sentence-transformers/all-MiniLM-L6-v2
Sentence Similarity β’ 22.7M β’ Updated β’ 197M β’ β’ 4.66k
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values | func_code_string stringlengths 52 3.87M | func_code_tokens listlengths 15 672k | func_documentation_string stringlengths 1 47.2k | func_documentation_tokens listlengths 1 3.92k | split_name stringclasses 1
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__msgc_step3_discontinuity_localization | def __msgc_step3_discontinuity_localization(self):
"""
Estimate discontinuity in basis of low resolution image segmentation.
:return: discontinuity in low resolution
"""
import scipy
start = self._start_time
seg = 1 - self.segmentation.astype(np.int8)
sel... | python | def __msgc_step3_discontinuity_localization(self):
"""
Estimate discontinuity in basis of low resolution image segmentation.
:return: discontinuity in low resolution
"""
import scipy
start = self._start_time
seg = 1 - self.segmentation.astype(np.int8)
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__multiscale_gc_lo2hi_run | def __multiscale_gc_lo2hi_run(self): # , pyed):
"""
Run Graph-Cut segmentation with refinement of low resolution multiscale graph.
In first step is performed normal GC on low resolution data
Second step construct finer grid on edges of segmentation from first
step.
There... | python | def __multiscale_gc_lo2hi_run(self): # , pyed):
"""
Run Graph-Cut segmentation with refinement of low resolution multiscale graph.
In first step is performed normal GC on low resolution data
Second step construct finer grid on edges of segmentation from first
step.
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__multiscale_gc_hi2lo_run | def __multiscale_gc_hi2lo_run(self): # , pyed):
"""
Run Graph-Cut segmentation with simplifiyng of high resolution multiscale graph.
In first step is performed normal GC on low resolution data
Second step construct finer grid on edges of segmentation from first
step.
The... | python | def __multiscale_gc_hi2lo_run(self): # , pyed):
"""
Run Graph-Cut segmentation with simplifiyng of high resolution multiscale graph.
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__ordered_values_by_indexes | def __ordered_values_by_indexes(self, data, inds):
"""
Return values (intensities) by indexes.
Used for multiscale graph cut.
data = [[0 1 1],
[0 2 2],
[0 2 2]]
inds = [[0 1 2],
[3 4 4],
[5 4 4]]
return: [... | python | def __ordered_values_by_indexes(self, data, inds):
"""
Return values (intensities) by indexes.
Used for multiscale graph cut.
data = [[0 1 1],
[0 2 2],
[0 2 2]]
inds = [[0 1 2],
[3 4 4],
[5 4 4]]
return: [... | [
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__hi2lo_multiscale_indexes | def __hi2lo_multiscale_indexes(self, mask, orig_shape): # , zoom):
"""
Function computes multiscale indexes of ndarray.
mask: Says where is original resolution (0) and where is small
resolution (1). Mask is in small resolution.
orig_shape: Original shape of input data.
... | python | def __hi2lo_multiscale_indexes(self, mask, orig_shape): # , zoom):
"""
Function computes multiscale indexes of ndarray.
mask: Says where is original resolution (0) and where is small
resolution (1). Mask is in small resolution.
orig_shape: Original shape of input data.
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.interactivity | def interactivity(self, min_val=None, max_val=None, qt_app=None):
"""
Interactive seed setting with 3d seed editor
"""
from .seed_editor_qt import QTSeedEditor
from PyQt4.QtGui import QApplication
if min_val is None:
min_val = np.min(self.img)
if max... | python | def interactivity(self, min_val=None, max_val=None, qt_app=None):
"""
Interactive seed setting with 3d seed editor
"""
from .seed_editor_qt import QTSeedEditor
from PyQt4.QtGui import QApplication
if min_val is None:
min_val = np.min(self.img)
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.set_seeds | def set_seeds(self, seeds):
"""
Function for manual seed setting. Sets variable seeds and prepares
voxels for density model.
:param seeds: ndarray (0 - nothing, 1 - object, 2 - background,
3 - object just hard constraints, no model training, 4 - background
just hard cons... | python | def set_seeds(self, seeds):
"""
Function for manual seed setting. Sets variable seeds and prepares
voxels for density model.
:param seeds: ndarray (0 - nothing, 1 - object, 2 - background,
3 - object just hard constraints, no model training, 4 - background
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.run | def run(self, run_fit_model=True):
"""
Run the Graph Cut segmentation according to preset parameters.
:param run_fit_model: Allow to skip model fit when the model is prepared before
:return:
"""
if run_fit_model:
self.fit_model(self.img, self.voxelsize, self... | python | def run(self, run_fit_model=True):
"""
Run the Graph Cut segmentation according to preset parameters.
:param run_fit_model: Allow to skip model fit when the model is prepared before
:return:
"""
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__set_hard_hard_constraints | def __set_hard_hard_constraints(self, tdata1, tdata2, seeds):
"""
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2: object 2 - full seeds
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"""
seeds_mask = (seeds ... | python | def __set_hard_hard_constraints(self, tdata1, tdata2, seeds):
"""
it works with seed labels:
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2: object 2 - full seeds
3: object 1 - not a training seeds
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__similarity_for_tlinks_obj_bgr | def __similarity_for_tlinks_obj_bgr(
self,
data,
voxelsize,
# voxels1, voxels2,
# seeds, otherfeatures=None
):
"""
Compute edge values for graph cut tlinks based on image intensity
and texture.
"""
# self.fit_model(data, voxelsize, seed... | python | def __similarity_for_tlinks_obj_bgr(
self,
data,
voxelsize,
# voxels1, voxels2,
# seeds, otherfeatures=None
):
"""
Compute edge values for graph cut tlinks based on image intensity
and texture.
"""
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__create_nlinks | def __create_nlinks(self, data, inds=None, boundary_penalties_fcn=None):
"""
Compute nlinks grid from data shape information. For boundary penalties
are data (intensities) values are used.
ins: Default is None. Used for multiscale GC. This are indexes of
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"""
Compute nlinks grid from data shape information. For boundary penalties
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ins: Default is None. Used for multiscale GC. This are indexes of
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.debug_get_reconstructed_similarity | def debug_get_reconstructed_similarity(
self,
data3d=None,
voxelsize=None,
seeds=None,
area_weight=1,
hard_constraints=True,
return_unariesalt=False,
):
"""
Use actual model to calculate similarity. If no input is given the last image is used.
... | python | def debug_get_reconstructed_similarity(
self,
data3d=None,
voxelsize=None,
seeds=None,
area_weight=1,
hard_constraints=True,
return_unariesalt=False,
):
"""
Use actual model to calculate similarity. If no input is given the last image is used.
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.debug_show_reconstructed_similarity | def debug_show_reconstructed_similarity(
self,
data3d=None,
voxelsize=None,
seeds=None,
area_weight=1,
hard_constraints=True,
show=True,
bins=20,
slice_number=None,
):
"""
Show tlinks.
:param data3d: ndarray with input d... | python | def debug_show_reconstructed_similarity(
self,
data3d=None,
voxelsize=None,
seeds=None,
area_weight=1,
hard_constraints=True,
show=True,
bins=20,
slice_number=None,
):
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Show tlinks.
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.debug_inspect_node | def debug_inspect_node(self, node_msindex):
"""
Get info about the node. See pycut.inspect_node() for details.
Processing is done in temporary shape.
:param node_seed:
:return: node_unariesalt, node_neighboor_edges_and_weights, node_neighboor_seeds
"""
return ins... | python | def debug_inspect_node(self, node_msindex):
"""
Get info about the node. See pycut.inspect_node() for details.
Processing is done in temporary shape.
:param node_seed:
:return: node_unariesalt, node_neighboor_edges_and_weights, node_neighboor_seeds
"""
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.debug_interactive_inspect_node | def debug_interactive_inspect_node(self):
"""
Call after segmentation to see selected node neighborhood.
User have to select one node by click.
:return:
"""
if (
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np.abs(
np.asarray(self.msinds.shape) - np.asarray(sel... | python | def debug_interactive_inspect_node(self):
"""
Call after segmentation to see selected node neighborhood.
User have to select one node by click.
:return:
"""
if (
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mjirik/imcut | imcut/pycut.py | ImageGraphCut._ssgc_prepare_data_and_run_computation | def _ssgc_prepare_data_and_run_computation(
self,
# voxels1, voxels2,
hard_constraints=True,
area_weight=1,
):
"""
Setting of data.
You need set seeds if you want use hard_constraints.
"""
# from PyQt4.QtCore import pyqtRemoveInputHook
... | python | def _ssgc_prepare_data_and_run_computation(
self,
# voxels1, voxels2,
hard_constraints=True,
area_weight=1,
):
"""
Setting of data.
You need set seeds if you want use hard_constraints.
"""
# from PyQt4.QtCore import pyqtRemoveInputHook
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mjirik/imcut | imcut/image_manipulation.py | resize_to_shape | def resize_to_shape(data, shape, zoom=None, mode="nearest", order=0):
"""
Function resize input data to specific shape.
:param data: input 3d array-like data
:param shape: shape of output data
:param zoom: zoom is used for back compatibility
:mode: default is 'nearest'
"""
# @TODO remove... | python | def resize_to_shape(data, shape, zoom=None, mode="nearest", order=0):
"""
Function resize input data to specific shape.
:param data: input 3d array-like data
:param shape: shape of output data
:param zoom: zoom is used for back compatibility
:mode: default is 'nearest'
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mjirik/imcut | imcut/image_manipulation.py | seed_zoom | def seed_zoom(seeds, zoom):
"""
Smart zoom for sparse matrix. If there is resize to bigger resolution
thin line of label could be lost. This function prefers labels larger
then zero. If there is only one small voxel in larger volume with zeros
it is selected.
"""
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# loseeds... | python | def seed_zoom(seeds, zoom):
"""
Smart zoom for sparse matrix. If there is resize to bigger resolution
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mjirik/imcut | imcut/image_manipulation.py | zoom_to_shape | def zoom_to_shape(data, shape, dtype=None):
"""
Zoom data to specific shape.
"""
import scipy
import scipy.ndimage
zoomd = np.array(shape) / np.array(data.shape, dtype=np.double)
import warnings
datares = scipy.ndimage.interpolation.zoom(data, zoomd, order=0, mode="reflect")
if da... | python | def zoom_to_shape(data, shape, dtype=None):
"""
Zoom data to specific shape.
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import scipy.ndimage
zoomd = np.array(shape) / np.array(data.shape, dtype=np.double)
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datares = scipy.ndimage.interpolation.zoom(data, zoomd, order=0, mode="reflect")
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mjirik/imcut | imcut/image_manipulation.py | crop | def crop(data, crinfo):
"""
Crop the data.
crop(data, crinfo)
:param crinfo: min and max for each axis - [[minX, maxX], [minY, maxY], [minZ, maxZ]]
"""
crinfo = fix_crinfo(crinfo)
return data[
__int_or_none(crinfo[0][0]) : __int_or_none(crinfo[0][1]),
__int_or_none(crinfo[... | python | def crop(data, crinfo):
"""
Crop the data.
crop(data, crinfo)
:param crinfo: min and max for each axis - [[minX, maxX], [minY, maxY], [minZ, maxZ]]
"""
crinfo = fix_crinfo(crinfo)
return data[
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mjirik/imcut | imcut/image_manipulation.py | combinecrinfo | def combinecrinfo(crinfo1, crinfo2):
"""
Combine two crinfos. First used is crinfo1, second used is crinfo2.
"""
crinfo1 = fix_crinfo(crinfo1)
crinfo2 = fix_crinfo(crinfo2)
crinfo = [
[crinfo1[0][0] + crinfo2[0][0], crinfo1[0][0] + crinfo2[0][1]],
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"""
Combine two crinfos. First used is crinfo1, second used is crinfo2.
"""
crinfo1 = fix_crinfo(crinfo1)
crinfo2 = fix_crinfo(crinfo2)
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mjirik/imcut | imcut/image_manipulation.py | crinfo_from_specific_data | def crinfo_from_specific_data(data, margin=0):
"""
Create crinfo of minimum orthogonal nonzero block in input data.
:param data: input data
:param margin: add margin to minimum block
:return:
"""
# hledΓ‘me automatickΓ½ oΕez, nonzero dΓ‘ indexy
logger.debug("crinfo")
logger.debug(str(m... | python | def crinfo_from_specific_data(data, margin=0):
"""
Create crinfo of minimum orthogonal nonzero block in input data.
:param data: input data
:param margin: add margin to minimum block
:return:
"""
# hledΓ‘me automatickΓ½ oΕez, nonzero dΓ‘ indexy
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mjirik/imcut | imcut/image_manipulation.py | uncrop | def uncrop(data, crinfo, orig_shape, resize=False, outside_mode="constant", cval=0):
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Put some boundary to input image.
:param data: input data
:param crinfo: array with minimum and maximum index along each axis
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mjirik/imcut | imcut/image_manipulation.py | fix_crinfo | def fix_crinfo(crinfo, to="axis"):
"""
Function recognize order of crinfo and convert it to proper format.
"""
crinfo = np.asarray(crinfo)
if crinfo.shape[0] == 2:
crinfo = crinfo.T
return crinfo | python | def fix_crinfo(crinfo, to="axis"):
"""
Function recognize order of crinfo and convert it to proper format.
"""
crinfo = np.asarray(crinfo)
if crinfo.shape[0] == 2:
crinfo = crinfo.T
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mjirik/imcut | imcut/graph.py | grid_edges | def grid_edges(shape, inds=None, return_directions=True):
"""
Get list of grid edges
:param shape:
:param inds:
:param return_directions:
:return:
"""
if inds is None:
inds = np.arange(np.prod(shape)).reshape(shape)
# if not self.segparams['use_boundary_penalties'] and \
... | python | def grid_edges(shape, inds=None, return_directions=True):
"""
Get list of grid edges
:param shape:
:param inds:
:param return_directions:
:return:
"""
if inds is None:
inds = np.arange(np.prod(shape)).reshape(shape)
# if not self.segparams['use_boundary_penalties'] and \
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mjirik/imcut | imcut/graph.py | gen_grid_2d | def gen_grid_2d(shape, voxelsize):
"""
Generate list of edges for a base grid.
"""
nr, nc = shape
nrm1, ncm1 = nr - 1, nc - 1
# sh = nm.asarray(shape)
# calculate number of edges, in 2D: (nrows * (ncols - 1)) + ((nrows - 1) * ncols)
nedges = 0
for direction in range(len(shape)):
... | python | def gen_grid_2d(shape, voxelsize):
"""
Generate list of edges for a base grid.
"""
nr, nc = shape
nrm1, ncm1 = nr - 1, nc - 1
# sh = nm.asarray(shape)
# calculate number of edges, in 2D: (nrows * (ncols - 1)) + ((nrows - 1) * ncols)
nedges = 0
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mjirik/imcut | imcut/graph.py | write_grid_to_vtk | def write_grid_to_vtk(fname, nodes, edges, node_flag=None, edge_flag=None):
"""
Write nodes and edges to VTK file
:param fname: VTK filename
:param nodes:
:param edges:
:param node_flag: set if this node is really used in output
:param edge_flag: set if this flag is used in output
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"""
Write nodes and edges to VTK file
:param fname: VTK filename
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:param edges:
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mjirik/imcut | imcut/graph.py | Graph.add_nodes | def add_nodes(self, coors, node_low_or_high=None):
"""
Add new nodes at the end of the list.
"""
last = self.lastnode
if type(coors) is nm.ndarray:
if len(coors.shape) == 1:
coors = coors.reshape((1, coors.size))
nadd = coors.shape[0]
... | python | def add_nodes(self, coors, node_low_or_high=None):
"""
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mjirik/imcut | imcut/graph.py | Graph._edge_group_substitution | def _edge_group_substitution(
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Reconnect edges.
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Reconnect edges.
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mjirik/imcut | imcut/graph.py | Graph.generate_base_grid | def generate_base_grid(self, vtk_filename=None):
"""
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:return:
"""
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"""
Run first step of algorithm. Next step is split_voxels
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:return:
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mjirik/imcut | imcut/graph.py | Graph.split_voxels | def split_voxels(self, vtk_filename=None):
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Second step of algorithm
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mjirik/imcut | imcut/graph.py | MultiscaleArray.mul_block | def mul_block(self, index, val):
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mjirik/imcut | imcut/features.py | select_from_fv_by_seeds | def select_from_fv_by_seeds(fv, seeds, unique_cls):
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chitamoor/Rester | rester/manifest.py | Variables.expand | def expand(self, expression):
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disqus/gutter | gutter/client/__init__.py | get_gutter_client | def get_gutter_client(
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Creates gutter clients and memoizes them in a registry for future quick access.
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disqus/gutter | gutter/client/operators/misc.py | PercentRange._modulo | def _modulo(self, decimal_argument):
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disqus/gutter | gutter/client/models.py | Switch.enabled_for | def enabled_for(self, inpt):
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disqus/gutter | gutter/client/models.py | Manager.switches | def switches(self):
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List of all switches currently registered.
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List of all switches currently registered.
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disqus/gutter | gutter/client/models.py | Manager.switch | def switch(self, name):
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kaste/mockito-python | mockito/mockito.py | verify | def verify(obj, times=1, atleast=None, atmost=None, between=None,
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`args` can be as concrete as necessary. Often a catch-all is enough,
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End of preview. Expand in Data Studio
Dataset Card for CodeSearchNet corpus
Dataset Summary
CodeSearchNet corpus is a dataset of 2 milllion (comment, code) pairs from opensource libraries hosted on GitHub. It contains code and documentation for several programming languages.
CodeSearchNet corpus was gathered to support the CodeSearchNet challenge, to explore the problem of code retrieval using natural language.
Supported Tasks and Leaderboards
language-modeling: The dataset can be used to train a model for modelling programming languages, which consists in building language models for programming languages.
Languages
- Go programming language
- Java programming language
- Javascript programming language
- PHP programming language
- Python programming language
- Ruby programming language
Dataset Structure
Data Instances
A data point consists of a function code along with its documentation. Each data point also contains meta data on the function, such as the repository it was extracted from.
{
'id': '0',
'repository_name': 'organisation/repository',
'func_path_in_repository': 'src/path/to/file.py',
'func_name': 'func',
'whole_func_string': 'def func(args):\n"""Docstring"""\n [...]',
'language': 'python',
'func_code_string': '[...]',
'func_code_tokens': ['def', 'func', '(', 'args', ')', ...],
'func_documentation_string': 'Docstring',
'func_documentation_string_tokens': ['Docstring'],
'split_name': 'train',
'func_code_url': 'https://github.com/<org>/<repo>/blob/<hash>/src/path/to/file.py#L111-L150'
}
Data Fields
id: Arbitrary numberrepository_name: name of the GitHub repositoryfunc_path_in_repository: tl;dr: path to the file which holds the function in the repositoryfunc_name: name of the function in the filewhole_func_string: Code + documentation of the functionlanguage: Programming language in whoch the function is writtenfunc_code_string: Function codefunc_code_tokens: Tokens yielded by Treesitterfunc_documentation_string: Function documentationfunc_documentation_string_tokens: Tokens yielded by Treesittersplit_name: Name of the split to which the example belongs (one of train, test or valid)func_code_url: URL to the function code on Github
Data Splits
Three splits are available:
- train
- test
- valid
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
All information can be retrieved in the original technical review
Corpus collection:
Corpus has been collected from publicly available open-source non-fork GitHub repositories, using libraries.io to identify all projects which are used by at least one other project, and sort them by βpopularityβ as indicated by the number of stars and forks.
Then, any projects that do not have a license or whose license does not explicitly permit the re-distribution of parts of the project were removed. Treesitter - GitHub's universal parser - has been used to then tokenize all Go, Java, JavaScript, Python, PHP and Ruby functions (or methods) using and, where available, their respective documentation text using a heuristic regular expression.
Corpus filtering:
Functions without documentation are removed from the corpus. This yields a set of pairs ($c_i$, $d_i$) where ci is some function documented by di. Pairs ($c_i$, $d_i$) are passed through the folllowing preprocessing tasks:
- Documentation $d_i$ is truncated to the first full paragraph to remove in-depth discussion of function arguments and return values
- Pairs in which $d_i$ is shorter than three tokens are removed
- Functions $c_i$ whose implementation is shorter than three lines are removed
- Functions whose name contains the substring βtestβ are removed
- Constructors and standard extenion methods (eg
__str__in Python ortoStringin Java) are removed - Duplicates and near duplicates functions are removed, in order to keep only one version of the function
Who are the source language producers?
OpenSource contributors produced the code and documentations.
The dataset was gatherered and preprocessed automatically.
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
Each example in the dataset has is extracted from a GitHub repository, and each repository has its own license. Example-wise license information is not (yet) included in this dataset: you will need to find out yourself which license the code is using.
Citation Information
@article{husain2019codesearchnet, title={{CodeSearchNet} challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} }
Contributions
Thanks to @SBrandeis for adding this dataset.
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Homepage:
wandb.ai
Repository:
github.com
Paper:
arxiv.org
Data:
doi.org
Leaderboard:
wandb.ai
Size of downloaded dataset files:
3.93 GB
Size of the auto-converted Parquet files:
3.93 GB
Number of rows:
4,141,072
