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dmbee/seglearn | seglearn/pipe.py | Pype.predict_log_proba | def predict_log_proba(self, X):
"""
Apply transforms, and predict_log_proba of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
... | python | def predict_log_proba(self, X):
"""
Apply transforms, and predict_log_proba of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
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dmbee/seglearn | seglearn/feature_functions.py | base_features | def base_features():
''' Returns dictionary of some basic features that can be calculated for segmented time
series data '''
features = {'mean': mean,
'median': median,
'abs_energy': abs_energy,
'std': std,
'var': var,
'min': mi... | python | def base_features():
''' Returns dictionary of some basic features that can be calculated for segmented time
series data '''
features = {'mean': mean,
'median': median,
'abs_energy': abs_energy,
'std': std,
'var': var,
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dmbee/seglearn | seglearn/feature_functions.py | all_features | def all_features():
''' Returns dictionary of all features in the module
.. note:: Some of the features (hist4, corr) are relatively expensive to compute
'''
features = {'mean': mean,
'median': median,
'gmean': gmean,
'hmean': hmean,
'vec_... | python | def all_features():
''' Returns dictionary of all features in the module
.. note:: Some of the features (hist4, corr) are relatively expensive to compute
'''
features = {'mean': mean,
'median': median,
'gmean': gmean,
'hmean': hmean,
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dmbee/seglearn | seglearn/feature_functions.py | emg_features | def emg_features(threshold=0):
'''Return a dictionary of popular features used for EMG time series classification.'''
return {
'mean_abs_value': mean_abs,
'zero_crossings': zero_crossing(threshold),
'slope_sign_changes': slope_sign_changes(threshold),
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'''Return a dictionary of popular features used for EMG time series classification.'''
return {
'mean_abs_value': mean_abs,
'zero_crossings': zero_crossing(threshold),
'slope_sign_changes': slope_sign_changes(threshold),
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dmbee/seglearn | seglearn/feature_functions.py | means_abs_diff | def means_abs_diff(X):
''' mean absolute temporal derivative '''
return np.mean(np.abs(np.diff(X, axis=1)), axis=1) | python | def means_abs_diff(X):
''' mean absolute temporal derivative '''
return np.mean(np.abs(np.diff(X, axis=1)), axis=1) | [
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dmbee/seglearn | seglearn/feature_functions.py | mse | def mse(X):
''' computes mean spectral energy for each variable in a segmented time series '''
return np.mean(np.square(np.abs(np.fft.fft(X, axis=1))), axis=1) | python | def mse(X):
''' computes mean spectral energy for each variable in a segmented time series '''
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dmbee/seglearn | seglearn/feature_functions.py | mean_crossings | def mean_crossings(X):
''' Computes number of mean crossings for each variable in a segmented time series '''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
mnx = np.zeros((N, D))
for i in range(D):
pos = X[:, :, i] > 0
npos = ~pos
c = (pos[:, :-1] & npos[:, 1:]) | (n... | python | def mean_crossings(X):
''' Computes number of mean crossings for each variable in a segmented time series '''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
mnx = np.zeros((N, D))
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pos = X[:, :, i] > 0
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c = (pos[:, :-1] & npos[:, 1:]) | (n... | [
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dmbee/seglearn | seglearn/feature_functions.py | corr2 | def corr2(X):
''' computes correlations between all variable pairs in a segmented time series
.. note:: this feature is expensive to compute with the current implementation, and cannot be
used with univariate time series
'''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
if D == 1:... | python | def corr2(X):
''' computes correlations between all variable pairs in a segmented time series
.. note:: this feature is expensive to compute with the current implementation, and cannot be
used with univariate time series
'''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
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dmbee/seglearn | seglearn/feature_functions.py | waveform_length | def waveform_length(X):
''' cumulative length of the waveform over a segment for each variable in the segmented time
series '''
return np.sum(np.abs(np.diff(X, axis=1)), axis=1) | python | def waveform_length(X):
''' cumulative length of the waveform over a segment for each variable in the segmented time
series '''
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dmbee/seglearn | seglearn/feature_functions.py | root_mean_square | def root_mean_square(X):
''' root mean square for each variable in the segmented time series '''
segment_width = X.shape[1]
return np.sqrt(np.sum(X * X, axis=1) / segment_width) | python | def root_mean_square(X):
''' root mean square for each variable in the segmented time series '''
segment_width = X.shape[1]
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dmbee/seglearn | seglearn/split.py | TemporalKFold.split | def split(self, X, y):
'''
Splits time series data and target arrays, and generates splitting indices
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like shape [n_series, ]
ta... | python | def split(self, X, y):
'''
Splits time series data and target arrays, and generates splitting indices
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like shape [n_series, ]
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dmbee/seglearn | seglearn/split.py | TemporalKFold._ts_slice | def _ts_slice(self, Xt, y):
''' takes time series data, and splits each series into temporal folds '''
Ns = len(Xt)
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for i in range(self.n_splits):
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Xt_new.append(Xt[j][(Njs * ... | python | def _ts_slice(self, Xt, y):
''' takes time series data, and splits each series into temporal folds '''
Ns = len(Xt)
Xt_new = []
for i in range(self.n_splits):
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dmbee/seglearn | seglearn/split.py | TemporalKFold._make_indices | def _make_indices(self, Ns):
''' makes indices for cross validation '''
N_new = int(Ns * self.n_splits)
test = [np.full(N_new, False) for i in range(self.n_splits)]
for i in range(self.n_splits):
test[i][np.arange(Ns * i, Ns * (i + 1))] = True
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''' makes indices for cross validation '''
N_new = int(Ns * self.n_splits)
test = [np.full(N_new, False) for i in range(self.n_splits)]
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test[i][np.arange(Ns * i, Ns * (i + 1))] = True
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dmbee/seglearn | seglearn/preprocessing.py | TargetRunLengthEncoder.transform | def transform(self, X, y, sample_weight=None):
'''
Transforms the time series data with run length encoding of the target variable
Note this transformation changes the number of samples in the data
If sample_weight is provided, it is transformed to align to the new target encoding
... | python | def transform(self, X, y, sample_weight=None):
'''
Transforms the time series data with run length encoding of the target variable
Note this transformation changes the number of samples in the data
If sample_weight is provided, it is transformed to align to the new target encoding
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dmbee/seglearn | seglearn/preprocessing.py | TargetRunLengthEncoder._rle | def _rle(self, a):
'''
rle implementation credit to Thomas Browne from his SOF post Sept 2015
Parameters
----------
a : array, shape[n,]
input vector
Returns
-------
z : array, shape[nt,]
run lengths
p : array, shape[nt,]
... | python | def _rle(self, a):
'''
rle implementation credit to Thomas Browne from his SOF post Sept 2015
Parameters
----------
a : array, shape[n,]
input vector
Returns
-------
z : array, shape[nt,]
run lengths
p : array, shape[nt,]
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dmbee/seglearn | seglearn/preprocessing.py | TargetRunLengthEncoder._transform | def _transform(self, X, y):
'''
Transforms single series
'''
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p = np.append(p, len(y))
big_enough = p[1:] - p[:-1] >= self.min_length
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if (big_enough[i]):
Xt.append(X... | python | def _transform(self, X, y):
'''
Transforms single series
'''
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p = np.append(p, len(y))
big_enough = p[1:] - p[:-1] >= self.min_length
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'''
Separates time series data object into time series variables and contextual variables
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X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
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Time series data and (optionally) contextual data
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dmbee/seglearn | seglearn/util.py | check_ts_data_with_ts_target | def check_ts_data_with_ts_target(X, y=None):
'''
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X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series, ...]
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Checks time series data with time series target is good. If not raises value error.
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X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series]
target data
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target data
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dmbee/seglearn | seglearn/datasets.py | load_watch | def load_watch():
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The study can be found here: https://arxiv.... | python | def load_watch():
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Loads some of the 6-axis inertial sensor data from my smartwatch project. The sensor data was
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dmbee/seglearn | seglearn/transform.py | shuffle_data | def shuffle_data(X, y=None, sample_weight=None):
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dmbee/seglearn | seglearn/transform.py | expand_variables_to_segments | def expand_variables_to_segments(v, Nt):
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''' expands contextual variables v, by repeating each instance as specified in Nt '''
N_v = len(np.atleast_1d(v[0]))
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dmbee/seglearn | seglearn/transform.py | sliding_window | def sliding_window(time_series, width, step, order='F'):
'''
Segments univariate time series with sliding window
Parameters
----------
time_series : array like shape [n_samples]
time series or sequence
width : int > 0
segment width in samples
step : int > 0
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'''
Segments univariate time series with sliding window
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time_series : array like shape [n_samples]
time series or sequence
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segment width in samples
step : int > 0
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'''
segments multivariate time series with sliding window
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mv_time_series : array like shape [n_samples, n_variables]
multivariate time series or sequence
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segment width in samples
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'''
segments multivariate time series with sliding window
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dmbee/seglearn | seglearn/transform.py | InterpLongToWide._check_data | def _check_data(self, X):
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Checks that unique identifiers vaf_types are consistent between time series.
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X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
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dmbee/seglearn | seglearn/transform.py | FeatureRep._check_features | def _check_features(self, features, Xti):
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Parameters
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features : dict
feature function dictionary
Xti : array-like, shape [n_samples, segment_width, n_variables]
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dmbee/seglearn | seglearn/transform.py | FeatureRep._generate_feature_labels | def _generate_feature_labels(self, X):
'''
Generates string feature labels
'''
Xt, Xc = get_ts_data_parts(X)
ftr_sizes = self._check_features(self.features, Xt[0:3])
f_labels = []
# calculated features
for key in ftr_sizes:
for i in range(ftr... | python | def _generate_feature_labels(self, X):
'''
Generates string feature labels
'''
Xt, Xc = get_ts_data_parts(X)
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dmbee/seglearn | seglearn/transform.py | FeatureRepMix._retrieve_indices | def _retrieve_indices(cols):
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Retrieve a list of indices corresponding to the provided column specification.
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Internal function to validate the transformer before applying all internal transformers.
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SAP/PyHDB | pyhdb/protocol/segments.py | RequestSegment.build_payload | def build_payload(self, payload):
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SAP/PyHDB | pyhdb/protocol/types.py | escape | def escape(value):
"""
Escape a single value.
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typ = by_python_type.get(value.__class__)
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Escape a single value.
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SAP/PyHDB | pyhdb/protocol/types.py | escape_values | def escape_values(values):
"""
Escape multiple values from a list, tuple or dict.
"""
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Escape multiple values from a list, tuple or dict.
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SAP/PyHDB | pyhdb/protocol/types.py | Date.prepare | def prepare(cls, value):
"""Pack datetime value into proper binary format"""
pfield = struct.pack('b', cls.type_code)
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value = datetime.datetime.strptime(value, "%Y-%m-%d")
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"""Pack datetime value into proper binary format"""
pfield = struct.pack('b', cls.type_code)
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SAP/PyHDB | pyhdb/protocol/types.py | MixinLobType.prepare | def prepare(cls, value, length=0, position=0, is_last_data=True):
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SAP/PyHDB | pyhdb/protocol/lobs.py | Lob.seek | def seek(self, offset, whence=SEEK_SET):
"""Seek pointer in lob data buffer to requested position.
Might trigger further loading of data from the database if the pointer is beyond currently read data.
"""
# A nice trick is to (ab)use BytesIO.seek() to go to the desired position for easie... | python | def seek(self, offset, whence=SEEK_SET):
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SAP/PyHDB | pyhdb/protocol/lobs.py | Lob._read_missing_lob_data_from_db | def _read_missing_lob_data_from_db(self, readoffset, readlength):
"""Read LOB request part from database"""
logger.debug('Reading missing lob data from db. Offset: %d, readlength: %d' % (readoffset, readlength))
lob_data = self._make_read_lob_request(readoffset, readlength)
# make sure ... | python | def _read_missing_lob_data_from_db(self, readoffset, readlength):
"""Read LOB request part from database"""
logger.debug('Reading missing lob data from db. Offset: %d, readlength: %d' % (readoffset, readlength))
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SAP/PyHDB | pyhdb/protocol/lobs.py | Clob._init_io_container | def _init_io_container(self, init_value):
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For CLobs ensure that an initial unicode value only contains valid ascii chars.
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SAP/PyHDB | pyhdb/cursor.py | Cursor._handle_upsert | def _handle_upsert(self, parts, unwritten_lobs=()):
"""Handle reply messages from INSERT or UPDATE statements"""
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if part.kind... | python | def _handle_upsert(self, parts, unwritten_lobs=()):
"""Handle reply messages from INSERT or UPDATE statements"""
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SAP/PyHDB | pyhdb/cursor.py | Cursor._handle_select | def _handle_select(self, parts, result_metadata=None):
"""Handle reply messages from SELECT statements"""
self.rowcount = -1
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self.description, self._column_types = self._han... | python | def _handle_select(self, parts, result_metadata=None):
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SAP/PyHDB | pyhdb/cursor.py | Cursor._handle_dbproc_call | def _handle_dbproc_call(self, parts, parameters_metadata):
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SAP/PyHDB | pyhdb/lib/stringlib.py | allhexlify | def allhexlify(data):
"""Hexlify given data into a string representation with hex values for all chars
Input like
'ab\x04ce'
becomes
'\x61\x62\x04\x63\x65'
"""
hx = binascii.hexlify(data)
return b''.join([b'\\x' + o for o in re.findall(b'..', hx)]) | python | def allhexlify(data):
"""Hexlify given data into a string representation with hex values for all chars
Input like
'ab\x04ce'
becomes
'\x61\x62\x04\x63\x65'
"""
hx = binascii.hexlify(data)
return b''.join([b'\\x' + o for o in re.findall(b'..', hx)]) | [
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SAP/PyHDB | pyhdb/protocol/parts.py | Part.pack | def pack(self, remaining_size):
"""Pack data of part into binary format"""
arguments_count, payload = self.pack_data(remaining_size - self.header_size)
payload_length = len(payload)
# align payload length to multiple of 8
if payload_length % 8 != 0:
payload += b"\x00... | python | def pack(self, remaining_size):
"""Pack data of part into binary format"""
arguments_count, payload = self.pack_data(remaining_size - self.header_size)
payload_length = len(payload)
# align payload length to multiple of 8
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SAP/PyHDB | pyhdb/protocol/parts.py | Part.unpack_from | def unpack_from(cls, payload, expected_parts):
"""Unpack parts from payload"""
for num_part in iter_range(expected_parts):
hdr = payload.read(cls.header_size)
try:
part_header = PartHeader(*cls.header_struct.unpack(hdr))
except struct.error:
... | python | def unpack_from(cls, payload, expected_parts):
"""Unpack parts from payload"""
for num_part in iter_range(expected_parts):
hdr = payload.read(cls.header_size)
try:
part_header = PartHeader(*cls.header_struct.unpack(hdr))
except struct.error:
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SAP/PyHDB | pyhdb/protocol/parts.py | ReadLobRequest.pack_data | def pack_data(self, remaining_size):
"""Pack data. readoffset has to be increased by one, seems like HANA starts from 1, not zero."""
payload = self.part_struct.pack(self.locator_id, self.readoffset + 1, self.readlength, b' ')
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SAP/PyHDB | pyhdb/protocol/message.py | RequestMessage.build_payload | def build_payload(self, payload):
""" Build payload of message. """
for segment in self.segments:
segment.pack(payload, commit=self.autocommit) | python | def build_payload(self, payload):
""" Build payload of message. """
for segment in self.segments:
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SAP/PyHDB | pyhdb/protocol/message.py | RequestMessage.pack | def pack(self):
""" Pack message to binary stream. """
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# Advance num bytes equal to header size - the header is written later
# after the payload of all segments and parts has been written:
payload.seek(self.header_size, io.SEEK_CUR)
# Write out pa... | python | def pack(self):
""" Pack message to binary stream. """
payload = io.BytesIO()
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serge-sans-paille/pythran | pythran/syntax.py | check_specs | def check_specs(specs, renamings, types):
'''
Does nothing but raising PythranSyntaxError if specs
are incompatible with the actual code
'''
from pythran.types.tog import unify, clone, tr
from pythran.types.tog import Function, TypeVariable, InferenceError
functions = {renamings.get(k, k): ... | python | def check_specs(specs, renamings, types):
'''
Does nothing but raising PythranSyntaxError if specs
are incompatible with the actual code
'''
from pythran.types.tog import unify, clone, tr
from pythran.types.tog import Function, TypeVariable, InferenceError
functions = {renamings.get(k, k): ... | [
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serge-sans-paille/pythran | pythran/syntax.py | check_exports | def check_exports(mod, specs, renamings):
'''
Does nothing but raising PythranSyntaxError if specs
references an undefined global
'''
functions = {renamings.get(k, k): v for k, v in specs.functions.items()}
mod_functions = {node.name: node for node in mod.body
if isinstance... | python | def check_exports(mod, specs, renamings):
'''
Does nothing but raising PythranSyntaxError if specs
references an undefined global
'''
functions = {renamings.get(k, k): v for k, v in specs.functions.items()}
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serge-sans-paille/pythran | pythran/syntax.py | SyntaxChecker.visit_Import | def visit_Import(self, node):
""" Check if imported module exists in MODULES. """
for alias in node.names:
current_module = MODULES
# Recursive check for submodules
for path in alias.name.split('.'):
if path not in current_module:
r... | python | def visit_Import(self, node):
""" Check if imported module exists in MODULES. """
for alias in node.names:
current_module = MODULES
# Recursive check for submodules
for path in alias.name.split('.'):
if path not in current_module:
r... | [
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serge-sans-paille/pythran | pythran/syntax.py | SyntaxChecker.visit_ImportFrom | def visit_ImportFrom(self, node):
"""
Check validity of imported functions.
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- no level specific value are provided.
- a module is provided
- module/submodule exists in MODULES
- imported function exists in the given ... | python | def visit_ImportFrom(self, node):
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Check validity of imported functions.
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- no level specific value are provided.
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serge-sans-paille/pythran | pythran/passmanager.py | uncamel | def uncamel(name):
"""Transform CamelCase naming convention into C-ish convention."""
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower() | python | def uncamel(name):
"""Transform CamelCase naming convention into C-ish convention."""
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower() | [
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serge-sans-paille/pythran | pythran/passmanager.py | ContextManager.verify_dependencies | def verify_dependencies(self):
"""
Checks no analysis are called before a transformation,
as the transformation could invalidate the analysis.
"""
for i in range(1, len(self.deps)):
assert(not (isinstance(self.deps[i], Transformation) and
isinstanc... | python | def verify_dependencies(self):
"""
Checks no analysis are called before a transformation,
as the transformation could invalidate the analysis.
"""
for i in range(1, len(self.deps)):
assert(not (isinstance(self.deps[i], Transformation) and
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serge-sans-paille/pythran | pythran/passmanager.py | ContextManager.prepare | def prepare(self, node):
'''Gather analysis result required by this analysis'''
if isinstance(node, ast.Module):
self.ctx.module = node
elif isinstance(node, ast.FunctionDef):
self.ctx.function = node
for D in self.deps:
d = D()
d.attach(s... | python | def prepare(self, node):
'''Gather analysis result required by this analysis'''
if isinstance(node, ast.Module):
self.ctx.module = node
elif isinstance(node, ast.FunctionDef):
self.ctx.function = node
for D in self.deps:
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serge-sans-paille/pythran | pythran/passmanager.py | Transformation.run | def run(self, node):
""" Apply transformation and dependencies and fix new node location."""
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""" Apply transformation and dependencies and fix new node location."""
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serge-sans-paille/pythran | pythran/passmanager.py | Transformation.apply | def apply(self, node):
""" Apply transformation and return if an update happened. """
new_node = self.run(node)
return self.update, new_node | python | def apply(self, node):
""" Apply transformation and return if an update happened. """
new_node = self.run(node)
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serge-sans-paille/pythran | pythran/passmanager.py | PassManager.gather | def gather(self, analysis, node):
"High-level function to call an `analysis' on a `node'"
assert issubclass(analysis, Analysis)
a = analysis()
a.attach(self)
return a.run(node) | python | def gather(self, analysis, node):
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serge-sans-paille/pythran | pythran/passmanager.py | PassManager.dump | def dump(self, backend, node):
'''High-level function to call a `backend' on a `node' to generate
code for module `module_name'.'''
assert issubclass(backend, Backend)
b = backend()
b.attach(self)
return b.run(node) | python | def dump(self, backend, node):
'''High-level function to call a `backend' on a `node' to generate
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b.attach(self)
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High-level function to call a `transformation' on a `node'.
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High-level function to call a `transformation' on a `node'.
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serge-sans-paille/pythran | pythran/types/conversion.py | pytype_to_ctype | def pytype_to_ctype(t):
""" Python -> pythonic type binding. """
if isinstance(t, List):
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pytype_to_ctype(t.__args__[0])
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""" Python -> pythonic type binding. """
if isinstance(t, List):
return 'pythonic::types::list<{0}>'.format(
pytype_to_ctype(t.__args__[0])
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serge-sans-paille/pythran | pythran/types/conversion.py | pytype_to_pretty_type | def pytype_to_pretty_type(t):
""" Python -> docstring type. """
if isinstance(t, List):
return '{0} list'.format(pytype_to_pretty_type(t.__args__[0]))
elif isinstance(t, Set):
return '{0} set'.format(pytype_to_pretty_type(t.__args__[0]))
elif isinstance(t, Dict):
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""" Python -> docstring type. """
if isinstance(t, List):
return '{0} list'.format(pytype_to_pretty_type(t.__args__[0]))
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serge-sans-paille/pythran | pythran/types/tog.py | get_type | def get_type(name, env, non_generic):
"""Get the type of identifier name from the type environment env.
Args:
name: The identifier name
env: The type environment mapping from identifier names to types
non_generic: A set of non-generic TypeVariables
Raises:
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"""Get the type of identifier name from the type environment env.
Args:
name: The identifier name
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non_generic: A set of non-generic TypeVariables
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serge-sans-paille/pythran | pythran/types/tog.py | fresh | def fresh(t, non_generic):
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Args:
t: A type to be copied.
non_generic: A set of non-generic TypeVariables
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"""Makes a copy of a type expression.
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t: A type to be copied.
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serge-sans-paille/pythran | pythran/types/tog.py | prune | def prune(t):
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serge-sans-paille/pythran | pythran/types/tog.py | occurs_in_type | def occurs_in_type(v, type2):
"""Checks whether a type variable occurs in a type expression.
Note: Must be called with v pre-pruned
Args:
v: The TypeVariable to be tested for
type2: The type in which to search
Returns:
True if v occurs in type2, otherwise False
"""
pr... | python | def occurs_in_type(v, type2):
"""Checks whether a type variable occurs in a type expression.
Note: Must be called with v pre-pruned
Args:
v: The TypeVariable to be tested for
type2: The type in which to search
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serge-sans-paille/pythran | pythran/transformations/expand_imports.py | ExpandImports.visit_Module | def visit_Module(self, node):
"""
Visit the whole module and add all import at the top level.
>> import numpy.linalg
Becomes
>> import numpy
"""
node.body = [k for k in (self.visit(n) for n in node.body) if k]
imports = [ast.Import([ast.alias(i, mangle... | python | def visit_Module(self, node):
"""
Visit the whole module and add all import at the top level.
>> import numpy.linalg
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>> import numpy
"""
node.body = [k for k in (self.visit(n) for n in node.body) if k]
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serge-sans-paille/pythran | pythran/transformations/expand_imports.py | ExpandImports.visit_Name | def visit_Name(self, node):
"""
Replace name with full expanded name.
Examples
--------
>> from numpy.linalg import det
>> det(a)
Becomes
>> numpy.linalg.det(a)
"""
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symbol = path_to_node(self.symb... | python | def visit_Name(self, node):
"""
Replace name with full expanded name.
Examples
--------
>> from numpy.linalg import det
>> det(a)
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>> numpy.linalg.det(a)
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serge-sans-paille/pythran | pythran/analyses/argument_effects.py | save_function_effect | def save_function_effect(module):
""" Recursively save function effect for pythonic functions. """
for intr in module.values():
if isinstance(intr, dict): # Submodule case
save_function_effect(intr)
else:
fe = FunctionEffects(intr)
IntrinsicArgumentEffects[in... | python | def save_function_effect(module):
""" Recursively save function effect for pythonic functions. """
for intr in module.values():
if isinstance(intr, dict): # Submodule case
save_function_effect(intr)
else:
fe = FunctionEffects(intr)
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serge-sans-paille/pythran | pythran/analyses/argument_effects.py | ArgumentEffects.prepare | def prepare(self, node):
"""
Initialise arguments effects as this analyse is inter-procedural.
Initialisation done for Pythonic functions and default value set for
user defined functions.
"""
super(ArgumentEffects, self).prepare(node)
for n in self.global_declara... | python | def prepare(self, node):
"""
Initialise arguments effects as this analyse is inter-procedural.
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.process_locals | def process_locals(self, node, node_visited, *skipped):
"""
Declare variable local to node and insert declaration before.
Not possible for function yielding values.
"""
local_vars = self.scope[node].difference(skipped)
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Declare variable local to node and insert declaration before.
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.process_omp_attachements | def process_omp_attachements(self, node, stmt, index=None):
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"""
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.visit_Assign | def visit_Assign(self, node):
"""
Create Assign node for final Cxx representation.
It tries to handle multi assignment like:
>> a = b = c = 2
If only one local variable is assigned, typing is added:
>> int a = 2;
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"""
Create Assign node for final Cxx representation.
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.gen_for | def gen_for(self, node, target, local_iter, local_iter_decl, loop_body):
"""
Create For representation on iterator for Cxx generation.
Examples
--------
>> "omp parallel for"
>> for i in xrange(10):
>> ... do things ...
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"""
Create For representation on iterator for Cxx generation.
Examples
--------
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Handle comparison for real loops.
Add the correct comparison operator if possible.
"""
# order is 1 for increasing loop, -1 for decreasing loop and 0 if it is
# not known at compile time
if len... | python | def handle_real_loop_comparison(self, args, target, upper_bound):
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Add the correct comparison operator if possible.
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.gen_c_for | def gen_c_for(self, node, local_iter, loop_body):
"""
Create C For representation for Cxx generation.
Examples
--------
>> for i in xrange(10):
>> ... do things ...
Becomes
>> for(long i = 0, __targetX = 10; i < __targetX; i += 1)
>> ...... | python | def gen_c_for(self, node, local_iter, loop_body):
"""
Create C For representation for Cxx generation.
Examples
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>> ... do things ...
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.handle_omp_for | def handle_omp_for(self, node, local_iter):
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Fix OpenMP directives on For loops.
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Also, add the iterator as shared variable as all 'parallel for chunck'
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"""
Fix OpenMP directives on For loops.
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.can_use_autofor | def can_use_autofor(self, node):
"""
Check if given for Node can use autoFor syntax.
To use auto_for:
- iterator should have local scope
- yield should not be use
- OpenMP pragma should not be use
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"""
Check if given for Node can use autoFor syntax.
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- yield should not be use
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.can_use_c_for | def can_use_c_for(self, node):
"""
Check if a for loop can use classic C syntax.
To use C syntax:
- target should not be assign in the loop
- xrange should be use as iterator
- order have to be known at compile time
"""
assert isinstance(node.... | python | def can_use_c_for(self, node):
"""
Check if a for loop can use classic C syntax.
To use C syntax:
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- order have to be known at compile time
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.visit_For | def visit_For(self, node):
"""
Create For representation for Cxx generation.
Examples
--------
>> for i in xrange(10):
>> ... work ...
Becomes
>> typename returnable<decltype(__builtin__.xrange(10))>::type __iterX
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"""
Create For representation for Cxx generation.
Examples
--------
>> for i in xrange(10):
>> ... work ...
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.visit_While | def visit_While(self, node):
"""
Create While node for Cxx generation.
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"""
test = self.visit(node.test)
body = [self.visit(n) for n in node.body]
stmt = While(test, Block(body))
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"""
Create While node for Cxx generation.
It is a cxx_loop to handle else clause.
"""
test = self.visit(node.test)
body = [self.visit(n) for n in node.body]
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serge-sans-paille/pythran | pythran/backend.py | CxxFunction.visit_Break | def visit_Break(self, _):
"""
Generate break statement in most case and goto for orelse clause.
See Also : cxx_loop
"""
if self.break_handlers and self.break_handlers[-1]:
return Statement("goto {0}".format(self.break_handlers[-1]))
else:
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"""
Generate break statement in most case and goto for orelse clause.
See Also : cxx_loop
"""
if self.break_handlers and self.break_handlers[-1]:
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serge-sans-paille/pythran | pythran/backend.py | Cxx.visit_Module | def visit_Module(self, node):
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# build all types
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serge-sans-paille/pythran | pythran/middlend.py | refine | def refine(pm, node, optimizations):
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""" Refine node in place until it matches pythran's expectations. """
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serge-sans-paille/pythran | pythran/analyses/global_effects.py | GlobalEffects.prepare | def prepare(self, node):
"""
Initialise globals effects as this analyse is inter-procedural.
Initialisation done for Pythonic functions and default value set for
user defined functions.
"""
super(GlobalEffects, self).prepare(node)
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"""
Initialise globals effects as this analyse is inter-procedural.
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serge-sans-paille/pythran | pythran/types/types.py | Types.prepare | def prepare(self, node):
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"""
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serge-sans-paille/pythran | pythran/types/types.py | Types.register | def register(self, ptype):
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self.typedefs.append(ptype)
return True
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"""register ptype as a local typedef"""
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serge-sans-paille/pythran | pythran/types/types.py | Types.isargument | def isargument(self, node):
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node_id, _ = self.node_to_id(node)
return (node_id in self.name_to_nodes and
any([isinstance(n, ast.Name) and
isinstance(n.ctx, ast.Param)
... | python | def isargument(self, node):
""" checks whether node aliases to a parameter."""
try:
node_id, _ = self.node_to_id(node)
return (node_id in self.name_to_nodes and
any([isinstance(n, ast.Name) and
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serge-sans-paille/pythran | pythran/types/types.py | Types.combine | def combine(self, node, othernode, op=None, unary_op=None, register=False,
aliasing_type=False):
"""
Change `node` typing with combination of `node` and `othernode`.
Parameters
----------
aliasing_type : bool
All node aliasing to `node` have to be upd... | python | def combine(self, node, othernode, op=None, unary_op=None, register=False,
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"""
Change `node` typing with combination of `node` and `othernode`.
Parameters
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serge-sans-paille/pythran | pythran/types/types.py | Types.visit_Return | def visit_Return(self, node):
""" Compute return type and merges with others possible return type."""
self.generic_visit(node)
# No merge are done if the function is a generator.
if not self.yield_points:
assert node.value, "Values were added in each return statement."
... | python | def visit_Return(self, node):
""" Compute return type and merges with others possible return type."""
self.generic_visit(node)
# No merge are done if the function is a generator.
if not self.yield_points:
assert node.value, "Values were added in each return statement."
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serge-sans-paille/pythran | pythran/types/types.py | Types.visit_Yield | def visit_Yield(self, node):
""" Compute yield type and merges it with others yield type. """
self.generic_visit(node)
self.combine(self.current, node.value) | python | def visit_Yield(self, node):
""" Compute yield type and merges it with others yield type. """
self.generic_visit(node)
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serge-sans-paille/pythran | pythran/types/types.py | Types.visit_BoolOp | def visit_BoolOp(self, node):
"""
Merge BoolOp operand type.
BoolOp are "and" and "or" and may return any of these results so all
operands should have the combinable type.
"""
# Visit subnodes
self.generic_visit(node)
# Merge all operands types.
[... | python | def visit_BoolOp(self, node):
"""
Merge BoolOp operand type.
BoolOp are "and" and "or" and may return any of these results so all
operands should have the combinable type.
"""
# Visit subnodes
self.generic_visit(node)
# Merge all operands types.
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serge-sans-paille/pythran | pythran/types/types.py | Types.visit_Num | def visit_Num(self, node):
"""
Set type for number.
It could be int, long or float so we use the default python to pythonic
type converter.
"""
ty = type(node.n)
sty = pytype_to_ctype(ty)
if node in self.immediates:
sty = "std::integral_consta... | python | def visit_Num(self, node):
"""
Set type for number.
It could be int, long or float so we use the default python to pythonic
type converter.
"""
ty = type(node.n)
sty = pytype_to_ctype(ty)
if node in self.immediates:
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serge-sans-paille/pythran | pythran/types/types.py | Types.visit_Str | def visit_Str(self, node):
""" Set the pythonic string type. """
self.result[node] = self.builder.NamedType(pytype_to_ctype(str)) | python | def visit_Str(self, node):
""" Set the pythonic string type. """
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serge-sans-paille/pythran | pythran/types/types.py | Types.visit_Attribute | def visit_Attribute(self, node):
""" Compute typing for an attribute node. """
obj, path = attr_to_path(node)
# If no type is given, use a decltype
if obj.isliteral():
typename = pytype_to_ctype(obj.signature)
self.result[node] = self.builder.NamedType(typename)
... | python | def visit_Attribute(self, node):
""" Compute typing for an attribute node. """
obj, path = attr_to_path(node)
# If no type is given, use a decltype
if obj.isliteral():
typename = pytype_to_ctype(obj.signature)
self.result[node] = self.builder.NamedType(typename)
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serge-sans-paille/pythran | pythran/types/types.py | Types.visit_Slice | def visit_Slice(self, node):
"""
Set slicing type using continuous information if provided.
Also visit subnodes as they may contains relevant typing information.
"""
self.generic_visit(node)
if node.step is None or (isinstance(node.step, ast.Num) and
... | python | def visit_Slice(self, node):
"""
Set slicing type using continuous information if provided.
Also visit subnodes as they may contains relevant typing information.
"""
self.generic_visit(node)
if node.step is None or (isinstance(node.step, ast.Num) and
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serge-sans-paille/pythran | omp/__init__.py | OpenMP.init_not_msvc | def init_not_msvc(self):
""" Find OpenMP library and try to load if using ctype interface. """
# find_library() does not search automatically LD_LIBRARY_PATH
paths = os.environ.get('LD_LIBRARY_PATH', '').split(':')
for gomp in ('libgomp.so', 'libgomp.dylib'):
if cxx is None:
... | python | def init_not_msvc(self):
""" Find OpenMP library and try to load if using ctype interface. """
# find_library() does not search automatically LD_LIBRARY_PATH
paths = os.environ.get('LD_LIBRARY_PATH', '').split(':')
for gomp in ('libgomp.so', 'libgomp.dylib'):
if cxx is None:
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serge-sans-paille/pythran | pythran/analyses/inlinable.py | Inlinable.visit_FunctionDef | def visit_FunctionDef(self, node):
""" Determine this function definition can be inlined. """
if (len(node.body) == 1 and
isinstance(node.body[0], (ast.Call, ast.Return))):
ids = self.gather(Identifiers, node.body[0])
# FIXME : It mark "not inlinable" def foo(foo)... | python | def visit_FunctionDef(self, node):
""" Determine this function definition can be inlined. """
if (len(node.body) == 1 and
isinstance(node.body[0], (ast.Call, ast.Return))):
ids = self.gather(Identifiers, node.body[0])
# FIXME : It mark "not inlinable" def foo(foo)... | [
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