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sashs/filebytes | filebytes/pe.py | PE._getSectionForDataDirectoryEntry | def _getSectionForDataDirectoryEntry(self, data_directory_entry, sections):
"""Returns the section which contains the data of DataDirectory"""
for section in sections:
if data_directory_entry.VirtualAddress >= section.header.VirtualAddress and \
data_directory_entry.VirtualAddres... | python | def _getSectionForDataDirectoryEntry(self, data_directory_entry, sections):
"""Returns the section which contains the data of DataDirectory"""
for section in sections:
if data_directory_entry.VirtualAddress >= section.header.VirtualAddress and \
data_directory_entry.VirtualAddres... | [
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sashs/filebytes | filebytes/pe.py | PE._parseDataDirectory | def _parseDataDirectory(self, data, sections, imageNtHeaders):
"""Parses the entries of the DataDirectory and returns a list of the content"""
data_directory_data_list = [None for i in range(15)]
# parse DataDirectory[Export]
export_data_directory = imageNtHeaders.header.OptionalHeader.... | python | def _parseDataDirectory(self, data, sections, imageNtHeaders):
"""Parses the entries of the DataDirectory and returns a list of the content"""
data_directory_data_list = [None for i in range(15)]
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sashs/filebytes | filebytes/pe.py | PE._parseDataDirectoryExport | def _parseDataDirectoryExport(self, data, dataDirectoryEntry, exportSection):
"""Parses the EmportDataDirectory and returns an instance of ExportDirectoryData"""
if not exportSection:
return
functions = []
export_directory = IMAGE_EXPORT_DIRECTORY.from_buffer(exportSection.ra... | python | def _parseDataDirectoryExport(self, data, dataDirectoryEntry, exportSection):
"""Parses the EmportDataDirectory and returns an instance of ExportDirectoryData"""
if not exportSection:
return
functions = []
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sashs/filebytes | filebytes/pe.py | PE._parseDataDirectoryImport | def _parseDataDirectoryImport(self, dataDirectoryEntry, importSection):
"""Parses the ImportDataDirectory and returns a list of ImportDescriptorData"""
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raw_bytes = (c_ubyte * dataDirectoryEntry.Size).from_buffer(importSection.raw, to_offset(dataDirector... | python | def _parseDataDirectoryImport(self, dataDirectoryEntry, importSection):
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sashs/filebytes | filebytes/pe.py | PE.__parseThunks | def __parseThunks(self, thunkRVA, importSection):
"""Parses the thunks and returns a list"""
offset = to_offset(thunkRVA, importSection)
table_offset = 0
thunks = []
while True:
thunk = IMAGE_THUNK_DATA.from_buffer(importSection.raw, offset)
offset += size... | python | def __parseThunks(self, thunkRVA, importSection):
"""Parses the thunks and returns a list"""
offset = to_offset(thunkRVA, importSection)
table_offset = 0
thunks = []
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offset += size... | [
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sashs/filebytes | filebytes/pe.py | PE.__parseThunkData | def __parseThunkData(self, thunk,importSection):
"""Parses the data of a thunk and sets the data"""
offset = to_offset(thunk.header.AddressOfData, importSection)
if 0xf0000000 & thunk.header.AddressOfData == 0x80000000:
thunk.ordinal = thunk.header.AddressOfData & 0x0fffffff
... | python | def __parseThunkData(self, thunk,importSection):
"""Parses the data of a thunk and sets the data"""
offset = to_offset(thunk.header.AddressOfData, importSection)
if 0xf0000000 & thunk.header.AddressOfData == 0x80000000:
thunk.ordinal = thunk.header.AddressOfData & 0x0fffffff
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sashs/filebytes | filebytes/ctypes_helper.py | get_ptr | def get_ptr(data, offset=None, ptr_type=ctypes.c_void_p):
"""Returns a void pointer to the data"""
ptr = ctypes.cast(ctypes.pointer(data), ctypes.c_void_p)
if offset:
ptr = ctypes.c_void_p(ptr.value + offset)
if ptr_type != ctypes.c_void_p:
ptr = ctypes.cast(ptr, ptr_type)
return ... | python | def get_ptr(data, offset=None, ptr_type=ctypes.c_void_p):
"""Returns a void pointer to the data"""
ptr = ctypes.cast(ctypes.pointer(data), ctypes.c_void_p)
if offset:
ptr = ctypes.c_void_p(ptr.value + offset)
if ptr_type != ctypes.c_void_p:
ptr = ctypes.cast(ptr, ptr_type)
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sashs/filebytes | filebytes/ctypes_helper.py | to_ubyte_array | def to_ubyte_array(barray):
"""Returns a c_ubyte_array filled with the given data of a bytearray or bytes"""
bs = (ctypes.c_ubyte * len(barray))()
pack_into('%ds' % len(barray), bs, 0, barray)
return bs | python | def to_ubyte_array(barray):
"""Returns a c_ubyte_array filled with the given data of a bytearray or bytes"""
bs = (ctypes.c_ubyte * len(barray))()
pack_into('%ds' % len(barray), bs, 0, barray)
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sashs/filebytes | filebytes/binary.py | Binary._readFile | def _readFile(self, fileName):
"""
Returns the bytes of the file.
"""
with open(fileName, 'rb') as binFile:
b = binFile.read()
return to_ubyte_array(b) | python | def _readFile(self, fileName):
"""
Returns the bytes of the file.
"""
with open(fileName, 'rb') as binFile:
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sashs/filebytes | filebytes/elf.py | ELF._getSuitableClasses | def _getSuitableClasses(self, data):
"""Returns the class which holds the suitable classes for the loaded file"""
classes = None
if data[EI.CLASS] == ELFCLASS.BITS_32:
if data[EI.DATA] == ELFDATA.LSB:
classes = LSB_32
elif data[EI.DATA] == ELFDATA.MSB:
... | python | def _getSuitableClasses(self, data):
"""Returns the class which holds the suitable classes for the loaded file"""
classes = None
if data[EI.CLASS] == ELFCLASS.BITS_32:
if data[EI.DATA] == ELFDATA.LSB:
classes = LSB_32
elif data[EI.DATA] == ELFDATA.MSB:
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sashs/filebytes | filebytes/elf.py | ELF._parseElfHeader | def _parseElfHeader(self, data):
"""Returns the elf header"""
ehdr = self.__classes.EHDR.from_buffer(data)
return EhdrData(header=ehdr) | python | def _parseElfHeader(self, data):
"""Returns the elf header"""
ehdr = self.__classes.EHDR.from_buffer(data)
return EhdrData(header=ehdr) | [
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sashs/filebytes | filebytes/elf.py | ELF._parseSegments | def _parseSegments(self, data, elfHeader):
"""Return a list of segments"""
offset = elfHeader.header.e_phoff
segments = []
for i in range(elfHeader.header.e_phnum):
phdr = self.__classes.PHDR.from_buffer(data, offset)
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sashs/filebytes | filebytes/elf.py | ELF._parseSections | def _parseSections(self, data, elfHeader):
"""Returns a list of sections"""
offset = elfHeader.header.e_shoff
shdrs = []
for i in range(elfHeader.header.e_shnum):
shdr = self.__classes.SHDR.from_buffer(data, offset)
section_bytes = None
ba_section_byte... | python | def _parseSections(self, data, elfHeader):
"""Returns a list of sections"""
offset = elfHeader.header.e_shoff
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sashs/filebytes | filebytes/elf.py | ELF._parseSymbols | def _parseSymbols(self, sections):
"""Sets a list of symbols in each DYNSYM and SYMTAB section"""
for section in sections:
strtab = sections[section.header.sh_link]
if section.header.sh_type in (int(SHT.DYNSYM), int(SHT.SYMTAB)):
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sashs/filebytes | filebytes/elf.py | ELF._parseRelocations | def _parseRelocations(self, sections):
"""Parses the relocations and add those to the section"""
for section in sections:
if section.header.sh_link != SHN.UNDEF and section.header.sh_type in (SHT.REL, SHT.RELA):
symbols = sections[section.header.sh_link].symbols
... | python | def _parseRelocations(self, sections):
"""Parses the relocations and add those to the section"""
for section in sections:
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pyqg/pyqg | pyqg/model.py | run_with_snapshots | def run_with_snapshots(self, tsnapstart=0., tsnapint=432000.):
"""Run the model forward, yielding to user code at specified intervals.
Parameters
----------
tsnapstart : int
The timestep at which to begin yielding.
tstapint : int
The interval at which to... | python | def run_with_snapshots(self, tsnapstart=0., tsnapint=432000.):
"""Run the model forward, yielding to user code at specified intervals.
Parameters
----------
tsnapstart : int
The timestep at which to begin yielding.
tstapint : int
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pyqg/pyqg | pyqg/model.py | vertical_modes | def vertical_modes(self):
""" Calculate standard vertical modes. Simply
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evals,evecs = np.linalg.eig(-self.S)
asort = evals.argsort()
# deformation wavenumbers and radii
self.kdi2 = evals[asort]
self.radii = np... | python | def vertical_modes(self):
""" Calculate standard vertical modes. Simply
the eigenvectors of the stretching matrix S """
evals,evecs = np.linalg.eig(-self.S)
asort = evals.argsort()
# deformation wavenumbers and radii
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pyqg/pyqg | pyqg/model.py | modal_projection | def modal_projection(self,p,forward=True):
""" Performs a field p into modal amplitudes pn
using the basis [pmodes]. The inverse
transform calculates p from pn"""
if forward:
pt = np.linalg.solve(self.pmodes[np.newaxis,np.newaxis],p.T).T
else:
... | python | def modal_projection(self,p,forward=True):
""" Performs a field p into modal amplitudes pn
using the basis [pmodes]. The inverse
transform calculates p from pn"""
if forward:
pt = np.linalg.solve(self.pmodes[np.newaxis,np.newaxis],p.T).T
else:
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pyqg/pyqg | pyqg/sqg_model.py | SQGModel._initialize_background | def _initialize_background(self):
"""Set up background state (zonal flow and PV gradients)."""
# background vel.
if len(np.shape(self.U)) == 0:
self.U = (self.U * np.ones((self.ny)))
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# the meridional PV gradients in e... | python | def _initialize_background(self):
"""Set up background state (zonal flow and PV gradients)."""
# background vel.
if len(np.shape(self.U)) == 0:
self.U = (self.U * np.ones((self.ny)))
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pyqg/pyqg | pyqg/sqg_model.py | SQGModel._initialize_inversion_matrix | def _initialize_inversion_matrix(self):
""" the inversion """
# The sqg model is diagonal. The inversion is simply qh = -kappa**2 ph
self.a = np.asarray(self.Nb*np.sqrt(self.wv2i))[np.newaxis, np.newaxis, :, :] | python | def _initialize_inversion_matrix(self):
""" the inversion """
# The sqg model is diagonal. The inversion is simply qh = -kappa**2 ph
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pyqg/pyqg | pyqg/sqg_model.py | SQGModel.set_U | def set_U(self, U):
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pyqg/pyqg | pyqg/particles.py | LagrangianParticleArray2D.step_forward_with_function | def step_forward_with_function(self, uv0fun, uv1fun, dt):
"""Advance particles using a function to determine u and v.
Parameters
----------
uv0fun : function
Called like ``uv0fun(x,y)``. Should return the velocity field
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uv1fun(x,y)... | python | def step_forward_with_function(self, uv0fun, uv1fun, dt):
"""Advance particles using a function to determine u and v.
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----------
uv0fun : function
Called like ``uv0fun(x,y)``. Should return the velocity field
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pyqg/pyqg | pyqg/particles.py | LagrangianParticleArray2D._rk4_integrate | def _rk4_integrate(self, x, y, uv0fun, uv1fun, dt):
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u0, v0 = uv0fun(x, y)
k1u = dt*u0
k1v = dt*v0
x11 = self._wrap_x(x + 0.5*k1u)
y11 = self._wrap_y(y... | python | def _rk4_integrate(self, x, y, uv0fun, uv1fun, dt):
"""Integrates positions x, y using velocity functions
uv0fun, uv1fun. Returns dx and dy, the displacements."""
u0, v0 = uv0fun(x, y)
k1u = dt*u0
k1v = dt*v0
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pyqg/pyqg | pyqg/particles.py | LagrangianParticleArray2D._distance | def _distance(self, x0, y0, x1, y1):
"""Utitlity function to compute distance between points."""
dx = x1-x0
dy = y1-y0
# roll displacements across the borders
if self.pix:
dx[ dx > self.Lx/2 ] -= self.Lx
dx[ dx < -self.Lx/2 ] += self.Lx
if self.piy... | python | def _distance(self, x0, y0, x1, y1):
"""Utitlity function to compute distance between points."""
dx = x1-x0
dy = y1-y0
# roll displacements across the borders
if self.pix:
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pyqg/pyqg | pyqg/particles.py | GriddedLagrangianParticleArray2D.interpolate_gridded_scalar | def interpolate_gridded_scalar(self, x, y, c, order=1, pad=1, offset=0):
"""Interpolate gridded scalar C to points x,y.
Parameters
----------
x, y : array-like
Points at which to interpolate
c : array-like
The scalar, assumed to be defined on the ... | python | def interpolate_gridded_scalar(self, x, y, c, order=1, pad=1, offset=0):
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Parameters
----------
x, y : array-like
Points at which to interpolate
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pyqg/pyqg | pyqg/particles.py | GriddedLagrangianParticleArray2D.step_forward_with_gridded_uv | def step_forward_with_gridded_uv(self, U0, V0, U1, V1, dt, order=1):
"""Advance particles using a gridded velocity field. Because of the
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Parameters
----------
U0, V0 : array-like
... | python | def step_forward_with_gridded_uv(self, U0, V0, U1, V1, dt, order=1):
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Runga-Kutta timestepping, we need two velocity fields at different
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pyqg/pyqg | pyqg/diagnostic_tools.py | spec_var | def spec_var(model, ph):
"""Compute variance of ``p`` from Fourier coefficients ``ph``.
Parameters
----------
model : pyqg.Model instance
The model object from which `ph` originates
ph : complex array
The field on which to compute the variance
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-------
var_dens :... | python | def spec_var(model, ph):
"""Compute variance of ``p`` from Fourier coefficients ``ph``.
Parameters
----------
model : pyqg.Model instance
The model object from which `ph` originates
ph : complex array
The field on which to compute the variance
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pyqg/pyqg | pyqg/diagnostic_tools.py | spec_sum | def spec_sum(ph2):
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Parameters
----------
model : pyqg.Model instance
The model object from which `ph` originates
ph2 : real array
The field on which to compute the sum
Returns
-------
var_dens : float
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"""Compute total spectral sum of the real spectral quantity``ph^2``.
Parameters
----------
model : pyqg.Model instance
The model object from which `ph` originates
ph2 : real array
The field on which to compute the sum
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pyqg/pyqg | pyqg/diagnostic_tools.py | calc_ispec | def calc_ispec(model, ph):
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Parameters
----------
model : pyqg.Model instance
The model object from which `ph` originates
ph : complex array
The field on which to compute the variance
Returns
-------
kr : arra... | python | def calc_ispec(model, ph):
"""Compute isotropic spectrum `phr` of `ph` from 2D spectrum.
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model : pyqg.Model instance
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ph : complex array
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pyqg/pyqg | pyqg/layered_model.py | LayeredModel._initialize_stretching_matrix | def _initialize_stretching_matrix(self):
""" Set up the stretching matrix """
self.S = np.zeros((self.nz, self.nz))
if (self.nz==2) and (self.rd) and (self.delta):
self.del1 = self.delta/(self.delta+1.)
self.del2 = (self.delta+1.)**-1
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""" Set up the stretching matrix """
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pyqg/pyqg | pyqg/layered_model.py | LayeredModel._initialize_background | def _initialize_background(self):
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self.Ubg = self.U * np.ones((self.ny))
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self.Ubg = np.expand_dims(self.U,axis=1) * np.ones((self.ny))
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""" estimate the eddy turn-over time in days """
ens = 0.
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ens = .5*self.Hi[j] * self.spec_var(self.wv2*self.ph[j])
return 2.*pi*np.sqrt( self.H / ens.sum() ) / 86400 | python | def _calc_eddy_time(self):
""" estimate the eddy turn-over time in days """
ens = 0.
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pyqg/pyqg | pyqg/qg_model.py | QGModel.set_q1q2 | def set_q1q2(self, q1, q2, check=False):
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q1 : array-like
Upper layer PV anomaly in spatial coordinates.
q1 : array-like
Lower layer PV anomaly in spatial coordinates.
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q1 : array-like
Upper layer PV anomaly in spatial coordinates.
q1 : array-like
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Lower layer flow. Units: m/s
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U1 : number
Upper layer flow. Units: m/s
U2 : number
Lower layer flow. Units: m/s
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pyqg/pyqg | pyqg/qg_model.py | QGModel._calc_eddy_time | def _calc_eddy_time(self):
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""" estimate the eddy turn-over time in days """
ens = .5*self.Hi[0] * self.spec_var(self.wv2*self.ph1) + \
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pyqg/pyqg | pyqg/bt_model.py | BTModel._initialize_inversion_matrix | def _initialize_inversion_matrix(self):
""" the inversion """
# The bt model is diagonal. The inversion is simply qh = -kappa**2 ph
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pyqg/pyqg | pyqg/point_vortex.py | PointVortexArray2D.calc_uv | def calc_uv(self, x, y, prev=False):
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Assumes x and y are vortex positions and are ordered the same as
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assert len(x) == self.N
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pyqg/pyqg | pyqg/point_vortex.py | PointVortexArray2D.uv_at_xy | def uv_at_xy(self, x, y, x0, y0, s0):
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brentp/interlap | interlap.py | reduce | def reduce(args):
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[(2, 10)]
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[(2, 10)]
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iset = self._iset
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iset = self._iset
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gumblex/zhconv | zhconv/zhconv.py | loaddict | def loaddict(filename=DICTIONARY):
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gumblex/zhconv | zhconv/zhconv.py | getdict | def getdict(locale):
"""
Generate or get convertion dict cache for certain locale.
Dictionaries are loaded on demand.
"""
global zhcdicts, dict_zhcn, dict_zhsg, dict_zhtw, dict_zhhk, pfsdict
if zhcdicts is None:
loaddict(DICTIONARY)
if locale == 'zh-cn':
if dict_zhcn:
... | python | def getdict(locale):
"""
Generate or get convertion dict cache for certain locale.
Dictionaries are loaded on demand.
"""
global zhcdicts, dict_zhcn, dict_zhsg, dict_zhtw, dict_zhhk, pfsdict
if zhcdicts is None:
loaddict(DICTIONARY)
if locale == 'zh-cn':
if dict_zhcn:
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gumblex/zhconv | zhconv/zhconv.py | issimp | def issimp(s, full=False):
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Detect text is whether Simplified Chinese or Traditional Chinese.
Returns True for Simplified; False for Traditional; None for unknown.
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"""
Detect text is whether Simplified Chinese or Traditional Chinese.
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gumblex/zhconv | zhconv/zhconv.py | convtable2dict | def convtable2dict(convtable, locale, update=None):
"""
Convert a list of conversion dict to a dict for a certain locale.
>>> sorted(convtable2dict([{'zh-hk': '列斯', 'zh-hans': '利兹', 'zh': '利兹', 'zh-tw': '里茲'}, {':uni': '巨集', 'zh-cn': '宏'}], 'zh-cn').items())
[('列斯', '利兹'), ('利兹', '利兹'), ('巨集', '宏'), ('... | python | def convtable2dict(convtable, locale, update=None):
"""
Convert a list of conversion dict to a dict for a certain locale.
>>> sorted(convtable2dict([{'zh-hk': '列斯', 'zh-hans': '利兹', 'zh': '利兹', 'zh-tw': '里茲'}, {':uni': '巨集', 'zh-cn': '宏'}], 'zh-cn').items())
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gumblex/zhconv | zhconv/zhconv.py | tokenize | def tokenize(s, locale, update=None):
"""
Tokenize `s` according to corresponding locale dictionary.
Don't use this for serious text processing.
"""
zhdict = getdict(locale)
pfset = pfsdict[locale]
if update:
zhdict = zhdict.copy()
zhdict.update(update)
newset = set()... | python | def tokenize(s, locale, update=None):
"""
Tokenize `s` according to corresponding locale dictionary.
Don't use this for serious text processing.
"""
zhdict = getdict(locale)
pfset = pfsdict[locale]
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gumblex/zhconv | zhconv/zhconv.py | convert_for_mw | def convert_for_mw(s, locale, update=None):
"""
Recognizes MediaWiki's human conversion format.
Use locale='zh' for no conversion.
Reference: (all tests passed)
https://zh.wikipedia.org/wiki/Help:高级字词转换语法
https://www.mediawiki.org/wiki/Writing_systems/Syntax
>>> print(convert_for_mw('在现代，机... | python | def convert_for_mw(s, locale, update=None):
"""
Recognizes MediaWiki's human conversion format.
Use locale='zh' for no conversion.
Reference: (all tests passed)
https://zh.wikipedia.org/wiki/Help:高级字词转换语法
https://www.mediawiki.org/wiki/Writing_systems/Syntax
>>> print(convert_for_mw('在现代，机... | [
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https://www.mediawiki.org/wiki/Writing_systems/Syntax
>>> print(convert_for_mw('在现代，机械计算-{}-机的应用已经完全被电子计算-{}-机所取代', 'zh-hk'))
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gumblex/zhconv | zhconv/zhconv.py | main | def main():
"""
Simple stdin/stdout interface.
"""
if len(sys.argv) == 2 and sys.argv[1] in Locales:
locale = sys.argv[1]
convertfunc = convert
elif len(sys.argv) == 3 and sys.argv[1] == '-w' and sys.argv[2] in Locales:
locale = sys.argv[2]
convertfunc = convert_for_m... | python | def main():
"""
Simple stdin/stdout interface.
"""
if len(sys.argv) == 2 and sys.argv[1] in Locales:
locale = sys.argv[1]
convertfunc = convert
elif len(sys.argv) == 3 and sys.argv[1] == '-w' and sys.argv[2] in Locales:
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glasslion/django-qiniu-storage | qiniustorage/utils.py | bucket_lister | def bucket_lister(manager, bucket_name, prefix=None, marker=None, limit=None):
"""
A generator function for listing keys in a bucket.
"""
eof = False
while not eof:
ret, eof, info = manager.list(bucket_name, prefix=prefix, limit=limit,
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"""
A generator function for listing keys in a bucket.
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glasslion/django-qiniu-storage | qiniustorage/backends.py | get_qiniu_config | def get_qiniu_config(name, default=None):
"""
Get configuration variable from environment variable
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"""
config = os.environ.get(name, getattr(settings, name, default))
if config is not None:
if isinstance(config, six.string_types):
return config.strip()
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"""
Get configuration variable from environment variable
or django setting.py
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config = os.environ.get(name, getattr(settings, name, default))
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non-Jedi/gyr | gyr/api.py | MatrixASHttpAPI.register | def register(self, username=""):
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Args:
username(str): Username to register.
"""
if not username:
username = utils.mxid2localpart(self.identity)
content = {
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"""Performs /register with type: m.login.application_service
Args:
username(str): Username to register.
"""
if not username:
username = utils.mxid2localpart(self.identity)
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ocaballeror/LyricFetch | lyricfetch/cli.py | load_from_file | def load_from_file(filename):
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Load a list of filenames from an external text file.
"""
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logger.error("Err: File '%s' is a directory", filename)
return None
if not os.path.isfile(filename):
logger.error("Err: File '%s' does not exist", filename)
... | python | def load_from_file(filename):
"""
Load a list of filenames from an external text file.
"""
if os.path.isdir(filename):
logger.error("Err: File '%s' is a directory", filename)
return None
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ocaballeror/LyricFetch | lyricfetch/cli.py | parse_argv | def parse_argv():
"""
Parse command line arguments. Settings will be stored in the global
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"""
parser = argparse.ArgumentParser(description='Find lyrics for a set of mp3'
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parser.add_argument('-j', ... | python | def parse_argv():
"""
Parse command line arguments. Settings will be stored in the global
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parser = argparse.ArgumentParser(description='Find lyrics for a set of mp3'
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ocaballeror/LyricFetch | lyricfetch/cli.py | main | def main():
"""
Main function.
"""
msg = ''
try:
songs = parse_argv()
if not songs:
msg = 'No songs specified'
except ValueError as error:
msg = str(error)
if msg:
logger.error('%s: Error: %s', sys.argv[0], msg)
return 1
logger.debug('... | python | def main():
"""
Main function.
"""
msg = ''
try:
songs = parse_argv()
if not songs:
msg = 'No songs specified'
except ValueError as error:
msg = str(error)
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taskcluster/slugid.py | slugid/slugid.py | decode | def decode(slug):
"""
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if sys.version_info.major != 2 and isinstance(slug, bytes):
slug = slug.decode('ascii')
slug = slug + '==' # base64 padding
return uuid.UUID(bytes=base64.urlsafe_b64decode(slug)) | python | def decode(slug):
"""
Returns the uuid.UUID object represented by the given v4 or "nice" slug
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if sys.version_info.major != 2 and isinstance(slug, bytes):
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taskcluster/slugid.py | slugid/slugid.py | nice | def nice():
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inodb/sufam | sufam/mutation.py | MutationsAtSinglePosition.filter_against_normal | def filter_against_normal(self, normal_mutations, maf_min=0.2,
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"""Filters mutations that are in the given normal"""
assert(normal_mutations.chrom == self.chrom)
assert(normal_mutations.pos == self.pos)
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tipsi/tipsi_tools | tipsi_tools/monitoring.py | log_mon_value | def log_mon_value(name, value=1, **kwargs):
"""
simplest monitoring function to be aggregated with sum
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simplest monitoring function to be aggregated with sum
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alfredodeza/notario | notario/store.py | create_store | def create_store():
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tipsi/tipsi_tools | tipsi_tools/drf/__init__.py | use_form | def use_form(form_class, request=None, **top_kwargs):
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craigahobbs/chisel | src/chisel/request.py | request | def request(request_callback=None, **kwargs):
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Chisel request decorator
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bierschenk/ode | examples/example_2d_orbit.py | dx_orbit_sys | def dx_orbit_sys(t, X):
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Parsely/redis-fluster | fluster/penalty_box.py | PenaltyBox.add | def add(self, client):
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log.info("%r is already in the penalty box. Ignoring.", client)
return
release = time.time() + self._min_wait
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"""Add a client to the penalty box."""
if client.pool_id in self._client_ids:
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alfredodeza/notario | notario/validators/types.py | string | def string(_object):
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.. note::
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alfredodeza/notario | notario/validators/types.py | array | def array(_object):
"""
Validates a given input is of type list.
Example usage::
data = {'a' : [1,2]}
schema = ('a', array)
You can also use this as a decorator, as a way to check for the
input before it even hits a validator you may be writing.
.. note::
If the argum... | python | def array(_object):
"""
Validates a given input is of type list.
Example usage::
data = {'a' : [1,2]}
schema = ('a', array)
You can also use this as a decorator, as a way to check for the
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"""
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Example usage::
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schema = ('a', integer)
You can also use this as a decorator, as a way to check for the
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.. note::
If the argu... | python | def integer(_object):
"""
Validates a given input is of type int..
Example usage::
data = {'a' : 21}
schema = ('a', integer)
You can also use this as a decorator, as a way to check for the
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.constant | def constant(cls,
value: Value,
dtype: tf.DType = tf.float32) -> 'TensorFluent':
'''Returns a constant `value` TensorFluent with given `dtype`.
Args:
value: The constant value.
dtype: The output's data type.
Returns:
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value: Value,
dtype: tf.DType = tf.float32) -> 'TensorFluent':
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value: The constant value.
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batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
'''Returns a TensorFluent for the Bernoulli sampling op with given mean parameter.
Args:
mean: The mean parameter of the Bernoulli distribution.
bat... | python | def Bernoulli(cls,
mean: 'TensorFluent',
batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.Uniform | def Uniform(cls,
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batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
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low: 'TensorFluent', high: 'TensorFluent',
batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.Normal | def Normal(cls,
mean: 'TensorFluent', variance: 'TensorFluent',
batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
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mean: The mean parameter of the Normal d... | python | def Normal(cls,
mean: 'TensorFluent', variance: 'TensorFluent',
batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.Gamma | def Gamma(cls,
shape: 'TensorFluent',
scale: 'TensorFluent',
batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
'''Returns a TensorFluent for the Gamma sampling op with given shape and scale parameters.
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shape: The shape parame... | python | def Gamma(cls,
shape: 'TensorFluent',
scale: 'TensorFluent',
batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.Exponential | def Exponential(cls,
mean: 'TensorFluent',
batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
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Args:
mean: The mean parameter of the Exponential distribution.
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mean: 'TensorFluent',
batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.stop_gradient | def stop_gradient(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a copy of the input fluent with stop_gradient at tensor level.
Args:
x: The input fluent.
Returns:
A TensorFluent that stops backpropagation of gradient computations.
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scope = x.s... | python | def stop_gradient(cls, x: 'TensorFluent') -> 'TensorFluent':
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Returns:
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.exp | def exp(cls, x: 'TensorFluent') -> 'TensorFluent':
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Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the exp function.
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x: The input fluent.
Returns:
A TensorFluent wrapping the log function.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.sqrt | def sqrt(cls, x: 'TensorFluent') -> 'TensorFluent':
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x: The input fluent.
Returns:
A TensorFluent wrapping the sqrt function.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.cos | def cos(cls, x: 'TensorFluent') -> 'TensorFluent':
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Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the cos function.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.sin | def sin(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the sin function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the sin function.
'''
return cls._unary_op(x, tf.sin, tf.float32) | python | def sin(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the sin function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the sin function.
'''
return cls._unary_op(x, tf.sin, tf.float32) | [
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.tan | def tan(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the tan function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the tan function.
'''
return cls._unary_op(x, tf.tan, tf.float32) | python | def tan(cls, x: 'TensorFluent') -> 'TensorFluent':
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.acos | def acos(cls, x: 'TensorFluent') -> 'TensorFluent':
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Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the arccos function.
'''
return cls._unary_op(x, tf.acos, tf.float32) | python | def acos(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the arccos function.
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A TensorFluent wrapping the arccos function.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.asin | def asin(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the arcsin function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the arcsin function.
'''
return cls._unary_op(x, tf.asin, tf.float32) | python | def asin(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the arcsin function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the arcsin function.
'''
return cls._unary_op(x, tf.asin, tf.float32) | [
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.atan | def atan(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the arctan function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the arctan function.
'''
return cls._unary_op(x, tf.atan2, tf.float32) | python | def atan(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the arctan function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the arctan function.
'''
return cls._unary_op(x, tf.atan2, tf.float32) | [
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.round | def round(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the round function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the round function.
'''
return cls._unary_op(x, tf.round, tf.float32) | python | def round(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the round function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the round function.
'''
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.ceil | def ceil(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the ceil function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the ceil function.
'''
return cls._unary_op(x, tf.ceil, tf.float32) | python | def ceil(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the ceil function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the ceil function.
'''
return cls._unary_op(x, tf.ceil, tf.float32) | [
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.floor | def floor(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the floor function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the floor function.
'''
return cls._unary_op(x, tf.floor, tf.float32) | python | def floor(cls, x: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the floor function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the floor function.
'''
return cls._unary_op(x, tf.floor, tf.float32) | [
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.pow | def pow(cls, x: 'TensorFluent', y: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the pow function.TensorFluent
Args:
x: The first operand.
y: The second operand.
Returns:
A TensorFluent wrapping the pow function.
'''
return... | python | def pow(cls, x: 'TensorFluent', y: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the pow function.TensorFluent
Args:
x: The first operand.
y: The second operand.
Returns:
A TensorFluent wrapping the pow function.
'''
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.max | def max(cls, x: 'TensorFluent', y: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the maximum function.TensorFluent
Args:
x: The first operand.
y: The second operand.
Returns:
A TensorFluent wrapping the maximum function.
'''
... | python | def max(cls, x: 'TensorFluent', y: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the maximum function.TensorFluent
Args:
x: The first operand.
y: The second operand.
Returns:
A TensorFluent wrapping the maximum function.
'''
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