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obulkin/string-dist | stringdist/pystringdist/rdlevenshtein.py | rdlevenshtein_norm | def rdlevenshtein_norm(source, target):
"""Calculates the normalized restricted Damerau-Levenshtein distance
(a.k.a. the normalized optimal string alignment distance) between two
string arguments. The result will be a float in the range [0.0, 1.0], with
1.0 signifying the maximum distance between string... | python | def rdlevenshtein_norm(source, target):
"""Calculates the normalized restricted Damerau-Levenshtein distance
(a.k.a. the normalized optimal string alignment distance) between two
string arguments. The result will be a float in the range [0.0, 1.0], with
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obulkin/string-dist | stringdist/pystringdist/levenshtein_shared.py | _levenshtein_compute | def _levenshtein_compute(source, target, rd_flag):
"""Computes the Levenshtein
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and restricted Damerau-Levenshtein
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mretegan/crispy | setup.py | main | def main():
"""The main entry point."""
if sys.version_info < (2, 7):
sys.exit('crispy requires at least Python 2.7')
elif sys.version_info[0] == 3 and sys.version_info < (3, 4):
sys.exit('crispy requires at least Python 3.4')
kwargs = dict(
name='crispy',
version=get_ve... | python | def main():
"""The main entry point."""
if sys.version_info < (2, 7):
sys.exit('crispy requires at least Python 2.7')
elif sys.version_info[0] == 3 and sys.version_info < (3, 4):
sys.exit('crispy requires at least Python 3.4')
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mretegan/crispy | crispy/gui/quanty.py | QuantySpectra.loadFromDisk | def loadFromDisk(self, calculation):
"""
Read the spectra from the files generated by Quanty and store them
as a list of spectum objects.
"""
suffixes = {
'Isotropic': 'iso',
'Circular Dichroism (R-L)': 'cd',
'Right Polarized (R)': 'r',
... | python | def loadFromDisk(self, calculation):
"""
Read the spectra from the files generated by Quanty and store them
as a list of spectum objects.
"""
suffixes = {
'Isotropic': 'iso',
'Circular Dichroism (R-L)': 'cd',
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mretegan/crispy | crispy/gui/quanty.py | QuantyDockWidget.populateWidget | def populateWidget(self):
"""
Populate the widget using data stored in the state
object. The order in which the individual widgets are populated
follows their arrangment.
The models are recreated every time the function is called.
This might seem to be an overkill, but i... | python | def populateWidget(self):
"""
Populate the widget using data stored in the state
object. The order in which the individual widgets are populated
follows their arrangment.
The models are recreated every time the function is called.
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mretegan/crispy | crispy/gui/quanty.py | QuantyDockWidget.updateResultsView | def updateResultsView(self, index):
"""
Update the selection to contain only the result specified by
the index. This should be the last index of the model. Finally updade
the context menu.
The selectionChanged signal is used to trigger the update of
the Quanty dock widge... | python | def updateResultsView(self, index):
"""
Update the selection to contain only the result specified by
the index. This should be the last index of the model. Finally updade
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mretegan/crispy | crispy/gui/quanty.py | QuantyDockWidget.updatePlotWidget | def updatePlotWidget(self):
"""Updating the plotting widget should not require any information
about the current state of the widget."""
pw = self.getPlotWidget()
pw.reset()
results = self.resultsModel.getCheckedItems()
for result in results:
if isinstance(r... | python | def updatePlotWidget(self):
"""Updating the plotting widget should not require any information
about the current state of the widget."""
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pw.reset()
results = self.resultsModel.getCheckedItems()
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mretegan/crispy | crispy/gui/models.py | HamiltonianItem.row | def row(self):
"""Return the row of the child."""
if self.parent is not None:
children = self.parent.getChildren()
# The index method of the list object.
return children.index(self)
else:
return 0 | python | def row(self):
"""Return the row of the child."""
if self.parent is not None:
children = self.parent.getChildren()
# The index method of the list object.
return children.index(self)
else:
return 0 | [
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mretegan/crispy | crispy/gui/models.py | HamiltonianModel.index | def index(self, row, column, parent=QModelIndex()):
"""Return the index of the item in the model specified by the
given row, column, and parent index.
"""
if parent is not None and not parent.isValid():
parentItem = self.rootItem
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mretegan/crispy | crispy/gui/models.py | HamiltonianModel.parent | def parent(self, index):
"""Return the index of the parent for a given index of the
child. Unfortunately, the name of the method has to be parent,
even though a more verbose name like parentIndex, would avoid
confusion about what parent actually is - an index or an item.
"""
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"""Return the index of the parent for a given index of the
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mretegan/crispy | crispy/gui/models.py | HamiltonianModel.rowCount | def rowCount(self, parentIndex):
"""Return the number of rows under the given parent. When the
parentIndex is valid, rowCount() returns the number of children
of the parent. For this it uses item() method to extract the
parentItem from the parentIndex, and calls the childCount() of
... | python | def rowCount(self, parentIndex):
"""Return the number of rows under the given parent. When the
parentIndex is valid, rowCount() returns the number of children
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mretegan/crispy | crispy/gui/models.py | HamiltonianModel.data | def data(self, index, role):
"""Return role specific data for the item referred by
index.column()."""
if not index.isValid():
return
item = self.item(index)
column = index.column()
value = item.getItemData(column)
if role == Qt.DisplayRole:
... | python | def data(self, index, role):
"""Return role specific data for the item referred by
index.column()."""
if not index.isValid():
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item = self.item(index)
column = index.column()
value = item.getItemData(column)
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mretegan/crispy | crispy/gui/models.py | HamiltonianModel.setData | def setData(self, index, value, role):
"""Set the role data for the item at index to value."""
if not index.isValid():
return False
item = self.item(index)
column = index.column()
if role == Qt.EditRole:
items = list()
items.append(item)
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if not index.isValid():
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mretegan/crispy | crispy/gui/models.py | HamiltonianModel.flags | def flags(self, index):
"""Return the active flags for the given index. Add editable
flag to items other than the first column.
"""
activeFlags = (Qt.ItemIsEnabled | Qt.ItemIsSelectable |
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column = ind... | python | def flags(self, index):
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"""
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mretegan/crispy | crispy/gui/models.py | HamiltonianModel._getModelData | def _getModelData(self, modelData, parentItem=None):
"""Return the data contained in the model."""
if parentItem is None:
parentItem = self.rootItem
for item in parentItem.getChildren():
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mretegan/crispy | crispy/gui/models.py | HamiltonianModel.getNodesCheckState | def getNodesCheckState(self, parentItem=None):
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"""
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parentItem = self.rootItem
checkStates = odict()
children = parentItem.getChildren()
... | python | def getNodesCheckState(self, parentItem=None):
"""Return the check state (disabled, tristate, enable) of all items
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mretegan/crispy | crispy/version.py | calc_hexversion | def calc_hexversion(major=0, minor=0, micro=0, releaselevel='dev', serial=0):
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:param minor: integer
:param micro: integer
:param relev: integer or string
:param serial: integer
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mretegan/crispy | crispy/gui/plot.py | MainPlotWidget._contextMenu | def _contextMenu(self, pos):
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:param QPoint pos: Mouse position relative to plot area
"""
# Create the context menu.
menu = QMenu(self)
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mretegan/crispy | crispy/utils/broaden.py | convolve_fft | def convolve_fft(array, kernel):
"""
Convolve an array with a kernel using FFT.
Implemntation based on the convolve_fft function from astropy.
https://github.com/astropy/astropy/blob/master/astropy/convolution/convolve.py
"""
array = np.asarray(array, dtype=np.complex)
kernel = np.asarray(... | python | def convolve_fft(array, kernel):
"""
Convolve an array with a kernel using FFT.
Implemntation based on the convolve_fft function from astropy.
https://github.com/astropy/astropy/blob/master/astropy/convolution/convolve.py
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mretegan/crispy | crispy/modules/orca/parser.py | Tensor.diagonalize | def diagonalize(self):
'''Diagonalize the tensor.'''
self.eigvals, self.eigvecs = np.linalg.eig(
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'''Diagonalize the tensor.'''
self.eigvals, self.eigvecs = np.linalg.eig(
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mretegan/crispy | crispy/modules/orca/parser.py | Tensor.euler_angles_and_eigenframes | def euler_angles_and_eigenframes(self):
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mretegan/crispy | crispy/modules/orca/parser.py | OutputData._skip_lines | def _skip_lines(self, n):
'''Skip a number of lines from the output.'''
for i in range(n):
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return self.line | python | def _skip_lines(self, n):
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mretegan/crispy | crispy/modules/orca/parser.py | OutputData._parse_tensor | def _parse_tensor(self, indices=False):
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tensor = np.zeros((3, 3))
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mretegan/crispy | crispy/modules/orca/parser.py | OutputData.parse | def parse(self):
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for self.line in self.output:
# Parse general data: charge, multiplicity, coordinates, etc.
self.index = 0
if self.line[1:13] == 'Total Charge':
tokens = self.line.split()
... | python | def parse(self):
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xeBuz/Flask-Validator | flask_validator/validator.py | FlaskValidator.__validate | def __validate(self, target, value, oldvalue, initiator):
""" Method executed when the event 'set' is triggered.
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:param oldvalue: Previous value
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xeBuz/Flask-Validator | flask_validator/validator.py | FlaskValidator.__create_event | def __create_event(self):
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xeBuz/Flask-Validator | flask_validator/validator.py | FlaskValidator.stop | def stop(self):
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doanguyen/lasotuvi | lasotuvi/DiaBan.py | diaBan.nhapDaiHan | def nhapDaiHan(self, cucSo, gioiTinh):
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cucSo (TYPE): Description
gioiTinh (TYPE): Description
Returns:
TYPE: Description
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"""Nhap dai han
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cucSo (TYPE): Description
gioiTinh (TYPE): Description
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TYPE: Description
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tansey/gfl | pygfl/easy.py | solve_gfl | def solve_gfl(data, edges=None, weights=None,
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doanguyen/lasotuvi | lasotuvi/AmDuong.py | ngayThangNam | def ngayThangNam(nn, tt, nnnn, duongLich=True, timeZone=7):
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nn (TYPE): ngay
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tt (TYPE): thang
nnnn (TYPE): nam
duongLich (bool, optional): bool
timeZone (int, optional): +7 Vietnam
Returns:
TYPE: Description
Raises:
Exception: Description | [
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] | train | https://github.com/doanguyen/lasotuvi/blob/98383a3056f0a0633d6937d364c37eb788661c0d/lasotuvi/AmDuong.py#L218-L242 |
doanguyen/lasotuvi | lasotuvi/AmDuong.py | canChiNgay | def canChiNgay(nn, tt, nnnn, duongLich=True, timeZone=7, thangNhuan=False):
"""Summary
Args:
nn (int): ngày
tt (int): tháng
nnnn (int): năm
duongLich (bool, optional): True nếu là dương lịch, False âm lịch
timeZone (int, optional): Múi giờ
thangNhuan (bool, optio... | python | def canChiNgay(nn, tt, nnnn, duongLich=True, timeZone=7, thangNhuan=False):
"""Summary
Args:
nn (int): ngày
tt (int): tháng
nnnn (int): năm
duongLich (bool, optional): True nếu là dương lịch, False âm lịch
timeZone (int, optional): Múi giờ
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doanguyen/lasotuvi | lasotuvi/AmDuong.py | ngayThangNamCanChi | def ngayThangNamCanChi(nn, tt, nnnn, duongLich=True, timeZone=7):
"""chuyển đổi năm, tháng âm/dương lịch sang Can, Chi trong tiếng Việt.
Không tính đến can ngày vì phải chuyển đổi qua lịch Julius.
Hàm tìm can ngày là hàm canChiNgay(nn, tt, nnnn, duongLich=True,\
timeZone... | python | def ngayThangNamCanChi(nn, tt, nnnn, duongLich=True, timeZone=7):
"""chuyển đổi năm, tháng âm/dương lịch sang Can, Chi trong tiếng Việt.
Không tính đến can ngày vì phải chuyển đổi qua lịch Julius.
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doanguyen/lasotuvi | lasotuvi/AmDuong.py | nguHanh | def nguHanh(tenHanh):
"""
Args:
tenHanh (string): Tên Hành trong ngũ hành, Kim hoặc K, Moc hoặc M,
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Raises:
Exception: Description
"""
if tenHanh in ["Kim",... | python | def nguHanh(tenHanh):
"""
Args:
tenHanh (string): Tên Hành trong ngũ hành, Kim hoặc K, Moc hoặc M,
Thuy hoặc T, Hoa hoặc H, Tho hoặc O
Returns:
Dictionary: ID của Hành, tên đầy đủ của Hành, số Cục của Hành
Raises:
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doanguyen/lasotuvi | lasotuvi/AmDuong.py | nguHanhNapAm | def nguHanhNapAm(diaChi, thienCan, xuatBanMenh=False):
"""Sử dụng Ngũ Hành nạp âm để tính Hành của năm.
Args:
diaChi (integer): Số thứ tự của địa chi (Tý=1, Sửu=2,...)
thienCan (integer): Số thứ tự của thiên can (Giáp=1, Ất=2,...)
Returns:
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"""Sử dụng Ngũ Hành nạp âm để tính Hành của năm.
Args:
diaChi (integer): Số thứ tự của địa chi (Tý=1, Sửu=2,...)
thienCan (integer): Số thứ tự của thiên can (Giáp=1, Ất=2,...)
Returns:
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doanguyen/lasotuvi | lasotuvi/AmDuong.py | timTuVi | def timTuVi(cuc, ngaySinhAmLich):
"""Tìm vị trí của sao Tử vi
Args:
cuc (TYPE): Description
ngaySinhAmLich (TYPE): Description
Returns:
TYPE: Description
Raises:
Exception: Description
"""
cungDan = 3 # Vị trí cung Dần ban đầu là 3
cucBanDau = cuc
if c... | python | def timTuVi(cuc, ngaySinhAmLich):
"""Tìm vị trí của sao Tử vi
Args:
cuc (TYPE): Description
ngaySinhAmLich (TYPE): Description
Returns:
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zsethna/OLGA | olga/utils.py | nt2aa | def nt2aa(ntseq):
"""Translate a nucleotide sequence into an amino acid sequence.
Parameters
----------
ntseq : str
Nucleotide sequence composed of A, C, G, or T (uppercase or lowercase)
Returns
-------
aaseq : str
Amino acid sequence
Example
--------
>>> n... | python | def nt2aa(ntseq):
"""Translate a nucleotide sequence into an amino acid sequence.
Parameters
----------
ntseq : str
Nucleotide sequence composed of A, C, G, or T (uppercase or lowercase)
Returns
-------
aaseq : str
Amino acid sequence
Example
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zsethna/OLGA | olga/utils.py | nt2codon_rep | def nt2codon_rep(ntseq):
"""Represent nucleotide sequence by sequence of codon symbols.
'Translates' the nucleotide sequence into a symbolic representation of
'amino acids' where each codon gets its own unique character symbol. These
characters should be reserved only for representing the 64 individua... | python | def nt2codon_rep(ntseq):
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zsethna/OLGA | olga/utils.py | cutR_seq | def cutR_seq(seq, cutR, max_palindrome):
"""Cut genomic sequence from the right.
Parameters
----------
seq : str
Nucleotide sequence to be cut from the right
cutR : int
cutR - max_palindrome = how many nucleotides to cut from the right.
Negative cutR implies complementary pa... | python | def cutR_seq(seq, cutR, max_palindrome):
"""Cut genomic sequence from the right.
Parameters
----------
seq : str
Nucleotide sequence to be cut from the right
cutR : int
cutR - max_palindrome = how many nucleotides to cut from the right.
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zsethna/OLGA | olga/utils.py | cutL_seq | def cutL_seq(seq, cutL, max_palindrome):
"""Cut genomic sequence from the left.
Parameters
----------
seq : str
Nucleotide sequence to be cut from the right
cutL : int
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"""Cut genomic sequence from the left.
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seq : str
Nucleotide sequence to be cut from the right
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cutL - max_palindrome = how many nucleotides to cut from the left.
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zsethna/OLGA | olga/utils.py | construct_codons_dict | def construct_codons_dict(alphabet_file = None):
"""Generate the sub_codons_right dictionary of codon suffixes.
syntax of custom alphabet_files:
char: list,of,amino,acids,or,codons,separated,by,commas
Parameters
----------
alphabet_file : str
File name for a custom al... | python | def construct_codons_dict(alphabet_file = None):
"""Generate the sub_codons_right dictionary of codon suffixes.
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char: list,of,amino,acids,or,codons,separated,by,commas
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zsethna/OLGA | olga/utils.py | generate_sub_codons_left | def generate_sub_codons_left(codons_dict):
"""Generate the sub_codons_left dictionary of codon prefixes.
Parameters
----------
codons_dict : dict
Dictionary, keyed by the allowed 'amino acid' symbols with the values
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Returns
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codons_dict : dict
Dictionary, keyed by the allowed 'amino acid' symbols with the values
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zsethna/OLGA | olga/utils.py | generate_sub_codons_right | def generate_sub_codons_right(codons_dict):
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codons_dict : dict
Dictionary, keyed by the allowed 'amino acid' symbols with the values
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zsethna/OLGA | olga/utils.py | determine_seq_type | def determine_seq_type(seq, aa_alphabet):
"""Determine the type of a sequence.
Parameters
----------
seq : str
Sequence to be typed.
aa_alphabet : str
String of all characters recoginized as 'amino acids'. (i.e. the keys
of codons_dict: aa_alphabet = ''.join(codons_dict... | python | def determine_seq_type(seq, aa_alphabet):
"""Determine the type of a sequence.
Parameters
----------
seq : str
Sequence to be typed.
aa_alphabet : str
String of all characters recoginized as 'amino acids'. (i.e. the keys
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zsethna/OLGA | olga/utils.py | calc_steady_state_dist | def calc_steady_state_dist(R):
"""Calculate the steady state dist of a 4 state markov transition matrix.
Parameters
----------
R : ndarray
Markov transition matrix
Returns
-------
p_ss : ndarray
Steady state probability distribution
"""
#Calc steady state d... | python | def calc_steady_state_dist(R):
"""Calculate the steady state dist of a 4 state markov transition matrix.
Parameters
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R : ndarray
Markov transition matrix
Returns
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p_ss : ndarray
Steady state probability distribution
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zsethna/OLGA | olga/sequence_generation.py | rnd_ins_seq | def rnd_ins_seq(ins_len, C_R, CP_first_nt):
"""Generate a random insertion nucleotide sequence of length ins_len.
Draws the sequence identity (for a set length) from the distribution
defined by the dinucleotide markov model of transition matrix R.
Parameters
----------
ins_len : int
Le... | python | def rnd_ins_seq(ins_len, C_R, CP_first_nt):
"""Generate a random insertion nucleotide sequence of length ins_len.
Draws the sequence identity (for a set length) from the distribution
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zsethna/OLGA | olga/sequence_generation.py | SequenceGenerationVDJ.gen_rnd_prod_CDR3 | def gen_rnd_prod_CDR3(self, conserved_J_residues = 'FVW'):
"""Generate a productive CDR3 seq from a Monte Carlo draw of the model.
Parameters
----------
conserved_J_residues : str, optional
Conserved amino acid residues defining the CDR3 on the J side (normally
F... | python | def gen_rnd_prod_CDR3(self, conserved_J_residues = 'FVW'):
"""Generate a productive CDR3 seq from a Monte Carlo draw of the model.
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zsethna/OLGA | olga/sequence_generation.py | SequenceGenerationVDJ.choose_random_recomb_events | def choose_random_recomb_events(self):
"""Sample the genomic model for VDJ recombination events.
Returns
-------
recomb_events : dict
Dictionary of the VDJ recombination events. These are
integers determining gene choice, deletions, and number of insertions.
... | python | def choose_random_recomb_events(self):
"""Sample the genomic model for VDJ recombination events.
Returns
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zsethna/OLGA | olga/sequence_generation.py | SequenceGenerationVJ.gen_rnd_prod_CDR3 | def gen_rnd_prod_CDR3(self, conserved_J_residues = 'FVW'):
"""Generate a productive CDR3 seq from a Monte Carlo draw of the model.
Parameters
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conserved_J_residues : str, optional
Conserved amino acid residues defining the CDR3 on the J side (normally
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zsethna/OLGA | olga/sequence_generation.py | SequenceGenerationVJ.choose_random_recomb_events | def choose_random_recomb_events(self):
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Returns
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recomb_events : dict
Dictionary of the VDJ recombination events. These are
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evonove/django-money-rates | djmoney_rates/backends.py | BaseRateBackend.update_rates | def update_rates(self):
"""
Creates or updates rates for a source
"""
source, created = RateSource.objects.get_or_create(name=self.get_source_name())
source.base_currency = self.get_base_currency()
source.save()
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"""
Creates or updates rates for a source
"""
source, created = RateSource.objects.get_or_create(name=self.get_source_name())
source.base_currency = self.get_base_currency()
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tansey/gfl | pygfl/bayes.py | sample_gtf | def sample_gtf(data, D, k, likelihood='gaussian', prior='laplace',
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dp_hyperparameter=None, w_hyperparameters=None,
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zsethna/OLGA | olga/generation_probability.py | GenerationProbability.compute_regex_CDR3_template_pgen | def compute_regex_CDR3_template_pgen(self, regex_seq, V_usage_mask_in = None, J_usage_mask_in = None, print_warnings = True, raise_overload_warning = True):
"""Compute Pgen for all seqs consistent with regular expression regex_seq.
Computes Pgen for a (limited vocabulary) regular expression of CDR3... | python | def compute_regex_CDR3_template_pgen(self, regex_seq, V_usage_mask_in = None, J_usage_mask_in = None, print_warnings = True, raise_overload_warning = True):
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zsethna/OLGA | olga/generation_probability.py | GenerationProbability.compute_aa_CDR3_pgen | def compute_aa_CDR3_pgen(self, CDR3_seq, V_usage_mask_in = None, J_usage_mask_in = None, print_warnings = True):
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"""Compute Pgen for the amino acid sequence CDR3_seq.
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zsethna/OLGA | olga/generation_probability.py | GenerationProbability.compute_hamming_dist_1_pgen | def compute_hamming_dist_1_pgen(self, CDR3_seq, V_usage_mask_in = None, J_usage_mask_in = None, print_warnings = True):
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Please note that this function will list out all the
sequences that are hamming distance 1 from... | python | def compute_hamming_dist_1_pgen(self, CDR3_seq, V_usage_mask_in = None, J_usage_mask_in = None, print_warnings = True):
"""Compute Pgen of all seqs hamming dist 1 (in amino acids) from CDR3_seq.
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zsethna/OLGA | olga/generation_probability.py | GenerationProbability.compute_nt_CDR3_pgen | def compute_nt_CDR3_pgen(self, CDR3_ntseq, V_usage_mask_in = None, J_usage_mask_in = None, print_warnings = True):
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zsethna/OLGA | olga/generation_probability.py | GenerationProbability.format_usage_masks | def format_usage_masks(self, V_usage_mask_in, J_usage_mask_in, print_warnings = True):
"""Format raw usage masks into lists of indices.
Usage masks allows the Pgen computation to be conditioned on the V and J
gene/allele identities. The inputted masks are lists of strings, or a
si... | python | def format_usage_masks(self, V_usage_mask_in, J_usage_mask_in, print_warnings = True):
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zsethna/OLGA | olga/generation_probability.py | GenerationProbability.list_seqs_from_regex | def list_seqs_from_regex(self, regex_seq, print_warnings = True, raise_overload_warning = True):
"""List sequences that match regular expression template.
This function parses a limited regular expression vocabulary, and
lists all the sequences consistent with the regular expression. Suppo... | python | def list_seqs_from_regex(self, regex_seq, print_warnings = True, raise_overload_warning = True):
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zsethna/OLGA | olga/generation_probability.py | GenerationProbability.max_nt_to_aa_alignment_left | def max_nt_to_aa_alignment_left(self, CDR3_seq, ntseq):
"""Find maximum match between CDR3_seq and ntseq from the left.
This function returns the length of the maximum length nucleotide
subsequence of ntseq contiguous from the left (or 5' end) that is
consistent with the 'amino... | python | def max_nt_to_aa_alignment_left(self, CDR3_seq, ntseq):
"""Find maximum match between CDR3_seq and ntseq from the left.
This function returns the length of the maximum length nucleotide
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zsethna/OLGA | olga/generation_probability.py | GenerationProbability.max_nt_to_aa_alignment_right | def max_nt_to_aa_alignment_right(self, CDR3_seq, ntseq):
"""Find maximum match between CDR3_seq and ntseq from the right.
This function returns the length of the maximum length nucleotide
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVDJ.compute_CDR3_pgen | def compute_CDR3_pgen(self, CDR3_seq, V_usage_mask, J_usage_mask):
"""Compute Pgen for CDR3 'amino acid' sequence CDR3_seq from VDJ model.
Conditioned on the already formatted V genes/alleles indicated in
V_usage_mask and the J genes/alleles in J_usage_mask.
(Examples are TCRB seq... | python | def compute_CDR3_pgen(self, CDR3_seq, V_usage_mask, J_usage_mask):
"""Compute Pgen for CDR3 'amino acid' sequence CDR3_seq from VDJ model.
Conditioned on the already formatted V genes/alleles indicated in
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVDJ.compute_Pi_V | def compute_Pi_V(self, CDR3_seq, V_usage_mask):
"""Compute Pi_V.
This function returns the Pi array from the model factors of the V genomic
contributions, P(V)*P(delV|V). This corresponds to V_{x_1}.
For clarity in parsing the algorithm implementation, we include which
... | python | def compute_Pi_V(self, CDR3_seq, V_usage_mask):
"""Compute Pi_V.
This function returns the Pi array from the model factors of the V genomic
contributions, P(V)*P(delV|V). This corresponds to V_{x_1}.
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVDJ.compute_Pi_L | def compute_Pi_L(self, CDR3_seq, Pi_V, max_V_align):
"""Compute Pi_L.
This function returns the Pi array from the model factors of the V genomic
contributions, P(V)*P(delV|V), and the VD (N1) insertions,
first_nt_bias_insVD(m_1)PinsVD(\ell_{VD})\prod_{i=2}^{\ell_{VD}}Rvd(m_i|m_{i-1... | python | def compute_Pi_L(self, CDR3_seq, Pi_V, max_V_align):
"""Compute Pi_L.
This function returns the Pi array from the model factors of the V genomic
contributions, P(V)*P(delV|V), and the VD (N1) insertions,
first_nt_bias_insVD(m_1)PinsVD(\ell_{VD})\prod_{i=2}^{\ell_{VD}}Rvd(m_i|m_{i-1... | [
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVDJ.compute_Pi_J_given_D | def compute_Pi_J_given_D(self, CDR3_seq, J_usage_mask):
"""Compute Pi_J conditioned on D.
This function returns the Pi array from the model factors of the D and J
genomic contributions, P(D, J)*P(delJ|J) = P(D|J)P(J)P(delJ|J). This
corresponds to J(D)^{x_4}.
For c... | python | def compute_Pi_J_given_D(self, CDR3_seq, J_usage_mask):
"""Compute Pi_J conditioned on D.
This function returns the Pi array from the model factors of the D and J
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVDJ.compute_Pi_JinsDJ_given_D | def compute_Pi_JinsDJ_given_D(self, CDR3_seq, Pi_J_given_D, max_J_align):
"""Compute Pi_JinsDJ conditioned on D.
This function returns the Pi array from the model factors of the J genomic
contributions, P(D,J)*P(delJ|J), and the DJ (N2) insertions,
first_nt_bias_insDJ(n_1)PinsDJ(\e... | python | def compute_Pi_JinsDJ_given_D(self, CDR3_seq, Pi_J_given_D, max_J_align):
"""Compute Pi_JinsDJ conditioned on D.
This function returns the Pi array from the model factors of the J genomic
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVDJ.compute_Pi_R | def compute_Pi_R(self, CDR3_seq, Pi_JinsDJ_given_D):
"""Compute Pi_R.
This function returns the Pi array from the model factors of the D and J
genomic contributions, P(D, J)*P(delJ|J)P(delDl, delDr |D) and
the DJ (N2) insertions,
first_nt_bias_insDJ(n_1)PinsDJ(\ell_{DJ})\pr... | python | def compute_Pi_R(self, CDR3_seq, Pi_JinsDJ_given_D):
"""Compute Pi_R.
This function returns the Pi array from the model factors of the D and J
genomic contributions, P(D, J)*P(delJ|J)P(delDl, delDr |D) and
the DJ (N2) insertions,
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVJ.compute_CDR3_pgen | def compute_CDR3_pgen(self, CDR3_seq, V_usage_mask, J_usage_mask):
"""Compute Pgen for CDR3 'amino acid' sequence CDR3_seq from VJ model.
Conditioned on the already formatted V genes/alleles indicated in
V_usage_mask and the J genes/alleles in J_usage_mask.
Parameters
... | python | def compute_CDR3_pgen(self, CDR3_seq, V_usage_mask, J_usage_mask):
"""Compute Pgen for CDR3 'amino acid' sequence CDR3_seq from VJ model.
Conditioned on the already formatted V genes/alleles indicated in
V_usage_mask and the J genes/alleles in J_usage_mask.
Parameters
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVJ.compute_Pi_V_given_J | def compute_Pi_V_given_J(self, CDR3_seq, V_usage_mask, J_usage_mask):
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This function returns the Pi array from the model factors of the V genomic
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For clarity in parsing the a... | python | def compute_Pi_V_given_J(self, CDR3_seq, V_usage_mask, J_usage_mask):
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVJ.compute_Pi_V_insVJ_given_J | def compute_Pi_V_insVJ_given_J(self, CDR3_seq, Pi_V_given_J, max_V_align):
"""Compute Pi_V_insVJ conditioned on J.
This function returns the Pi array from the model factors of the V genomic
contributions, P(V, J)*P(delV|V), and the VJ (N) insertions,
first_nt_bias_insVJ(m_1)PinsVJ(... | python | def compute_Pi_V_insVJ_given_J(self, CDR3_seq, Pi_V_given_J, max_V_align):
"""Compute Pi_V_insVJ conditioned on J.
This function returns the Pi array from the model factors of the V genomic
contributions, P(V, J)*P(delV|V), and the VJ (N) insertions,
first_nt_bias_insVJ(m_1)PinsVJ(... | [
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zsethna/OLGA | olga/generation_probability.py | GenerationProbabilityVJ.compute_Pi_J | def compute_Pi_J(self, CDR3_seq, J_usage_mask):
"""Compute Pi_J.
This function returns the Pi array from the model factors of the J genomic
contributions, P(delJ|J). This corresponds to J(D)^{x_4}.
For clarity in parsing the algorithm implementation, we include which
... | python | def compute_Pi_J(self, CDR3_seq, J_usage_mask):
"""Compute Pi_J.
This function returns the Pi array from the model factors of the J genomic
contributions, P(delJ|J). This corresponds to J(D)^{x_4}.
For clarity in parsing the algorithm implementation, we include which
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tansey/gfl | pygfl/trendfiltering.py | TrendFilteringSolver.solve | def solve(self, lam):
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tansey/gfl | pygfl/trendfiltering.py | TrendFilteringSolver.solution_path | def solution_path(self, min_lambda, max_lambda, lambda_bins, verbose=0):
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'''Follows the solution path to find the best lambda value.'''
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tansey/gfl | pygfl/trendfiltering.py | LogitTrendFilteringSolver.solve | def solve(self, lam):
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zsethna/OLGA | olga/preprocess_generative_model_and_data.py | PreprocessedParameters.make_V_and_J_mask_mapping | def make_V_and_J_mask_mapping(self, genV, genJ):
"""Constructs the V and J mask mapping dictionaries.
Parameters
----------
genV : list
List of genomic V information.
genJ : list
List of genomic J information.
"""
... | python | def make_V_and_J_mask_mapping(self, genV, genJ):
"""Constructs the V and J mask mapping dictionaries.
Parameters
----------
genV : list
List of genomic V information.
genJ : list
List of genomic J information.
"""
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zsethna/OLGA | olga/preprocess_generative_model_and_data.py | PreprocessedParametersVDJ.preprocess_D_segs | def preprocess_D_segs(self, generative_model, genomic_data):
"""Process P(delDl, delDr|D) into Pi arrays.
Sets the attributes PD_nt_pos_vec, PD_2nd_nt_pos_per_aa_vec,
min_delDl_given_DdelDr, max_delDl_given_DdelDr, and zeroD_given_D.
Parameters
----------
g... | python | def preprocess_D_segs(self, generative_model, genomic_data):
"""Process P(delDl, delDr|D) into Pi arrays.
Sets the attributes PD_nt_pos_vec, PD_2nd_nt_pos_per_aa_vec,
min_delDl_given_DdelDr, max_delDl_given_DdelDr, and zeroD_given_D.
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zsethna/OLGA | olga/preprocess_generative_model_and_data.py | PreprocessedParametersVDJ.generate_PJdelJ_nt_pos_vecs | def generate_PJdelJ_nt_pos_vecs(self, generative_model, genomic_data):
"""Process P(J)*P(delJ|J) into Pi arrays.
Sets the attributes PJdelJ_nt_pos_vec and PJdelJ_2nd_nt_pos_per_aa_vec.
Parameters
----------
generative_model : GenerativeModelVDJ
VDJ gener... | python | def generate_PJdelJ_nt_pos_vecs(self, generative_model, genomic_data):
"""Process P(J)*P(delJ|J) into Pi arrays.
Sets the attributes PJdelJ_nt_pos_vec and PJdelJ_2nd_nt_pos_per_aa_vec.
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generative_model : GenerativeModelVDJ
VDJ gener... | [
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zsethna/OLGA | olga/preprocess_generative_model_and_data.py | PreprocessedParametersVDJ.generate_VD_junction_transfer_matrices | def generate_VD_junction_transfer_matrices(self):
"""Compute the transfer matrices for the VD junction.
Sets the attributes Tvd, Svd, Dvd, lTvd, and lDvd.
"""
nt2num = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
#Compute Tvd
Tvd ... | python | def generate_VD_junction_transfer_matrices(self):
"""Compute the transfer matrices for the VD junction.
Sets the attributes Tvd, Svd, Dvd, lTvd, and lDvd.
"""
nt2num = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
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zsethna/OLGA | olga/preprocess_generative_model_and_data.py | PreprocessedParametersVDJ.generate_DJ_junction_transfer_matrices | def generate_DJ_junction_transfer_matrices(self):
"""Compute the transfer matrices for the VD junction.
Sets the attributes Tdj, Sdj, Ddj, rTdj, and rDdj.
"""
nt2num = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
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Tdj = {}
for aa... | python | def generate_DJ_junction_transfer_matrices(self):
"""Compute the transfer matrices for the VD junction.
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zsethna/OLGA | olga/preprocess_generative_model_and_data.py | PreprocessedParametersVJ.generate_PVdelV_nt_pos_vecs | def generate_PVdelV_nt_pos_vecs(self, generative_model, genomic_data):
"""Process P(delV|V) into Pi arrays.
Set the attributes PVdelV_nt_pos_vec and PVdelV_2nd_nt_pos_per_aa_vec.
Parameters
----------
generative_model : GenerativeModelVJ
VJ generative mo... | python | def generate_PVdelV_nt_pos_vecs(self, generative_model, genomic_data):
"""Process P(delV|V) into Pi arrays.
Set the attributes PVdelV_nt_pos_vec and PVdelV_2nd_nt_pos_per_aa_vec.
Parameters
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generative_model : GenerativeModelVJ
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zsethna/OLGA | olga/preprocess_generative_model_and_data.py | PreprocessedParametersVJ.generate_PJdelJ_nt_pos_vecs | def generate_PJdelJ_nt_pos_vecs(self, generative_model, genomic_data):
"""Process P(delJ|J) into Pi arrays.
Set the attributes PJdelJ_nt_pos_vec and PJdelJ_2nd_nt_pos_per_aa_vec.
Parameters
----------
generative_model : GenerativeModelVJ
VJ generative mo... | python | def generate_PJdelJ_nt_pos_vecs(self, generative_model, genomic_data):
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generative_model : GenerativeModelVJ
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Set the attributes PJdelJ_nt_pos_vec and PJdelJ_2nd_nt_pos_per_aa_vec.
Parameters
----------
generative_model : GenerativeModelVJ
VJ generative model class containing the model parameters.
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zsethna/OLGA | olga/preprocess_generative_model_and_data.py | PreprocessedParametersVJ.generate_VJ_junction_transfer_matrices | def generate_VJ_junction_transfer_matrices(self):
"""Compute the transfer matrices for the VJ junction.
Sets the attributes Tvj, Svj, Dvj, lTvj, and lDvj.
"""
nt2num = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
#Compute Tvj
Tv... | python | def generate_VJ_junction_transfer_matrices(self):
"""Compute the transfer matrices for the VJ junction.
Sets the attributes Tvj, Svj, Dvj, lTvj, and lDvj.
"""
nt2num = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
#Compute Tvj
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crackinglandia/pype32 | tools/readpe.py | showDosHeaderData | def showDosHeaderData(peInstance):
""" Prints IMAGE_DOS_HEADER fields. """
dosFields = peInstance.dosHeader.getFields()
print "[+] IMAGE_DOS_HEADER values:\n"
for field in dosFields:
if isinstance(dosFields[field], datatypes.Array):
print "--> %s - Array of length %d" % (field,... | python | def showDosHeaderData(peInstance):
""" Prints IMAGE_DOS_HEADER fields. """
dosFields = peInstance.dosHeader.getFields()
print "[+] IMAGE_DOS_HEADER values:\n"
for field in dosFields:
if isinstance(dosFields[field], datatypes.Array):
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crackinglandia/pype32 | tools/readpe.py | showFileHeaderData | def showFileHeaderData(peInstance):
""" Prints IMAGE_FILE_HEADER fields. """
fileHeaderFields = peInstance.ntHeaders.fileHeader.getFields()
print "[+] IMAGE_FILE_HEADER values:\n"
for field in fileHeaderFields:
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""" Prints IMAGE_FILE_HEADER fields. """
fileHeaderFields = peInstance.ntHeaders.fileHeader.getFields()
print "[+] IMAGE_FILE_HEADER values:\n"
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crackinglandia/pype32 | tools/readpe.py | showOptionalHeaderData | def showOptionalHeaderData(peInstance):
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print "[+] IMAGE_OPTIONAL_HEADER:\n"
ohFields = peInstance.ntHeaders.optionalHeader.getFields()
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print "--> %s ... | python | def showOptionalHeaderData(peInstance):
""" Prints IMAGE_OPTIONAL_HEADER fields. """
print "[+] IMAGE_OPTIONAL_HEADER:\n"
ohFields = peInstance.ntHeaders.optionalHeader.getFields()
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crackinglandia/pype32 | tools/readpe.py | showDataDirectoriesData | def showDataDirectoriesData(peInstance):
""" Prints the DATA_DIRECTORY fields. """
print "[+] Data directories:\n"
dirs = peInstance.ntHeaders.optionalHeader.dataDirectory
counter = 1
for dir in dirs:
print "[%d] --> Name: %s -- RVA: 0x%08x -- SIZE: 0x%08x" % (counter, dir.name.value, ... | python | def showDataDirectoriesData(peInstance):
""" Prints the DATA_DIRECTORY fields. """
print "[+] Data directories:\n"
dirs = peInstance.ntHeaders.optionalHeader.dataDirectory
counter = 1
for dir in dirs:
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crackinglandia/pype32 | tools/readpe.py | showSectionsHeaders | def showSectionsHeaders(peInstance):
""" Prints IMAGE_SECTION_HEADER for every section present in the file. """
print "[+] Sections information:\n"
print "--> NumberOfSections: %d\n" % peInstance.ntHeaders.fileHeader.numberOfSections.value
for section in peInstance.sectionHeaders:
fields = ... | python | def showSectionsHeaders(peInstance):
""" Prints IMAGE_SECTION_HEADER for every section present in the file. """
print "[+] Sections information:\n"
print "--> NumberOfSections: %d\n" % peInstance.ntHeaders.fileHeader.numberOfSections.value
for section in peInstance.sectionHeaders:
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crackinglandia/pype32 | tools/readpe.py | showImports | def showImports(peInstance):
""" Shows imports information. """
iidEntries = peInstance.ntHeaders.optionalHeader.dataDirectory[consts.IMPORT_DIRECTORY].info
if iidEntries:
for iidEntry in iidEntries:
fields = iidEntry.getFields()
print "module: %s" % iidEntry.metaData.mo... | python | def showImports(peInstance):
""" Shows imports information. """
iidEntries = peInstance.ntHeaders.optionalHeader.dataDirectory[consts.IMPORT_DIRECTORY].info
if iidEntries:
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crackinglandia/pype32 | tools/readpe.py | showExports | def showExports(peInstance):
""" Show exports information """
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... | python | def showExports(peInstance):
""" Show exports information """
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exp_fields = exports.getFields()
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crackinglandia/pype32 | pype32/baseclasses.py | BaseStructClass.getFields | def getFields(self):
"""
Returns all the class attributues.
@rtype: dict
@return: A dictionary containing all the class attributes.
"""
d = {}
for i in self._attrsList:
key = i
value = getattr(self, i)
d[key] = value
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"""
Returns all the class attributues.
@rtype: dict
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crackinglandia/pype32 | pype32/datatypes.py | Array.parse | def parse(readDataInstance, arrayType, arrayLength):
"""
Returns a new L{Array} object.
@type readDataInstance: L{ReadData}
@param readDataInstance: The L{ReadData} object containing the array data.
@type arrayType: int
@param arrayType: The type of L{... | python | def parse(readDataInstance, arrayType, arrayLength):
"""
Returns a new L{Array} object.
@type readDataInstance: L{ReadData}
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tansey/gfl | pygfl/trails.py | calc_euler_tour | def calc_euler_tour(g, start, end):
'''Calculates an Euler tour over the graph g from vertex start to vertex end.
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# I... | python | def calc_euler_tour(g, start, end):
'''Calculates an Euler tour over the graph g from vertex start to vertex end.
Assumes start and end are odd-degree vertices and that there are no other odd-degree
vertices.'''
even_g = nx.subgraph(g, g.nodes()).copy()
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tansey/gfl | pygfl/trails.py | greedy_trails | def greedy_trails(subg, odds, verbose):
'''Greedily select trails by making the longest you can until the end'''
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edges = defaultdict(list)
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if verbose:
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'''Greedily select trails by making the longest you can until the end'''
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print('\tCreating edge map')
edges = defaultdict(list)
for x,y in subg.edges():
edges[x].append(y)
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tansey/gfl | pygfl/trails.py | decompose_graph | def decompose_graph(g, heuristic='tour', max_odds=20, verbose=0):
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subgraphs = [nx.subgraph(g, x).copy() for x in nx.connected_components(g)]
chains = []
num_subgraphs = len(subgraphs)
step = 0
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'''Decompose a graph into a set of non-overlapping trails.'''
# Get the connected subgraphs
subgraphs = [nx.subgraph(g, x).copy() for x in nx.connected_components(g)]
chains = []
num_subgraphs = len(subgraphs)
step = 0
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timknip/pycsg | csg/geom.py | Vector.plus | def plus(self, a):
""" Add. """
return Vector(self.x+a.x, self.y+a.y, self.z+a.z) | python | def plus(self, a):
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timknip/pycsg | csg/geom.py | Vector.times | def times(self, a):
""" Multiply. """
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timknip/pycsg | csg/geom.py | Vector.dividedBy | def dividedBy(self, a):
""" Divide. """
return Vector(self.x/a, self.y/a, self.z/a) | python | def dividedBy(self, a):
""" Divide. """
return Vector(self.x/a, self.y/a, self.z/a) | [
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timknip/pycsg | csg/geom.py | Vector.lerp | def lerp(self, a, t):
""" Lerp. Linear interpolation from self to a"""
return self.plus(a.minus(self).times(t)); | python | def lerp(self, a, t):
""" Lerp. Linear interpolation from self to a"""
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timknip/pycsg | csg/geom.py | Vertex.interpolate | def interpolate(self, other, t):
"""
Create a new vertex between this vertex and `other` by linearly
interpolating all properties using a parameter of `t`. Subclasses should
override this to interpolate additional properties.
"""
return Vertex(self.pos.lerp(other.pos, t),... | python | def interpolate(self, other, t):
"""
Create a new vertex between this vertex and `other` by linearly
interpolating all properties using a parameter of `t`. Subclasses should
override this to interpolate additional properties.
"""
return Vertex(self.pos.lerp(other.pos, t),... | [
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