(actual[0][1], FailedCast)
assert len(errors) == 1
expect_row_data = OrderedDict([('key', 'one'), ('value', 'not_an_int')])
expect_error_data = OrderedDict([('value', 'not_an_int')])
_check_error(errors[0],
expect_exc_class=exceptions.CastError,
expect_exc_str='There are 1 cast errors',
expect_row_number=2,
expect_row_data=expect_row_data,
expect_error_data=expect_error_data)
def test_iter_invalid_extra_cols_stream_closed():
table = Table('data/data_invalid_extra_cols.csv', schema=SCHEMA_MIN)
with pytest.raises(exceptions.CastError) as excinfo:
for _ in table.iter():
pass
# Circumvent name mangling to get at (overly private ;-))
# __stream attribute
assert table._Table__stream.closed
def test_iter_unique_primary_key_violation_stream_closed():
schema = deepcopy(SCHEMA_CSV)
schema['primaryKey'] = 'id'
table = Table('data/data_unique_primary_key_violation.csv', schema=schema)
with pytest.raises(exceptions.TableSchemaException) as excinfo:
for _ in table.iter():
pass
assert table._Table__stream.closed
def test_iter_single_field_foreign_key_invalid():
relations = deepcopy(FK_RELATIONS)
relations['people'][2]['firstname'] = 'Max'
table = Table(FK_SOURCE, schema=FK_SCHEMA)
with pytest.raises(exceptions.RelationError) as excinfo:
for _ in table.iter(relations=relations):
pass
assert isinstance(excinfo.value, exceptions.UnresolvedFKError)
assert 'Foreign key' in str(excinfo.value)
def test_iter_with_headers_field_names_mismatch():
source = [
['id', 'bad', 'name'],
[1, 39, 'Paul'],
]
table = Table(source, schema=SCHEMA_CSV)
with pytest.raises(exceptions.CastError) as excinfo:
for _ in table.iter():
pass
assert 'match schema field names' in str(excinfo.value)
def test_iter_invalid_extra_cols():
source = [
['key', 'value'],
['one', 1, 'unexpected'],
['two', 2],
]
table = Table(source, schema=SCHEMA_MIN)
with pytest.raises(exceptions.CastError) as excinfo:
for _ in table.iter():
pass
assert 'Row length' in str(excinfo.value)
def reindex(self):
file_path, params = self.get_file_info()
t = Table(file_path, ignore_blank_headers=True, **params)
schema = t.infer()
data = t.iter(keyed=True)
self.storage.create(self.index_name,
schema,
reindex=True,
always_recreate=True)
self.storage.write(self.index_name, data)
for res in self.storage.write(self.index_name, data):
pass
def test_iter_unique_primary_key_violation():
schema = deepcopy(SCHEMA_CSV)
schema['primaryKey'] = 'id'
source = [
['id', 'age', 'name'],
[1, 39, 'Paul'],
[1, 36, 'Jane'],
]
table = Table(source, schema=schema)
with pytest.raises(exceptions.TableSchemaException) as excinfo:
for _ in table.iter():
pass
assert isinstance(excinfo.value, exceptions.UniqueKeyError)
assert 'duplicates' in str(excinfo.value)
def test_iter_invalid_col_value_no_cast():
# Test a schema-invalid column value in one row, without value-casting
source = [
['key', 'value'],
['one', 'not_an_int'],
['two', 2],
]
expect = [
['one', 'not_an_int'],
['two', 2],
]
table = Table(source, schema=SCHEMA_MIN)
actual = list(table.iter(cast=False))
# no actual casting, no cast errors
assert actual == expect
def test_iter_invalid_col_value():
# Test a schema-invalid column value in one row
source = [
['key', 'value'],
['one', 'not_an_int'],
['two', 2],
]
table = Table(source, schema=SCHEMA_MIN)
with pytest.raises(exceptions.CastError) as excinfo:
for _ in table.iter():
pass
assert 'There are 1 cast errors' in str(excinfo.value)
error = excinfo.value.errors[0]
assert isinstance(error, exceptions.CastError)
assert ('Field "value" can\'t cast value "not_an_int" for type "integer"'
in str(error))
def load_data_from_local_csv(csv_file=ASSET_DATA_FILE):
table = Table(csv_file, schema=SCHEMA_FILE)
try:
valid = validate(table.schema.descriptor)
if valid:
for keyed_row in table.iter(keyed=True):
yield keyed_row
except exceptions.ValidationError as exception:
for error in exception.errors:
print(error)
except exceptions.CastError as exception:
if not exception.errors:
print(exception)
for error in exception.errors:
write_skipped_assets(error, [])
def test_iter_invalid_col_value_handled_no_cast():
# Test a schema-invalid column value in one row, without value-casting
source = [
['key', 'value'],
['one', 'not_an_int'],
['two', 2],
]
expect = [
['one', 'not_an_int'],
['two', 2],
]
table = Table(source, schema=SCHEMA_MIN)
errors = []
def handler(exc, row_number, row_data, error_data):
errors.append((exc, row_number, row_data, error_data))
actual = list(table.iter(cast=False, exc_handler=handler))
# no actual casting, no cast errors
assert len(errors) == 0
assert actual == expect
class CsvReader(DataReader):
'''this is a wrapper class for tableschema library'''
def __init__(self, filePath):
self._filePath = filePath
self._table = Table(filePath)
def close(self):
pass
def getColumns(self):
if not self._table.headers:
self._table.infer(
settings.SCHEMA_INFER_LIMIT)
# clean the headers
result = []
for header in self._table.headers:
tmpheader = header.lower()
tmpheader = tmpheader.replace(' ', '_').replace('-', '_')
r = re.search('\w+', tmpheader)
if r:
result.append(r.group())
else:
raise InvalidCsvHeaderException(
'%s is not a valid header' % header)
return result
def requery(self):
self._table = Table(self._filePath)
def getRow(self):
i = self._table.iter(cast=True)
return next(i)
def getRowsList(self):
self._table.infer()
self._table.schema.descriptor[
'missingValues'] = settings.SCHEMA_CSV_MISSING_VALUES
self._table.schema.commit()
i = self._table.iter(cast=True)
return list(map(tuple, i))
def getSchema(self):
'''
Get data schema infered from records,
the number of records is defined by SCHEMA_INFER_LIMIT
the confidence trashold is defined by SCHEMA_INFER_CONFIDENCE
'''
t = Table(self._filePath)
t.infer()
t.schema.descriptor[
'missingValues'] = settings.SCHEMA_CSV_MISSING_VALUES
t.schema.commit()
return t.infer(
settings.SCHEMA_INFER_LIMIT,
confidence=settings.SCHEMA_INFER_CONFIDENCE)
def __repr__(self):
return (settings.SOURCE_TYPE_CSV_PREFIX +
self._filePath)
from tableschema import Table
# Data from WEB, schema from MEMORY
SOURCE = 'https://raw.githubusercontent.com/frictionlessdata/tableschema-py/master/data/data_infer.csv'
SCHEMA = {
'fields': [{
'name': 'id',
'type': 'integer'
}, {
'name': 'age',
'type': 'integer'
}, {
'name': 'name',
'type': 'string'
}]
}
# If schema is not passed it will be inferred
table = Table(SOURCE, schema=SCHEMA)
rows = table.iter()
while True:
try:
print(next(rows))
except StopIteration:
break
except Exception as exception:
print(exception)
from tableschema import Table
fileCSV = 'D:\dct\enem-microdados\DADOS_ENEM_2009.csv'
fileJSON = 'D:\dct\enem-microdados\DADOS_ENEM_2009.json'
# Create table
table = Table(fileCSV, schema=fileJSON)
# Print schema descriptor
print(table.schema.descriptor)
print "\n"
# Print cast rows in a dict form
for keyed_row in table.iter(keyed=True):
print(keyed_row)
print "\n"
from tableschema import Table
# Data from WEB, schema from MEMORY
SOURCE = 'https://raw.githubusercontent.com/frictionlessdata/tableschema-py/master/data/data_infer.csv'
SCHEMA = {'fields': [{'name': 'id', 'type': 'integer'}, {'name': 'age', 'type': 'integer'}, {'name': 'name', 'type': 'string'}] }
# If schema is not passed it will be inferred
table = Table(SOURCE, schema=SCHEMA)
rows = table.iter()
while True:
try:
print(next(rows))
except StopIteration:
break
except Exception as exception:
print(exception)
def loadPeptides(self, datapackage, datasetId, row_start=0, row_stop=None):
"""Load Peptide Data"""
# Get the Ontology Version
ontology_version = oceanproteinportal.datapackage.getDatapackageOntologyVersion(datapackage)
peptideResource = oceanproteinportal.datapackage.findResource(datapackage=datapackage, resource_type='peptide')
if peptideResource is None:
return
datasetCruises = datapackageCruises(datapackage)
table = Table( peptideResource.descriptor['path'], schema=peptideResource.descriptor['schema'] )
if (0 < row_start):
logging.info("Skipping rows until # %s" % (row_start))
row_count = 0
data = None
PEPTIDE_FIELDS = getOntologyMappingFields(type='peptide', ontology_version=ontology_version)
for keyed_row in table.iter(keyed=True):
row_count += 1
if row_count < row_start:
logging.debug("Skipping Row # %s" % (row_count))
continue
if row_stop is not None and row_count > row_stop:
logging.info("Stopping at Row# %s" % (row_count))
break
logging.debug("Reading Row# %s" % (row_count))
data = readKeyedTableRow(keyed_row=keyed_row, elastic_mappings=PEPTIDE_FIELDS)
primaryKey = datasetId + data.get('sampleName') + data.get('proteinId') + data.get('peptideSequence')
data['guid'] = generateGuid( datapackage.descriptor['name'] + '_peptide_' + primaryKey )
filterSize = {}
minimumFilterSize = data.get('filterSize:minimum', None)
maximumFilterSize = data.get('filterSize:maximum', None)
filterSizeLabel = ''
if minimumFilterSize is not None:
del data['filterSize:minimum']
filterSize['minimum'] = minimumFilterSize
filterSizeLabel += str(minimumFilterSize)
if maximumFilterSize is not None:
del data['filterSize:maximum']
filterSize['maximum'] = maximumFilterSize
if filterSizeLabel != '':
filterSizeLabel += ' - ' + str(maximumFilterSize)
else:
filterSize += str(maximumFilterSize)
filterSize['label'] = filterSizeLabel
data['filterSize'] = filterSize
if ('coordinate:lat' in data and 'coordinate:lon' in data):
data['coordinate'] = {
'lat': data['coordinate:lat'],
'lon': data['coordinate:lon']
}
del data['coordinate:lat']
del data['coordinate:lon']
# load in ES
self.load(data=data, type='peptide', id=data['guid'])
logging.info(res['result'])
def loadProteins(self, datapackage, datasetId, row_start=0, row_stop=None):
"""Load Protein Data
Tabular data, so proteins may be repeated for different samples, stations, depths, etc.
1) Build proteinId first, then lookup if it exists in the store
2) If not exists, build a new document. Else, update the spectral counts of existing doc
"""
es = self.getStore()
index = self.getIndex()
# Get the Ontology Version
ontology_version = oceanproteinportal.datapackage.getDatapackageOntologyVersion(datapackage)
proteinResource = oceanproteinportal.datapackage.findResource(datapackage=datapackage, resource_type='protein')
if proteinResource is None:
return
datasetCruises = oceanproteinportal.datapackage.datapackageCruises(datapackage)
table = Table(proteinResource.descriptor['path'], schema=proteinResource.descriptor['schema'])
if (0 < row_start):
logging.info("Skipping rows until # %s" % (row_start))
row_count = 0
proteinId = None
data = None
PROTEIN_FIELDS = getOntologyMappingFields(type='protein', ontology_version=ontology_version)
try:
for keyed_row in table.iter(keyed=True):
row_count += 1
if row_count < row_start:
logging.debug("Skipping Row # %s" % (row_count))
continue
if row_stop is not None and row_count > row_stop:
logging.info("Stopping at Row# %s" % (row_count))
break
logging.debug("Reading Row# %s" % (row_count))
row = readKeyedTableRow(keyed_row=keyed_row, elastic_mappings=PROTEIN_FIELDS)
# Get the unqiue identifier for this protein
proteinId = row['proteinId']
protein_guid = generateGuid( datapackage.descriptor['name'] + '_protein_' + datasetId + ':' + proteinId )
try:
res = es.get(index=index, doc_type='protein', id=protein_guid)
# Reuse existing protein document
data = res['_source']
except elasticsearch.exceptions.NotFoundError as exc:
# Build a new ES Protein document
data = {
'_dataset': datasetId,
'guid': protein_guid,
'proteinId': proteinId,
'spectralCount': []
}
if row['productName'] is not None:
data['productName'] = row['productName']
if row['molecularWeight'] is not None:
data['molecularWeight'] = row['molecularWeight']
if row['enzymeCommId'] is not None:
data['enzymeCommId'] = row['enzymeCommId']
if row['uniprotId'] is not None:
data['uniprotId'] = row['uniprotId']
if row['otherIdentifiedProteins'] is not None:
data['otherIdentifiedProteins'] = row['otherIdentifiedProteins']
# NCBI
ncbiTaxon = None
if 'ncbi:id' in row:
ncbiTaxon = {
'id': row['ncbi:id'],
'name': None
}
if 'ncbi:name' in row:
ncbiTaxon['name'] = row['ncbi:name']
if ncbiTaxon is not None:
data['ncbiTaxon'] = ncbiTaxon
# Kegg
kegg_pathway = None
pathway = row.get('kegg:path', None)
if pathway is not None:
kegg_pathway = []
for idx,path in enumerate(pathway):
kegg_pathway.append({'value': path, 'index': idx})
data['kegg'] = {
'id': row.get('kegg:id', None),
'description': row.get('kegg:desc', None),
'pathway': kegg_pathway
}
# PFams
if 'pfams:id' in row:
data['pfams'] = {
'id': data.get('pfams:id', None),
'name': data.get('pfams:name', None)
}
# END of initial protein data setup
# Handle all the unqiue row data for a certain protein
# FilterSize
filterSize = {}
minimumFilterSize = row.get('filterSize:minimum', None)
maximumFilterSize = row.get('filterSize:maximum', None)
filterSizeLabel = ''
if minimumFilterSize is not None:
filterSize['minimum'] = minimumFilterSize
filterSizeLabel += str(minimumFilterSize)
if maximumFilterSize is not None:
filterSize['maximum'] = maximumFilterSize
if filterSizeLabel != '':
filterSizeLabel += ' - ' + str(maximumFilterSize)
else:
filterSize += str(maximumFilterSize)
if filterSizeLabel != '':
filterSize['label'] = filterSizeLabel
data['filterSize'] = filterSize
# Cruise
cruise = {
'value': row.get('spectralCount:cruise', None),
}
if cruise['value'] in datasetCruises:
cruise['uri'] = datasetCruises[cruise['value']]['uri']
# To-do
# 1. Lookup the cruise URI in the datapackage
# Spectral Counts
# fix ISO DateTime
observationDateTime = None
if 'spectralCount:dateTime' in row and row['spectralCount:dateTime'] is not None:
observationDateTime = dateutil.parser.parse(row['spectralCount:dateTime'])
observationDateTime = observationDateTime.strftime(SPECTRAL_COUNT_DATE_TIME_FORMAT)
elif 'spectralCount:date' in row and row['spectralCount:date'] is not None:
time = row.get('spectralCount:time', None)
if (time is None):
time = '00:00:00'
observationDateTime = dateutil.parser.parse(row['spectralCount:date'] + 'T' + time)
observationDateTime = observationDateTime.strftime(SPECTRAL_COUNT_DATE_TIME_FORMAT)
spectralCount = {
'sampleId': row.get('spectralCount:sampleId', None),
'count': row.get('spectralCount:count', None),
'cruise': cruise,
'station': row.get('spectralCount:station', None),
'depth': row.get('spectralCount:depth', None),
'dateTime': observationDateTime,
}
if (spectralCount['depth'] is not None):
if 'min' not in dataset_depth_stats:
dataset_depth_stats['min'] = spectralCount['depth']
dataset_depth_stats['max'] = spectralCount['depth']
else:
if spectralCount['depth'] < dataset_depth_stats['min']:
dataset_depth_stats['min'] = spectralCount['depth']
if spectralCount['depth'] > dataset_depth_stats['max']:
dataset_depth_stats['max'] = spectralCount['depth']
if (row['spectralCount:coordinate:lat'] is not None and row['spectralCount:coordinate:lon'] is not None):
spectralCount['coordinate'] = {
'lat': row['spectralCount:coordinate:lat'],
'lon': row['spectralCount:coordinate:lon']
}
data['spectralCount'].append(spectralCount)
res = self.load(data=data, type='protein', id=data['guid'])
logging.info(res['result'])
# end of for loop of protein rows
except Exception as e:
logging.exception("Error with row[%s]: %s" % (row_count, keyed_row))
raise e
def test_iter():
table = Table(DATA_MIN, schema=SCHEMA_MIN)
expect = [['one', 1], ['two', 2]]
actual = list(table.iter())
def test_iter_web_csv():
table = Table(BASE_URL % 'data/data_infer.csv', schema=SCHEMA_CSV)
expect = [[1, 39, 'Paul'], [2, 23, 'Jimmy'], [3, 36, 'Jane'],
[4, 28, 'Judy']]
actual = list(table.iter())
assert actual == expect
def test_iter_missing_cols_stream_closed():
table = Table('data/data_missing_cols.csv', schema=SCHEMA_MIN)
with pytest.raises(exceptions.CastError) as excinfo:
for _ in table.iter():
pass
assert table._Table__stream.closed
def test_iter_keyed():
table = Table(DATA_MIN, schema=SCHEMA_MIN)
expect = [{'key': 'one', 'value': 1}, {'key': 'two', 'value': 2}]
actual = list(table.iter(keyed=True))
assert actual == expect
from tableschema import Table
table = Table("template.csv", schema="schema.json")
table.schema.valid
# True
for row in table.iter():
print(row)
