def preview_bam(filename):
import pysam
res = ''
with pysam.AlignmentFile(filename, 'rb') as bam:
headers = bam.header
for record_type in ('RG', 'PG', 'SQ'):
if record_type not in headers:
continue
else:
records = headers[record_type]
res += record_type + ':\n'
for i, record in enumerate(records):
if type(record) == str:
res += ' ' + short_repr(record) + '\n'
elif type(record) == dict:
res += ' '
for idx, (k, v) in enumerate(record.items()):
if idx < 4:
res += '{}: {} '.format(k, short_repr(v))
elif idx == 4:
res += '...'
break
if i > 4:
res += '\n ...\n'
break
else:
res += '\n'
return res
def log(self, stage=None, msg=None):
if stage == 'start':
env.logger.debug('{} ``{}``: {}'.format('Checking' if self.run_mode == 'dryrun' else 'Executing',
self.step.step_name(), self.step.comment.strip()))
elif stage == 'input':
if env.sos_dict['input'] is not None:
env.logger.debug('input: ``{}``'.format(short_repr(env.sos_dict['input'])))
elif stage == 'output':
if env.sos_dict['output'] is not None:
env.logger.debug('output: ``{}``'.format(short_repr(env.sos_dict['output'])))
def _R_repr(obj):
if isinstance(obj, bool):
return 'TRUE' if obj else 'FALSE'
elif isinstance(obj, (int, float, str)):
return repr(obj)
elif isinstance(obj, Sequence):
if len(obj) == 0:
return 'c()'
# if the data is of homogeneous type, let us use c()
# otherwise use list()
# this can be confusion but list can be difficult to handle
if homogeneous_type(obj):
return 'c(' + ','.join(_R_repr(x) for x in obj) + ')'
else:
return 'list(' + ','.join(_R_repr(x) for x in obj) + ')'
elif obj is None:
return 'NULL'
elif isinstance(obj, dict):
return 'list(' + ','.join('{}={}'.format(x, _R_repr(y)) for x,y in obj.items()) + ')'
elif isinstance(obj, set):
return 'list(' + ','.join(_R_repr(x) for x in obj) + ')'
else:
import numpy
import pandas
if isinstance(obj, (numpy.intc, numpy.intp, numpy.int8, numpy.int16, numpy.int32, numpy.int64,\
numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64, numpy.float16, numpy.float32, \
numpy.float64)):
return repr(obj)
elif isinstance(obj, numpy.matrixlib.defmatrix.matrix):
try:
import feather
except ImportError:
raise UsageError('The feather-format module is required to pass numpy matrix as R matrix'
'See https://github.com/wesm/feather/tree/master/python for details.')
feather_tmp_ = tempfile.NamedTemporaryFile(suffix='.feather', delete=False).name
feather.write_dataframe(pandas.DataFrame(obj).copy(), feather_tmp_)
return 'data.matrix(read_feather("{}"))'.format(feather_tmp_)
elif isinstance(obj, numpy.ndarray):
return 'c(' + ','.join(_R_repr(x) for x in obj) + ')'
elif isinstance(obj, pandas.DataFrame):
try:
import feather
except ImportError:
raise UsageError('The feather-format module is required to pass pandas DataFrame as R data.frame'
'See https://github.com/wesm/feather/tree/master/python for details.')
feather_tmp_ = tempfile.NamedTemporaryFile(suffix='.feather', delete=False).name
try:
data = obj.copy()
feather.write_dataframe(data, feather_tmp_)
except:
# if data cannot be written, we try to manipulate data
# frame to have consistent types and try again
for c in data.columns:
if not homogeneous_type(data[c]):
data[c] = [str(x) for x in data[c]]
feather.write_dataframe(data, feather_tmp_)
return 'read_feather("{}")'.format(feather_tmp_)
else:
return repr('Unsupported datatype {}'.format(short_repr(obj)))
def load_pickled(self, item):
if isinstance(item, bytes):
return pickle.loads(item)
elif isinstance(item, str):
return pickle.loads(item.encode('utf-8'))
else:
self.sos_kernel.warn(
'Cannot restore from result of pickle.dumps: {}'.format(
short_repr(item)))
return {}
def get_vars(self, var_names):
"""
Functionality to transfer CAS objects and TypeSystem from SoS (python) kernel to the IRuta kernel.
This function is called when a use invokes the line magic %get or %with.
"""
if len(var_names) != 1:
raise Exception(
"%get takes exactly one variable name as argument."
"If you want to transfer multiple CAS, then please write them to a directory and use `%inputDir` in IRuta kernel."
)
var_name = var_names[0]
var_content = env.sos_dict[var_name]
# Step 1: Writing Cas and TypeSystem to disk using dkpro-cassis
temp_directory = tempfile.TemporaryDirectory()
temp_typesystem_file = tempfile.NamedTemporaryFile(
suffix=".xml", dir=temp_directory.name, delete=False)
temp_typesystem_file_path = os.path.normpath(
temp_typesystem_file.name).replace('\\', "/")
temp_xmi_file = tempfile.NamedTemporaryFile(suffix=".xmi",
dir=temp_directory.name,
delete=False)
temp_xmi_file_path = os.path.normpath(temp_xmi_file.name).replace(
'\\', "/")
if isinstance(var_content, cassis.Cas):
var_content.to_xmi(temp_xmi_file_path)
var_content.typesystem.to_xml(temp_typesystem_file_path)
cmd_transfer_var = "%displayMode NONE\n" \
f"%loadCas {temp_xmi_file_path}\n" \
f"%loadTypeSystem {temp_typesystem_file_path}"
elif isinstance(var_content, cassis.TypeSystem):
var_content.to_xml(temp_typesystem_file_path)
cmd_transfer_var = "%displayMode NONE\n" \
f"%loadTypeSystem {temp_typesystem_file_path}"
else:
raise Exception(
'%get only support transfering UIMA CAS objects or TypeSystem objects. '
'Use %expand for transfering string variables. Received datatype {}'
.format(short_repr(var_content)))
# Step 2: Loading files
env.log_to_file('KERNEL', f'Executing "{cmd_transfer_var}"')
self.ruta_kernel.run_cell(
cmd_transfer_var,
silent=True,
store_history=False,
on_error=f'Failed to get variable {var_name}')
# Step 3: Clean-up temp files
temp_typesystem_file.close()
temp_xmi_file.close()
temp_directory.cleanup()
def _Ruby_repr(self, obj):
if isinstance(obj, bool):
return 'true' if obj else 'false'
elif isinstance(obj, float) and numpy.isnan(obj):
return "Float::NAN"
elif isinstance(obj, (int, float)):
return repr(obj)
elif isinstance(obj, str):
return '%(' + obj + ')'
elif isinstance(obj, complex):
return 'Complex(' + str(obj.real) + ',' + str(obj.imag) + ')'
elif isinstance(obj, range):
return '(' + repr(min(obj)) + '...' + repr(max(obj)) + ')'
elif isinstance(obj, Sequence):
if len(obj) == 0:
return '[]'
else:
return '[' + ','.join(self._Ruby_repr(x) for x in obj) + ']'
elif obj is None:
return 'nil'
elif isinstance(obj, dict):
return '{' + ','.join('"{}" => {}'.format(x, self._Ruby_repr(y))
for x, y in obj.items()) + '}'
elif isinstance(obj, set):
return 'Set[' + ','.join(self._Ruby_repr(x) for x in obj) + ']'
else:
if isinstance(obj, (numpy.intc, numpy.intp, numpy.int8, numpy.int16, numpy.int32, numpy.int64,\
numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64, numpy.float16, numpy.float32, numpy.float64)):
return repr(obj)
elif isinstance(obj, numpy.matrixlib.defmatrix.matrix):
return 'N' + repr(obj.tolist())
elif isinstance(obj, numpy.ndarray):
return repr(obj.tolist())
elif isinstance(obj, pandas.DataFrame):
_beginning_result_string_dataframe_to_ruby = "Daru::DataFrame.new({"
_context_string_dataframe_to_ruby = str([
'"' + str(x).replace("'", '"') + '"' + "=>" + "[" +
str(",".join(
list(map(lambda y: self._Ruby_repr(y),
obj[x].tolist())))).replace("'", '"') + "]"
for x in obj.keys().tolist()
])[2:-2].replace("\', \'", ", ") + "},"
_indexing_result_string_dataframe_to_ruby = "index:" + str(
obj.index.values.tolist()).replace("'", '"') + ")"
_result_string_dataframe_to_ruby = _beginning_result_string_dataframe_to_ruby + _context_string_dataframe_to_ruby + _indexing_result_string_dataframe_to_ruby
return _result_string_dataframe_to_ruby
elif isinstance(obj, pandas.Series):
dat = list(obj.values)
ind = list(obj.index.values)
ans = "{" + ",".join(
[repr(x) + "=>" + repr(y) for x, y in zip(ind, dat)]) + "}"
return ans
else:
return repr('Unsupported datatype {}'.format(short_repr(obj)))
def _R_repr(obj, processed=None):
if isinstance(obj, bool):
return 'TRUE' if obj else 'FALSE'
elif isinstance(obj, (int, str)):
return repr(obj)
elif isinstance(obj, float):
if numpy.isnan(obj):
return 'NaN'
else:
return repr(obj)
elif isinstance(obj, complex):
return 'complex(real = ' + str(obj.real) + ', imaginary = ' + str(obj.imag) + ')'
elif isinstance(obj, Sequence):
if len(obj) == 0:
return 'c()'
# if the data is of homogeneous type, let us use c()
# otherwise use list()
# this can be confusion but list can be difficult to handle
if homogeneous_type(obj):
return 'c(' + ','.join(_R_repr(x) for x in obj) + ')'
else:
return 'list(' + ','.join(_R_repr(x) for x in obj) + ')'
elif obj is None:
return 'NULL'
elif isinstance(obj, dict):
if processed:
if id(obj) in processed:
return 'NULL'
else:
processed = set()
processed.add(id(obj))
return 'list(' + ','.join('{}={}'.format(make_name(str(x)), _R_repr(y, processed)) for x, y in obj.items()) + ')'
elif isinstance(obj, set):
return 'list(' + ','.join(_R_repr(x) for x in obj) + ')'
else:
if isinstance(obj, (numpy.intc, numpy.intp, numpy.int8, numpy.int16, numpy.int32, numpy.int64,
numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64, numpy.float16, numpy.float32,
numpy.float64)):
return repr(obj)
elif isinstance(obj, numpy.matrixlib.defmatrix.matrix):
try:
import feather
except ImportError:
raise UsageError('The feather-format module is required to pass numpy matrix as R matrix'
'See https://github.com/wesm/feather/tree/master/python for details.')
feather_tmp_ = tempfile.NamedTemporaryFile(
suffix='.feather', delete=False).name
feather.write_dataframe(pandas.DataFrame(obj).copy(), feather_tmp_)
return 'data.matrix(..read.feather({!r}))'.format(feather_tmp_)
elif isinstance(obj, numpy.ndarray):
if obj.ndim == 1:
return 'array(c(' + ','.join(_R_repr(x) for x in obj) + '))'
else:
return 'array(' + 'c(' + ','.join(repr(x) for x in obj.swapaxes(obj.ndim - 2, obj.ndim - 1).flatten(order='C')) + ')' + ', dim=(' + 'rev(c' + repr(obj.swapaxes(obj.ndim - 2, obj.ndim - 1).shape) + ')))'
elif isinstance(obj, pandas.DataFrame):
try:
import feather
except ImportError:
raise UsageError('The feather-format module is required to pass pandas DataFrame as R data.frame'
'See https://github.com/wesm/feather/tree/master/python for details.')
feather_tmp_ = tempfile.NamedTemporaryFile(
suffix='.feather', delete=False).name
try:
data = obj.copy()
# if the dataframe has index, it would not be transferred due to limitations
# of feather. We will have to do something to save the index separately and
# recreate it. (#397)
if isinstance(data.index, pandas.Index):
df_index = list(data.index)
elif not isinstance(data.index, pandas.RangeIndex):
# we should give a warning here
df_index = None
feather.write_dataframe(data, feather_tmp_)
except Exception:
# if data cannot be written, we try to manipulate data
# frame to have consistent types and try again
for c in data.columns:
if not homogeneous_type(data[c]):
data[c] = [str(x) for x in data[c]]
feather.write_dataframe(data, feather_tmp_)
# use {!r} for path because the string might contain c:\ which needs to be
# double quoted.
return '..read.feather({!r}, index={})'.format(feather_tmp_, _R_repr(df_index))
elif isinstance(obj, pandas.Series):
dat = list(obj.values)
ind = list(obj.index.values)
return 'setNames(' + 'c(' + ','.join(_R_repr(x) for x in dat) + ')' + ',c(' + ','.join(_R_repr(y) for y in ind) + '))'
else:
return repr('Unsupported datatype {}'.format(short_repr(obj)))
def _R_repr(obj):
if isinstance(obj, bool):
return 'TRUE' if obj else 'FALSE'
elif isinstance(obj, (int, float, str)):
return repr(obj)
elif isinstance(obj, Sequence):
if len(obj) == 0:
return 'c()'
# if the data is of homogeneous type, let us use c()
# otherwise use list()
# this can be confusion but list can be difficult to handle
if homogeneous_type(obj):
return 'c(' + ','.join(_R_repr(x) for x in obj) + ')'
else:
return 'list(' + ','.join(_R_repr(x) for x in obj) + ')'
elif obj is None:
return 'NULL'
elif isinstance(obj, dict):
return 'list(' + ','.join('{}={}'.format(x, _R_repr(y))
for x, y in obj.items()) + ')'
elif isinstance(obj, set):
return 'list(' + ','.join(_R_repr(x) for x in obj) + ')'
else:
import numpy
import pandas
if isinstance(obj, (numpy.intc, numpy.intp, numpy.int8, numpy.int16, numpy.int32, numpy.int64,\
numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64, numpy.float16, numpy.float32, \
numpy.float64)):
return repr(obj)
elif isinstance(obj, numpy.matrixlib.defmatrix.matrix):
try:
import feather
except ImportError:
raise UsageError(
'The feather-format module is required to pass numpy matrix as R matrix'
'See https://github.com/wesm/feather/tree/master/python for details.'
)
feather_tmp_ = tempfile.NamedTemporaryFile(suffix='.feather',
delete=False).name
feather.write_dataframe(pandas.DataFrame(obj).copy(), feather_tmp_)
return 'data.matrix(read_feather("{}"))'.format(feather_tmp_)
elif isinstance(obj, numpy.ndarray):
return 'c(' + ','.join(_R_repr(x) for x in obj) + ')'
elif isinstance(obj, pandas.DataFrame):
try:
import feather
except ImportError:
raise UsageError(
'The feather-format module is required to pass pandas DataFrame as R data.frame'
'See https://github.com/wesm/feather/tree/master/python for details.'
)
feather_tmp_ = tempfile.NamedTemporaryFile(suffix='.feather',
delete=False).name
try:
data = obj.copy()
feather.write_dataframe(data, feather_tmp_)
except:
# if data cannot be written, we try to manipulate data
# frame to have consistent types and try again
for c in data.columns:
if not homogeneous_type(data[c]):
data[c] = [str(x) for x in data[c]]
feather.write_dataframe(data, feather_tmp_)
return 'read_feather("{}")'.format(feather_tmp_)
else:
return repr('Unsupported datatype {}'.format(short_repr(obj)))
def _julia_repr(self, obj):
if isinstance(obj, bool):
return 'true' if obj else 'false'
elif isinstance(obj, (int, float)):
return repr(obj)
elif isinstance(obj, str):
# Not using repr() here becasue of the problem of qoutes in Julia.
return '"""' + obj + '"""'
elif isinstance(obj, complex):
return 'complex(' + str(obj.real) + ',' + str(obj.imag) + ')'
elif isinstance(obj, Sequence):
if len(obj) == 0:
return '[]'
else:
return '[' + ','.join(self._julia_repr(x) for x in obj) + ']'
elif obj is None:
return 'NaN'
elif isinstance(obj, dict):
return 'Dict(' + ','.join(
'"{}" => {}'.format(x, self._julia_repr(y))
for x, y in obj.items()) + ')'
elif isinstance(obj, set):
return 'Set([' + ','.join(self._julia_repr(x) for x in obj) + '])'
else:
if isinstance(obj, (numpy.intc, numpy.intp, numpy.int8, numpy.int16, numpy.int32, numpy.int64,\
numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64, numpy.float16, numpy.float32)):
return repr(obj)
# need to specify Float64() as the return to Julia in order to avoid losing precision
elif isinstance(obj, numpy.float64):
return 'Float64(' + obj + ')'
elif isinstance(obj, numpy.matrixlib.defmatrix.matrix):
try:
import feather
except ImportError:
raise UsageError(
'The feather-format module is required to pass numpy matrix as julia matrix(array)'
'See https://github.com/wesm/feather/tree/master/python for details.'
)
feather_tmp_ = tempfile.NamedTemporaryFile(suffix='.feather',
delete=False).name
feather.write_dataframe(
pandas.DataFrame(obj).copy(), feather_tmp_)
return 'convert(Matrix, Feather.read("' + feather_tmp_ + '"))'
elif isinstance(obj, numpy.ndarray):
return '[' + ','.join(self._julia_repr(x) for x in obj) + ']'
elif isinstance(obj, pandas.DataFrame):
try:
import feather
except ImportError:
raise UsageError(
'The feather-format module is required to pass pandas DataFrame as julia.DataFrames'
'See https://github.com/wesm/feather/tree/master/python for details.'
)
feather_tmp_ = tempfile.NamedTemporaryFile(suffix='.feather',
delete=False).name
try:
data = obj.copy()
# Julia DataFrame does not have index
if not isinstance(data.index, pandas.RangeIndex):
self.sos_kernel.warn(
'Raw index is ignored because Julia DataFrame does not support raw index.'
)
feather.write_dataframe(data, feather_tmp_)
except Exception:
# if data cannot be written, we try to manipulate data
# frame to have consistent types and try again
for c in data.columns:
if not homogeneous_type(data[c]):
data[c] = [str(x) for x in data[c]]
feather.write_dataframe(data, feather_tmp_)
# use {!r} for path because the string might contain c:\ which needs to be
# double quoted.
return 'Feather.read("' + feather_tmp_ + '")'
elif isinstance(obj, pandas.Series):
dat = list(obj.values)
ind = list(obj.index.values)
ans = 'NamedArray(' + '[' + ','.join(
self._julia_repr(x) for x in dat) + ']' + ',([' + ','.join(
self._julia_repr(y) for y in ind) + '],))'
return ans.replace("'", '"')
else:
return repr('Unsupported datatype {}'.format(short_repr(obj)))
