K00B404/GPT_2_CODE
Text Generation
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0 | getsentry/libsourcemap | libsourcemap/highlevel.py | View.get_original_function_name | def get_original_function_name(self, line, col, minified_name,
minified_source):
"""Given a token location and a minified function name and the
minified source file this returns the original function name if it
can be found of the minified function in scope.
"""
# Silently ignore underflows
if line < 0 or col < 0:
return None
minified_name = minified_name.encode('utf-8')
sout = _ffi.new('const char **')
try:
slen = rustcall(_lib.lsm_view_get_original_function_name,
self._get_ptr(), line, col, minified_name,
minified_source, sout)
if slen > 0:
return _ffi.unpack(sout[0], slen).decode('utf-8', 'replace')
except SourceMapError:
# In some rare cases the library is/was known to panic. We do
# not want to report this upwards (this happens on slicing
# out of range on older rust versions in the rust-sourcemap
# library)
pass | python | def get_original_function_name(self, line, col, minified_name,
minified_source):
"""Given a token location and a minified function name and the
minified source file this returns the original function name if it
can be found of the minified function in scope.
"""
# Silently ignore underflows
if line < 0 or col < 0:
return None
minified_name = minified_name.encode('utf-8')
sout = _ffi.new('const char **')
try:
slen = rustcall(_lib.lsm_view_get_original_function_name,
self._get_ptr(), line, col, minified_name,
minified_source, sout)
if slen > 0:
return _ffi.unpack(sout[0], slen).decode('utf-8', 'replace')
except SourceMapError:
# In some rare cases the library is/was known to panic. We do
# not want to report this upwards (this happens on slicing
# out of range on older rust versions in the rust-sourcemap
# library)
pass | ['def', 'get_original_function_name', '(', 'self', ',', 'line', ',', 'col', ',', 'minified_name', ',', 'minified_source', ')', ':', '# Silently ignore underflows', 'if', 'line', '<', '0', 'or', 'col', '<', '0', ':', 'return', 'None', 'minified_name', '=', 'minified_name', '.', 'encode', '(', "'utf-8'", ')', 'sout', '=', '_ffi', '.', 'new', '(', "'const char **'", ')', 'try', ':', 'slen', '=', 'rustcall', '(', '_lib', '.', 'lsm_view_get_original_function_name', ',', 'self', '.', '_get_ptr', '(', ')', ',', 'line', ',', 'col', ',', 'minified_name', ',', 'minified_source', ',', 'sout', ')', 'if', 'slen', '>', '0', ':', 'return', '_ffi', '.', 'unpack', '(', 'sout', '[', '0', ']', ',', 'slen', ')', '.', 'decode', '(', "'utf-8'", ',', "'replace'", ')', 'except', 'SourceMapError', ':', '# In some rare cases the library is/was known to panic. We do', '# not want to report this upwards (this happens on slicing', '# out of range on older rust versions in the rust-sourcemap', '# library)', 'pass'] | Given a token location and a minified function name and the
minified source file this returns the original function name if it
can be found of the minified function in scope. | ['Given', 'a', 'token', 'location', 'and', 'a', 'minified', 'function', 'name', 'and', 'the', 'minified', 'source', 'file', 'this', 'returns', 'the', 'original', 'function', 'name', 'if', 'it', 'can', 'be', 'found', 'of', 'the', 'minified', 'function', 'in', 'scope', '.'] | train | https://github.com/getsentry/libsourcemap/blob/94b5a34814fafee9dc23da8ec0ccca77f30e3370/libsourcemap/highlevel.py#L163-L185 |
1 | brocade/pynos | pynos/versions/ver_7/ver_7_1_0/yang/brocade_mac_address_table.py | brocade_mac_address_table.get_mac_address_table_input_request_type_get_interface_based_request_mac_type | def get_mac_address_table_input_request_type_get_interface_based_request_mac_type(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
get_mac_address_table = ET.Element("get_mac_address_table")
config = get_mac_address_table
input = ET.SubElement(get_mac_address_table, "input")
request_type = ET.SubElement(input, "request-type")
get_interface_based_request = ET.SubElement(request_type, "get-interface-based-request")
mac_type = ET.SubElement(get_interface_based_request, "mac-type")
mac_type.text = kwargs.pop('mac_type')
callback = kwargs.pop('callback', self._callback)
return callback(config) | python | def get_mac_address_table_input_request_type_get_interface_based_request_mac_type(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
get_mac_address_table = ET.Element("get_mac_address_table")
config = get_mac_address_table
input = ET.SubElement(get_mac_address_table, "input")
request_type = ET.SubElement(input, "request-type")
get_interface_based_request = ET.SubElement(request_type, "get-interface-based-request")
mac_type = ET.SubElement(get_interface_based_request, "mac-type")
mac_type.text = kwargs.pop('mac_type')
callback = kwargs.pop('callback', self._callback)
return callback(config) | ['def', 'get_mac_address_table_input_request_type_get_interface_based_request_mac_type', '(', 'self', ',', '*', '*', 'kwargs', ')', ':', 'config', '=', 'ET', '.', 'Element', '(', '"config"', ')', 'get_mac_address_table', '=', 'ET', '.', 'Element', '(', '"get_mac_address_table"', ')', 'config', '=', 'get_mac_address_table', 'input', '=', 'ET', '.', 'SubElement', '(', 'get_mac_address_table', ',', '"input"', ')', 'request_type', '=', 'ET', '.', 'SubElement', '(', 'input', ',', '"request-type"', ')', 'get_interface_based_request', '=', 'ET', '.', 'SubElement', '(', 'request_type', ',', '"get-interface-based-request"', ')', 'mac_type', '=', 'ET', '.', 'SubElement', '(', 'get_interface_based_request', ',', '"mac-type"', ')', 'mac_type', '.', 'text', '=', 'kwargs', '.', 'pop', '(', "'mac_type'", ')', 'callback', '=', 'kwargs', '.', 'pop', '(', "'callback'", ',', 'self', '.', '_callback', ')', 'return', 'callback', '(', 'config', ')'] | Auto Generated Code | ['Auto', 'Generated', 'Code'] | train | https://github.com/brocade/pynos/blob/bd8a34e98f322de3fc06750827d8bbc3a0c00380/pynos/versions/ver_7/ver_7_1_0/yang/brocade_mac_address_table.py#L297-L310 |
2 | nicolargo/glances | glances/plugins/glances_memswap.py | Plugin.update_views | def update_views(self):
"""Update stats views."""
# Call the father's method
super(Plugin, self).update_views()
# Add specifics informations
# Alert and log
self.views['used']['decoration'] = self.get_alert_log(self.stats['used'], maximum=self.stats['total']) | python | def update_views(self):
"""Update stats views."""
# Call the father's method
super(Plugin, self).update_views()
# Add specifics informations
# Alert and log
self.views['used']['decoration'] = self.get_alert_log(self.stats['used'], maximum=self.stats['total']) | ['def', 'update_views', '(', 'self', ')', ':', "# Call the father's method", 'super', '(', 'Plugin', ',', 'self', ')', '.', 'update_views', '(', ')', '# Add specifics informations', '# Alert and log', 'self', '.', 'views', '[', "'used'", ']', '[', "'decoration'", ']', '=', 'self', '.', 'get_alert_log', '(', 'self', '.', 'stats', '[', "'used'", ']', ',', 'maximum', '=', 'self', '.', 'stats', '[', "'total'", ']', ')'] | Update stats views. | ['Update', 'stats', 'views', '.'] | train | https://github.com/nicolargo/glances/blob/5bd4d587a736e0d2b03170b56926841d2a3eb7ee/glances/plugins/glances_memswap.py#L130-L137 |
3 | DLR-RM/RAFCON | source/rafcon/gui/helpers/meta_data.py | contains_geometric_info | def contains_geometric_info(var):
""" Check whether the passed variable is a tuple with two floats or integers """
return isinstance(var, tuple) and len(var) == 2 and all(isinstance(val, (int, float)) for val in var) | python | def contains_geometric_info(var):
""" Check whether the passed variable is a tuple with two floats or integers """
return isinstance(var, tuple) and len(var) == 2 and all(isinstance(val, (int, float)) for val in var) | ['def', 'contains_geometric_info', '(', 'var', ')', ':', 'return', 'isinstance', '(', 'var', ',', 'tuple', ')', 'and', 'len', '(', 'var', ')', '==', '2', 'and', 'all', '(', 'isinstance', '(', 'val', ',', '(', 'int', ',', 'float', ')', ')', 'for', 'val', 'in', 'var', ')'] | Check whether the passed variable is a tuple with two floats or integers | ['Check', 'whether', 'the', 'passed', 'variable', 'is', 'a', 'tuple', 'with', 'two', 'floats', 'or', 'integers'] | train | https://github.com/DLR-RM/RAFCON/blob/24942ef1a904531f49ab8830a1dbb604441be498/source/rafcon/gui/helpers/meta_data.py#L55-L57 |
4 | ejeschke/ginga | ginga/gtk3w/ImageViewGtk.py | ImageViewGtk.save_plain_image_as_file | def save_plain_image_as_file(self, filepath, format='png', quality=90):
"""Used for generating thumbnails. Does not include overlaid
graphics.
"""
pixbuf = self.get_plain_image_as_pixbuf()
options, values = [], []
if format == 'jpeg':
options.append('quality')
values.append(str(quality))
pixbuf.savev(filepath, format, options, values) | python | def save_plain_image_as_file(self, filepath, format='png', quality=90):
"""Used for generating thumbnails. Does not include overlaid
graphics.
"""
pixbuf = self.get_plain_image_as_pixbuf()
options, values = [], []
if format == 'jpeg':
options.append('quality')
values.append(str(quality))
pixbuf.savev(filepath, format, options, values) | ['def', 'save_plain_image_as_file', '(', 'self', ',', 'filepath', ',', 'format', '=', "'png'", ',', 'quality', '=', '90', ')', ':', 'pixbuf', '=', 'self', '.', 'get_plain_image_as_pixbuf', '(', ')', 'options', ',', 'values', '=', '[', ']', ',', '[', ']', 'if', 'format', '==', "'jpeg'", ':', 'options', '.', 'append', '(', "'quality'", ')', 'values', '.', 'append', '(', 'str', '(', 'quality', ')', ')', 'pixbuf', '.', 'savev', '(', 'filepath', ',', 'format', ',', 'options', ',', 'values', ')'] | Used for generating thumbnails. Does not include overlaid
graphics. | ['Used', 'for', 'generating', 'thumbnails', '.', 'Does', 'not', 'include', 'overlaid', 'graphics', '.'] | train | https://github.com/ejeschke/ginga/blob/a78c893ec6f37a837de851947e9bb4625c597915/ginga/gtk3w/ImageViewGtk.py#L75-L84 |
5 | poppy-project/pypot | pypot/vrep/remoteApiBindings/vrep.py | simxClearFloatSignal | def simxClearFloatSignal(clientID, signalName, operationMode):
'''
Please have a look at the function description/documentation in the V-REP user manual
'''
if (sys.version_info[0] == 3) and (type(signalName) is str):
signalName=signalName.encode('utf-8')
return c_ClearFloatSignal(clientID, signalName, operationMode) | python | def simxClearFloatSignal(clientID, signalName, operationMode):
'''
Please have a look at the function description/documentation in the V-REP user manual
'''
if (sys.version_info[0] == 3) and (type(signalName) is str):
signalName=signalName.encode('utf-8')
return c_ClearFloatSignal(clientID, signalName, operationMode) | ['def', 'simxClearFloatSignal', '(', 'clientID', ',', 'signalName', ',', 'operationMode', ')', ':', 'if', '(', 'sys', '.', 'version_info', '[', '0', ']', '==', '3', ')', 'and', '(', 'type', '(', 'signalName', ')', 'is', 'str', ')', ':', 'signalName', '=', 'signalName', '.', 'encode', '(', "'utf-8'", ')', 'return', 'c_ClearFloatSignal', '(', 'clientID', ',', 'signalName', ',', 'operationMode', ')'] | Please have a look at the function description/documentation in the V-REP user manual | ['Please', 'have', 'a', 'look', 'at', 'the', 'function', 'description', '/', 'documentation', 'in', 'the', 'V', '-', 'REP', 'user', 'manual'] | train | https://github.com/poppy-project/pypot/blob/d9c6551bbc87d45d9d1f0bc15e35b616d0002afd/pypot/vrep/remoteApiBindings/vrep.py#L900-L907 |
6 | cloudify-cosmo/repex | repex.py | Repex.find_matches | def find_matches(self, content, file_to_handle):
"""Find all matches of an expression in a file
"""
# look for all match groups in the content
groups = [match.groupdict() for match in
self.match_expression.finditer(content)]
# filter out content not in the matchgroup
matches = [group['matchgroup'] for group in groups
if group.get('matchgroup')]
logger.info('Found %s matches in %s', len(matches), file_to_handle)
# We only need the unique strings found as we'll be replacing each
# of them. No need to replace the ones already replaced.
return list(set(matches)) | python | def find_matches(self, content, file_to_handle):
"""Find all matches of an expression in a file
"""
# look for all match groups in the content
groups = [match.groupdict() for match in
self.match_expression.finditer(content)]
# filter out content not in the matchgroup
matches = [group['matchgroup'] for group in groups
if group.get('matchgroup')]
logger.info('Found %s matches in %s', len(matches), file_to_handle)
# We only need the unique strings found as we'll be replacing each
# of them. No need to replace the ones already replaced.
return list(set(matches)) | ['def', 'find_matches', '(', 'self', ',', 'content', ',', 'file_to_handle', ')', ':', '# look for all match groups in the content', 'groups', '=', '[', 'match', '.', 'groupdict', '(', ')', 'for', 'match', 'in', 'self', '.', 'match_expression', '.', 'finditer', '(', 'content', ')', ']', '# filter out content not in the matchgroup', 'matches', '=', '[', 'group', '[', "'matchgroup'", ']', 'for', 'group', 'in', 'groups', 'if', 'group', '.', 'get', '(', "'matchgroup'", ')', ']', 'logger', '.', 'info', '(', "'Found %s matches in %s'", ',', 'len', '(', 'matches', ')', ',', 'file_to_handle', ')', "# We only need the unique strings found as we'll be replacing each", '# of them. No need to replace the ones already replaced.', 'return', 'list', '(', 'set', '(', 'matches', ')', ')'] | Find all matches of an expression in a file | ['Find', 'all', 'matches', 'of', 'an', 'expression', 'in', 'a', 'file'] | train | https://github.com/cloudify-cosmo/repex/blob/589e442857fa4a99fa88670d7df1a72f983bbd28/repex.py#L605-L618 |
7 | mariocj89/github-token | github_token/__init__.py | TokenFactory.create | def create(self):
"""Creates a token
It uses the app_name as the notes and the scopes are
the permissions required by the application. See those
in github when configuring an app token
Raises a TFARequired if a two factor is required after
the atempt to create it without having call tfa before
"""
headers = dict()
if self.tfa_token:
headers["X-GitHub-OTP"] = self.tfa_token
token_name = self.app_name + platform.node() # node specific in case the user has multiple hosts
payload = dict(note=token_name, scopes=self.scopes)
response = requests.post(
self.api_url + "authorizations", auth=(self.user, self.password),
headers=headers, json=payload
)
if response.status_code == 401 and "required" in response.headers.get("X-GitHub-OTP", ""):
raise TFARequired("TFA required for the user")
if response.status_code == 422:
raise AlreadyExistsError("APP already exists. Please delete {} token".format(token_name))
if response.status_code == 401:
raise BadPassword("Bad User/Password")
response.raise_for_status()
return response.json()["token"] | python | def create(self):
"""Creates a token
It uses the app_name as the notes and the scopes are
the permissions required by the application. See those
in github when configuring an app token
Raises a TFARequired if a two factor is required after
the atempt to create it without having call tfa before
"""
headers = dict()
if self.tfa_token:
headers["X-GitHub-OTP"] = self.tfa_token
token_name = self.app_name + platform.node() # node specific in case the user has multiple hosts
payload = dict(note=token_name, scopes=self.scopes)
response = requests.post(
self.api_url + "authorizations", auth=(self.user, self.password),
headers=headers, json=payload
)
if response.status_code == 401 and "required" in response.headers.get("X-GitHub-OTP", ""):
raise TFARequired("TFA required for the user")
if response.status_code == 422:
raise AlreadyExistsError("APP already exists. Please delete {} token".format(token_name))
if response.status_code == 401:
raise BadPassword("Bad User/Password")
response.raise_for_status()
return response.json()["token"] | ['def', 'create', '(', 'self', ')', ':', 'headers', '=', 'dict', '(', ')', 'if', 'self', '.', 'tfa_token', ':', 'headers', '[', '"X-GitHub-OTP"', ']', '=', 'self', '.', 'tfa_token', 'token_name', '=', 'self', '.', 'app_name', '+', 'platform', '.', 'node', '(', ')', '# node specific in case the user has multiple hosts', 'payload', '=', 'dict', '(', 'note', '=', 'token_name', ',', 'scopes', '=', 'self', '.', 'scopes', ')', 'response', '=', 'requests', '.', 'post', '(', 'self', '.', 'api_url', '+', '"authorizations"', ',', 'auth', '=', '(', 'self', '.', 'user', ',', 'self', '.', 'password', ')', ',', 'headers', '=', 'headers', ',', 'json', '=', 'payload', ')', 'if', 'response', '.', 'status_code', '==', '401', 'and', '"required"', 'in', 'response', '.', 'headers', '.', 'get', '(', '"X-GitHub-OTP"', ',', '""', ')', ':', 'raise', 'TFARequired', '(', '"TFA required for the user"', ')', 'if', 'response', '.', 'status_code', '==', '422', ':', 'raise', 'AlreadyExistsError', '(', '"APP already exists. Please delete {} token"', '.', 'format', '(', 'token_name', ')', ')', 'if', 'response', '.', 'status_code', '==', '401', ':', 'raise', 'BadPassword', '(', '"Bad User/Password"', ')', 'response', '.', 'raise_for_status', '(', ')', 'return', 'response', '.', 'json', '(', ')', '[', '"token"', ']'] | Creates a token
It uses the app_name as the notes and the scopes are
the permissions required by the application. See those
in github when configuring an app token
Raises a TFARequired if a two factor is required after
the atempt to create it without having call tfa before | ['Creates', 'a', 'token'] | train | https://github.com/mariocj89/github-token/blob/8ca85fa51a52aef94cfb4f851eb229ee500bc28f/github_token/__init__.py#L81-L108 |
8 | brycedrennan/eulerian-magnification | eulerian_magnification/base.py | combine_pyramid_and_save | def combine_pyramid_and_save(g_video, orig_video, enlarge_multiple, fps, save_filename='media/output.avi'):
"""Combine a gaussian video representation with the original and save to file"""
width, height = get_frame_dimensions(orig_video[0])
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
print("Outputting to %s" % save_filename)
writer = cv2.VideoWriter(save_filename, fourcc, fps, (width, height), 1)
for x in range(0, g_video.shape[0]):
img = np.ndarray(shape=g_video[x].shape, dtype='float')
img[:] = g_video[x]
for i in range(enlarge_multiple):
img = cv2.pyrUp(img)
img[:height, :width] = img[:height, :width] + orig_video[x]
res = cv2.convertScaleAbs(img[:height, :width])
writer.write(res) | python | def combine_pyramid_and_save(g_video, orig_video, enlarge_multiple, fps, save_filename='media/output.avi'):
"""Combine a gaussian video representation with the original and save to file"""
width, height = get_frame_dimensions(orig_video[0])
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
print("Outputting to %s" % save_filename)
writer = cv2.VideoWriter(save_filename, fourcc, fps, (width, height), 1)
for x in range(0, g_video.shape[0]):
img = np.ndarray(shape=g_video[x].shape, dtype='float')
img[:] = g_video[x]
for i in range(enlarge_multiple):
img = cv2.pyrUp(img)
img[:height, :width] = img[:height, :width] + orig_video[x]
res = cv2.convertScaleAbs(img[:height, :width])
writer.write(res) | ['def', 'combine_pyramid_and_save', '(', 'g_video', ',', 'orig_video', ',', 'enlarge_multiple', ',', 'fps', ',', 'save_filename', '=', "'media/output.avi'", ')', ':', 'width', ',', 'height', '=', 'get_frame_dimensions', '(', 'orig_video', '[', '0', ']', ')', 'fourcc', '=', 'cv2', '.', 'VideoWriter_fourcc', '(', '*', "'MJPG'", ')', 'print', '(', '"Outputting to %s"', '%', 'save_filename', ')', 'writer', '=', 'cv2', '.', 'VideoWriter', '(', 'save_filename', ',', 'fourcc', ',', 'fps', ',', '(', 'width', ',', 'height', ')', ',', '1', ')', 'for', 'x', 'in', 'range', '(', '0', ',', 'g_video', '.', 'shape', '[', '0', ']', ')', ':', 'img', '=', 'np', '.', 'ndarray', '(', 'shape', '=', 'g_video', '[', 'x', ']', '.', 'shape', ',', 'dtype', '=', "'float'", ')', 'img', '[', ':', ']', '=', 'g_video', '[', 'x', ']', 'for', 'i', 'in', 'range', '(', 'enlarge_multiple', ')', ':', 'img', '=', 'cv2', '.', 'pyrUp', '(', 'img', ')', 'img', '[', ':', 'height', ',', ':', 'width', ']', '=', 'img', '[', ':', 'height', ',', ':', 'width', ']', '+', 'orig_video', '[', 'x', ']', 'res', '=', 'cv2', '.', 'convertScaleAbs', '(', 'img', '[', ':', 'height', ',', ':', 'width', ']', ')', 'writer', '.', 'write', '(', 'res', ')'] | Combine a gaussian video representation with the original and save to file | ['Combine', 'a', 'gaussian', 'video', 'representation', 'with', 'the', 'original', 'and', 'save', 'to', 'file'] | train | https://github.com/brycedrennan/eulerian-magnification/blob/9ae0651fe3334176300d183f8240ad36d77759a9/eulerian_magnification/base.py#L108-L122 |
9 | PmagPy/PmagPy | pmagpy/pmag.py | PintPars | def PintPars(datablock, araiblock, zijdblock, start, end, accept, **kwargs):
"""
calculate the paleointensity magic parameters make some definitions
"""
if 'version' in list(kwargs.keys()) and kwargs['version'] == 3:
meth_key = 'method_codes'
beta_key = 'int_b_beta'
temp_key, min_key, max_key = 'treat_temp', 'meas_step_min', 'meas_step_max'
dc_theta_key, dc_phi_key = 'treat_dc_field_theta', 'treat_dc_field_phi'
# convert dataframe to list of dictionaries
datablock = datablock.to_dict('records')
z_key = 'int_z'
drats_key = 'int_drats'
drat_key = 'int_drat'
md_key = 'int_md'
dec_key = 'dir_dec'
inc_key = 'dir_inc'
mad_key = 'int_mad_free'
dang_key = 'int_dang'
ptrm_key = 'int_n_ptrm'
theta_key = 'int_theta'
gamma_key = 'int_gamma'
delta_key = 'int_delta'
frac_key = 'int_frac'
gmax_key = 'int_gmax'
scat_key = 'int_scat'
else:
beta_key = 'specimen_b_beta'
meth_key = 'magic_method_codes'
temp_key, min_key, max_key = 'treatment_temp', 'measurement_step_min', 'measurement_step_max'
z_key = 'specimen_z'
drats_key = 'specimen_drats'
drat_key = 'specimen_drat'
md_key = 'specimen_md'
dec_key = 'specimen_dec'
inc_key = 'specimen_inc'
mad_key = 'specimen_int_mad'
dang_key = 'specimen_dang'
ptrm_key = 'specimen_int_ptrm_n'
theta_key = 'specimen_theta'
gamma_key = 'specimen_gamma'
delta_key = 'specimen_delta'
frac_key = 'specimen_frac'
gmax_key = 'specimen_gmax'
scat_key = 'specimen_scat'
first_Z, first_I, zptrm_check, ptrm_check, ptrm_tail = [], [], [], [], []
methcode, ThetaChecks, DeltaChecks, GammaChecks = "", "", "", ""
zptrm_check = []
first_Z, first_I, ptrm_check, ptrm_tail, zptrm_check, GammaChecks = araiblock[
0], araiblock[1], araiblock[2], araiblock[3], araiblock[4], araiblock[5]
if len(araiblock) > 6:
# used only for perpendicular method of paleointensity
ThetaChecks = araiblock[6]
# used only for perpendicular method of paleointensity
DeltaChecks = araiblock[7]
xi, yi, diffcum = [], [], 0
xiz, xzi, yiz, yzi = [], [], [], []
Nptrm, dmax = 0, -1e-22
# check if even zero and infield steps
if len(first_Z) > len(first_I):
maxe = len(first_I) - 1
else:
maxe = len(first_Z) - 1
if end == 0 or end > maxe:
end = maxe
# get the MAD, DANG, etc. for directional data
bstep = araiblock[0][start][0]
estep = araiblock[0][end][0]
zstart, zend = 0, len(zijdblock)
for k in range(len(zijdblock)):
zrec = zijdblock[k]
if zrec[0] == bstep:
zstart = k
if zrec[0] == estep:
zend = k
PCA = domean(zijdblock, zstart, zend, 'DE-BFL')
D, Diz, Dzi, Du = [], [], [], [] # list of NRM vectors, and separated by zi and iz
for rec in zijdblock:
D.append((rec[1], rec[2], rec[3]))
Du.append((rec[1], rec[2]))
if rec[4] == 1:
Dzi.append((rec[1], rec[2])) # if this is ZI step
else:
Diz.append((rec[1], rec[2])) # if this is IZ step
# calculate the vector difference sum
vds = dovds(D)
b_zi, b_iz = [], []
# collect data included in ZigZag calculation
if end + 1 >= len(first_Z):
stop = end - 1
else:
stop = end
for k in range(start, end + 1):
for l in range(len(first_I)):
irec = first_I[l]
if irec[0] == first_Z[k][0]:
xi.append(irec[3])
yi.append(first_Z[k][3])
pars, errcode = int_pars(xi, yi, vds)
if errcode == 1:
return pars, errcode
# for k in range(start,end+1):
for k in range(len(first_Z) - 1):
for l in range(k):
# only go down to 10% of NRM.....
if old_div(first_Z[k][3], vds) > 0.1:
irec = first_I[l]
if irec[4] == 1 and first_I[l + 1][4] == 0: # a ZI step
xzi = irec[3]
yzi = first_Z[k][3]
xiz = first_I[l + 1][3]
yiz = first_Z[k + 1][3]
slope = np.arctan2((yzi - yiz), (xiz - xzi))
r = np.sqrt((yzi - yiz)**2 + (xiz - xzi)**2)
if r > .1 * vds:
b_zi.append(slope) # suppress noise
elif irec[4] == 0 and first_I[l + 1][4] == 1: # an IZ step
xiz = irec[3]
yiz = first_Z[k][3]
xzi = first_I[l + 1][3]
yzi = first_Z[k + 1][3]
slope = np.arctan2((yiz - yzi), (xzi - xiz))
r = np.sqrt((yiz - yzi)**2 + (xzi - xiz)**2)
if r > .1 * vds:
b_iz.append(slope) # suppress noise
#
ZigZag, Frat, Trat = -1, 0, 0
if len(Diz) > 2 and len(Dzi) > 2:
ZigZag = 0
dizp = fisher_mean(Diz) # get Fisher stats on IZ steps
dzip = fisher_mean(Dzi) # get Fisher stats on ZI steps
dup = fisher_mean(Du) # get Fisher stats on all steps
#
# if directions are TOO well grouped, can get false positive for ftest, so
# angles must be > 3 degrees apart.
#
if angle([dizp['dec'], dizp['inc']], [dzip['dec'], dzip['inc']]) > 3.:
F = (dup['n'] - 2.) * (dzip['r'] + dizp['r'] - dup['r']) / \
(dup['n'] - dzip['r'] - dizp['r']
) # Watson test for common mean
nf = 2. * (dup['n'] - 2.) # number of degees of freedom
ftest = fcalc(2, nf)
Frat = old_div(F, ftest)
if Frat > 1.:
ZigZag = Frat # fails zigzag on directions
methcode = "SM-FTEST"
# now do slopes
if len(b_zi) > 2 and len(b_iz) > 2:
bzi_m, bzi_sig = gausspars(b_zi) # mean, std dev
biz_m, biz_sig = gausspars(b_iz)
n_zi = float(len(b_zi))
n_iz = float(len(b_iz))
b_diff = abs(bzi_m - biz_m) # difference in means
#
# avoid false positives - set 3 degree slope difference here too
if b_diff > 3 * np.pi / 180.:
nf = n_zi + n_iz - 2. # degrees of freedom
svar = old_div(((n_zi - 1.) * bzi_sig**2 +
(n_iz - 1.) * biz_sig**2), nf)
T = old_div((b_diff), np.sqrt(
svar * (old_div(1.0, n_zi) + old_div(1.0, n_iz)))) # student's t
ttest = tcalc(nf, .05) # t-test at 95% conf.
Trat = old_div(T, ttest)
if Trat > 1 and Trat > Frat:
ZigZag = Trat # fails zigzag on directions
methcode = "SM-TTEST"
pars[z_key] = ZigZag
pars[meth_key] = methcode
# do drats
if len(ptrm_check) != 0:
diffcum, drat_max = 0, 0
for prec in ptrm_check:
step = prec[0]
endbak = end
zend = end
while zend > len(zijdblock) - 1:
zend = zend - 2 # don't count alteration that happens after this step
if step < zijdblock[zend][0]:
Nptrm += 1
for irec in first_I:
if irec[0] == step:
break
diffcum += prec[3] - irec[3]
if abs(prec[3] - irec[3]) > drat_max:
drat_max = abs(prec[3] - irec[3])
pars[drats_key] = (100 * abs(diffcum) / first_I[zend][3])
pars[drat_key] = (100 * abs(drat_max) / first_I[zend][3])
elif len(zptrm_check) != 0:
diffcum = 0
for prec in zptrm_check:
step = prec[0]
endbak = end
zend = end
while zend > len(zijdblock) - 1:
zend = zend - 1
if step < zijdblock[zend][0]:
Nptrm += 1
for irec in first_I:
if irec[0] == step:
break
diffcum += prec[3] - irec[3]
pars[drats_key] = (100 * abs(diffcum) / first_I[zend][3])
else:
pars[drats_key] = -1
pars[drat_key] = -1
# and the pTRM tails
if len(ptrm_tail) != 0:
for trec in ptrm_tail:
step = trec[0]
for irec in first_I:
if irec[0] == step:
break
if abs(trec[3]) > dmax:
dmax = abs(trec[3])
pars[md_key] = (100 * dmax / vds)
else:
pars[md_key] = -1
pars[min_key] = bstep
pars[max_key] = estep
pars[dec_key] = PCA["specimen_dec"]
pars[inc_key] = PCA["specimen_inc"]
pars[mad_key] = PCA["specimen_mad"]
pars[dang_key] = PCA["specimen_dang"]
pars[ptrm_key] = Nptrm
# and the ThetaChecks
if ThetaChecks != "":
t = 0
for theta in ThetaChecks:
if theta[0] >= bstep and theta[0] <= estep and theta[1] > t:
t = theta[1]
pars[theta_key] = t
else:
pars[theta_key] = -1
# and the DeltaChecks
if DeltaChecks != "":
d = 0
for delta in DeltaChecks:
if delta[0] >= bstep and delta[0] <= estep and delta[1] > d:
d = delta[1]
pars[delta_key]
else:
pars[delta_key] = -1
pars[gamma_key] = -1
if GammaChecks != "":
for gamma in GammaChecks:
if gamma[0] <= estep:
pars['specimen_gamma'] = gamma[1]
# --------------------------------------------------------------
# From here added By Ron Shaar 11-Dec 2012
# New parameters defined in Shaar and Tauxe (2012):
# FRAC (specimen_frac) - ranges from 0. to 1.
# SCAT (specimen_scat) - takes 1/0
# gap_max (specimen_gmax) - ranges from 0. to 1.
# --------------------------------------------------------------
# --------------------------------------------------------------
# FRAC is similar to Fvds, but the numerator is the vds fraction:
# FRAC= [ vds (start,end)] / total vds ]
# gap_max= max [ (vector difference) / vds (start,end)]
# --------------------------------------------------------------
# collect all zijderveld data to arrays and calculate VDS
z_temperatures = [row[0] for row in zijdblock]
zdata = [] # array of zero-fields measurements in Cartezian coordinates
# array of vector differences (for vds calculation)
vector_diffs = []
NRM = zijdblock[0][3] # NRM
for k in range(len(zijdblock)):
DIR = [zijdblock[k][1], zijdblock[k][2], old_div(zijdblock[k][3], NRM)]
cart = dir2cart(DIR)
zdata.append(np.array([cart[0], cart[1], cart[2]]))
if k > 0:
vector_diffs.append(
np.sqrt(sum((np.array(zdata[-2]) - np.array(zdata[-1]))**2)))
# last vector difference: from the last point to the origin.
vector_diffs.append(np.sqrt(sum(np.array(zdata[-1])**2)))
vds = sum(vector_diffs) # vds calculation
zdata = np.array(zdata)
vector_diffs = np.array(vector_diffs)
# calculate the vds within the chosen segment
vector_diffs_segment = vector_diffs[zstart:zend]
# FRAC calculation
FRAC = old_div(sum(vector_diffs_segment), vds)
pars[frac_key] = FRAC
# gap_max calculation
max_FRAC_gap = max(
old_div(vector_diffs_segment, sum(vector_diffs_segment)))
pars[gmax_key] = max_FRAC_gap
# ---------------------------------------------------------------------
# Calculate the "scat box"
# all data-points, pTRM checks, and tail-checks, should be inside a "scat box"
# ---------------------------------------------------------------------
# intialization
# fail scat due to arai plot data points
pars["fail_arai_beta_box_scatter"] = False
pars["fail_ptrm_beta_box_scatter"] = False # fail scat due to pTRM checks
pars["fail_tail_beta_box_scatter"] = False # fail scat due to tail checks
pars[scat_key] = "t" # Pass by default
# --------------------------------------------------------------
# collect all Arai plot data points in arrays
x_Arai, y_Arai, t_Arai, steps_Arai = [], [], [], []
NRMs = araiblock[0]
PTRMs = araiblock[1]
ptrm_checks = araiblock[2]
ptrm_tail = araiblock[3]
PTRMs_temperatures = [row[0] for row in PTRMs]
NRMs_temperatures = [row[0] for row in NRMs]
NRM = NRMs[0][3]
for k in range(len(NRMs)):
index_pTRMs = PTRMs_temperatures.index(NRMs[k][0])
x_Arai.append(old_div(PTRMs[index_pTRMs][3], NRM))
y_Arai.append(old_div(NRMs[k][3], NRM))
t_Arai.append(NRMs[k][0])
if NRMs[k][4] == 1:
steps_Arai.append('ZI')
else:
steps_Arai.append('IZ')
x_Arai = np.array(x_Arai)
y_Arai = np.array(y_Arai)
# --------------------------------------------------------------
# collect all pTRM check to arrays
x_ptrm_check, y_ptrm_check, ptrm_checks_temperatures, = [], [], []
x_ptrm_check_starting_point, y_ptrm_check_starting_point, ptrm_checks_starting_temperatures = [], [], []
for k in range(len(ptrm_checks)):
if ptrm_checks[k][0] in NRMs_temperatures:
# find the starting point of the pTRM check:
for i in range(len(datablock)):
rec = datablock[i]
if "LT-PTRM-I" in rec[meth_key] and float(rec[temp_key]) == ptrm_checks[k][0]:
starting_temperature = (float(datablock[i - 1][temp_key]))
try:
index = t_Arai.index(starting_temperature)
x_ptrm_check_starting_point.append(x_Arai[index])
y_ptrm_check_starting_point.append(y_Arai[index])
ptrm_checks_starting_temperatures.append(
starting_temperature)
index_zerofield = zerofield_temperatures.index(
ptrm_checks[k][0])
x_ptrm_check.append(old_div(ptrm_checks[k][3], NRM))
y_ptrm_check.append(
old_div(zerofields[index_zerofield][3], NRM))
ptrm_checks_temperatures.append(ptrm_checks[k][0])
break
except:
pass
x_ptrm_check_starting_point = np.array(x_ptrm_check_starting_point)
y_ptrm_check_starting_point = np.array(y_ptrm_check_starting_point)
ptrm_checks_starting_temperatures = np.array(
ptrm_checks_starting_temperatures)
x_ptrm_check = np.array(x_ptrm_check)
y_ptrm_check = np.array(y_ptrm_check)
ptrm_checks_temperatures = np.array(ptrm_checks_temperatures)
# --------------------------------------------------------------
# collect tail checks to arrays
x_tail_check, y_tail_check, tail_check_temperatures = [], [], []
x_tail_check_starting_point, y_tail_check_starting_point, tail_checks_starting_temperatures = [], [], []
for k in range(len(ptrm_tail)):
if ptrm_tail[k][0] in NRMs_temperatures:
# find the starting point of the pTRM check:
for i in range(len(datablock)):
rec = datablock[i]
if "LT-PTRM-MD" in rec[meth_key] and float(rec[temp_key]) == ptrm_tail[k][0]:
starting_temperature = (float(datablock[i - 1][temp_key]))
try:
index = t_Arai.index(starting_temperature)
x_tail_check_starting_point.append(x_Arai[index])
y_tail_check_starting_point.append(y_Arai[index])
tail_checks_starting_temperatures.append(
starting_temperature)
index_infield = infield_temperatures.index(
ptrm_tail[k][0])
x_tail_check.append(
old_div(infields[index_infield][3], NRM))
y_tail_check.append(
old_div(ptrm_tail[k][3], NRM) + old_div(zerofields[index_infield][3], NRM))
tail_check_temperatures.append(ptrm_tail[k][0])
break
except:
pass
x_tail_check = np.array(x_tail_check)
y_tail_check = np.array(y_tail_check)
tail_check_temperatures = np.array(tail_check_temperatures)
x_tail_check_starting_point = np.array(x_tail_check_starting_point)
y_tail_check_starting_point = np.array(y_tail_check_starting_point)
tail_checks_starting_temperatures = np.array(
tail_checks_starting_temperatures)
# --------------------------------------------------------------
# collect the chosen segment in the Arai plot to arrays
x_Arai_segment = x_Arai[start:end + 1] # chosen segent in the Arai plot
y_Arai_segment = y_Arai[start:end + 1] # chosen segent in the Arai plot
# --------------------------------------------------------------
# collect pTRM checks in segment to arrays
# notice, this is different than the conventional DRATS.
# for scat calculation we take only the pTRM checks which were carried out
# before reaching the highest temperature in the chosen segment
x_ptrm_check_for_SCAT, y_ptrm_check_for_SCAT = [], []
for k in range(len(ptrm_checks_temperatures)):
if ptrm_checks_temperatures[k] >= pars[min_key] and ptrm_checks_starting_temperatures <= pars[max_key]:
x_ptrm_check_for_SCAT.append(x_ptrm_check[k])
y_ptrm_check_for_SCAT.append(y_ptrm_check[k])
x_ptrm_check_for_SCAT = np.array(x_ptrm_check_for_SCAT)
y_ptrm_check_for_SCAT = np.array(y_ptrm_check_for_SCAT)
# --------------------------------------------------------------
# collect Tail checks in segment to arrays
# for scat calculation we take only the tail checks which were carried out
# before reaching the highest temperature in the chosen segment
x_tail_check_for_SCAT, y_tail_check_for_SCAT = [], []
for k in range(len(tail_check_temperatures)):
if tail_check_temperatures[k] >= pars[min_key] and tail_checks_starting_temperatures[k] <= pars[max_key]:
x_tail_check_for_SCAT.append(x_tail_check[k])
y_tail_check_for_SCAT.append(y_tail_check[k])
x_tail_check_for_SCAT = np.array(x_tail_check_for_SCAT)
y_tail_check_for_SCAT = np.array(y_tail_check_for_SCAT)
# --------------------------------------------------------------
# calculate the lines that define the scat box:
# if threshold value for beta is not defined, then scat cannot be calculated (pass)
# in this case, scat pass
if beta_key in list(accept.keys()) and accept[beta_key] != "":
b_beta_threshold = float(accept[beta_key])
b = pars[b_key] # best fit line
cm_x = np.mean(np.array(x_Arai_segment)) # x center of mass
cm_y = np.mean(np.array(y_Arai_segment)) # y center of mass
a = cm_y - b * cm_x
# lines with slope = slope +/- 2*(specimen_b_beta)
two_sigma_beta_threshold = 2 * b_beta_threshold
two_sigma_slope_threshold = abs(two_sigma_beta_threshold * b)
# a line with a shallower slope (b + 2*beta*b) passing through the center of mass
# y=a1+b1x
b1 = b + two_sigma_slope_threshold
a1 = cm_y - b1 * cm_x
# bounding line with steeper slope (b - 2*beta*b) passing through the center of mass
# y=a2+b2x
b2 = b - two_sigma_slope_threshold
a2 = cm_y - b2 * cm_x
# lower bounding line of the 'beta box'
# y=intercept1+slop1x
slop1 = old_div(a1, ((old_div(a2, b2))))
intercept1 = a1
# higher bounding line of the 'beta box'
# y=intercept2+slop2x
slop2 = old_div(a2, ((old_div(a1, b1))))
intercept2 = a2
pars['specimen_scat_bounding_line_high'] = [intercept2, slop2]
pars['specimen_scat_bounding_line_low'] = [intercept1, slop1]
# --------------------------------------------------------------
# check if the Arai data points are in the 'box'
# the two bounding lines
ymin = intercept1 + x_Arai_segment * slop1
ymax = intercept2 + x_Arai_segment * slop2
# arrays of "True" or "False"
check_1 = y_Arai_segment > ymax
check_2 = y_Arai_segment < ymin
# check if at least one "True"
if (sum(check_1) + sum(check_2)) > 0:
pars["fail_arai_beta_box_scatter"] = True
# --------------------------------------------------------------
# check if the pTRM checks data points are in the 'box'
if len(x_ptrm_check_for_SCAT) > 0:
# the two bounding lines
ymin = intercept1 + x_ptrm_check_for_SCAT * slop1
ymax = intercept2 + x_ptrm_check_for_SCAT * slop2
# arrays of "True" or "False"
check_1 = y_ptrm_check_for_SCAT > ymax
check_2 = y_ptrm_check_for_SCAT < ymin
# check if at least one "True"
if (sum(check_1) + sum(check_2)) > 0:
pars["fail_ptrm_beta_box_scatter"] = True
# --------------------------------------------------------------
# check if the tail checks data points are in the 'box'
if len(x_tail_check_for_SCAT) > 0:
# the two bounding lines
ymin = intercept1 + x_tail_check_for_SCAT * slop1
ymax = intercept2 + x_tail_check_for_SCAT * slop2
# arrays of "True" or "False"
check_1 = y_tail_check_for_SCAT > ymax
check_2 = y_tail_check_for_SCAT < ymin
# check if at least one "True"
if (sum(check_1) + sum(check_2)) > 0:
pars["fail_tail_beta_box_scatter"] = True
# --------------------------------------------------------------
# check if specimen_scat is PASS or FAIL:
if pars["fail_tail_beta_box_scatter"] or pars["fail_ptrm_beta_box_scatter"] or pars["fail_arai_beta_box_scatter"]:
pars[scat_key] = 'f'
else:
pars[scat_key] = 't'
return pars, 0 | python | def PintPars(datablock, araiblock, zijdblock, start, end, accept, **kwargs):
"""
calculate the paleointensity magic parameters make some definitions
"""
if 'version' in list(kwargs.keys()) and kwargs['version'] == 3:
meth_key = 'method_codes'
beta_key = 'int_b_beta'
temp_key, min_key, max_key = 'treat_temp', 'meas_step_min', 'meas_step_max'
dc_theta_key, dc_phi_key = 'treat_dc_field_theta', 'treat_dc_field_phi'
# convert dataframe to list of dictionaries
datablock = datablock.to_dict('records')
z_key = 'int_z'
drats_key = 'int_drats'
drat_key = 'int_drat'
md_key = 'int_md'
dec_key = 'dir_dec'
inc_key = 'dir_inc'
mad_key = 'int_mad_free'
dang_key = 'int_dang'
ptrm_key = 'int_n_ptrm'
theta_key = 'int_theta'
gamma_key = 'int_gamma'
delta_key = 'int_delta'
frac_key = 'int_frac'
gmax_key = 'int_gmax'
scat_key = 'int_scat'
else:
beta_key = 'specimen_b_beta'
meth_key = 'magic_method_codes'
temp_key, min_key, max_key = 'treatment_temp', 'measurement_step_min', 'measurement_step_max'
z_key = 'specimen_z'
drats_key = 'specimen_drats'
drat_key = 'specimen_drat'
md_key = 'specimen_md'
dec_key = 'specimen_dec'
inc_key = 'specimen_inc'
mad_key = 'specimen_int_mad'
dang_key = 'specimen_dang'
ptrm_key = 'specimen_int_ptrm_n'
theta_key = 'specimen_theta'
gamma_key = 'specimen_gamma'
delta_key = 'specimen_delta'
frac_key = 'specimen_frac'
gmax_key = 'specimen_gmax'
scat_key = 'specimen_scat'
first_Z, first_I, zptrm_check, ptrm_check, ptrm_tail = [], [], [], [], []
methcode, ThetaChecks, DeltaChecks, GammaChecks = "", "", "", ""
zptrm_check = []
first_Z, first_I, ptrm_check, ptrm_tail, zptrm_check, GammaChecks = araiblock[
0], araiblock[1], araiblock[2], araiblock[3], araiblock[4], araiblock[5]
if len(araiblock) > 6:
# used only for perpendicular method of paleointensity
ThetaChecks = araiblock[6]
# used only for perpendicular method of paleointensity
DeltaChecks = araiblock[7]
xi, yi, diffcum = [], [], 0
xiz, xzi, yiz, yzi = [], [], [], []
Nptrm, dmax = 0, -1e-22
# check if even zero and infield steps
if len(first_Z) > len(first_I):
maxe = len(first_I) - 1
else:
maxe = len(first_Z) - 1
if end == 0 or end > maxe:
end = maxe
# get the MAD, DANG, etc. for directional data
bstep = araiblock[0][start][0]
estep = araiblock[0][end][0]
zstart, zend = 0, len(zijdblock)
for k in range(len(zijdblock)):
zrec = zijdblock[k]
if zrec[0] == bstep:
zstart = k
if zrec[0] == estep:
zend = k
PCA = domean(zijdblock, zstart, zend, 'DE-BFL')
D, Diz, Dzi, Du = [], [], [], [] # list of NRM vectors, and separated by zi and iz
for rec in zijdblock:
D.append((rec[1], rec[2], rec[3]))
Du.append((rec[1], rec[2]))
if rec[4] == 1:
Dzi.append((rec[1], rec[2])) # if this is ZI step
else:
Diz.append((rec[1], rec[2])) # if this is IZ step
# calculate the vector difference sum
vds = dovds(D)
b_zi, b_iz = [], []
# collect data included in ZigZag calculation
if end + 1 >= len(first_Z):
stop = end - 1
else:
stop = end
for k in range(start, end + 1):
for l in range(len(first_I)):
irec = first_I[l]
if irec[0] == first_Z[k][0]:
xi.append(irec[3])
yi.append(first_Z[k][3])
pars, errcode = int_pars(xi, yi, vds)
if errcode == 1:
return pars, errcode
# for k in range(start,end+1):
for k in range(len(first_Z) - 1):
for l in range(k):
# only go down to 10% of NRM.....
if old_div(first_Z[k][3], vds) > 0.1:
irec = first_I[l]
if irec[4] == 1 and first_I[l + 1][4] == 0: # a ZI step
xzi = irec[3]
yzi = first_Z[k][3]
xiz = first_I[l + 1][3]
yiz = first_Z[k + 1][3]
slope = np.arctan2((yzi - yiz), (xiz - xzi))
r = np.sqrt((yzi - yiz)**2 + (xiz - xzi)**2)
if r > .1 * vds:
b_zi.append(slope) # suppress noise
elif irec[4] == 0 and first_I[l + 1][4] == 1: # an IZ step
xiz = irec[3]
yiz = first_Z[k][3]
xzi = first_I[l + 1][3]
yzi = first_Z[k + 1][3]
slope = np.arctan2((yiz - yzi), (xzi - xiz))
r = np.sqrt((yiz - yzi)**2 + (xzi - xiz)**2)
if r > .1 * vds:
b_iz.append(slope) # suppress noise
#
ZigZag, Frat, Trat = -1, 0, 0
if len(Diz) > 2 and len(Dzi) > 2:
ZigZag = 0
dizp = fisher_mean(Diz) # get Fisher stats on IZ steps
dzip = fisher_mean(Dzi) # get Fisher stats on ZI steps
dup = fisher_mean(Du) # get Fisher stats on all steps
#
# if directions are TOO well grouped, can get false positive for ftest, so
# angles must be > 3 degrees apart.
#
if angle([dizp['dec'], dizp['inc']], [dzip['dec'], dzip['inc']]) > 3.:
F = (dup['n'] - 2.) * (dzip['r'] + dizp['r'] - dup['r']) / \
(dup['n'] - dzip['r'] - dizp['r']
) # Watson test for common mean
nf = 2. * (dup['n'] - 2.) # number of degees of freedom
ftest = fcalc(2, nf)
Frat = old_div(F, ftest)
if Frat > 1.:
ZigZag = Frat # fails zigzag on directions
methcode = "SM-FTEST"
# now do slopes
if len(b_zi) > 2 and len(b_iz) > 2:
bzi_m, bzi_sig = gausspars(b_zi) # mean, std dev
biz_m, biz_sig = gausspars(b_iz)
n_zi = float(len(b_zi))
n_iz = float(len(b_iz))
b_diff = abs(bzi_m - biz_m) # difference in means
#
# avoid false positives - set 3 degree slope difference here too
if b_diff > 3 * np.pi / 180.:
nf = n_zi + n_iz - 2. # degrees of freedom
svar = old_div(((n_zi - 1.) * bzi_sig**2 +
(n_iz - 1.) * biz_sig**2), nf)
T = old_div((b_diff), np.sqrt(
svar * (old_div(1.0, n_zi) + old_div(1.0, n_iz)))) # student's t
ttest = tcalc(nf, .05) # t-test at 95% conf.
Trat = old_div(T, ttest)
if Trat > 1 and Trat > Frat:
ZigZag = Trat # fails zigzag on directions
methcode = "SM-TTEST"
pars[z_key] = ZigZag
pars[meth_key] = methcode
# do drats
if len(ptrm_check) != 0:
diffcum, drat_max = 0, 0
for prec in ptrm_check:
step = prec[0]
endbak = end
zend = end
while zend > len(zijdblock) - 1:
zend = zend - 2 # don't count alteration that happens after this step
if step < zijdblock[zend][0]:
Nptrm += 1
for irec in first_I:
if irec[0] == step:
break
diffcum += prec[3] - irec[3]
if abs(prec[3] - irec[3]) > drat_max:
drat_max = abs(prec[3] - irec[3])
pars[drats_key] = (100 * abs(diffcum) / first_I[zend][3])
pars[drat_key] = (100 * abs(drat_max) / first_I[zend][3])
elif len(zptrm_check) != 0:
diffcum = 0
for prec in zptrm_check:
step = prec[0]
endbak = end
zend = end
while zend > len(zijdblock) - 1:
zend = zend - 1
if step < zijdblock[zend][0]:
Nptrm += 1
for irec in first_I:
if irec[0] == step:
break
diffcum += prec[3] - irec[3]
pars[drats_key] = (100 * abs(diffcum) / first_I[zend][3])
else:
pars[drats_key] = -1
pars[drat_key] = -1
# and the pTRM tails
if len(ptrm_tail) != 0:
for trec in ptrm_tail:
step = trec[0]
for irec in first_I:
if irec[0] == step:
break
if abs(trec[3]) > dmax:
dmax = abs(trec[3])
pars[md_key] = (100 * dmax / vds)
else:
pars[md_key] = -1
pars[min_key] = bstep
pars[max_key] = estep
pars[dec_key] = PCA["specimen_dec"]
pars[inc_key] = PCA["specimen_inc"]
pars[mad_key] = PCA["specimen_mad"]
pars[dang_key] = PCA["specimen_dang"]
pars[ptrm_key] = Nptrm
# and the ThetaChecks
if ThetaChecks != "":
t = 0
for theta in ThetaChecks:
if theta[0] >= bstep and theta[0] <= estep and theta[1] > t:
t = theta[1]
pars[theta_key] = t
else:
pars[theta_key] = -1
# and the DeltaChecks
if DeltaChecks != "":
d = 0
for delta in DeltaChecks:
if delta[0] >= bstep and delta[0] <= estep and delta[1] > d:
d = delta[1]
pars[delta_key]
else:
pars[delta_key] = -1
pars[gamma_key] = -1
if GammaChecks != "":
for gamma in GammaChecks:
if gamma[0] <= estep:
pars['specimen_gamma'] = gamma[1]
# --------------------------------------------------------------
# From here added By Ron Shaar 11-Dec 2012
# New parameters defined in Shaar and Tauxe (2012):
# FRAC (specimen_frac) - ranges from 0. to 1.
# SCAT (specimen_scat) - takes 1/0
# gap_max (specimen_gmax) - ranges from 0. to 1.
# --------------------------------------------------------------
# --------------------------------------------------------------
# FRAC is similar to Fvds, but the numerator is the vds fraction:
# FRAC= [ vds (start,end)] / total vds ]
# gap_max= max [ (vector difference) / vds (start,end)]
# --------------------------------------------------------------
# collect all zijderveld data to arrays and calculate VDS
z_temperatures = [row[0] for row in zijdblock]
zdata = [] # array of zero-fields measurements in Cartezian coordinates
# array of vector differences (for vds calculation)
vector_diffs = []
NRM = zijdblock[0][3] # NRM
for k in range(len(zijdblock)):
DIR = [zijdblock[k][1], zijdblock[k][2], old_div(zijdblock[k][3], NRM)]
cart = dir2cart(DIR)
zdata.append(np.array([cart[0], cart[1], cart[2]]))
if k > 0:
vector_diffs.append(
np.sqrt(sum((np.array(zdata[-2]) - np.array(zdata[-1]))**2)))
# last vector difference: from the last point to the origin.
vector_diffs.append(np.sqrt(sum(np.array(zdata[-1])**2)))
vds = sum(vector_diffs) # vds calculation
zdata = np.array(zdata)
vector_diffs = np.array(vector_diffs)
# calculate the vds within the chosen segment
vector_diffs_segment = vector_diffs[zstart:zend]
# FRAC calculation
FRAC = old_div(sum(vector_diffs_segment), vds)
pars[frac_key] = FRAC
# gap_max calculation
max_FRAC_gap = max(
old_div(vector_diffs_segment, sum(vector_diffs_segment)))
pars[gmax_key] = max_FRAC_gap
# ---------------------------------------------------------------------
# Calculate the "scat box"
# all data-points, pTRM checks, and tail-checks, should be inside a "scat box"
# ---------------------------------------------------------------------
# intialization
# fail scat due to arai plot data points
pars["fail_arai_beta_box_scatter"] = False
pars["fail_ptrm_beta_box_scatter"] = False # fail scat due to pTRM checks
pars["fail_tail_beta_box_scatter"] = False # fail scat due to tail checks
pars[scat_key] = "t" # Pass by default
# --------------------------------------------------------------
# collect all Arai plot data points in arrays
x_Arai, y_Arai, t_Arai, steps_Arai = [], [], [], []
NRMs = araiblock[0]
PTRMs = araiblock[1]
ptrm_checks = araiblock[2]
ptrm_tail = araiblock[3]
PTRMs_temperatures = [row[0] for row in PTRMs]
NRMs_temperatures = [row[0] for row in NRMs]
NRM = NRMs[0][3]
for k in range(len(NRMs)):
index_pTRMs = PTRMs_temperatures.index(NRMs[k][0])
x_Arai.append(old_div(PTRMs[index_pTRMs][3], NRM))
y_Arai.append(old_div(NRMs[k][3], NRM))
t_Arai.append(NRMs[k][0])
if NRMs[k][4] == 1:
steps_Arai.append('ZI')
else:
steps_Arai.append('IZ')
x_Arai = np.array(x_Arai)
y_Arai = np.array(y_Arai)
# --------------------------------------------------------------
# collect all pTRM check to arrays
x_ptrm_check, y_ptrm_check, ptrm_checks_temperatures, = [], [], []
x_ptrm_check_starting_point, y_ptrm_check_starting_point, ptrm_checks_starting_temperatures = [], [], []
for k in range(len(ptrm_checks)):
if ptrm_checks[k][0] in NRMs_temperatures:
# find the starting point of the pTRM check:
for i in range(len(datablock)):
rec = datablock[i]
if "LT-PTRM-I" in rec[meth_key] and float(rec[temp_key]) == ptrm_checks[k][0]:
starting_temperature = (float(datablock[i - 1][temp_key]))
try:
index = t_Arai.index(starting_temperature)
x_ptrm_check_starting_point.append(x_Arai[index])
y_ptrm_check_starting_point.append(y_Arai[index])
ptrm_checks_starting_temperatures.append(
starting_temperature)
index_zerofield = zerofield_temperatures.index(
ptrm_checks[k][0])
x_ptrm_check.append(old_div(ptrm_checks[k][3], NRM))
y_ptrm_check.append(
old_div(zerofields[index_zerofield][3], NRM))
ptrm_checks_temperatures.append(ptrm_checks[k][0])
break
except:
pass
x_ptrm_check_starting_point = np.array(x_ptrm_check_starting_point)
y_ptrm_check_starting_point = np.array(y_ptrm_check_starting_point)
ptrm_checks_starting_temperatures = np.array(
ptrm_checks_starting_temperatures)
x_ptrm_check = np.array(x_ptrm_check)
y_ptrm_check = np.array(y_ptrm_check)
ptrm_checks_temperatures = np.array(ptrm_checks_temperatures)
# --------------------------------------------------------------
# collect tail checks to arrays
x_tail_check, y_tail_check, tail_check_temperatures = [], [], []
x_tail_check_starting_point, y_tail_check_starting_point, tail_checks_starting_temperatures = [], [], []
for k in range(len(ptrm_tail)):
if ptrm_tail[k][0] in NRMs_temperatures:
# find the starting point of the pTRM check:
for i in range(len(datablock)):
rec = datablock[i]
if "LT-PTRM-MD" in rec[meth_key] and float(rec[temp_key]) == ptrm_tail[k][0]:
starting_temperature = (float(datablock[i - 1][temp_key]))
try:
index = t_Arai.index(starting_temperature)
x_tail_check_starting_point.append(x_Arai[index])
y_tail_check_starting_point.append(y_Arai[index])
tail_checks_starting_temperatures.append(
starting_temperature)
index_infield = infield_temperatures.index(
ptrm_tail[k][0])
x_tail_check.append(
old_div(infields[index_infield][3], NRM))
y_tail_check.append(
old_div(ptrm_tail[k][3], NRM) + old_div(zerofields[index_infield][3], NRM))
tail_check_temperatures.append(ptrm_tail[k][0])
break
except:
pass
x_tail_check = np.array(x_tail_check)
y_tail_check = np.array(y_tail_check)
tail_check_temperatures = np.array(tail_check_temperatures)
x_tail_check_starting_point = np.array(x_tail_check_starting_point)
y_tail_check_starting_point = np.array(y_tail_check_starting_point)
tail_checks_starting_temperatures = np.array(
tail_checks_starting_temperatures)
# --------------------------------------------------------------
# collect the chosen segment in the Arai plot to arrays
x_Arai_segment = x_Arai[start:end + 1] # chosen segent in the Arai plot
y_Arai_segment = y_Arai[start:end + 1] # chosen segent in the Arai plot
# --------------------------------------------------------------
# collect pTRM checks in segment to arrays
# notice, this is different than the conventional DRATS.
# for scat calculation we take only the pTRM checks which were carried out
# before reaching the highest temperature in the chosen segment
x_ptrm_check_for_SCAT, y_ptrm_check_for_SCAT = [], []
for k in range(len(ptrm_checks_temperatures)):
if ptrm_checks_temperatures[k] >= pars[min_key] and ptrm_checks_starting_temperatures <= pars[max_key]:
x_ptrm_check_for_SCAT.append(x_ptrm_check[k])
y_ptrm_check_for_SCAT.append(y_ptrm_check[k])
x_ptrm_check_for_SCAT = np.array(x_ptrm_check_for_SCAT)
y_ptrm_check_for_SCAT = np.array(y_ptrm_check_for_SCAT)
# --------------------------------------------------------------
# collect Tail checks in segment to arrays
# for scat calculation we take only the tail checks which were carried out
# before reaching the highest temperature in the chosen segment
x_tail_check_for_SCAT, y_tail_check_for_SCAT = [], []
for k in range(len(tail_check_temperatures)):
if tail_check_temperatures[k] >= pars[min_key] and tail_checks_starting_temperatures[k] <= pars[max_key]:
x_tail_check_for_SCAT.append(x_tail_check[k])
y_tail_check_for_SCAT.append(y_tail_check[k])
x_tail_check_for_SCAT = np.array(x_tail_check_for_SCAT)
y_tail_check_for_SCAT = np.array(y_tail_check_for_SCAT)
# --------------------------------------------------------------
# calculate the lines that define the scat box:
# if threshold value for beta is not defined, then scat cannot be calculated (pass)
# in this case, scat pass
if beta_key in list(accept.keys()) and accept[beta_key] != "":
b_beta_threshold = float(accept[beta_key])
b = pars[b_key] # best fit line
cm_x = np.mean(np.array(x_Arai_segment)) # x center of mass
cm_y = np.mean(np.array(y_Arai_segment)) # y center of mass
a = cm_y - b * cm_x
# lines with slope = slope +/- 2*(specimen_b_beta)
two_sigma_beta_threshold = 2 * b_beta_threshold
two_sigma_slope_threshold = abs(two_sigma_beta_threshold * b)
# a line with a shallower slope (b + 2*beta*b) passing through the center of mass
# y=a1+b1x
b1 = b + two_sigma_slope_threshold
a1 = cm_y - b1 * cm_x
# bounding line with steeper slope (b - 2*beta*b) passing through the center of mass
# y=a2+b2x
b2 = b - two_sigma_slope_threshold
a2 = cm_y - b2 * cm_x
# lower bounding line of the 'beta box'
# y=intercept1+slop1x
slop1 = old_div(a1, ((old_div(a2, b2))))
intercept1 = a1
# higher bounding line of the 'beta box'
# y=intercept2+slop2x
slop2 = old_div(a2, ((old_div(a1, b1))))
intercept2 = a2
pars['specimen_scat_bounding_line_high'] = [intercept2, slop2]
pars['specimen_scat_bounding_line_low'] = [intercept1, slop1]
# --------------------------------------------------------------
# check if the Arai data points are in the 'box'
# the two bounding lines
ymin = intercept1 + x_Arai_segment * slop1
ymax = intercept2 + x_Arai_segment * slop2
# arrays of "True" or "False"
check_1 = y_Arai_segment > ymax
check_2 = y_Arai_segment < ymin
# check if at least one "True"
if (sum(check_1) + sum(check_2)) > 0:
pars["fail_arai_beta_box_scatter"] = True
# --------------------------------------------------------------
# check if the pTRM checks data points are in the 'box'
if len(x_ptrm_check_for_SCAT) > 0:
# the two bounding lines
ymin = intercept1 + x_ptrm_check_for_SCAT * slop1
ymax = intercept2 + x_ptrm_check_for_SCAT * slop2
# arrays of "True" or "False"
check_1 = y_ptrm_check_for_SCAT > ymax
check_2 = y_ptrm_check_for_SCAT < ymin
# check if at least one "True"
if (sum(check_1) + sum(check_2)) > 0:
pars["fail_ptrm_beta_box_scatter"] = True
# --------------------------------------------------------------
# check if the tail checks data points are in the 'box'
if len(x_tail_check_for_SCAT) > 0:
# the two bounding lines
ymin = intercept1 + x_tail_check_for_SCAT * slop1
ymax = intercept2 + x_tail_check_for_SCAT * slop2
# arrays of "True" or "False"
check_1 = y_tail_check_for_SCAT > ymax
check_2 = y_tail_check_for_SCAT < ymin
# check if at least one "True"
if (sum(check_1) + sum(check_2)) > 0:
pars["fail_tail_beta_box_scatter"] = True
# --------------------------------------------------------------
# check if specimen_scat is PASS or FAIL:
if pars["fail_tail_beta_box_scatter"] or pars["fail_ptrm_beta_box_scatter"] or pars["fail_arai_beta_box_scatter"]:
pars[scat_key] = 'f'
else:
pars[scat_key] = 't'
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--------------------------------------------------------------', '# --------------------------------------------------------------', '# FRAC is similar to Fvds, but the numerator is the vds fraction:', '# FRAC= [ vds (start,end)] / total vds ]', '# gap_max= max [ (vector difference) / vds (start,end)]', '# --------------------------------------------------------------', '# collect all zijderveld data to arrays and calculate VDS', 'z_temperatures', '=', '[', 'row', '[', '0', ']', 'for', 'row', 'in', 'zijdblock', ']', 'zdata', '=', '[', ']', '# array of zero-fields measurements in Cartezian coordinates', '# array of vector differences (for vds calculation)', 'vector_diffs', '=', '[', ']', 'NRM', '=', 'zijdblock', '[', '0', ']', '[', '3', ']', '# NRM', 'for', 'k', 'in', 'range', '(', 'len', '(', 'zijdblock', ')', ')', ':', 'DIR', '=', '[', 'zijdblock', '[', 'k', ']', '[', '1', ']', ',', 'zijdblock', '[', 'k', ']', '[', '2', ']', ',', 'old_div', '(', 'zijdblock', '[', 'k', ']', '[', '3', 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Pass by default', '# --------------------------------------------------------------', '# collect all Arai plot data points in arrays', 'x_Arai', ',', 'y_Arai', ',', 't_Arai', ',', 'steps_Arai', '=', '[', ']', ',', '[', ']', ',', '[', ']', ',', '[', ']', 'NRMs', '=', 'araiblock', '[', '0', ']', 'PTRMs', '=', 'araiblock', '[', '1', ']', 'ptrm_checks', '=', 'araiblock', '[', '2', ']', 'ptrm_tail', '=', 'araiblock', '[', '3', ']', 'PTRMs_temperatures', '=', '[', 'row', '[', '0', ']', 'for', 'row', 'in', 'PTRMs', ']', 'NRMs_temperatures', '=', '[', 'row', '[', '0', ']', 'for', 'row', 'in', 'NRMs', ']', 'NRM', '=', 'NRMs', '[', '0', ']', '[', '3', ']', 'for', 'k', 'in', 'range', '(', 'len', '(', 'NRMs', ')', ')', ':', 'index_pTRMs', '=', 'PTRMs_temperatures', '.', 'index', '(', 'NRMs', '[', 'k', ']', '[', '0', ']', ')', 'x_Arai', '.', 'append', '(', 'old_div', '(', 'PTRMs', '[', 'index_pTRMs', ']', '[', '3', ']', ',', 'NRM', ')', ')', 'y_Arai', '.', 'append', '(', 'old_div', '(', 'NRMs', 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'y_tail_check_starting_point', '=', 'np', '.', 'array', '(', 'y_tail_check_starting_point', ')', 'tail_checks_starting_temperatures', '=', 'np', '.', 'array', '(', 'tail_checks_starting_temperatures', ')', '# --------------------------------------------------------------', '# collect the chosen segment in the Arai plot to arrays', 'x_Arai_segment', '=', 'x_Arai', '[', 'start', ':', 'end', '+', '1', ']', '# chosen segent in the Arai plot', 'y_Arai_segment', '=', 'y_Arai', '[', 'start', ':', 'end', '+', '1', ']', '# chosen segent in the Arai plot', '# --------------------------------------------------------------', '# collect pTRM checks in segment to arrays', '# notice, this is different than the conventional DRATS.', '# for scat calculation we take only the pTRM checks which were carried out', '# before reaching the highest temperature in the chosen segment', 'x_ptrm_check_for_SCAT', ',', 'y_ptrm_check_for_SCAT', '=', '[', ']', ',', '[', ']', 'for', 'k', 'in', 'range', '(', 'len', '(', 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line', 'cm_x', '=', 'np', '.', 'mean', '(', 'np', '.', 'array', '(', 'x_Arai_segment', ')', ')', '# x center of mass', 'cm_y', '=', 'np', '.', 'mean', '(', 'np', '.', 'array', '(', 'y_Arai_segment', ')', ')', '# y center of mass', 'a', '=', 'cm_y', '-', 'b', '*', 'cm_x', '# lines with slope = slope +/- 2*(specimen_b_beta)', 'two_sigma_beta_threshold', '=', '2', '*', 'b_beta_threshold', 'two_sigma_slope_threshold', '=', 'abs', '(', 'two_sigma_beta_threshold', '*', 'b', ')', '# a line with a shallower slope (b + 2*beta*b) passing through the center of mass', '# y=a1+b1x', 'b1', '=', 'b', '+', 'two_sigma_slope_threshold', 'a1', '=', 'cm_y', '-', 'b1', '*', 'cm_x', '# bounding line with steeper slope (b - 2*beta*b) passing through the center of mass', '# y=a2+b2x', 'b2', '=', 'b', '-', 'two_sigma_slope_threshold', 'a2', '=', 'cm_y', '-', 'b2', '*', 'cm_x', "# lower bounding line of the 'beta box'", '# y=intercept1+slop1x', 'slop1', '=', 'old_div', '(', 'a1', ',', '(', '(', 'old_div', '(', 'a2', ',', 'b2', ')', ')', ')', ')', 'intercept1', '=', 'a1', "# higher bounding line of the 'beta box'", '# y=intercept2+slop2x', 'slop2', '=', 'old_div', '(', 'a2', ',', '(', '(', 'old_div', '(', 'a1', ',', 'b1', ')', ')', ')', ')', 'intercept2', '=', 'a2', 'pars', '[', "'specimen_scat_bounding_line_high'", ']', '=', '[', 'intercept2', ',', 'slop2', ']', 'pars', '[', "'specimen_scat_bounding_line_low'", ']', '=', '[', 'intercept1', ',', 'slop1', ']', '# --------------------------------------------------------------', "# check if the Arai data points are in the 'box'", '# the two bounding lines', 'ymin', '=', 'intercept1', '+', 'x_Arai_segment', '*', 'slop1', 'ymax', '=', 'intercept2', '+', 'x_Arai_segment', '*', 'slop2', '# arrays of "True" or "False"', 'check_1', '=', 'y_Arai_segment', '>', 'ymax', 'check_2', '=', 'y_Arai_segment', '<', 'ymin', '# check if at least one "True"', 'if', '(', 'sum', '(', 'check_1', ')', '+', 'sum', '(', 'check_2', ')', ')', '>', '0', ':', 'pars', '[', '"fail_arai_beta_box_scatter"', ']', '=', 'True', '# --------------------------------------------------------------', "# check if the pTRM checks data points are in the 'box'", 'if', 'len', '(', 'x_ptrm_check_for_SCAT', ')', '>', '0', ':', '# the two bounding lines', 'ymin', '=', 'intercept1', '+', 'x_ptrm_check_for_SCAT', '*', 'slop1', 'ymax', '=', 'intercept2', '+', 'x_ptrm_check_for_SCAT', '*', 'slop2', '# arrays of "True" or "False"', 'check_1', '=', 'y_ptrm_check_for_SCAT', '>', 'ymax', 'check_2', '=', 'y_ptrm_check_for_SCAT', '<', 'ymin', '# check if at least one "True"', 'if', '(', 'sum', '(', 'check_1', ')', '+', 'sum', '(', 'check_2', ')', ')', '>', '0', ':', 'pars', '[', '"fail_ptrm_beta_box_scatter"', ']', '=', 'True', '# --------------------------------------------------------------', "# check if the tail checks data points are in the 'box'", 'if', 'len', '(', 'x_tail_check_for_SCAT', ')', '>', '0', ':', '# the two bounding lines', 'ymin', '=', 'intercept1', '+', 'x_tail_check_for_SCAT', '*', 'slop1', 'ymax', '=', 'intercept2', '+', 'x_tail_check_for_SCAT', '*', 'slop2', '# arrays of "True" or "False"', 'check_1', '=', 'y_tail_check_for_SCAT', '>', 'ymax', 'check_2', '=', 'y_tail_check_for_SCAT', '<', 'ymin', '# check if at least one "True"', 'if', '(', 'sum', '(', 'check_1', ')', '+', 'sum', '(', 'check_2', ')', ')', '>', '0', ':', 'pars', '[', '"fail_tail_beta_box_scatter"', ']', '=', 'True', '# --------------------------------------------------------------', '# check if specimen_scat is PASS or FAIL:', 'if', 'pars', '[', '"fail_tail_beta_box_scatter"', ']', 'or', 'pars', '[', '"fail_ptrm_beta_box_scatter"', ']', 'or', 'pars', '[', '"fail_arai_beta_box_scatter"', ']', ':', 'pars', '[', 'scat_key', ']', '=', "'f'", 'else', ':', 'pars', '[', 'scat_key', ']', '=', "'t'", 'return', 'pars', ',', '0'] | calculate the paleointensity magic parameters make some definitions | ['calculate', 'the', 'paleointensity', 'magic', 'parameters', 'make', 'some', 'definitions'] | train | https://github.com/PmagPy/PmagPy/blob/c7984f8809bf40fe112e53dcc311a33293b62d0b/pmagpy/pmag.py#L2827-L3374 |
10 | lowandrew/OLCTools | spadespipeline/vtyper.py | Vtyper.epcrparse | def epcrparse(self):
"""
Parse the ePCR text file outputs
"""
logging.info('Parsing ePCR results')
for sample in self.metadata:
if sample.general.bestassemblyfile != 'NA':
if 'stx' in sample.general.datastore:
# Initialise count - this allows for the population of vtyperresults with unique values
uniquecount = 0
# This populates vtyperresults with the verotoxin subtypes
toxinlist = []
if os.path.isfile(sample[self.analysistype].resultsfile):
epcrresults = open(sample[self.analysistype].resultsfile, 'r')
for result in epcrresults:
# Only the lines without a # contain results
if "#" not in result:
uniquecount += 1
# Split on \t
data = result.split('\t')
# The subtyping primer pair is the first entry on lines with results
vttype = data[0].split('_')[0]
# Push the name of the primer pair - stripped of anything after a _ to the dictionary
if vttype not in toxinlist:
toxinlist.append(vttype)
# Create a string of the entries in list1 joined with ";"
toxinstring = ";".join(sorted(toxinlist))
# Save the string to the metadata
sample[self.analysistype].toxinprofile = toxinstring
else:
setattr(sample, self.analysistype, GenObject())
sample[self.analysistype].toxinprofile = 'NA'
else:
setattr(sample, self.analysistype, GenObject())
sample[self.analysistype].toxinprofile = 'NA' | python | def epcrparse(self):
"""
Parse the ePCR text file outputs
"""
logging.info('Parsing ePCR results')
for sample in self.metadata:
if sample.general.bestassemblyfile != 'NA':
if 'stx' in sample.general.datastore:
# Initialise count - this allows for the population of vtyperresults with unique values
uniquecount = 0
# This populates vtyperresults with the verotoxin subtypes
toxinlist = []
if os.path.isfile(sample[self.analysistype].resultsfile):
epcrresults = open(sample[self.analysistype].resultsfile, 'r')
for result in epcrresults:
# Only the lines without a # contain results
if "#" not in result:
uniquecount += 1
# Split on \t
data = result.split('\t')
# The subtyping primer pair is the first entry on lines with results
vttype = data[0].split('_')[0]
# Push the name of the primer pair - stripped of anything after a _ to the dictionary
if vttype not in toxinlist:
toxinlist.append(vttype)
# Create a string of the entries in list1 joined with ";"
toxinstring = ";".join(sorted(toxinlist))
# Save the string to the metadata
sample[self.analysistype].toxinprofile = toxinstring
else:
setattr(sample, self.analysistype, GenObject())
sample[self.analysistype].toxinprofile = 'NA'
else:
setattr(sample, self.analysistype, GenObject())
sample[self.analysistype].toxinprofile = 'NA' | ['def', 'epcrparse', '(', 'self', ')', ':', 'logging', '.', 'info', '(', "'Parsing ePCR results'", ')', 'for', 'sample', 'in', 'self', '.', 'metadata', ':', 'if', 'sample', '.', 'general', '.', 'bestassemblyfile', '!=', "'NA'", ':', 'if', "'stx'", 'in', 'sample', '.', 'general', '.', 'datastore', ':', '# Initialise count - this allows for the population of vtyperresults with unique values', 'uniquecount', '=', '0', '# This populates vtyperresults with the verotoxin subtypes', 'toxinlist', '=', '[', ']', 'if', 'os', '.', 'path', '.', 'isfile', '(', 'sample', '[', 'self', '.', 'analysistype', ']', '.', 'resultsfile', ')', ':', 'epcrresults', '=', 'open', '(', 'sample', '[', 'self', '.', 'analysistype', ']', '.', 'resultsfile', ',', "'r'", ')', 'for', 'result', 'in', 'epcrresults', ':', '# Only the lines without a # contain results', 'if', '"#"', 'not', 'in', 'result', ':', 'uniquecount', '+=', '1', '# Split on \\t', 'data', '=', 'result', '.', 'split', '(', "'\\t'", ')', '# The subtyping primer pair is the first entry on lines with results', 'vttype', '=', 'data', '[', '0', ']', '.', 'split', '(', "'_'", ')', '[', '0', ']', '# Push the name of the primer pair - stripped of anything after a _ to the dictionary', 'if', 'vttype', 'not', 'in', 'toxinlist', ':', 'toxinlist', '.', 'append', '(', 'vttype', ')', '# Create a string of the entries in list1 joined with ";"', 'toxinstring', '=', '";"', '.', 'join', '(', 'sorted', '(', 'toxinlist', ')', ')', '# Save the string to the metadata', 'sample', '[', 'self', '.', 'analysistype', ']', '.', 'toxinprofile', '=', 'toxinstring', 'else', ':', 'setattr', '(', 'sample', ',', 'self', '.', 'analysistype', ',', 'GenObject', '(', ')', ')', 'sample', '[', 'self', '.', 'analysistype', ']', '.', 'toxinprofile', '=', "'NA'", 'else', ':', 'setattr', '(', 'sample', ',', 'self', '.', 'analysistype', ',', 'GenObject', '(', ')', ')', 'sample', '[', 'self', '.', 'analysistype', ']', '.', 'toxinprofile', '=', "'NA'"] | Parse the ePCR text file outputs | ['Parse', 'the', 'ePCR', 'text', 'file', 'outputs'] | train | https://github.com/lowandrew/OLCTools/blob/88aa90ac85f84d0bbeb03e43c29b0a9d36e4ce2a/spadespipeline/vtyper.py#L96-L131 |
11 | atztogo/phonopy | phonopy/structure/spglib.py | get_pointgroup | def get_pointgroup(rotations):
"""Return point group in international table symbol and number.
The symbols are mapped to the numbers as follows:
1 "1 "
2 "-1 "
3 "2 "
4 "m "
5 "2/m "
6 "222 "
7 "mm2 "
8 "mmm "
9 "4 "
10 "-4 "
11 "4/m "
12 "422 "
13 "4mm "
14 "-42m "
15 "4/mmm"
16 "3 "
17 "-3 "
18 "32 "
19 "3m "
20 "-3m "
21 "6 "
22 "-6 "
23 "6/m "
24 "622 "
25 "6mm "
26 "-62m "
27 "6/mmm"
28 "23 "
29 "m-3 "
30 "432 "
31 "-43m "
32 "m-3m "
"""
_set_no_error()
# (symbol, pointgroup_number, transformation_matrix)
pointgroup = spg.pointgroup(np.array(rotations, dtype='intc', order='C'))
_set_error_message()
return pointgroup | python | def get_pointgroup(rotations):
"""Return point group in international table symbol and number.
The symbols are mapped to the numbers as follows:
1 "1 "
2 "-1 "
3 "2 "
4 "m "
5 "2/m "
6 "222 "
7 "mm2 "
8 "mmm "
9 "4 "
10 "-4 "
11 "4/m "
12 "422 "
13 "4mm "
14 "-42m "
15 "4/mmm"
16 "3 "
17 "-3 "
18 "32 "
19 "3m "
20 "-3m "
21 "6 "
22 "-6 "
23 "6/m "
24 "622 "
25 "6mm "
26 "-62m "
27 "6/mmm"
28 "23 "
29 "m-3 "
30 "432 "
31 "-43m "
32 "m-3m "
"""
_set_no_error()
# (symbol, pointgroup_number, transformation_matrix)
pointgroup = spg.pointgroup(np.array(rotations, dtype='intc', order='C'))
_set_error_message()
return pointgroup | ['def', 'get_pointgroup', '(', 'rotations', ')', ':', '_set_no_error', '(', ')', '# (symbol, pointgroup_number, transformation_matrix)', 'pointgroup', '=', 'spg', '.', 'pointgroup', '(', 'np', '.', 'array', '(', 'rotations', ',', 'dtype', '=', "'intc'", ',', 'order', '=', "'C'", ')', ')', '_set_error_message', '(', ')', 'return', 'pointgroup'] | Return point group in international table symbol and number.
The symbols are mapped to the numbers as follows:
1 "1 "
2 "-1 "
3 "2 "
4 "m "
5 "2/m "
6 "222 "
7 "mm2 "
8 "mmm "
9 "4 "
10 "-4 "
11 "4/m "
12 "422 "
13 "4mm "
14 "-42m "
15 "4/mmm"
16 "3 "
17 "-3 "
18 "32 "
19 "3m "
20 "-3m "
21 "6 "
22 "-6 "
23 "6/m "
24 "622 "
25 "6mm "
26 "-62m "
27 "6/mmm"
28 "23 "
29 "m-3 "
30 "432 "
31 "-43m "
32 "m-3m " | ['Return', 'point', 'group', 'in', 'international', 'table', 'symbol', 'and', 'number', '.'] | train | https://github.com/atztogo/phonopy/blob/869cc2ba9e7d495d5f4cf6942415ab3fc9e2a10f/phonopy/structure/spglib.py#L301-L343 |
12 | automl/HpBandSter | hpbandster/core/nameserver.py | NameServer.start | def start(self):
"""
starts a Pyro4 nameserver in a separate thread
Returns
-------
tuple (str, int):
the host name and the used port
"""
if self.host is None:
if self.nic_name is None:
self.host = 'localhost'
else:
self.host = nic_name_to_host(self.nic_name)
uri, self.pyro_ns, _ = Pyro4.naming.startNS(host=self.host, port=self.port)
self.host, self.port = self.pyro_ns.locationStr.split(':')
self.port = int(self.port)
thread = threading.Thread(target=self.pyro_ns.requestLoop, name='Pyro4 nameserver started by HpBandSter')
thread.start()
if not self.dir is None:
os.makedirs(self.dir, exist_ok=True)
self.conf_fn = os.path.join(self.dir, 'HPB_run_%s_pyro.pkl'%self.run_id)
with open(self.conf_fn, 'wb') as fh:
pickle.dump((self.host, self.port), fh)
return(self.host, self.port) | python | def start(self):
"""
starts a Pyro4 nameserver in a separate thread
Returns
-------
tuple (str, int):
the host name and the used port
"""
if self.host is None:
if self.nic_name is None:
self.host = 'localhost'
else:
self.host = nic_name_to_host(self.nic_name)
uri, self.pyro_ns, _ = Pyro4.naming.startNS(host=self.host, port=self.port)
self.host, self.port = self.pyro_ns.locationStr.split(':')
self.port = int(self.port)
thread = threading.Thread(target=self.pyro_ns.requestLoop, name='Pyro4 nameserver started by HpBandSter')
thread.start()
if not self.dir is None:
os.makedirs(self.dir, exist_ok=True)
self.conf_fn = os.path.join(self.dir, 'HPB_run_%s_pyro.pkl'%self.run_id)
with open(self.conf_fn, 'wb') as fh:
pickle.dump((self.host, self.port), fh)
return(self.host, self.port) | ['def', 'start', '(', 'self', ')', ':', 'if', 'self', '.', 'host', 'is', 'None', ':', 'if', 'self', '.', 'nic_name', 'is', 'None', ':', 'self', '.', 'host', '=', "'localhost'", 'else', ':', 'self', '.', 'host', '=', 'nic_name_to_host', '(', 'self', '.', 'nic_name', ')', 'uri', ',', 'self', '.', 'pyro_ns', ',', '_', '=', 'Pyro4', '.', 'naming', '.', 'startNS', '(', 'host', '=', 'self', '.', 'host', ',', 'port', '=', 'self', '.', 'port', ')', 'self', '.', 'host', ',', 'self', '.', 'port', '=', 'self', '.', 'pyro_ns', '.', 'locationStr', '.', 'split', '(', "':'", ')', 'self', '.', 'port', '=', 'int', '(', 'self', '.', 'port', ')', 'thread', '=', 'threading', '.', 'Thread', '(', 'target', '=', 'self', '.', 'pyro_ns', '.', 'requestLoop', ',', 'name', '=', "'Pyro4 nameserver started by HpBandSter'", ')', 'thread', '.', 'start', '(', ')', 'if', 'not', 'self', '.', 'dir', 'is', 'None', ':', 'os', '.', 'makedirs', '(', 'self', '.', 'dir', ',', 'exist_ok', '=', 'True', ')', 'self', '.', 'conf_fn', '=', 'os', '.', 'path', '.', 'join', '(', 'self', '.', 'dir', ',', "'HPB_run_%s_pyro.pkl'", '%', 'self', '.', 'run_id', ')', 'with', 'open', '(', 'self', '.', 'conf_fn', ',', "'wb'", ')', 'as', 'fh', ':', 'pickle', '.', 'dump', '(', '(', 'self', '.', 'host', ',', 'self', '.', 'port', ')', ',', 'fh', ')', 'return', '(', 'self', '.', 'host', ',', 'self', '.', 'port', ')'] | starts a Pyro4 nameserver in a separate thread
Returns
-------
tuple (str, int):
the host name and the used port | ['starts', 'a', 'Pyro4', 'nameserver', 'in', 'a', 'separate', 'thread', 'Returns', '-------', 'tuple', '(', 'str', 'int', ')', ':', 'the', 'host', 'name', 'and', 'the', 'used', 'port'] | train | https://github.com/automl/HpBandSter/blob/841db4b827f342e5eb7f725723ea6461ac52d45a/hpbandster/core/nameserver.py#L48-L79 |
13 | PixelwarStudio/PyTree | Tree/core.py | Tree.grow | def grow(self, times=1):
"""Let the tree grow.
Args:
times (integer): Indicate how many times the tree will grow.
"""
self.nodes.append([])
for n, node in enumerate(self.nodes[self.age]):
if self.age == 0:
p_node = Node(self.pos[:2])
else:
p_node = self._get_node_parent(self.age-1, n)
angle = node.get_node_angle(p_node)
for i in range(self.comp):
tot_angle = self.__get_total_angle(angle, i)
length = self.__get_total_length(self.age+1, i)
self.nodes[self.age+1].append(node.make_new_node(length, tot_angle))
self.age += 1
if times > 1:
self.grow(times-1) | python | def grow(self, times=1):
"""Let the tree grow.
Args:
times (integer): Indicate how many times the tree will grow.
"""
self.nodes.append([])
for n, node in enumerate(self.nodes[self.age]):
if self.age == 0:
p_node = Node(self.pos[:2])
else:
p_node = self._get_node_parent(self.age-1, n)
angle = node.get_node_angle(p_node)
for i in range(self.comp):
tot_angle = self.__get_total_angle(angle, i)
length = self.__get_total_length(self.age+1, i)
self.nodes[self.age+1].append(node.make_new_node(length, tot_angle))
self.age += 1
if times > 1:
self.grow(times-1) | ['def', 'grow', '(', 'self', ',', 'times', '=', '1', ')', ':', 'self', '.', 'nodes', '.', 'append', '(', '[', ']', ')', 'for', 'n', ',', 'node', 'in', 'enumerate', '(', 'self', '.', 'nodes', '[', 'self', '.', 'age', ']', ')', ':', 'if', 'self', '.', 'age', '==', '0', ':', 'p_node', '=', 'Node', '(', 'self', '.', 'pos', '[', ':', '2', ']', ')', 'else', ':', 'p_node', '=', 'self', '.', '_get_node_parent', '(', 'self', '.', 'age', '-', '1', ',', 'n', ')', 'angle', '=', 'node', '.', 'get_node_angle', '(', 'p_node', ')', 'for', 'i', 'in', 'range', '(', 'self', '.', 'comp', ')', ':', 'tot_angle', '=', 'self', '.', '__get_total_angle', '(', 'angle', ',', 'i', ')', 'length', '=', 'self', '.', '__get_total_length', '(', 'self', '.', 'age', '+', '1', ',', 'i', ')', 'self', '.', 'nodes', '[', 'self', '.', 'age', '+', '1', ']', '.', 'append', '(', 'node', '.', 'make_new_node', '(', 'length', ',', 'tot_angle', ')', ')', 'self', '.', 'age', '+=', '1', 'if', 'times', '>', '1', ':', 'self', '.', 'grow', '(', 'times', '-', '1', ')'] | Let the tree grow.
Args:
times (integer): Indicate how many times the tree will grow. | ['Let', 'the', 'tree', 'grow', '.'] | train | https://github.com/PixelwarStudio/PyTree/blob/f14b25ea145da6b00d836e34251d2a4c823766dc/Tree/core.py#L167-L189 |
14 | saltstack/salt | salt/modules/osquery.py | kernel_integrity | def kernel_integrity(attrs=None, where=None):
'''
Return kernel_integrity information from osquery
CLI Example:
.. code-block:: bash
salt '*' osquery.kernel_integrity
'''
if __grains__['os_family'] in ['RedHat', 'Debian']:
return _osquery_cmd(table='kernel_integrity', attrs=attrs, where=where)
return {'result': False, 'comment': 'Only available on Red Hat or Debian based systems.'} | python | def kernel_integrity(attrs=None, where=None):
'''
Return kernel_integrity information from osquery
CLI Example:
.. code-block:: bash
salt '*' osquery.kernel_integrity
'''
if __grains__['os_family'] in ['RedHat', 'Debian']:
return _osquery_cmd(table='kernel_integrity', attrs=attrs, where=where)
return {'result': False, 'comment': 'Only available on Red Hat or Debian based systems.'} | ['def', 'kernel_integrity', '(', 'attrs', '=', 'None', ',', 'where', '=', 'None', ')', ':', 'if', '__grains__', '[', "'os_family'", ']', 'in', '[', "'RedHat'", ',', "'Debian'", ']', ':', 'return', '_osquery_cmd', '(', 'table', '=', "'kernel_integrity'", ',', 'attrs', '=', 'attrs', ',', 'where', '=', 'where', ')', 'return', '{', "'result'", ':', 'False', ',', "'comment'", ':', "'Only available on Red Hat or Debian based systems.'", '}'] | Return kernel_integrity information from osquery
CLI Example:
.. code-block:: bash
salt '*' osquery.kernel_integrity | ['Return', 'kernel_integrity', 'information', 'from', 'osquery'] | train | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/osquery.py#L149-L161 |
15 | rackerlabs/lambda-uploader | lambda_uploader/package.py | Package.virtualenv | def virtualenv(self, virtualenv):
'''
Sets the virtual environment for the lambda package
If this is not set then package_dependencies will create a new one.
Takes a path to a virtualenv or a boolean if the virtualenv creation
should be skipped.
'''
# If a boolean is passed then set the internal _skip_virtualenv flag
if isinstance(virtualenv, bool):
self._skip_virtualenv = virtualenv
else:
self._virtualenv = virtualenv
if not os.path.isdir(self._virtualenv):
raise Exception("virtualenv %s not found" % self._virtualenv)
LOG.info("Using existing virtualenv at %s" % self._virtualenv)
# use supplied virtualenv path
self._pkg_venv = self._virtualenv
self._skip_virtualenv = True | python | def virtualenv(self, virtualenv):
'''
Sets the virtual environment for the lambda package
If this is not set then package_dependencies will create a new one.
Takes a path to a virtualenv or a boolean if the virtualenv creation
should be skipped.
'''
# If a boolean is passed then set the internal _skip_virtualenv flag
if isinstance(virtualenv, bool):
self._skip_virtualenv = virtualenv
else:
self._virtualenv = virtualenv
if not os.path.isdir(self._virtualenv):
raise Exception("virtualenv %s not found" % self._virtualenv)
LOG.info("Using existing virtualenv at %s" % self._virtualenv)
# use supplied virtualenv path
self._pkg_venv = self._virtualenv
self._skip_virtualenv = True | ['def', 'virtualenv', '(', 'self', ',', 'virtualenv', ')', ':', '# If a boolean is passed then set the internal _skip_virtualenv flag', 'if', 'isinstance', '(', 'virtualenv', ',', 'bool', ')', ':', 'self', '.', '_skip_virtualenv', '=', 'virtualenv', 'else', ':', 'self', '.', '_virtualenv', '=', 'virtualenv', 'if', 'not', 'os', '.', 'path', '.', 'isdir', '(', 'self', '.', '_virtualenv', ')', ':', 'raise', 'Exception', '(', '"virtualenv %s not found"', '%', 'self', '.', '_virtualenv', ')', 'LOG', '.', 'info', '(', '"Using existing virtualenv at %s"', '%', 'self', '.', '_virtualenv', ')', '# use supplied virtualenv path', 'self', '.', '_pkg_venv', '=', 'self', '.', '_virtualenv', 'self', '.', '_skip_virtualenv', '=', 'True'] | Sets the virtual environment for the lambda package
If this is not set then package_dependencies will create a new one.
Takes a path to a virtualenv or a boolean if the virtualenv creation
should be skipped. | ['Sets', 'the', 'virtual', 'environment', 'for', 'the', 'lambda', 'package'] | train | https://github.com/rackerlabs/lambda-uploader/blob/a5036e60d45d1a4fdc07df071f5b6e3b113388d4/lambda_uploader/package.py#L114-L133 |
16 | agoragames/kairos | kairos/cassandra_backend.py | CassandraSet._insert_stmt | def _insert_stmt(self, name, value, timestamp, interval, config):
'''Helper to generate the insert statement.'''
# Calculate the TTL and abort if inserting into the past
expire, ttl = config['expire'], config['ttl'](timestamp)
if expire and not ttl:
return None
i_time = config['i_calc'].to_bucket(timestamp)
if not config['coarse']:
r_time = config['r_calc'].to_bucket(timestamp)
else:
r_time = -1
# TODO: figure out escaping rules of CQL
stmt = '''INSERT INTO %s (name, interval, i_time, r_time, value)
VALUES ('%s', '%s', %s, %s, %s)'''%(self._table, name, interval, i_time, r_time, value)
expire = config['expire']
if ttl:
stmt += " USING TTL %s"%(ttl)
return stmt | python | def _insert_stmt(self, name, value, timestamp, interval, config):
'''Helper to generate the insert statement.'''
# Calculate the TTL and abort if inserting into the past
expire, ttl = config['expire'], config['ttl'](timestamp)
if expire and not ttl:
return None
i_time = config['i_calc'].to_bucket(timestamp)
if not config['coarse']:
r_time = config['r_calc'].to_bucket(timestamp)
else:
r_time = -1
# TODO: figure out escaping rules of CQL
stmt = '''INSERT INTO %s (name, interval, i_time, r_time, value)
VALUES ('%s', '%s', %s, %s, %s)'''%(self._table, name, interval, i_time, r_time, value)
expire = config['expire']
if ttl:
stmt += " USING TTL %s"%(ttl)
return stmt | ['def', '_insert_stmt', '(', 'self', ',', 'name', ',', 'value', ',', 'timestamp', ',', 'interval', ',', 'config', ')', ':', '# Calculate the TTL and abort if inserting into the past', 'expire', ',', 'ttl', '=', 'config', '[', "'expire'", ']', ',', 'config', '[', "'ttl'", ']', '(', 'timestamp', ')', 'if', 'expire', 'and', 'not', 'ttl', ':', 'return', 'None', 'i_time', '=', 'config', '[', "'i_calc'", ']', '.', 'to_bucket', '(', 'timestamp', ')', 'if', 'not', 'config', '[', "'coarse'", ']', ':', 'r_time', '=', 'config', '[', "'r_calc'", ']', '.', 'to_bucket', '(', 'timestamp', ')', 'else', ':', 'r_time', '=', '-', '1', '# TODO: figure out escaping rules of CQL', 'stmt', '=', "'''INSERT INTO %s (name, interval, i_time, r_time, value)\n VALUES ('%s', '%s', %s, %s, %s)'''", '%', '(', 'self', '.', '_table', ',', 'name', ',', 'interval', ',', 'i_time', ',', 'r_time', ',', 'value', ')', 'expire', '=', 'config', '[', "'expire'", ']', 'if', 'ttl', ':', 'stmt', '+=', '" USING TTL %s"', '%', '(', 'ttl', ')', 'return', 'stmt'] | Helper to generate the insert statement. | ['Helper', 'to', 'generate', 'the', 'insert', 'statement', '.'] | train | https://github.com/agoragames/kairos/blob/0b062d543b0f4a46df460fa0eb6ec281232ab179/kairos/cassandra_backend.py#L646-L665 |
17 | pavelsof/ipalint | ipalint/read.py | Reader._determine_dialect | def _determine_dialect(self, lines):
"""
Expects a non-empty [] of strings; these would normally be the first
few lines of a csv file. Returns the most likely Dialect named tuple or
None if the data seems to form a single column.
Ensures that using the returned dialect, all the lines given will have
the same number of columns.
Helper for the get_dialect method.
"""
permuts = [(quotechar, escapechar)
for quotechar in CSV_QUOTECHARS
for escapechar in CSV_ESCAPECHARS]
for delim in CSV_DELIMITERS:
counts = [line.count(delim) for line in lines]
if min(counts) == 0:
continue
for quotechar, escapechar in permuts:
doublequote = True if escapechar is None else False
reader = csv.reader(lines, delimiter=delim, quotechar=quotechar,
doublequote=doublequote, escapechar=escapechar)
try:
assert len(set([len(line) for line in reader])) == 1
except AssertionError:
continue
else:
break
else:
continue # no suitable quoting found
break # found it!
else:
return None
return Dialect(delim, quotechar, doublequote, escapechar) | python | def _determine_dialect(self, lines):
"""
Expects a non-empty [] of strings; these would normally be the first
few lines of a csv file. Returns the most likely Dialect named tuple or
None if the data seems to form a single column.
Ensures that using the returned dialect, all the lines given will have
the same number of columns.
Helper for the get_dialect method.
"""
permuts = [(quotechar, escapechar)
for quotechar in CSV_QUOTECHARS
for escapechar in CSV_ESCAPECHARS]
for delim in CSV_DELIMITERS:
counts = [line.count(delim) for line in lines]
if min(counts) == 0:
continue
for quotechar, escapechar in permuts:
doublequote = True if escapechar is None else False
reader = csv.reader(lines, delimiter=delim, quotechar=quotechar,
doublequote=doublequote, escapechar=escapechar)
try:
assert len(set([len(line) for line in reader])) == 1
except AssertionError:
continue
else:
break
else:
continue # no suitable quoting found
break # found it!
else:
return None
return Dialect(delim, quotechar, doublequote, escapechar) | ['def', '_determine_dialect', '(', 'self', ',', 'lines', ')', ':', 'permuts', '=', '[', '(', 'quotechar', ',', 'escapechar', ')', 'for', 'quotechar', 'in', 'CSV_QUOTECHARS', 'for', 'escapechar', 'in', 'CSV_ESCAPECHARS', ']', 'for', 'delim', 'in', 'CSV_DELIMITERS', ':', 'counts', '=', '[', 'line', '.', 'count', '(', 'delim', ')', 'for', 'line', 'in', 'lines', ']', 'if', 'min', '(', 'counts', ')', '==', '0', ':', 'continue', 'for', 'quotechar', ',', 'escapechar', 'in', 'permuts', ':', 'doublequote', '=', 'True', 'if', 'escapechar', 'is', 'None', 'else', 'False', 'reader', '=', 'csv', '.', 'reader', '(', 'lines', ',', 'delimiter', '=', 'delim', ',', 'quotechar', '=', 'quotechar', ',', 'doublequote', '=', 'doublequote', ',', 'escapechar', '=', 'escapechar', ')', 'try', ':', 'assert', 'len', '(', 'set', '(', '[', 'len', '(', 'line', ')', 'for', 'line', 'in', 'reader', ']', ')', ')', '==', '1', 'except', 'AssertionError', ':', 'continue', 'else', ':', 'break', 'else', ':', 'continue', '# no suitable quoting found', 'break', '# found it!', 'else', ':', 'return', 'None', 'return', 'Dialect', '(', 'delim', ',', 'quotechar', ',', 'doublequote', ',', 'escapechar', ')'] | Expects a non-empty [] of strings; these would normally be the first
few lines of a csv file. Returns the most likely Dialect named tuple or
None if the data seems to form a single column.
Ensures that using the returned dialect, all the lines given will have
the same number of columns.
Helper for the get_dialect method. | ['Expects', 'a', 'non', '-', 'empty', '[]', 'of', 'strings', ';', 'these', 'would', 'normally', 'be', 'the', 'first', 'few', 'lines', 'of', 'a', 'csv', 'file', '.', 'Returns', 'the', 'most', 'likely', 'Dialect', 'named', 'tuple', 'or', 'None', 'if', 'the', 'data', 'seems', 'to', 'form', 'a', 'single', 'column', '.'] | train | https://github.com/pavelsof/ipalint/blob/763e5979ede6980cbfc746b06fd007b379762eeb/ipalint/read.py#L177-L218 |
18 | angr/angr | angr/utils/graph.py | compute_dominance_frontier | def compute_dominance_frontier(graph, domtree):
"""
Compute a dominance frontier based on the given post-dominator tree.
This implementation is based on figure 2 of paper An Efficient Method of Computing Static Single Assignment
Form by Ron Cytron, etc.
:param graph: The graph where we want to compute the dominance frontier.
:param domtree: The dominator tree
:returns: A dict of dominance frontier
"""
df = {}
# Perform a post-order search on the dominator tree
for x in networkx.dfs_postorder_nodes(domtree):
if x not in graph:
# Skip nodes that are not in the graph
continue
df[x] = set()
# local set
for y in graph.successors(x):
if x not in domtree.predecessors(y):
df[x].add(y)
# up set
if x is None:
continue
for z in domtree.successors(x):
if z is x:
continue
if z not in df:
continue
for y in df[z]:
if x not in list(domtree.predecessors(y)):
df[x].add(y)
return df | python | def compute_dominance_frontier(graph, domtree):
"""
Compute a dominance frontier based on the given post-dominator tree.
This implementation is based on figure 2 of paper An Efficient Method of Computing Static Single Assignment
Form by Ron Cytron, etc.
:param graph: The graph where we want to compute the dominance frontier.
:param domtree: The dominator tree
:returns: A dict of dominance frontier
"""
df = {}
# Perform a post-order search on the dominator tree
for x in networkx.dfs_postorder_nodes(domtree):
if x not in graph:
# Skip nodes that are not in the graph
continue
df[x] = set()
# local set
for y in graph.successors(x):
if x not in domtree.predecessors(y):
df[x].add(y)
# up set
if x is None:
continue
for z in domtree.successors(x):
if z is x:
continue
if z not in df:
continue
for y in df[z]:
if x not in list(domtree.predecessors(y)):
df[x].add(y)
return df | ['def', 'compute_dominance_frontier', '(', 'graph', ',', 'domtree', ')', ':', 'df', '=', '{', '}', '# Perform a post-order search on the dominator tree', 'for', 'x', 'in', 'networkx', '.', 'dfs_postorder_nodes', '(', 'domtree', ')', ':', 'if', 'x', 'not', 'in', 'graph', ':', '# Skip nodes that are not in the graph', 'continue', 'df', '[', 'x', ']', '=', 'set', '(', ')', '# local set', 'for', 'y', 'in', 'graph', '.', 'successors', '(', 'x', ')', ':', 'if', 'x', 'not', 'in', 'domtree', '.', 'predecessors', '(', 'y', ')', ':', 'df', '[', 'x', ']', '.', 'add', '(', 'y', ')', '# up set', 'if', 'x', 'is', 'None', ':', 'continue', 'for', 'z', 'in', 'domtree', '.', 'successors', '(', 'x', ')', ':', 'if', 'z', 'is', 'x', ':', 'continue', 'if', 'z', 'not', 'in', 'df', ':', 'continue', 'for', 'y', 'in', 'df', '[', 'z', ']', ':', 'if', 'x', 'not', 'in', 'list', '(', 'domtree', '.', 'predecessors', '(', 'y', ')', ')', ':', 'df', '[', 'x', ']', '.', 'add', '(', 'y', ')', 'return', 'df'] | Compute a dominance frontier based on the given post-dominator tree.
This implementation is based on figure 2 of paper An Efficient Method of Computing Static Single Assignment
Form by Ron Cytron, etc.
:param graph: The graph where we want to compute the dominance frontier.
:param domtree: The dominator tree
:returns: A dict of dominance frontier | ['Compute', 'a', 'dominance', 'frontier', 'based', 'on', 'the', 'given', 'post', '-', 'dominator', 'tree', '.'] | train | https://github.com/angr/angr/blob/4e2f97d56af5419ee73bdb30482c8dd8ff5f3e40/angr/utils/graph.py#L63-L104 |
19 | CI-WATER/mapkit | mapkit/RasterConverter.py | RasterConverter.isNumber | def isNumber(self, value):
"""
Validate whether a value is a number or not
"""
try:
str(value)
float(value)
return True
except ValueError:
return False | python | def isNumber(self, value):
"""
Validate whether a value is a number or not
"""
try:
str(value)
float(value)
return True
except ValueError:
return False | ['def', 'isNumber', '(', 'self', ',', 'value', ')', ':', 'try', ':', 'str', '(', 'value', ')', 'float', '(', 'value', ')', 'return', 'True', 'except', 'ValueError', ':', 'return', 'False'] | Validate whether a value is a number or not | ['Validate', 'whether', 'a', 'value', 'is', 'a', 'number', 'or', 'not'] | train | https://github.com/CI-WATER/mapkit/blob/ce5fbded6af7adabdf1eec85631c6811ef8ecc34/mapkit/RasterConverter.py#L1097-L1107 |
20 | CamDavidsonPilon/lifelines | lifelines/utils/__init__.py | survival_table_from_events | def survival_table_from_events(
death_times,
event_observed,
birth_times=None,
columns=["removed", "observed", "censored", "entrance", "at_risk"],
weights=None,
collapse=False,
intervals=None,
): # pylint: disable=dangerous-default-value,too-many-locals
"""
Parameters
----------
death_times: (n,) array
represent the event times
event_observed: (n,) array
1 if observed event, 0 is censored event.
birth_times: a (n,) array, optional
representing when the subject was first observed. A subject's death event is then at [birth times + duration observed].
If None (default), birth_times are set to be the first observation or 0, which ever is smaller.
columns: iterable, optional
a 3-length array to call the, in order, removed individuals, observed deaths
and censorships.
weights: (n,1) array, optional
Optional argument to use weights for individuals. Assumes weights of 1 if not provided.
collapse: boolean, optional (default=False)
If True, collapses survival table into lifetable to show events in interval bins
intervals: iterable, optional
Default None, otherwise a list/(n,1) array of interval edge measures. If left as None
while collapse=True, then Freedman-Diaconis rule for histogram bins will be used to determine intervals.
Returns
-------
DataFrame
Pandas DataFrame with index as the unique times or intervals in event_times. The columns named
'removed' refers to the number of individuals who were removed from the population
by the end of the period. The column 'observed' refers to the number of removed
individuals who were observed to have died (i.e. not censored.) The column
'censored' is defined as 'removed' - 'observed' (the number of individuals who
left the population due to event_observed)
Example
-------
>>> #Uncollapsed output
>>> removed observed censored entrance at_risk
>>> event_at
>>> 0 0 0 0 11 11
>>> 6 1 1 0 0 11
>>> 7 2 2 0 0 10
>>> 9 3 3 0 0 8
>>> 13 3 3 0 0 5
>>> 15 2 2 0 0 2
>>> #Collapsed output
>>> removed observed censored at_risk
>>> sum sum sum max
>>> event_at
>>> (0, 2] 34 33 1 312
>>> (2, 4] 84 42 42 278
>>> (4, 6] 64 17 47 194
>>> (6, 8] 63 16 47 130
>>> (8, 10] 35 12 23 67
>>> (10, 12] 24 5 19 32
See Also
--------
group_survival_table_from_events
"""
removed, observed, censored, entrance, at_risk = columns
death_times = np.asarray(death_times)
if birth_times is None:
birth_times = min(0, death_times.min()) * np.ones(death_times.shape[0])
else:
birth_times = np.asarray(birth_times)
if np.any(birth_times > death_times):
raise ValueError("birth time must be less than time of death.")
if weights is None:
weights = 1
# deal with deaths and censorships
df = pd.DataFrame(death_times, columns=["event_at"])
df[removed] = np.asarray(weights)
df[observed] = np.asarray(weights) * (np.asarray(event_observed).astype(bool))
death_table = df.groupby("event_at").sum()
death_table[censored] = (death_table[removed] - death_table[observed]).astype(int)
# deal with late births
births = pd.DataFrame(birth_times, columns=["event_at"])
births[entrance] = np.asarray(weights)
births_table = births.groupby("event_at").sum()
event_table = death_table.join(births_table, how="outer", sort=True).fillna(0) # http://wesmckinney.com/blog/?p=414
event_table[at_risk] = event_table[entrance].cumsum() - event_table[removed].cumsum().shift(1).fillna(0)
# group by intervals
if (collapse) or (intervals is not None):
event_table = _group_event_table_by_intervals(event_table, intervals)
if (np.asarray(weights).astype(int) != weights).any():
return event_table.astype(float)
return event_table.astype(int) | python | def survival_table_from_events(
death_times,
event_observed,
birth_times=None,
columns=["removed", "observed", "censored", "entrance", "at_risk"],
weights=None,
collapse=False,
intervals=None,
): # pylint: disable=dangerous-default-value,too-many-locals
"""
Parameters
----------
death_times: (n,) array
represent the event times
event_observed: (n,) array
1 if observed event, 0 is censored event.
birth_times: a (n,) array, optional
representing when the subject was first observed. A subject's death event is then at [birth times + duration observed].
If None (default), birth_times are set to be the first observation or 0, which ever is smaller.
columns: iterable, optional
a 3-length array to call the, in order, removed individuals, observed deaths
and censorships.
weights: (n,1) array, optional
Optional argument to use weights for individuals. Assumes weights of 1 if not provided.
collapse: boolean, optional (default=False)
If True, collapses survival table into lifetable to show events in interval bins
intervals: iterable, optional
Default None, otherwise a list/(n,1) array of interval edge measures. If left as None
while collapse=True, then Freedman-Diaconis rule for histogram bins will be used to determine intervals.
Returns
-------
DataFrame
Pandas DataFrame with index as the unique times or intervals in event_times. The columns named
'removed' refers to the number of individuals who were removed from the population
by the end of the period. The column 'observed' refers to the number of removed
individuals who were observed to have died (i.e. not censored.) The column
'censored' is defined as 'removed' - 'observed' (the number of individuals who
left the population due to event_observed)
Example
-------
>>> #Uncollapsed output
>>> removed observed censored entrance at_risk
>>> event_at
>>> 0 0 0 0 11 11
>>> 6 1 1 0 0 11
>>> 7 2 2 0 0 10
>>> 9 3 3 0 0 8
>>> 13 3 3 0 0 5
>>> 15 2 2 0 0 2
>>> #Collapsed output
>>> removed observed censored at_risk
>>> sum sum sum max
>>> event_at
>>> (0, 2] 34 33 1 312
>>> (2, 4] 84 42 42 278
>>> (4, 6] 64 17 47 194
>>> (6, 8] 63 16 47 130
>>> (8, 10] 35 12 23 67
>>> (10, 12] 24 5 19 32
See Also
--------
group_survival_table_from_events
"""
removed, observed, censored, entrance, at_risk = columns
death_times = np.asarray(death_times)
if birth_times is None:
birth_times = min(0, death_times.min()) * np.ones(death_times.shape[0])
else:
birth_times = np.asarray(birth_times)
if np.any(birth_times > death_times):
raise ValueError("birth time must be less than time of death.")
if weights is None:
weights = 1
# deal with deaths and censorships
df = pd.DataFrame(death_times, columns=["event_at"])
df[removed] = np.asarray(weights)
df[observed] = np.asarray(weights) * (np.asarray(event_observed).astype(bool))
death_table = df.groupby("event_at").sum()
death_table[censored] = (death_table[removed] - death_table[observed]).astype(int)
# deal with late births
births = pd.DataFrame(birth_times, columns=["event_at"])
births[entrance] = np.asarray(weights)
births_table = births.groupby("event_at").sum()
event_table = death_table.join(births_table, how="outer", sort=True).fillna(0) # http://wesmckinney.com/blog/?p=414
event_table[at_risk] = event_table[entrance].cumsum() - event_table[removed].cumsum().shift(1).fillna(0)
# group by intervals
if (collapse) or (intervals is not None):
event_table = _group_event_table_by_intervals(event_table, intervals)
if (np.asarray(weights).astype(int) != weights).any():
return event_table.astype(float)
return event_table.astype(int) | ['def', 'survival_table_from_events', '(', 'death_times', ',', 'event_observed', ',', 'birth_times', '=', 'None', ',', 'columns', '=', '[', '"removed"', ',', '"observed"', ',', '"censored"', ',', '"entrance"', ',', '"at_risk"', ']', ',', 'weights', '=', 'None', ',', 'collapse', '=', 'False', ',', 'intervals', '=', 'None', ',', ')', ':', '# pylint: disable=dangerous-default-value,too-many-locals', 'removed', ',', 'observed', ',', 'censored', ',', 'entrance', ',', 'at_risk', '=', 'columns', 'death_times', '=', 'np', '.', 'asarray', '(', 'death_times', ')', 'if', 'birth_times', 'is', 'None', ':', 'birth_times', '=', 'min', '(', '0', ',', 'death_times', '.', 'min', '(', ')', ')', '*', 'np', '.', 'ones', '(', 'death_times', '.', 'shape', '[', '0', ']', ')', 'else', ':', 'birth_times', '=', 'np', '.', 'asarray', '(', 'birth_times', ')', 'if', 'np', '.', 'any', '(', 'birth_times', '>', 'death_times', ')', ':', 'raise', 'ValueError', '(', '"birth time must be less than time of death."', ')', 'if', 'weights', 'is', 'None', ':', 'weights', '=', '1', '# deal with deaths and censorships', 'df', '=', 'pd', '.', 'DataFrame', '(', 'death_times', ',', 'columns', '=', '[', '"event_at"', ']', ')', 'df', '[', 'removed', ']', '=', 'np', '.', 'asarray', '(', 'weights', ')', 'df', '[', 'observed', ']', '=', 'np', '.', 'asarray', '(', 'weights', ')', '*', '(', 'np', '.', 'asarray', '(', 'event_observed', ')', '.', 'astype', '(', 'bool', ')', ')', 'death_table', '=', 'df', '.', 'groupby', '(', '"event_at"', ')', '.', 'sum', '(', ')', 'death_table', '[', 'censored', ']', '=', '(', 'death_table', '[', 'removed', ']', '-', 'death_table', '[', 'observed', ']', ')', '.', 'astype', '(', 'int', ')', '# deal with late births', 'births', '=', 'pd', '.', 'DataFrame', '(', 'birth_times', ',', 'columns', '=', '[', '"event_at"', ']', ')', 'births', '[', 'entrance', ']', '=', 'np', '.', 'asarray', '(', 'weights', ')', 'births_table', '=', 'births', '.', 'groupby', '(', '"event_at"', ')', '.', 'sum', '(', ')', 'event_table', '=', 'death_table', '.', 'join', '(', 'births_table', ',', 'how', '=', '"outer"', ',', 'sort', '=', 'True', ')', '.', 'fillna', '(', '0', ')', '# http://wesmckinney.com/blog/?p=414', 'event_table', '[', 'at_risk', ']', '=', 'event_table', '[', 'entrance', ']', '.', 'cumsum', '(', ')', '-', 'event_table', '[', 'removed', ']', '.', 'cumsum', '(', ')', '.', 'shift', '(', '1', ')', '.', 'fillna', '(', '0', ')', '# group by intervals', 'if', '(', 'collapse', ')', 'or', '(', 'intervals', 'is', 'not', 'None', ')', ':', 'event_table', '=', '_group_event_table_by_intervals', '(', 'event_table', ',', 'intervals', ')', 'if', '(', 'np', '.', 'asarray', '(', 'weights', ')', '.', 'astype', '(', 'int', ')', '!=', 'weights', ')', '.', 'any', '(', ')', ':', 'return', 'event_table', '.', 'astype', '(', 'float', ')', 'return', 'event_table', '.', 'astype', '(', 'int', ')'] | Parameters
----------
death_times: (n,) array
represent the event times
event_observed: (n,) array
1 if observed event, 0 is censored event.
birth_times: a (n,) array, optional
representing when the subject was first observed. A subject's death event is then at [birth times + duration observed].
If None (default), birth_times are set to be the first observation or 0, which ever is smaller.
columns: iterable, optional
a 3-length array to call the, in order, removed individuals, observed deaths
and censorships.
weights: (n,1) array, optional
Optional argument to use weights for individuals. Assumes weights of 1 if not provided.
collapse: boolean, optional (default=False)
If True, collapses survival table into lifetable to show events in interval bins
intervals: iterable, optional
Default None, otherwise a list/(n,1) array of interval edge measures. If left as None
while collapse=True, then Freedman-Diaconis rule for histogram bins will be used to determine intervals.
Returns
-------
DataFrame
Pandas DataFrame with index as the unique times or intervals in event_times. The columns named
'removed' refers to the number of individuals who were removed from the population
by the end of the period. The column 'observed' refers to the number of removed
individuals who were observed to have died (i.e. not censored.) The column
'censored' is defined as 'removed' - 'observed' (the number of individuals who
left the population due to event_observed)
Example
-------
>>> #Uncollapsed output
>>> removed observed censored entrance at_risk
>>> event_at
>>> 0 0 0 0 11 11
>>> 6 1 1 0 0 11
>>> 7 2 2 0 0 10
>>> 9 3 3 0 0 8
>>> 13 3 3 0 0 5
>>> 15 2 2 0 0 2
>>> #Collapsed output
>>> removed observed censored at_risk
>>> sum sum sum max
>>> event_at
>>> (0, 2] 34 33 1 312
>>> (2, 4] 84 42 42 278
>>> (4, 6] 64 17 47 194
>>> (6, 8] 63 16 47 130
>>> (8, 10] 35 12 23 67
>>> (10, 12] 24 5 19 32
See Also
--------
group_survival_table_from_events | ['Parameters', '----------', 'death_times', ':', '(', 'n', ')', 'array', 'represent', 'the', 'event', 'times', 'event_observed', ':', '(', 'n', ')', 'array', '1', 'if', 'observed', 'event', '0', 'is', 'censored', 'event', '.', 'birth_times', ':', 'a', '(', 'n', ')', 'array', 'optional', 'representing', 'when', 'the', 'subject', 'was', 'first', 'observed', '.', 'A', 'subject', 's', 'death', 'event', 'is', 'then', 'at', '[', 'birth', 'times', '+', 'duration', 'observed', ']', '.', 'If', 'None', '(', 'default', ')', 'birth_times', 'are', 'set', 'to', 'be', 'the', 'first', 'observation', 'or', '0', 'which', 'ever', 'is', 'smaller', '.', 'columns', ':', 'iterable', 'optional', 'a', '3', '-', 'length', 'array', 'to', 'call', 'the', 'in', 'order', 'removed', 'individuals', 'observed', 'deaths', 'and', 'censorships', '.', 'weights', ':', '(', 'n', '1', ')', 'array', 'optional', 'Optional', 'argument', 'to', 'use', 'weights', 'for', 'individuals', '.', 'Assumes', 'weights', 'of', '1', 'if', 'not', 'provided', '.', 'collapse', ':', 'boolean', 'optional', '(', 'default', '=', 'False', ')', 'If', 'True', 'collapses', 'survival', 'table', 'into', 'lifetable', 'to', 'show', 'events', 'in', 'interval', 'bins', 'intervals', ':', 'iterable', 'optional', 'Default', 'None', 'otherwise', 'a', 'list', '/', '(', 'n', '1', ')', 'array', 'of', 'interval', 'edge', 'measures', '.', 'If', 'left', 'as', 'None', 'while', 'collapse', '=', 'True', 'then', 'Freedman', '-', 'Diaconis', 'rule', 'for', 'histogram', 'bins', 'will', 'be', 'used', 'to', 'determine', 'intervals', '.'] | train | https://github.com/CamDavidsonPilon/lifelines/blob/bdf6be6f1d10eea4c46365ee0ee6a47d8c30edf8/lifelines/utils/__init__.py#L262-L361 |
21 | django-blog-zinnia/cmsplugin-zinnia | cmsplugin_zinnia/cms_plugins.py | CMSRandomEntriesPlugin.render | def render(self, context, instance, placeholder):
"""
Update the context with plugin's data
"""
context = super(CMSRandomEntriesPlugin, self).render(
context, instance, placeholder)
context['template_to_render'] = (str(instance.template_to_render) or
'zinnia/tags/entries_random.html')
return context | python | def render(self, context, instance, placeholder):
"""
Update the context with plugin's data
"""
context = super(CMSRandomEntriesPlugin, self).render(
context, instance, placeholder)
context['template_to_render'] = (str(instance.template_to_render) or
'zinnia/tags/entries_random.html')
return context | ['def', 'render', '(', 'self', ',', 'context', ',', 'instance', ',', 'placeholder', ')', ':', 'context', '=', 'super', '(', 'CMSRandomEntriesPlugin', ',', 'self', ')', '.', 'render', '(', 'context', ',', 'instance', ',', 'placeholder', ')', 'context', '[', "'template_to_render'", ']', '=', '(', 'str', '(', 'instance', '.', 'template_to_render', ')', 'or', "'zinnia/tags/entries_random.html'", ')', 'return', 'context'] | Update the context with plugin's data | ['Update', 'the', 'context', 'with', 'plugin', 's', 'data'] | train | https://github.com/django-blog-zinnia/cmsplugin-zinnia/blob/7613c0d9ae29affe9ab97527e4b6d5bef124afdc/cmsplugin_zinnia/cms_plugins.py#L131-L139 |
22 | rjw57/throw | throw/minus/minus.py | Gallery.SaveGallery | def SaveGallery(self, name=None, items=None):
"""Use this to update the gallery name or change sort order.
Specify which attribute (name or items or both) you want to change."""
url = 'http://min.us/api/SaveGallery'
if not name:
if not self.name:
name = self.GetItems()[0]
if self.name:
name = self.name
if not items:
if not self.items:
items = self.GetItems()[1]
elif self.items:
items = self.items
params = {"name": name, "id":self.editor_id, "items":items}
try:
response = _dopost(url, params)
except:
pass
else:
self.name = name
self.items = items | python | def SaveGallery(self, name=None, items=None):
"""Use this to update the gallery name or change sort order.
Specify which attribute (name or items or both) you want to change."""
url = 'http://min.us/api/SaveGallery'
if not name:
if not self.name:
name = self.GetItems()[0]
if self.name:
name = self.name
if not items:
if not self.items:
items = self.GetItems()[1]
elif self.items:
items = self.items
params = {"name": name, "id":self.editor_id, "items":items}
try:
response = _dopost(url, params)
except:
pass
else:
self.name = name
self.items = items | ['def', 'SaveGallery', '(', 'self', ',', 'name', '=', 'None', ',', 'items', '=', 'None', ')', ':', 'url', '=', "'http://min.us/api/SaveGallery'", 'if', 'not', 'name', ':', 'if', 'not', 'self', '.', 'name', ':', 'name', '=', 'self', '.', 'GetItems', '(', ')', '[', '0', ']', 'if', 'self', '.', 'name', ':', 'name', '=', 'self', '.', 'name', 'if', 'not', 'items', ':', 'if', 'not', 'self', '.', 'items', ':', 'items', '=', 'self', '.', 'GetItems', '(', ')', '[', '1', ']', 'elif', 'self', '.', 'items', ':', 'items', '=', 'self', '.', 'items', 'params', '=', '{', '"name"', ':', 'name', ',', '"id"', ':', 'self', '.', 'editor_id', ',', '"items"', ':', 'items', '}', 'try', ':', 'response', '=', '_dopost', '(', 'url', ',', 'params', ')', 'except', ':', 'pass', 'else', ':', 'self', '.', 'name', '=', 'name', 'self', '.', 'items', '=', 'items'] | Use this to update the gallery name or change sort order.
Specify which attribute (name or items or both) you want to change. | ['Use', 'this', 'to', 'update', 'the', 'gallery', 'name', 'or', 'change', 'sort', 'order', '.', 'Specify', 'which', 'attribute', '(', 'name', 'or', 'items', 'or', 'both', ')', 'you', 'want', 'to', 'change', '.'] | train | https://github.com/rjw57/throw/blob/74a7116362ba5b45635ab247472b25cfbdece4ee/throw/minus/minus.py#L62-L88 |
23 | letuananh/chirptext | chirptext/cli.py | setup_logging | def setup_logging(filename, log_dir=None, force_setup=False):
''' Try to load logging configuration from a file. Set level to INFO if failed.
'''
if not force_setup and ChirpCLI.SETUP_COMPLETED:
logging.debug("Master logging has been setup. This call will be ignored.")
return
if log_dir and not os.path.exists(log_dir):
os.makedirs(log_dir)
if os.path.isfile(filename):
with open(filename) as config_file:
try:
config = json.load(config_file)
logging.config.dictConfig(config)
logging.info("logging was setup using {}".format(filename))
ChirpCLI.SETUP_COMPLETED = True
except Exception as e:
logging.exception("Could not load logging config")
# default logging config
logging.basicConfig(level=logging.INFO)
else:
logging.basicConfig(level=logging.INFO) | python | def setup_logging(filename, log_dir=None, force_setup=False):
''' Try to load logging configuration from a file. Set level to INFO if failed.
'''
if not force_setup and ChirpCLI.SETUP_COMPLETED:
logging.debug("Master logging has been setup. This call will be ignored.")
return
if log_dir and not os.path.exists(log_dir):
os.makedirs(log_dir)
if os.path.isfile(filename):
with open(filename) as config_file:
try:
config = json.load(config_file)
logging.config.dictConfig(config)
logging.info("logging was setup using {}".format(filename))
ChirpCLI.SETUP_COMPLETED = True
except Exception as e:
logging.exception("Could not load logging config")
# default logging config
logging.basicConfig(level=logging.INFO)
else:
logging.basicConfig(level=logging.INFO) | ['def', 'setup_logging', '(', 'filename', ',', 'log_dir', '=', 'None', ',', 'force_setup', '=', 'False', ')', ':', 'if', 'not', 'force_setup', 'and', 'ChirpCLI', '.', 'SETUP_COMPLETED', ':', 'logging', '.', 'debug', '(', '"Master logging has been setup. This call will be ignored."', ')', 'return', 'if', 'log_dir', 'and', 'not', 'os', '.', 'path', '.', 'exists', '(', 'log_dir', ')', ':', 'os', '.', 'makedirs', '(', 'log_dir', ')', 'if', 'os', '.', 'path', '.', 'isfile', '(', 'filename', ')', ':', 'with', 'open', '(', 'filename', ')', 'as', 'config_file', ':', 'try', ':', 'config', '=', 'json', '.', 'load', '(', 'config_file', ')', 'logging', '.', 'config', '.', 'dictConfig', '(', 'config', ')', 'logging', '.', 'info', '(', '"logging was setup using {}"', '.', 'format', '(', 'filename', ')', ')', 'ChirpCLI', '.', 'SETUP_COMPLETED', '=', 'True', 'except', 'Exception', 'as', 'e', ':', 'logging', '.', 'exception', '(', '"Could not load logging config"', ')', '# default logging config', 'logging', '.', 'basicConfig', '(', 'level', '=', 'logging', '.', 'INFO', ')', 'else', ':', 'logging', '.', 'basicConfig', '(', 'level', '=', 'logging', '.', 'INFO', ')'] | Try to load logging configuration from a file. Set level to INFO if failed. | ['Try', 'to', 'load', 'logging', 'configuration', 'from', 'a', 'file', '.', 'Set', 'level', 'to', 'INFO', 'if', 'failed', '.'] | train | https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/cli.py#L35-L55 |
24 | doconix/django-mako-plus | django_mako_plus/template/adapter.py | MakoTemplateAdapter.name | def name(self):
'''Returns the name of this template (if created from a file) or "string" if not'''
if self.mako_template.filename:
return os.path.basename(self.mako_template.filename)
return 'string' | python | def name(self):
'''Returns the name of this template (if created from a file) or "string" if not'''
if self.mako_template.filename:
return os.path.basename(self.mako_template.filename)
return 'string' | ['def', 'name', '(', 'self', ')', ':', 'if', 'self', '.', 'mako_template', '.', 'filename', ':', 'return', 'os', '.', 'path', '.', 'basename', '(', 'self', '.', 'mako_template', '.', 'filename', ')', 'return', "'string'"] | Returns the name of this template (if created from a file) or "string" if not | ['Returns', 'the', 'name', 'of', 'this', 'template', '(', 'if', 'created', 'from', 'a', 'file', ')', 'or', 'string', 'if', 'not'] | train | https://github.com/doconix/django-mako-plus/blob/a90f9b4af19e5fa9f83452989cdcaed21569a181/django_mako_plus/template/adapter.py#L39-L43 |
25 | HazyResearch/fonduer | src/fonduer/learning/disc_models/sparse_lstm.py | SparseLSTM.forward | def forward(self, X):
"""Forward function.
:param X: The input (batch) of the model contains word sequences for lstm,
features and feature weights.
:type X: For word sequences: a list of torch.Tensor pair (word sequence
and word mask) of shape (batch_size, sequence_length).
For features: torch.Tensor of shape (batch_size, sparse_feature_size).
For feature weights: torch.Tensor of shape
(batch_size, sparse_feature_size).
:return: The output of LSTM layer.
:rtype: torch.Tensor of shape (batch_size, num_classes)
"""
s = X[:-2]
f = X[-2]
w = X[-1]
batch_size = len(f)
# Generate lstm weight indices
x_idx = self._cuda(
torch.as_tensor(np.arange(1, self.settings["lstm_dim"] + 1)).repeat(
batch_size, 1
)
)
outputs = self._cuda(torch.Tensor([]))
# Calculate textual features from LSTMs
for i in range(len(s)):
state_word = self.lstms[0].init_hidden(batch_size)
output = self.lstms[0].forward(s[i][0], s[i][1], state_word)
outputs = torch.cat((outputs, output), 1)
# Concatenate textual features with multi-modal features
feaures = torch.cat((x_idx, f), 1)
weights = torch.cat((outputs, w), 1)
return self.sparse_linear(feaures, weights) | python | def forward(self, X):
"""Forward function.
:param X: The input (batch) of the model contains word sequences for lstm,
features and feature weights.
:type X: For word sequences: a list of torch.Tensor pair (word sequence
and word mask) of shape (batch_size, sequence_length).
For features: torch.Tensor of shape (batch_size, sparse_feature_size).
For feature weights: torch.Tensor of shape
(batch_size, sparse_feature_size).
:return: The output of LSTM layer.
:rtype: torch.Tensor of shape (batch_size, num_classes)
"""
s = X[:-2]
f = X[-2]
w = X[-1]
batch_size = len(f)
# Generate lstm weight indices
x_idx = self._cuda(
torch.as_tensor(np.arange(1, self.settings["lstm_dim"] + 1)).repeat(
batch_size, 1
)
)
outputs = self._cuda(torch.Tensor([]))
# Calculate textual features from LSTMs
for i in range(len(s)):
state_word = self.lstms[0].init_hidden(batch_size)
output = self.lstms[0].forward(s[i][0], s[i][1], state_word)
outputs = torch.cat((outputs, output), 1)
# Concatenate textual features with multi-modal features
feaures = torch.cat((x_idx, f), 1)
weights = torch.cat((outputs, w), 1)
return self.sparse_linear(feaures, weights) | ['def', 'forward', '(', 'self', ',', 'X', ')', ':', 's', '=', 'X', '[', ':', '-', '2', ']', 'f', '=', 'X', '[', '-', '2', ']', 'w', '=', 'X', '[', '-', '1', ']', 'batch_size', '=', 'len', '(', 'f', ')', '# Generate lstm weight indices', 'x_idx', '=', 'self', '.', '_cuda', '(', 'torch', '.', 'as_tensor', '(', 'np', '.', 'arange', '(', '1', ',', 'self', '.', 'settings', '[', '"lstm_dim"', ']', '+', '1', ')', ')', '.', 'repeat', '(', 'batch_size', ',', '1', ')', ')', 'outputs', '=', 'self', '.', '_cuda', '(', 'torch', '.', 'Tensor', '(', '[', ']', ')', ')', '# Calculate textual features from LSTMs', 'for', 'i', 'in', 'range', '(', 'len', '(', 's', ')', ')', ':', 'state_word', '=', 'self', '.', 'lstms', '[', '0', ']', '.', 'init_hidden', '(', 'batch_size', ')', 'output', '=', 'self', '.', 'lstms', '[', '0', ']', '.', 'forward', '(', 's', '[', 'i', ']', '[', '0', ']', ',', 's', '[', 'i', ']', '[', '1', ']', ',', 'state_word', ')', 'outputs', '=', 'torch', '.', 'cat', '(', '(', 'outputs', ',', 'output', ')', ',', '1', ')', '# Concatenate textual features with multi-modal features', 'feaures', '=', 'torch', '.', 'cat', '(', '(', 'x_idx', ',', 'f', ')', ',', '1', ')', 'weights', '=', 'torch', '.', 'cat', '(', '(', 'outputs', ',', 'w', ')', ',', '1', ')', 'return', 'self', '.', 'sparse_linear', '(', 'feaures', ',', 'weights', ')'] | Forward function.
:param X: The input (batch) of the model contains word sequences for lstm,
features and feature weights.
:type X: For word sequences: a list of torch.Tensor pair (word sequence
and word mask) of shape (batch_size, sequence_length).
For features: torch.Tensor of shape (batch_size, sparse_feature_size).
For feature weights: torch.Tensor of shape
(batch_size, sparse_feature_size).
:return: The output of LSTM layer.
:rtype: torch.Tensor of shape (batch_size, num_classes) | ['Forward', 'function', '.'] | train | https://github.com/HazyResearch/fonduer/blob/4520f86a716f03dcca458a9f4bddac75b4e7068f/src/fonduer/learning/disc_models/sparse_lstm.py#L25-L64 |
26 | google/grr | grr/server/grr_response_server/databases/mysql_flows.py | MySQLDBFlowMixin.CountFlowResultsByType | def CountFlowResultsByType(self, client_id, flow_id, cursor=None):
"""Returns counts of flow results grouped by result type."""
query = ("SELECT type, COUNT(*) FROM flow_results "
"FORCE INDEX (flow_results_by_client_id_flow_id_timestamp) "
"WHERE client_id = %s AND flow_id = %s "
"GROUP BY type")
args = [db_utils.ClientIDToInt(client_id), db_utils.FlowIDToInt(flow_id)]
cursor.execute(query, args)
return dict(cursor.fetchall()) | python | def CountFlowResultsByType(self, client_id, flow_id, cursor=None):
"""Returns counts of flow results grouped by result type."""
query = ("SELECT type, COUNT(*) FROM flow_results "
"FORCE INDEX (flow_results_by_client_id_flow_id_timestamp) "
"WHERE client_id = %s AND flow_id = %s "
"GROUP BY type")
args = [db_utils.ClientIDToInt(client_id), db_utils.FlowIDToInt(flow_id)]
cursor.execute(query, args)
return dict(cursor.fetchall()) | ['def', 'CountFlowResultsByType', '(', 'self', ',', 'client_id', ',', 'flow_id', ',', 'cursor', '=', 'None', ')', ':', 'query', '=', '(', '"SELECT type, COUNT(*) FROM flow_results "', '"FORCE INDEX (flow_results_by_client_id_flow_id_timestamp) "', '"WHERE client_id = %s AND flow_id = %s "', '"GROUP BY type"', ')', 'args', '=', '[', 'db_utils', '.', 'ClientIDToInt', '(', 'client_id', ')', ',', 'db_utils', '.', 'FlowIDToInt', '(', 'flow_id', ')', ']', 'cursor', '.', 'execute', '(', 'query', ',', 'args', ')', 'return', 'dict', '(', 'cursor', '.', 'fetchall', '(', ')', ')'] | Returns counts of flow results grouped by result type. | ['Returns', 'counts', 'of', 'flow', 'results', 'grouped', 'by', 'result', 'type', '.'] | train | https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/server/grr_response_server/databases/mysql_flows.py#L1350-L1360 |
27 | google/grr | grr/client/grr_response_client/client_actions/artifact_collector.py | ArtifactCollector._ProcessGrepSource | def _ProcessGrepSource(self, source):
"""Find files fulfilling regex conditions."""
attributes = source.base_source.attributes
paths = artifact_utils.InterpolateListKbAttributes(
attributes["paths"], self.knowledge_base,
self.ignore_interpolation_errors)
regex = utils.RegexListDisjunction(attributes["content_regex_list"])
condition = rdf_file_finder.FileFinderCondition.ContentsRegexMatch(
regex=regex, mode="ALL_HITS")
file_finder_action = rdf_file_finder.FileFinderAction.Stat()
request = rdf_file_finder.FileFinderArgs(
paths=paths,
action=file_finder_action,
conditions=[condition],
follow_links=True)
action = file_finder.FileFinderOSFromClient
yield action, request | python | def _ProcessGrepSource(self, source):
"""Find files fulfilling regex conditions."""
attributes = source.base_source.attributes
paths = artifact_utils.InterpolateListKbAttributes(
attributes["paths"], self.knowledge_base,
self.ignore_interpolation_errors)
regex = utils.RegexListDisjunction(attributes["content_regex_list"])
condition = rdf_file_finder.FileFinderCondition.ContentsRegexMatch(
regex=regex, mode="ALL_HITS")
file_finder_action = rdf_file_finder.FileFinderAction.Stat()
request = rdf_file_finder.FileFinderArgs(
paths=paths,
action=file_finder_action,
conditions=[condition],
follow_links=True)
action = file_finder.FileFinderOSFromClient
yield action, request | ['def', '_ProcessGrepSource', '(', 'self', ',', 'source', ')', ':', 'attributes', '=', 'source', '.', 'base_source', '.', 'attributes', 'paths', '=', 'artifact_utils', '.', 'InterpolateListKbAttributes', '(', 'attributes', '[', '"paths"', ']', ',', 'self', '.', 'knowledge_base', ',', 'self', '.', 'ignore_interpolation_errors', ')', 'regex', '=', 'utils', '.', 'RegexListDisjunction', '(', 'attributes', '[', '"content_regex_list"', ']', ')', 'condition', '=', 'rdf_file_finder', '.', 'FileFinderCondition', '.', 'ContentsRegexMatch', '(', 'regex', '=', 'regex', ',', 'mode', '=', '"ALL_HITS"', ')', 'file_finder_action', '=', 'rdf_file_finder', '.', 'FileFinderAction', '.', 'Stat', '(', ')', 'request', '=', 'rdf_file_finder', '.', 'FileFinderArgs', '(', 'paths', '=', 'paths', ',', 'action', '=', 'file_finder_action', ',', 'conditions', '=', '[', 'condition', ']', ',', 'follow_links', '=', 'True', ')', 'action', '=', 'file_finder', '.', 'FileFinderOSFromClient', 'yield', 'action', ',', 'request'] | Find files fulfilling regex conditions. | ['Find', 'files', 'fulfilling', 'regex', 'conditions', '.'] | train | https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/client/grr_response_client/client_actions/artifact_collector.py#L208-L225 |
28 | F5Networks/f5-common-python | f5/multi_device/cluster/__init__.py | ClusterManager.manage_extant | def manage_extant(self, **kwargs):
'''Manage an existing cluster
:param kwargs: dict -- keyword args in dict
'''
self._check_device_number(kwargs['devices'])
self.trust_domain = TrustDomain(
devices=kwargs['devices'],
partition=kwargs['device_group_partition']
)
self.device_group = DeviceGroup(**kwargs)
self.cluster = Cluster(**kwargs) | python | def manage_extant(self, **kwargs):
'''Manage an existing cluster
:param kwargs: dict -- keyword args in dict
'''
self._check_device_number(kwargs['devices'])
self.trust_domain = TrustDomain(
devices=kwargs['devices'],
partition=kwargs['device_group_partition']
)
self.device_group = DeviceGroup(**kwargs)
self.cluster = Cluster(**kwargs) | ['def', 'manage_extant', '(', 'self', ',', '*', '*', 'kwargs', ')', ':', 'self', '.', '_check_device_number', '(', 'kwargs', '[', "'devices'", ']', ')', 'self', '.', 'trust_domain', '=', 'TrustDomain', '(', 'devices', '=', 'kwargs', '[', "'devices'", ']', ',', 'partition', '=', 'kwargs', '[', "'device_group_partition'", ']', ')', 'self', '.', 'device_group', '=', 'DeviceGroup', '(', '*', '*', 'kwargs', ')', 'self', '.', 'cluster', '=', 'Cluster', '(', '*', '*', 'kwargs', ')'] | Manage an existing cluster
:param kwargs: dict -- keyword args in dict | ['Manage', 'an', 'existing', 'cluster'] | train | https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/cluster/__init__.py#L136-L148 |
29 | google/python-gflags | gflags/flagvalues.py | FlagValues._GetFlagsDefinedByModule | def _GetFlagsDefinedByModule(self, module):
"""Returns the list of flags defined by a module.
Args:
module: A module object or a module name (a string).
Returns:
A new list of Flag objects. Caller may update this list as he
wishes: none of those changes will affect the internals of this
FlagValue object.
"""
if not isinstance(module, str):
module = module.__name__
return list(self.FlagsByModuleDict().get(module, [])) | python | def _GetFlagsDefinedByModule(self, module):
"""Returns the list of flags defined by a module.
Args:
module: A module object or a module name (a string).
Returns:
A new list of Flag objects. Caller may update this list as he
wishes: none of those changes will affect the internals of this
FlagValue object.
"""
if not isinstance(module, str):
module = module.__name__
return list(self.FlagsByModuleDict().get(module, [])) | ['def', '_GetFlagsDefinedByModule', '(', 'self', ',', 'module', ')', ':', 'if', 'not', 'isinstance', '(', 'module', ',', 'str', ')', ':', 'module', '=', 'module', '.', '__name__', 'return', 'list', '(', 'self', '.', 'FlagsByModuleDict', '(', ')', '.', 'get', '(', 'module', ',', '[', ']', ')', ')'] | Returns the list of flags defined by a module.
Args:
module: A module object or a module name (a string).
Returns:
A new list of Flag objects. Caller may update this list as he
wishes: none of those changes will affect the internals of this
FlagValue object. | ['Returns', 'the', 'list', 'of', 'flags', 'defined', 'by', 'a', 'module', '.'] | train | https://github.com/google/python-gflags/blob/4f06c3d0d6cbe9b1fb90ee9fb1c082b3bf9285f6/gflags/flagvalues.py#L265-L279 |
30 | openp2pdesign/makerlabs | makerlabs/hackaday_io.py | get_labs | def get_labs(format):
"""Gets Hackerspaces data from hackaday.io."""
hackerspaces_json = data_from_hackaday_io(hackaday_io_labs_map_url)
hackerspaces = {}
# Load all the Hackerspaces
for i in hackerspaces_json:
current_lab = Hackerspace()
current_lab.id = i["id"]
current_lab.url = "https://hackaday.io/hackerspace/" + current_lab.id
current_lab.name = i["name"]
if len(i["description"]) != 0:
current_lab.description = i["description"]
elif len(i["summary"]) != 0:
current_lab.description = i["summary"]
current_lab.created_at = i["moments"]["exact"]
# Check if there are coordinates
if i["latlon"] is not None:
latlon = json.loads(i["latlon"])
current_lab.latitude = latlon["lat"]
current_lab.longitude = latlon["lng"]
# Get country, county and city from them
country = geolocator.reverse(
[latlon["lat"], latlon["lng"]])
current_lab.country = country.raw[
"address"]["country"]
current_lab.address = country.raw["display_name"]
current_lab.address_1 = country.raw["display_name"]
current_lab.country_code = country.raw[
"address"]["country_code"]
current_lab.county = country.raw[
"address"]["state_district"]
current_lab.city = country.raw[
"address"]["city"]
current_lab.postal_code = country.raw[
"address"]["postcode"]
else:
# For labs without a location or coordinates
# add 0,0 as coordinates
current_lab.latitude = 0.0
current_lab.longitude = 0.0
# Add the lab
hackerspaces[i["name"]] = current_lab
# Return a dictiornary / json
if format.lower() == "dict" or format.lower() == "json":
output = {}
for j in hackerspaces:
output[j] = hackerspaces[j].__dict__
# Return a geojson
elif format.lower() == "geojson" or format.lower() == "geo":
labs_list = []
for l in hackerspaces:
single = hackerspaces[l].__dict__
single_lab = Feature(
type="Feature",
geometry=Point((single["latitude"], single["longitude"])),
properties=single)
labs_list.append(single_lab)
output = dumps(FeatureCollection(labs_list))
# Return a Pandas DataFrame
elif format.lower() == "pandas" or format.lower() == "dataframe":
output = {}
for j in hackerspaces:
output[j] = hackerspaces[j].__dict__
# Transform the dict into a Pandas DataFrame
output = pd.DataFrame.from_dict(output)
output = output.transpose()
# Return an object
elif format.lower() == "object" or format.lower() == "obj":
output = hackerspaces
# Default: return an oject
else:
output = hackerspaces
# Return a proper json
if format.lower() == "json":
output = json.dumps(output)
return output | python | def get_labs(format):
"""Gets Hackerspaces data from hackaday.io."""
hackerspaces_json = data_from_hackaday_io(hackaday_io_labs_map_url)
hackerspaces = {}
# Load all the Hackerspaces
for i in hackerspaces_json:
current_lab = Hackerspace()
current_lab.id = i["id"]
current_lab.url = "https://hackaday.io/hackerspace/" + current_lab.id
current_lab.name = i["name"]
if len(i["description"]) != 0:
current_lab.description = i["description"]
elif len(i["summary"]) != 0:
current_lab.description = i["summary"]
current_lab.created_at = i["moments"]["exact"]
# Check if there are coordinates
if i["latlon"] is not None:
latlon = json.loads(i["latlon"])
current_lab.latitude = latlon["lat"]
current_lab.longitude = latlon["lng"]
# Get country, county and city from them
country = geolocator.reverse(
[latlon["lat"], latlon["lng"]])
current_lab.country = country.raw[
"address"]["country"]
current_lab.address = country.raw["display_name"]
current_lab.address_1 = country.raw["display_name"]
current_lab.country_code = country.raw[
"address"]["country_code"]
current_lab.county = country.raw[
"address"]["state_district"]
current_lab.city = country.raw[
"address"]["city"]
current_lab.postal_code = country.raw[
"address"]["postcode"]
else:
# For labs without a location or coordinates
# add 0,0 as coordinates
current_lab.latitude = 0.0
current_lab.longitude = 0.0
# Add the lab
hackerspaces[i["name"]] = current_lab
# Return a dictiornary / json
if format.lower() == "dict" or format.lower() == "json":
output = {}
for j in hackerspaces:
output[j] = hackerspaces[j].__dict__
# Return a geojson
elif format.lower() == "geojson" or format.lower() == "geo":
labs_list = []
for l in hackerspaces:
single = hackerspaces[l].__dict__
single_lab = Feature(
type="Feature",
geometry=Point((single["latitude"], single["longitude"])),
properties=single)
labs_list.append(single_lab)
output = dumps(FeatureCollection(labs_list))
# Return a Pandas DataFrame
elif format.lower() == "pandas" or format.lower() == "dataframe":
output = {}
for j in hackerspaces:
output[j] = hackerspaces[j].__dict__
# Transform the dict into a Pandas DataFrame
output = pd.DataFrame.from_dict(output)
output = output.transpose()
# Return an object
elif format.lower() == "object" or format.lower() == "obj":
output = hackerspaces
# Default: return an oject
else:
output = hackerspaces
# Return a proper json
if format.lower() == "json":
output = json.dumps(output)
return output | ['def', 'get_labs', '(', 'format', ')', ':', 'hackerspaces_json', '=', 'data_from_hackaday_io', '(', 'hackaday_io_labs_map_url', ')', 'hackerspaces', '=', '{', '}', '# Load all the Hackerspaces', 'for', 'i', 'in', 'hackerspaces_json', ':', 'current_lab', '=', 'Hackerspace', '(', ')', 'current_lab', '.', 'id', '=', 'i', '[', '"id"', ']', 'current_lab', '.', 'url', '=', '"https://hackaday.io/hackerspace/"', '+', 'current_lab', '.', 'id', 'current_lab', '.', 'name', '=', 'i', '[', '"name"', ']', 'if', 'len', '(', 'i', '[', '"description"', ']', ')', '!=', '0', ':', 'current_lab', '.', 'description', '=', 'i', '[', '"description"', ']', 'elif', 'len', '(', 'i', '[', '"summary"', ']', ')', '!=', '0', ':', 'current_lab', '.', 'description', '=', 'i', '[', '"summary"', ']', 'current_lab', '.', 'created_at', '=', 'i', '[', '"moments"', ']', '[', '"exact"', ']', '# Check if there are coordinates', 'if', 'i', '[', '"latlon"', ']', 'is', 'not', 'None', ':', 'latlon', '=', 'json', '.', 'loads', '(', 'i', '[', '"latlon"', ']', ')', 'current_lab', '.', 'latitude', '=', 'latlon', '[', '"lat"', ']', 'current_lab', '.', 'longitude', '=', 'latlon', '[', '"lng"', ']', '# Get country, county and city from them', 'country', '=', 'geolocator', '.', 'reverse', '(', '[', 'latlon', '[', '"lat"', ']', ',', 'latlon', '[', '"lng"', ']', ']', ')', 'current_lab', '.', 'country', '=', 'country', '.', 'raw', '[', '"address"', ']', '[', '"country"', ']', 'current_lab', '.', 'address', '=', 'country', '.', 'raw', '[', '"display_name"', ']', 'current_lab', '.', 'address_1', '=', 'country', '.', 'raw', '[', '"display_name"', ']', 'current_lab', '.', 'country_code', '=', 'country', '.', 'raw', '[', '"address"', ']', '[', '"country_code"', ']', 'current_lab', '.', 'county', '=', 'country', '.', 'raw', '[', '"address"', ']', '[', '"state_district"', ']', 'current_lab', '.', 'city', '=', 'country', '.', 'raw', '[', '"address"', ']', '[', '"city"', ']', 'current_lab', '.', 'postal_code', '=', 'country', '.', 'raw', '[', '"address"', ']', '[', '"postcode"', ']', 'else', ':', '# For labs without a location or coordinates', '# add 0,0 as coordinates', 'current_lab', '.', 'latitude', '=', '0.0', 'current_lab', '.', 'longitude', '=', '0.0', '# Add the lab', 'hackerspaces', '[', 'i', '[', '"name"', ']', ']', '=', 'current_lab', '# Return a dictiornary / json', 'if', 'format', '.', 'lower', '(', ')', '==', '"dict"', 'or', 'format', '.', 'lower', '(', ')', '==', '"json"', ':', 'output', '=', '{', '}', 'for', 'j', 'in', 'hackerspaces', ':', 'output', '[', 'j', ']', '=', 'hackerspaces', '[', 'j', ']', '.', '__dict__', '# Return a geojson', 'elif', 'format', '.', 'lower', '(', ')', '==', '"geojson"', 'or', 'format', '.', 'lower', '(', ')', '==', '"geo"', ':', 'labs_list', '=', '[', ']', 'for', 'l', 'in', 'hackerspaces', ':', 'single', '=', 'hackerspaces', '[', 'l', ']', '.', '__dict__', 'single_lab', '=', 'Feature', '(', 'type', '=', '"Feature"', ',', 'geometry', '=', 'Point', '(', '(', 'single', '[', '"latitude"', ']', ',', 'single', '[', '"longitude"', ']', ')', ')', ',', 'properties', '=', 'single', ')', 'labs_list', '.', 'append', '(', 'single_lab', ')', 'output', '=', 'dumps', '(', 'FeatureCollection', '(', 'labs_list', ')', ')', '# Return a Pandas DataFrame', 'elif', 'format', '.', 'lower', '(', ')', '==', '"pandas"', 'or', 'format', '.', 'lower', '(', ')', '==', '"dataframe"', ':', 'output', '=', '{', '}', 'for', 'j', 'in', 'hackerspaces', ':', 'output', '[', 'j', ']', '=', 'hackerspaces', '[', 'j', ']', '.', '__dict__', '# Transform the dict into a Pandas DataFrame', 'output', '=', 'pd', '.', 'DataFrame', '.', 'from_dict', '(', 'output', ')', 'output', '=', 'output', '.', 'transpose', '(', ')', '# Return an object', 'elif', 'format', '.', 'lower', '(', ')', '==', '"object"', 'or', 'format', '.', 'lower', '(', ')', '==', '"obj"', ':', 'output', '=', 'hackerspaces', '# Default: return an oject', 'else', ':', 'output', '=', 'hackerspaces', '# Return a proper json', 'if', 'format', '.', 'lower', '(', ')', '==', '"json"', ':', 'output', '=', 'json', '.', 'dumps', '(', 'output', ')', 'return', 'output'] | Gets Hackerspaces data from hackaday.io. | ['Gets', 'Hackerspaces', 'data', 'from', 'hackaday', '.', 'io', '.'] | train | https://github.com/openp2pdesign/makerlabs/blob/b5838440174f10d370abb671358db9a99d7739fd/makerlabs/hackaday_io.py#L57-L137 |
31 | Azure/blobxfer | cli/cli.py | upload | def upload(ctx):
"""Upload files to Azure Storage"""
settings.add_cli_options(ctx.cli_options, settings.TransferAction.Upload)
ctx.initialize(settings.TransferAction.Upload)
specs = settings.create_upload_specifications(
ctx.cli_options, ctx.config)
del ctx.cli_options
for spec in specs:
blobxfer.api.Uploader(
ctx.general_options, ctx.credentials, spec
).start() | python | def upload(ctx):
"""Upload files to Azure Storage"""
settings.add_cli_options(ctx.cli_options, settings.TransferAction.Upload)
ctx.initialize(settings.TransferAction.Upload)
specs = settings.create_upload_specifications(
ctx.cli_options, ctx.config)
del ctx.cli_options
for spec in specs:
blobxfer.api.Uploader(
ctx.general_options, ctx.credentials, spec
).start() | ['def', 'upload', '(', 'ctx', ')', ':', 'settings', '.', 'add_cli_options', '(', 'ctx', '.', 'cli_options', ',', 'settings', '.', 'TransferAction', '.', 'Upload', ')', 'ctx', '.', 'initialize', '(', 'settings', '.', 'TransferAction', '.', 'Upload', ')', 'specs', '=', 'settings', '.', 'create_upload_specifications', '(', 'ctx', '.', 'cli_options', ',', 'ctx', '.', 'config', ')', 'del', 'ctx', '.', 'cli_options', 'for', 'spec', 'in', 'specs', ':', 'blobxfer', '.', 'api', '.', 'Uploader', '(', 'ctx', '.', 'general_options', ',', 'ctx', '.', 'credentials', ',', 'spec', ')', '.', 'start', '(', ')'] | Upload files to Azure Storage | ['Upload', 'files', 'to', 'Azure', 'Storage'] | train | https://github.com/Azure/blobxfer/blob/3eccbe7530cc6a20ab2d30f9e034b6f021817f34/cli/cli.py#L1106-L1116 |
32 | mitsei/dlkit | dlkit/json_/resource/sessions.py | ResourceBinAssignmentSession.get_assignable_bin_ids | def get_assignable_bin_ids(self, bin_id):
"""Gets a list of bins including and under the given bin node in which any resource can be assigned.
arg: bin_id (osid.id.Id): the ``Id`` of the ``Bin``
return: (osid.id.IdList) - list of assignable bin ``Ids``
raise: NullArgument - ``bin_id`` is ``null``
raise: OperationFailed - unable to complete request
*compliance: mandatory -- This method must be implemented.*
"""
# Implemented from template for
# osid.resource.ResourceBinAssignmentSession.get_assignable_bin_ids
# This will likely be overridden by an authorization adapter
mgr = self._get_provider_manager('RESOURCE', local=True)
lookup_session = mgr.get_bin_lookup_session(proxy=self._proxy)
bins = lookup_session.get_bins()
id_list = []
for bin in bins:
id_list.append(bin.get_id())
return IdList(id_list) | python | def get_assignable_bin_ids(self, bin_id):
"""Gets a list of bins including and under the given bin node in which any resource can be assigned.
arg: bin_id (osid.id.Id): the ``Id`` of the ``Bin``
return: (osid.id.IdList) - list of assignable bin ``Ids``
raise: NullArgument - ``bin_id`` is ``null``
raise: OperationFailed - unable to complete request
*compliance: mandatory -- This method must be implemented.*
"""
# Implemented from template for
# osid.resource.ResourceBinAssignmentSession.get_assignable_bin_ids
# This will likely be overridden by an authorization adapter
mgr = self._get_provider_manager('RESOURCE', local=True)
lookup_session = mgr.get_bin_lookup_session(proxy=self._proxy)
bins = lookup_session.get_bins()
id_list = []
for bin in bins:
id_list.append(bin.get_id())
return IdList(id_list) | ['def', 'get_assignable_bin_ids', '(', 'self', ',', 'bin_id', ')', ':', '# Implemented from template for', '# osid.resource.ResourceBinAssignmentSession.get_assignable_bin_ids', '# This will likely be overridden by an authorization adapter', 'mgr', '=', 'self', '.', '_get_provider_manager', '(', "'RESOURCE'", ',', 'local', '=', 'True', ')', 'lookup_session', '=', 'mgr', '.', 'get_bin_lookup_session', '(', 'proxy', '=', 'self', '.', '_proxy', ')', 'bins', '=', 'lookup_session', '.', 'get_bins', '(', ')', 'id_list', '=', '[', ']', 'for', 'bin', 'in', 'bins', ':', 'id_list', '.', 'append', '(', 'bin', '.', 'get_id', '(', ')', ')', 'return', 'IdList', '(', 'id_list', ')'] | Gets a list of bins including and under the given bin node in which any resource can be assigned.
arg: bin_id (osid.id.Id): the ``Id`` of the ``Bin``
return: (osid.id.IdList) - list of assignable bin ``Ids``
raise: NullArgument - ``bin_id`` is ``null``
raise: OperationFailed - unable to complete request
*compliance: mandatory -- This method must be implemented.* | ['Gets', 'a', 'list', 'of', 'bins', 'including', 'and', 'under', 'the', 'given', 'bin', 'node', 'in', 'which', 'any', 'resource', 'can', 'be', 'assigned', '.'] | train | https://github.com/mitsei/dlkit/blob/445f968a175d61c8d92c0f617a3c17dc1dc7c584/dlkit/json_/resource/sessions.py#L1562-L1581 |
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