_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3
values | text stringlengths 75 19.8k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q5400 | get_thread_block_dimensions | train | def get_thread_block_dimensions(params, block_size_names=None):
"""thread block size from tuning params, currently using convention"""
if not block_size_names:
block_size_names = default_block_size_names
block_size_x = params.get(block_size_names[0], 256)
block_size_y = params.get(block_size_na... | python | {
"resource": ""
} |
q5401 | looks_like_a_filename | train | def looks_like_a_filename(kernel_source):
""" attempt to detect whether source code or a filename was passed """
logging.debug('looks_like_a_filename called')
result = False
if isinstance(kernel_source, str):
result = True
#test if not too long
if len(kernel_source) > 250:
... | python | {
"resource": ""
} |
q5402 | prepare_kernel_string | train | def prepare_kernel_string(kernel_name, kernel_string, params, grid, threads, block_size_names):
""" prepare kernel string for compilation
Prepends the kernel with a series of C preprocessor defines specific
to this kernel instance:
* the thread block dimensions
* the grid dimensions
* tunab... | python | {
"resource": ""
} |
q5403 | prepare_list_of_files | train | def prepare_list_of_files(kernel_name, kernel_file_list, params, grid, threads, block_size_names):
""" prepare the kernel string along with any additional files
The first file in the list is allowed to include or read in the others
The files beyond the first are considered additional files that may also co... | python | {
"resource": ""
} |
q5404 | read_file | train | def read_file(filename):
""" return the contents of the file named filename or None if file not found """
if os.path.isfile(filename):
with open(filename, 'r') as f:
return f.read() | python | {
"resource": ""
} |
q5405 | replace_param_occurrences | train | def replace_param_occurrences(string, params):
"""replace occurrences of the tuning params with their current value"""
for k, v in params.items():
string = string.replace(k, str(v))
return string | python | {
"resource": ""
} |
q5406 | setup_block_and_grid | train | def setup_block_and_grid(problem_size, grid_div, params, block_size_names=None):
"""compute problem size, thread block and grid dimensions for this kernel"""
threads = get_thread_block_dimensions(params, block_size_names)
current_problem_size = get_problem_size(problem_size, params)
grid = get_grid_dime... | python | {
"resource": ""
} |
q5407 | write_file | train | def write_file(filename, string):
"""dump the contents of string to a file called filename"""
import sys
#ugly fix, hopefully we can find a better one
if sys.version_info[0] >= 3:
with open(filename, 'w', encoding="utf-8") as f:
f.write(string)
else:
with open(filename, '... | python | {
"resource": ""
} |
q5408 | OpenCLFunctions.compile | train | def compile(self, kernel_name, kernel_string):
"""call the OpenCL compiler to compile the kernel, return the device function
:param kernel_name: The name of the kernel to be compiled, used to lookup the
function after compilation.
:type kernel_name: string
:param kernel_str... | python | {
"resource": ""
} |
q5409 | OpenCLFunctions.run_kernel | train | def run_kernel(self, func, gpu_args, threads, grid):
"""runs the OpenCL kernel passed as 'func'
:param func: An OpenCL Kernel
:type func: pyopencl.Kernel
:param gpu_args: A list of arguments to the kernel, order should match the
order in the code. Allowed values are either ... | python | {
"resource": ""
} |
q5410 | weighted_choice | train | def weighted_choice(population):
"""Randomly select, fitness determines probability of being selected"""
random_number = random.betavariate(1, 2.5) #increased probability of selecting members early in the list
#random_number = random.random()
ind = int(random_number*len(population))
ind = min(max(in... | python | {
"resource": ""
} |
q5411 | random_population | train | def random_population(dna_size, pop_size, tune_params):
"""create a random population"""
population = []
for _ in range(pop_size):
dna = []
for i in range(dna_size):
dna.append(random_val(i, tune_params))
population.append(dna)
return population | python | {
"resource": ""
} |
q5412 | random_val | train | def random_val(index, tune_params):
"""return a random value for a parameter"""
key = list(tune_params.keys())[index]
return random.choice(tune_params[key]) | python | {
"resource": ""
} |
q5413 | crossover | train | def crossover(dna1, dna2):
"""crossover dna1 and dna2 at a random index"""
pos = int(random.random()*len(dna1))
if random.random() < 0.5:
return (dna1[:pos]+dna2[pos:], dna2[:pos]+dna1[pos:])
else:
return (dna2[:pos]+dna1[pos:], dna1[:pos]+dna2[pos:]) | python | {
"resource": ""
} |
q5414 | _cost_func | train | def _cost_func(x, kernel_options, tuning_options, runner, results, cache):
""" Cost function used by minimize """
error_time = 1e20
logging.debug('_cost_func called')
logging.debug('x: ' + str(x))
x_key = ",".join([str(i) for i in x])
if x_key in cache:
return cache[x_key]
#snap v... | python | {
"resource": ""
} |
q5415 | get_bounds | train | def get_bounds(tune_params):
""" create a bounds array from the tunable parameters """
bounds = []
for values in tune_params.values():
sorted_values = numpy.sort(values)
bounds.append((sorted_values[0], sorted_values[-1]))
return bounds | python | {
"resource": ""
} |
q5416 | setup_method_options | train | def setup_method_options(method, tuning_options):
""" prepare method specific options """
kwargs = {}
#pass size of parameter space as max iterations to methods that support it
#it seems not all methods iterpret this value in the same manner
maxiter = numpy.prod([len(v) for v in tuning_options.tune... | python | {
"resource": ""
} |
q5417 | snap_to_nearest_config | train | def snap_to_nearest_config(x, tune_params):
"""helper func that for each param selects the closest actual value"""
params = []
for i, k in enumerate(tune_params.keys()):
values = numpy.array(tune_params[k])
idx = numpy.abs(values-x[i]).argmin()
params.append(int(values[idx]))
ret... | python | {
"resource": ""
} |
q5418 | unscale_and_snap_to_nearest | train | def unscale_and_snap_to_nearest(x, tune_params, eps):
"""helper func that snaps a scaled variable to the nearest config"""
x_u = [i for i in x]
for i, v in enumerate(tune_params.values()):
#create an evenly spaced linear space to map [0,1]-interval
#to actual values, giving each value an equ... | python | {
"resource": ""
} |
q5419 | SequentialRunner.run | train | def run(self, parameter_space, kernel_options, tuning_options):
""" Iterate through the entire parameter space using a single Python process
:param parameter_space: The parameter space as an iterable.
:type parameter_space: iterable
:param kernel_options: A dictionary with all options ... | python | {
"resource": ""
} |
q5420 | allocate | train | def allocate(n, dtype=numpy.float32):
""" allocate context-portable pinned host memory """
return drv.pagelocked_empty(int(n), dtype, order='C', mem_flags=drv.host_alloc_flags.PORTABLE) | python | {
"resource": ""
} |
q5421 | CudaFunctions.compile | train | def compile(self, kernel_name, kernel_string):
"""call the CUDA compiler to compile the kernel, return the device function
:param kernel_name: The name of the kernel to be compiled, used to lookup the
function after compilation.
:type kernel_name: string
:param kernel_strin... | python | {
"resource": ""
} |
q5422 | CudaFunctions.copy_constant_memory_args | train | def copy_constant_memory_args(self, cmem_args):
"""adds constant memory arguments to the most recently compiled module
:param cmem_args: A dictionary containing the data to be passed to the
device constant memory. The format to be used is as follows: A
string key is used to name... | python | {
"resource": ""
} |
q5423 | CudaFunctions.copy_texture_memory_args | train | def copy_texture_memory_args(self, texmem_args):
"""adds texture memory arguments to the most recently compiled module
:param texmem_args: A dictionary containing the data to be passed to the
device texture memory. TODO
"""
filter_mode_map = { 'point': drv.filter_mode.POIN... | python | {
"resource": ""
} |
q5424 | CudaFunctions.run_kernel | train | def run_kernel(self, func, gpu_args, threads, grid):
"""runs the CUDA kernel passed as 'func'
:param func: A PyCuda kernel compiled for this specific kernel configuration
:type func: pycuda.driver.Function
:param gpu_args: A list of arguments to the kernel, order should match the
... | python | {
"resource": ""
} |
q5425 | CudaFunctions.memcpy_htod | train | def memcpy_htod(self, dest, src):
"""perform a host to device memory copy
:param dest: A GPU memory allocation unit
:type dest: pycuda.driver.DeviceAllocation
:param src: A numpy array in host memory to store the data
:type src: numpy.ndarray
"""
if isinstance(d... | python | {
"resource": ""
} |
q5426 | acceptance_prob | train | def acceptance_prob(old_cost, new_cost, T):
"""annealing equation, with modifications to work towards a lower value"""
#if start pos is not valid, always move
if old_cost == 1e20:
return 1.0
#if we have found a valid ps before, never move to nonvalid pos
if new_cost == 1e20:
return 0... | python | {
"resource": ""
} |
q5427 | neighbor | train | def neighbor(pos, tune_params):
"""return a random neighbor of pos"""
size = len(pos)
pos_out = []
# random mutation
# expected value is set that values all dimensions attempt to get mutated
for i in range(size):
key = list(tune_params.keys())[i]
values = tune_params[key]
... | python | {
"resource": ""
} |
q5428 | tune | train | def tune(runner, kernel_options, device_options, tuning_options):
""" Tune all instances in the parameter space
:params runner: A runner from kernel_tuner.runners
:type runner: kernel_tuner.runner
:param kernel_options: A dictionary with all options for the kernel.
:type kernel_options: kernel_tun... | python | {
"resource": ""
} |
q5429 | CFunctions.ready_argument_list | train | def ready_argument_list(self, arguments):
"""ready argument list to be passed to the C function
:param arguments: List of arguments to be passed to the C function.
The order should match the argument list on the C function.
Allowed values are numpy.ndarray, and/or numpy.int32, n... | python | {
"resource": ""
} |
q5430 | CFunctions.compile | train | def compile(self, kernel_name, kernel_string):
"""call the C compiler to compile the kernel, return the function
:param kernel_name: The name of the kernel to be compiled, used to lookup the
function after compilation.
:type kernel_name: string
:param kernel_string: The C c... | python | {
"resource": ""
} |
q5431 | CFunctions.benchmark | train | def benchmark(self, func, c_args, threads, grid, times):
"""runs the kernel repeatedly, returns averaged returned value
The C function tuning is a little bit more flexible than direct CUDA
or OpenCL kernel tuning. The C function needs to measure time, or some
other quality metric you wi... | python | {
"resource": ""
} |
q5432 | CFunctions.run_kernel | train | def run_kernel(self, func, c_args, threads, grid):
"""runs the kernel once, returns whatever the kernel returns
:param func: A C function compiled for this specific configuration
:type func: ctypes._FuncPtr
:param c_args: A list of arguments to the function, order should match the
... | python | {
"resource": ""
} |
q5433 | CFunctions.cleanup_lib | train | def cleanup_lib(self):
""" unload the previously loaded shared library """
if not self.using_openmp:
#this if statement is necessary because shared libraries that use
#OpenMP will core dump when unloaded, this is a well-known issue with OpenMP
logging.debug('unloading... | python | {
"resource": ""
} |
q5434 | clock | train | def clock(rpc):
"""
This task runs forever and notifies all clients subscribed to
'clock' once a second.
"""
while True:
yield from rpc.notify('clock', str(datetime.datetime.now()))
yield from asyncio.sleep(1) | python | {
"resource": ""
} |
q5435 | patch_db_connections | train | def patch_db_connections():
"""
This wraps django.db.connections._connections with a TaskLocal object.
The Django transactions are only thread-safe, using threading.local,
and don't know about coroutines.
"""
global __already_patched
if not __already_patched:
from django.db import... | python | {
"resource": ""
} |
q5436 | decode_msg | train | def decode_msg(raw_msg):
"""
Decodes jsonrpc 2.0 raw message objects into JsonRpcMsg objects.
Examples:
Request:
{
"jsonrpc": "2.0",
"id": 1,
"method": "subtract",
"params": [42, 23]
}
Notification:
... | python | {
"resource": ""
} |
q5437 | switch_schema | train | def switch_schema(task, kwargs, **kw):
""" Switches schema of the task, before it has been run. """
# Lazily load needed functions, as they import django model functions which
# in turn load modules that need settings to be loaded and we can't
# guarantee this module was loaded when the settings were re... | python | {
"resource": ""
} |
q5438 | restore_schema | train | def restore_schema(task, **kwargs):
""" Switches the schema back to the one from before running the task. """
from .compat import get_public_schema_name
schema_name = get_public_schema_name()
include_public = True
if hasattr(task, '_old_schema'):
schema_name, include_public = task._old_sch... | python | {
"resource": ""
} |
q5439 | node_link_data | train | def node_link_data(G, attrs=_attrs):
"""Return data in node-link format that is suitable for JSON serialization
and use in Javascript documents.
Parameters
----------
G : DyNetx graph
attrs : dict
A dictionary that contains three keys 'id', 'source' and 'target'.
The correspon... | python | {
"resource": ""
} |
q5440 | compact_timeslot | train | def compact_timeslot(sind_list):
"""
Test method. Compact all snapshots into a single one.
:param sind_list:
:return:
"""
tls = sorted(sind_list)
conversion = {val: idx for idx, val in enumerate(tls)}
return conversion | python | {
"resource": ""
} |
q5441 | DynGraph.nodes_iter | train | def nodes_iter(self, t=None, data=False):
"""Return an iterator over the nodes with respect to a given temporal snapshot.
Parameters
----------
t : snapshot id (default=None).
If None the iterator returns all the nodes of the flattened graph.
data : boolean, optional... | python | {
"resource": ""
} |
q5442 | DynGraph.nodes | train | def nodes(self, t=None, data=False):
"""Return a list of the nodes in the graph at a given snapshot.
Parameters
----------
t : snapshot id (default=None)
If None the the method returns all the nodes of the flattened graph.
data : boolean, optional (default=False)
... | python | {
"resource": ""
} |
q5443 | DynGraph.add_interactions_from | train | def add_interactions_from(self, ebunch, t=None, e=None):
"""Add all the interaction in ebunch at time t.
Parameters
----------
ebunch : container of interaction
Each interaction given in the container will be added to the
graph. The interaction must be given as a... | python | {
"resource": ""
} |
q5444 | DynGraph.neighbors | train | def neighbors(self, n, t=None):
"""Return a list of the nodes connected to the node n at time t.
Parameters
----------
n : node
A node in the graph
t : snapshot id (default=None)
If None will be returned the neighbors of the node on the flattened graph.
... | python | {
"resource": ""
} |
q5445 | DynGraph.neighbors_iter | train | def neighbors_iter(self, n, t=None):
"""Return an iterator over all neighbors of node n at time t.
Parameters
----------
n : node
A node in the graph
t : snapshot id (default=None)
If None will be returned an iterator over the neighbors of the node on the ... | python | {
"resource": ""
} |
q5446 | DynGraph.degree | train | def degree(self, nbunch=None, t=None):
"""Return the degree of a node or nodes at time t.
The node degree is the number of interaction adjacent to that node in a given time frame.
Parameters
----------
nbunch : iterable container, optional (default=all nodes)
A cont... | python | {
"resource": ""
} |
q5447 | DynGraph.size | train | def size(self, t=None):
"""Return the number of edges at time t.
Parameters
----------
t : snapshot id (default=None)
If None will be returned the size of the flattened graph.
Returns
-------
nedges : int
The number of edges
See... | python | {
"resource": ""
} |
q5448 | DynGraph.number_of_nodes | train | def number_of_nodes(self, t=None):
"""Return the number of nodes in the t snpashot of a dynamic graph.
Parameters
----------
t : snapshot id (default=None)
If None return the number of nodes in the flattened graph.
Returns
-------
nnodes : int
... | python | {
"resource": ""
} |
q5449 | DynGraph.has_node | train | def has_node(self, n, t=None):
"""Return True if the graph, at time t, contains the node n.
Parameters
----------
n : node
t : snapshot id (default None)
If None return the presence of the node in the flattened graph.
Examples
--------
>>... | python | {
"resource": ""
} |
q5450 | DynGraph.to_directed | train | def to_directed(self):
"""Return a directed representation of the graph.
Returns
-------
G : DynDiGraph
A dynamic directed graph with the same name, same nodes, and with
each edge (u,v,data) replaced by two directed edges
(u,v,data) and (v,u,data).
... | python | {
"resource": ""
} |
q5451 | DynGraph.stream_interactions | train | def stream_interactions(self):
"""Generate a temporal ordered stream of interactions.
Returns
-------
nd_iter : an iterator
The iterator returns a 4-tuples of (node, node, op, timestamp).
Examples
--------
>>> G = dn.DynGraph()
>>> G.add_pat... | python | {
"resource": ""
} |
q5452 | DynGraph.interactions_per_snapshots | train | def interactions_per_snapshots(self, t=None):
"""Return the number of interactions within snapshot t.
Parameters
----------
t : snapshot id (default=None)
If None will be returned total number of interactions across all snapshots
Returns
-------
nd... | python | {
"resource": ""
} |
q5453 | DynDiGraph.in_interactions_iter | train | def in_interactions_iter(self, nbunch=None, t=None):
"""Return an iterator over the in interactions present in a given snapshot.
Edges are returned as tuples in the order (node, neighbor).
Parameters
----------
nbunch : iterable container, optional (default= all nodes)
... | python | {
"resource": ""
} |
q5454 | DynDiGraph.out_interactions_iter | train | def out_interactions_iter(self, nbunch=None, t=None):
"""Return an iterator over the out interactions present in a given snapshot.
Edges are returned as tuples
in the order (node, neighbor).
Parameters
----------
nbunch : iterable container, optional (default= all nodes... | python | {
"resource": ""
} |
q5455 | DynDiGraph.number_of_interactions | train | def number_of_interactions(self, u=None, v=None, t=None):
"""Return the number of interaction between two nodes at time t.
Parameters
----------
u, v : nodes, optional (default=all interaction)
If u and v are specified, return the number of interaction between
u ... | python | {
"resource": ""
} |
q5456 | DynDiGraph.in_degree | train | def in_degree(self, nbunch=None, t=None):
"""Return the in degree of a node or nodes at time t.
The node in degree is the number of incoming interaction to that node in a given time frame.
Parameters
----------
nbunch : iterable container, optional (default=all nodes)
... | python | {
"resource": ""
} |
q5457 | DynDiGraph.out_degree | train | def out_degree(self, nbunch=None, t=None):
"""Return the out degree of a node or nodes at time t.
The node degree is the number of interaction outgoing from that node in a given time frame.
Parameters
----------
nbunch : iterable container, optional (default=all nodes)
... | python | {
"resource": ""
} |
q5458 | DynDiGraph.to_undirected | train | def to_undirected(self, reciprocal=False):
"""Return an undirected representation of the dyndigraph.
Parameters
----------
reciprocal : bool (optional)
If True only keep edges that appear in both directions
in the original dyndigraph.
Returns
-------... | python | {
"resource": ""
} |
q5459 | write_interactions | train | def write_interactions(G, path, delimiter=' ', encoding='utf-8'):
"""Write a DyNetx graph in interaction list format.
Parameters
----------
G : graph
A DyNetx graph.
path : basestring
The desired output filename
delimiter : character
... | python | {
"resource": ""
} |
q5460 | read_interactions | train | def read_interactions(path, comments="#", directed=False, delimiter=None,
nodetype=None, timestamptype=None, encoding='utf-8', keys=False):
"""Read a DyNetx graph from interaction list format.
Parameters
----------
path : basestring
The desired output filenam... | python | {
"resource": ""
} |
q5461 | write_snapshots | train | def write_snapshots(G, path, delimiter=' ', encoding='utf-8'):
"""Write a DyNetx graph in snapshot graph list format.
Parameters
----------
G : graph
A DyNetx graph.
path : basestring
The desired output filename
delimiter : character
C... | python | {
"resource": ""
} |
q5462 | read_snapshots | train | def read_snapshots(path, comments="#", directed=False, delimiter=None,
nodetype=None, timestamptype=None, encoding='utf-8', keys=False):
"""Read a DyNetx graph from snapshot graph list format.
Parameters
----------
path : basestring
The desired output filena... | python | {
"resource": ""
} |
q5463 | open_file | train | def open_file(path_arg, mode='r'):
"""Decorator to ensure clean opening and closing of files.
Parameters
----------
path_arg : int
Location of the path argument in args. Even if the argument is a
named positional argument (with a default value), you must specify its
index as a ... | python | {
"resource": ""
} |
q5464 | number_of_interactions | train | def number_of_interactions(G, u=None, v=None, t=None):
"""Return the number of edges between two nodes at time t.
Parameters
----------
u, v : nodes, optional (default=all edges)
If u and v are specified, return the number of edges between
u and v. Otherwise return t... | python | {
"resource": ""
} |
q5465 | density | train | def density(G, t=None):
r"""Return the density of a graph at timestamp t.
The density for undirected graphs is
.. math::
d = \frac{2m}{n(n-1)},
and for directed graphs is
.. math::
d = \frac{m}{n(n-1)},
where `n` is the number of nodes and `m` is th... | python | {
"resource": ""
} |
q5466 | degree_histogram | train | def degree_histogram(G, t=None):
"""Return a list of the frequency of each degree value.
Parameters
----------
G : Graph opject
DyNetx graph object
t : snapshot id (default=None)
snapshot id
Returns
-------
hist : list
... | python | {
"resource": ""
} |
q5467 | freeze | train | def freeze(G):
"""Modify graph to prevent further change by adding or removing nodes or edges.
Node and edge data can still be modified.
Parameters
----------
G : graph
A NetworkX graph
Notes
-----
To "unfreeze" a graph you must make a... | python | {
"resource": ""
} |
q5468 | set_node_attributes | train | def set_node_attributes(G, values, name=None):
"""Set node attributes from dictionary of nodes and values
Parameters
----------
G : DyNetx Graph
name : string
Attribute name
values: dict
Dictionary of attribute values keyed by node. If `values` is not... | python | {
"resource": ""
} |
q5469 | get_node_attributes | train | def get_node_attributes(G, name):
"""Get node attributes from graph
Parameters
----------
G : DyNetx Graph
name : string
Attribute name
Returns
-------
Dictionary of attributes keyed by node.
"""
return {n: d[name] for n, d in G.node.item... | python | {
"resource": ""
} |
q5470 | all_neighbors | train | def all_neighbors(graph, node, t=None):
""" Returns all of the neighbors of a node in the graph at time t.
If the graph is directed returns predecessors as well as successors.
Parameters
----------
graph : DyNetx graph
Graph to find neighbors.
node : node
... | python | {
"resource": ""
} |
q5471 | non_neighbors | train | def non_neighbors(graph, node, t=None):
"""Returns the non-neighbors of the node in the graph at time t.
Parameters
----------
graph : DyNetx graph
Graph to find neighbors.
node : node
The node whose neighbors will be returned.
t : snapshot id (defa... | python | {
"resource": ""
} |
q5472 | non_interactions | train | def non_interactions(graph, t=None):
"""Returns the non-existent edges in the graph at time t.
Parameters
----------
graph : NetworkX graph.
Graph to find non-existent edges.
t : snapshot id (default=None)
If None the non-existent edges are identified on th... | python | {
"resource": ""
} |
q5473 | FunctionTimedOut.getMsg | train | def getMsg(self):
'''
getMsg - Generate a default message based on parameters to FunctionTimedOut exception'
@return <str> - Message
'''
return 'Function %s (args=%s) (kwargs=%s) timed out after %f seconds.\n' %(self.timedOutFunction.__name__, repr(self.timedOutArgs), re... | python | {
"resource": ""
} |
q5474 | FunctionTimedOut.retry | train | def retry(self, timeout=RETRY_SAME_TIMEOUT):
'''
retry - Retry the timed-out function with same arguments.
@param timeout <float/RETRY_SAME_TIMEOUT/None> Default RETRY_SAME_TIMEOUT
If RETRY_SAME_TIMEOUT : Will retry the function same args, same timeout
... | python | {
"resource": ""
} |
q5475 | JoinThread.run | train | def run(self):
'''
run - The thread main. Will attempt to stop and join the attached thread.
'''
# Try to silence default exception printing.
self.otherThread._Thread__stderr = self._stderr
if hasattr(self.otherThread, '_Thread__stop'):
# If py2, call thi... | python | {
"resource": ""
} |
q5476 | PDFIntegrator._make_map | train | def _make_map(self, limit):
""" Make vegas grid that is adapted to the pdf. """
ny = 2000
y = numpy.random.uniform(0., 1., (ny,1))
limit = numpy.arctan(limit)
m = AdaptiveMap([[-limit, limit]], ninc=100)
theta = numpy.empty(y.shape, float)
jac = numpy.empty(y.shap... | python | {
"resource": ""
} |
q5477 | PDFIntegrator._expval | train | def _expval(self, f, nopdf):
""" Return integrand using the tan mapping. """
def ff(theta, nopdf=nopdf):
tan_theta = numpy.tan(theta)
x = self.scale * tan_theta
jac = self.scale * (tan_theta ** 2 + 1.)
if nopdf:
pdf = jac * self.pdf.pjac[No... | python | {
"resource": ""
} |
q5478 | REPL.dump | train | def dump(self, function_name):
"""
Pretty-dump the bytecode for the function with the given name.
"""
assert isinstance(function_name, str)
self.stdout.write(function_name)
self.stdout.write("\n")
self.stdout.write("-" * len(function_name))
self.stdout.w... | python | {
"resource": ""
} |
q5479 | Command.handle | train | def handle(self, **options):
"""Call "startapp" to generate app with custom user model."""
template = os.path.dirname(os.path.abspath(__file__)) + "/app_template"
name = options.pop("name")
call_command("startapp", name, template=template, **options) | python | {
"resource": ""
} |
q5480 | BaseUserManager._create_user | train | def _create_user(self, email, password, **extra_fields):
"""Create and save a User with the given email and password."""
if not email:
raise ValueError("The given email must be set")
email = self.normalize_email(email)
user = self.model(email=email, **extra_fields)
us... | python | {
"resource": ""
} |
q5481 | nwise | train | def nwise(iterable, n):
"""
Iterate through a sequence with a defined length window
>>> list(nwise(range(8), 3))
[(0, 1, 2), (1, 2, 3), (2, 3, 4), (3, 4, 5), (5, 6, 7)]
>>> list(nwise(range(3), 5))
[]
Parameters
----------
iterable
n : length of each sequence
Yields
... | python | {
"resource": ""
} |
q5482 | step | train | def step(g, n1, n2, inbound=False, backward=False, continue_fn=None):
"""
Step along a path through a directed graph unless there is an intersection
Example graph:
Note that edge (1, 2) and (2, 3) are bidirectional, i.e., (2, 1) and
(3, 2) are also edges
1 -- 2 -- 3 -->-- 5 -->-- 7
... | python | {
"resource": ""
} |
q5483 | move | train | def move(g, n1, n2, **kwargs):
"""
Step along a graph until it ends or reach an intersection
Example graph:
Note that edge (1, 2) and (2, 3) are bidirectional, i.e., (2, 1) and
(3, 2) are also edges
1 -- 2 -- 3 -->-- 5 -->-- 7
| |
^ v
| ... | python | {
"resource": ""
} |
q5484 | is_intersection | train | def is_intersection(g, n):
"""
Determine if a node is an intersection
graph: 1 -->-- 2 -->-- 3
>>> is_intersection(g, 2)
False
graph:
1 -- 2 -- 3
|
4
>>> is_intersection(g, 2)
True
Parameters
----------
g : networkx DiGraph
n : node id
R... | python | {
"resource": ""
} |
q5485 | LazyModel.as_dict | train | def as_dict(self):
"""
Returns the model as a dict
"""
if not self._is_valid:
self.validate()
from .converters import to_dict
return to_dict(self) | python | {
"resource": ""
} |
q5486 | GraphImporter.coords_callback | train | def coords_callback(self, data):
""" Callback for nodes that have no tags """
for node_id, lon, lat in data:
self.coords[node_id] = (lon, lat) | python | {
"resource": ""
} |
q5487 | GraphImporter.nodes_callback | train | def nodes_callback(self, data):
""" Callback for nodes with tags """
for node_id, tags, coords in data:
# Discard the coords because they go into add_coords
self.nodes[node_id] = tags | python | {
"resource": ""
} |
q5488 | GraphImporter.ways_callback | train | def ways_callback(self, data):
""" Callback for all ways """
for way_id, tags, nodes in data:
# Imposm passes all ways through regardless of whether the tags
# have been filtered or not. It needs to do this in order to
# handle relations, but we don't care about relat... | python | {
"resource": ""
} |
q5489 | GraphImporter.get_graph | train | def get_graph(self, parse_direction=False):
""" Return the networkx directed graph of received data """
g = nx.DiGraph()
for way_id, (tags, nodes) in self.ways.items():
# If oneway is '-1', reverse the way and treat as a normal oneway
if tags.get('oneway') == '-1':
... | python | {
"resource": ""
} |
q5490 | parse_file | train | def parse_file(filename, parse_direction=False, **kwargs):
"""
Return an OSM networkx graph from the input OSM file
Only works with OSM xml, xml.bz2 and pbf files. This function cannot take
OSM QA tile files. Use parse_qa_tile() for QA tiles.
>>> graph = parse_file(filename)
"""
importer,... | python | {
"resource": ""
} |
q5491 | parse_data | train | def parse_data(data, type, **kwargs):
"""
Return an OSM networkx graph from the input OSM data
Parameters
----------
data : string
type : string ('xml' or 'pbf')
>>> graph = parse_data(data, 'xml')
"""
suffixes = {
'xml': '.osm',
'pbf': '.pbf',
}
try:
... | python | {
"resource": ""
} |
q5492 | parse_qa_tile | train | def parse_qa_tile(x, y, zoom, data, parse_direction=False, **kwargs):
"""
Return an OSM networkx graph from the input OSM QA tile data
Parameters
----------
data : string
x : int
tile's x coordinate
y : int
tile's y coordinate
zoom : int
tile's zoom level
>>... | python | {
"resource": ""
} |
q5493 | _basename | train | def _basename(fname):
"""Return file name without path."""
if not isinstance(fname, Path):
fname = Path(fname)
path, name, ext = fname.parent, fname.stem, fname.suffix
return path, name, ext | python | {
"resource": ""
} |
q5494 | from_btl | train | def from_btl(fname):
"""
DataFrame constructor to open Seabird CTD BTL-ASCII format.
Examples
--------
>>> from pathlib import Path
>>> import ctd
>>> data_path = Path(__file__).parents[1].joinpath("tests", "data")
>>> bottles = ctd.from_btl(data_path.joinpath('btl', 'bottletest.btl'))
... | python | {
"resource": ""
} |
q5495 | from_edf | train | def from_edf(fname):
"""
DataFrame constructor to open XBT EDF ASCII format.
Examples
--------
>>> from pathlib import Path
>>> import ctd
>>> data_path = Path(__file__).parents[1].joinpath("tests", "data")
>>> cast = ctd.from_edf(data_path.joinpath('XBT.EDF.gz'))
>>> ax = cast['tem... | python | {
"resource": ""
} |
q5496 | from_cnv | train | def from_cnv(fname):
"""
DataFrame constructor to open Seabird CTD CNV-ASCII format.
Examples
--------
>>> from pathlib import Path
>>> import ctd
>>> data_path = Path(__file__).parents[1].joinpath("tests", "data")
>>> cast = ctd.from_cnv(data_path.joinpath('CTD_big.cnv.bz2'))
>>> d... | python | {
"resource": ""
} |
q5497 | extrap_sec | train | def extrap_sec(data, dist, depth, w1=1.0, w2=0):
"""
Extrapolates `data` to zones where the shallow stations are shadowed by
the deep stations. The shadow region usually cannot be extrapolates via
linear interpolation.
The extrapolation is applied using the gradients of the `data` at a certain
... | python | {
"resource": ""
} |
q5498 | gen_topomask | train | def gen_topomask(h, lon, lat, dx=1.0, kind="linear", plot=False):
"""
Generates a topography mask from an oceanographic transect taking the
deepest CTD scan as the depth of each station.
Inputs
------
h : array
Pressure of the deepest CTD scan for each station [dbar].
lons : array
... | python | {
"resource": ""
} |
q5499 | plot_cast | train | def plot_cast(df, secondary_y=False, label=None, *args, **kwargs):
"""
Plot a CTD variable with the index in the y-axis instead of x-axis.
"""
ax = kwargs.pop("ax", None)
fignums = plt.get_fignums()
if ax is None and not fignums:
ax = plt.axes()
fig = ax.get_figure()
fi... | python | {
"resource": ""
} |
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