uid stringlengths 24 24 | split stringclasses 1
value | category stringclasses 2
values | content stringlengths 5 482k | signature stringlengths 1 14k | suffix stringlengths 1 482k | prefix stringlengths 9 14k | prefix_token_count int64 3 5.01k | prefix_token_budget int64 64 256 | element_token_count int64 1 292k | signature_token_count int64 1 5.01k | prefix_context_token_count int64 0 255 | repo stringlengths 7 112 | path stringlengths 4 208 | language stringclasses 1
value | name stringlengths 1 218 | qualname stringlengths 1 218 | start_line int64 1 26.7k | end_line int64 1 26.7k | signature_start_line int64 1 26.7k | signature_end_line int64 1 26.7k | source_hash stringlengths 40 40 | source_dataset stringclasses 1
value | source_split stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
81c2427071e10bdd2f91d805 | train | function | @contextlib.contextmanager
def profile_context(profile=True, profiler_path='./seq2seq.profile'):
if profile:
with profiler.profiler('All', 'total', profiler_path):
yield
else:
yield
| @contextlib.contextmanager
def profile_context(profile=True, profiler_path='./seq2seq.profile'):
| if profile:
with profiler.profiler('All', 'total', profiler_path):
yield
else:
yield
| [0] == '2':
reload(sys)
sys.setdefaultencoding("utf-8")
from args import *
from base_model import BaseModel
from attention_model import AttentionModel
import logging
import pickle
@contextlib.contextmanager
def profile_context(profile=True, profiler_path='./seq2seq.profile'):
| 64 | 64 | 47 | 20 | 43 | weiwei1115/models | PaddleNLP/legacy/seq2seq/seq2seq/train.py | Python | profile_context | profile_context | 49 | 55 | 49 | 50 | 96ce1a93a479da08a6d2b39d70f8e4627156ae2b | bigcode/the-stack | train |
c8ae8102a0cb2bc0e5aa9df8 | train | function | def binarize_categorical_columns(
input_train_df, input_test_df, categorical_columns):
"""Function to converting categorical features to one-hot encodings."""
# Binarize categorical columns.
binarized_train_df = pd.get_dummies(
input_train_df, columns=categorical_columns)
binarized_test_df = pd.get_d... | def binarize_categorical_columns(
input_train_df, input_test_df, categorical_columns):
| """Function to converting categorical features to one-hot encodings."""
# Binarize categorical columns.
binarized_train_df = pd.get_dummies(
input_train_df, columns=categorical_columns)
binarized_test_df = pd.get_dummies(
input_test_df, columns=categorical_columns)
# Make sure the train and test... | # Bucketize using provided bin cut-points.
input_train_df[continuous_column_name] = pd.cut(
input_train_df[continuous_column_name], bins, labels=False)
input_test_df[continuous_column_name] = pd.cut(
input_test_df[continuous_column_name], bins, labels=False)
def binarize_categorical_columns(
... | 84 | 84 | 281 | 20 | 64 | jrmendeshurb/google-research | generalized_rates/datasets/load_adult.py | Python | binarize_categorical_columns | binarize_categorical_columns | 72 | 96 | 72 | 73 | 95a253c9213da9dceab7578095ffcda96cc45aaf | bigcode/the-stack | train |
387f8dc9077095299bb2ca7e | train | function | def bucketize_continuous_column(
input_train_df, input_test_df, continuous_column_name, num_quantiles=None,
bins=None):
"""Bucketize continuous columns using either bin cut-points or quantiles."""
assert (num_quantiles is None or bins is None)
if num_quantiles is not None:
# Compute quantile cut-point... | def bucketize_continuous_column(
input_train_df, input_test_df, continuous_column_name, num_quantiles=None,
bins=None):
| """Bucketize continuous columns using either bin cut-points or quantiles."""
assert (num_quantiles is None or bins is None)
if num_quantiles is not None:
# Compute quantile cut-points and bucketize.
_, bins_quantized = pd.qcut(
input_train_df[continuous_column_name], num_quantiles, retbins=True,
... | test_file", "adult.test",
"Path to Adult test data file.")
flags.DEFINE_string("output_directory", "datasets/",
"Path to store processed dataset.")
FLAGS = flags.FLAGS
def bucketize_continuous_column(
input_train_df, input_test_df, continuous_column_name, num_quantiles=None... | 72 | 72 | 241 | 30 | 41 | jrmendeshurb/google-research | generalized_rates/datasets/load_adult.py | Python | bucketize_continuous_column | bucketize_continuous_column | 50 | 69 | 50 | 52 | 269a398a1f1836e51b7fe86edf72e1df240dda6f | bigcode/the-stack | train |
2aa58b2b080235eb5aca49f3 | train | function | def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
# Load and pre-process Adult train and test datasets.
train_set, test_set = load_data()
x_train, y_train, z_train = test_set
# Split train data into train and validation sets.
train_indices, vali_indices = model... | def main(argv):
| if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
# Load and pre-process Adult train and test datasets.
train_set, test_set = load_data()
x_train, y_train, z_train = test_set
# Split train data into train and validation sets.
train_indices, vali_indices = model_selection.train... | train_df[feature_names].to_numpy()
features_test = test_df[feature_names].to_numpy()
# Return train and test set tuples.
train_set = (features_train, labels_train, groups_train)
test_set = (features_test, labels_test, groups_test)
return train_set, test_set
def main(argv):
| 71 | 71 | 239 | 4 | 66 | jrmendeshurb/google-research | generalized_rates/datasets/load_adult.py | Python | main | main | 168 | 193 | 168 | 168 | 0d55fe737b8d2ba7efd7febb22526dc2937a5268 | bigcode/the-stack | train |
06ee11fc333c34f119e9a75f | train | function | def load_data():
"""Load and process train and test datasets at provided data paths."""
columns = [
"age", "workclass", "fnlwgt", "education", "education_num",
"marital_status", "occupation", "relationship", "race", "gender",
"capital_gain", "capital_loss", "hours_per_week", "native_country",
... | def load_data():
| """Load and process train and test datasets at provided data paths."""
columns = [
"age", "workclass", "fnlwgt", "education", "education_num",
"marital_status", "occupation", "relationship", "race", "gender",
"capital_gain", "capital_loss", "hours_per_week", "native_country",
"income_bracket... | )
binarized_test_df = pd.get_dummies(
input_test_df, columns=categorical_columns)
# Make sure the train and test dataframes have the same binarized columns.
# Identify columns in train set not in test set and fill them in test set.
test_df_missing_cols = set(binarized_train_df.columns) - set(
binar... | 223 | 223 | 744 | 4 | 218 | jrmendeshurb/google-research | generalized_rates/datasets/load_adult.py | Python | load_data | load_data | 99 | 165 | 99 | 99 | b11f23e80e031cca7de4f5732d30d87c5e862579 | bigcode/the-stack | train |
84c5aa9921d95108b7b3b450 | train | function | @mod.capture(rule="{self.ordinal_words}")
def ordinals(m) -> int:
"Returns a single ordinial as a digit"
o = m[0]
return int(ordinal_words[o])
| @mod.capture(rule="{self.ordinal_words}")
def ordinals(m) -> int:
| "Returns a single ordinial as a digit"
o = m[0]
return int(ordinal_words[o])
| join(ordinal_list)
return result
for n in range(1, 100):
ordinal_words[ordinal_word(n)] = n
mod = Module()
mod.list("ordinal_words", desc="list of ordinals")
@mod.capture(rule="{self.ordinal_words}")
def ordinals(m) -> int:
| 64 | 64 | 43 | 17 | 47 | gimpf/talon-conf | base/number_ordinals.py | Python | ordinals | ordinals | 95 | 99 | 95 | 96 | 888e96fdf11a105b17bd91603deb69e44048f6d9 | bigcode/the-stack | train |
366cdba4454cc7c47925ab83 | train | function | def ordinal_word(n):
n = int(n)
ordinal_list = []
if n > 19:
if n % 10 == 0:
ordinal_list.append(ordinal_tens[floor((n / 10)) - 2])
else:
ordinal_list.append(ordinal_tenty[floor(n / 10) - 2])
ordinal_list.append(ordinal_ones[(n % 10) - 1])
elif n > 9:
... | def ordinal_word(n):
| n = int(n)
ordinal_list = []
if n > 19:
if n % 10 == 0:
ordinal_list.append(ordinal_tens[floor((n / 10)) - 2])
else:
ordinal_list.append(ordinal_tenty[floor(n / 10) - 2])
ordinal_list.append(ordinal_ones[(n % 10) - 1])
elif n > 9:
ordinal_list.... | int(n)
suffix = ["th", "st", "nd", "rd", "th"][min(n % 10, 4)]
if 11 <= (n % 100) <= 13:
suffix = "th"
return str(n) + suffix
def ordinal_word(n):
| 64 | 64 | 143 | 5 | 58 | gimpf/talon-conf | base/number_ordinals.py | Python | ordinal_word | ordinal_word | 70 | 85 | 70 | 70 | 0206b767723b8967e26ce0526d3022816d1c9851 | bigcode/the-stack | train |
073af9de5acfcdcdc58a2c51 | train | function | def ordinal(n):
"""
Convert an integer into its ordinal representation::
ordinal(0) => '0th'
ordinal(3) => '3rd'
ordinal(122) => '122nd'
ordinal(213) => '213th'
"""
n = int(n)
suffix = ["th", "st", "nd", "rd", "th"][min(n % 10, 4)]
if 11 <= (n % 100) <= 13:
... | def ordinal(n):
| """
Convert an integer into its ordinal representation::
ordinal(0) => '0th'
ordinal(3) => '3rd'
ordinal(122) => '122nd'
ordinal(213) => '213th'
"""
n = int(n)
suffix = ["th", "st", "nd", "rd", "th"][min(n % 10, 4)]
if 11 <= (n % 100) <= 13:
suffix = "... | "eightieth",
"ninetieth",
]
ordinal_tenty = [
"twenty",
"thirty",
"forty",
"fifty",
"sixty",
"seventy",
"eighty",
"ninety",
]
def ordinal(n):
| 64 | 64 | 121 | 4 | 60 | gimpf/talon-conf | base/number_ordinals.py | Python | ordinal | ordinal | 55 | 67 | 55 | 55 | 164e4c6752ed19735c01eef6f2c79f77e4a921b0 | bigcode/the-stack | train |
6d9cfee1675f8207183f20da | train | function | def test_without_thickness_surface():
clientModel.service.begin_modification('new')
# Testing the standard surface function
Node(1, 0, -30, 0), Node(2, 10, -30, 0), Node(3, 10, -20, 0), Node(4, 0, -20, 0)
Line(1, '1 2'), Line(2, '2 3'), Line(3, '3 4'), Line(4, '4 1')
Material(name='C30/37')
Th... | def test_without_thickness_surface():
| clientModel.service.begin_modification('new')
# Testing the standard surface function
Node(1, 0, -30, 0), Node(2, 10, -30, 0), Node(3, 10, -20, 0), Node(4, 0, -20, 0)
Line(1, '1 2'), Line(2, '2 3'), Line(3, '3 4'), Line(4, '4 1')
Material(name='C30/37')
Thickness()
Surface()
# Standard... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
sys.path.append(".")
## Import the relevant Libraries
from os import name
from RFEM.enums import *
#from RFEM.window import *
from RFEM.dataTypes import *
from RFEM.initModel import *
from RFEM.BasicObjects.material import *
from RFEM.BasicObjects.section impor... | 253 | 256 | 955 | 7 | 246 | r0m30d4c/DlubalRFEM6 | UnitTests/test_WithoutThicknessSurface_Test.py | Python | test_without_thickness_surface | test_without_thickness_surface | 34 | 88 | 34 | 35 | cf99d2548bf65ac544ac028f4e8f75030cbc478c | bigcode/the-stack | train |
cdeb7a7c1ad3f73165a37aa2 | train | class | class Migration(migrations.Migration):
dependencies = [
('smart_contract', '0004_auto_20180711_2050'),
]
operations = [
migrations.AlterField(
model_name='comment',
name='date_update',
field=models.DateTimeField(auto_now=True, null=True),
),
... | class Migration(migrations.Migration):
| dependencies = [
('smart_contract', '0004_auto_20180711_2050'),
]
operations = [
migrations.AlterField(
model_name='comment',
name='date_update',
field=models.DateTimeField(auto_now=True, null=True),
),
]
| # Generated by Django 2.0.7 on 2018-07-12 18:41
from django.db import migrations, models
class Migration(migrations.Migration):
| 38 | 64 | 68 | 7 | 30 | RustamSultanov/Python-test-registry- | registry/smart_contract/migrations/0005_auto_20180712_1841.py | Python | Migration | Migration | 6 | 18 | 6 | 7 | f06bde29728215b44a171251d83c754b4a4540ee | bigcode/the-stack | train |
cb30cea972d3dfad86c6e101 | train | class | class ColorSchemeBuilder(object):
"""A class for building a color scheme."""
_scope_name_template = "CH_color_%s"
_color_scope_template = """
<dict>
<key>name</key>
<string>CH_color</string>
<key>scope</key>
<string>CH_color_%s</string>
<key>settings</key>
<dict>
<key>background</key>
<string>%s</string>
<... | class ColorSchemeBuilder(object):
| """A class for building a color scheme."""
_scope_name_template = "CH_color_%s"
_color_scope_template = """
<dict>
<key>name</key>
<string>CH_color</string>
<key>scope</key>
<string>CH_color_%s</string>
<key>settings</key>
<dict>
<key>background</key>
<string>%s</string>
<key>foreground</key>
<string>%s</s... | """A color highlighter that uses color scheme scopes to highlight colors."""
import threading
from xml.etree import ElementTree
try:
from .st_helper import running_in_st, is_st3
from . import colors
from .color_highlighter import ColorHighlighter
except ValueError:
from st_helper import running_in_st,... | 120 | 232 | 775 | 6 | 113 | EatBreatheCode/ColorHighlighter | color_scheme_color_highlighter.py | Python | ColorSchemeBuilder | ColorSchemeBuilder | 21 | 122 | 21 | 21 | 4f7c515161786a980405f812e146a3b15f0aa49b | bigcode/the-stack | train |
713e07e9ef2af62c3db34881 | train | class | class ColorSchemeColorHighlighter(ColorHighlighter):
"""A color highlighter that uses color scheme scopes to highlight colors."""
region_name_template = "CH_color_%s_%d_%d"
if is_st3():
_region_style_flags = {
"filled": sublime.DRAW_NO_OUTLINE,
"text": sublime.DRAW_NO_OUTLI... | class ColorSchemeColorHighlighter(ColorHighlighter):
| """A color highlighter that uses color scheme scopes to highlight colors."""
region_name_template = "CH_color_%s_%d_%d"
if is_st3():
_region_style_flags = {
"filled": sublime.DRAW_NO_OUTLINE,
"text": sublime.DRAW_NO_OUTLINE,
"outlined": sublime.DRAW_NO_FILL,
... | scopes = []
for color in for_colors:
if color in existing_colors:
continue
opposite_color = colors.complementary_color(color)
background_color = self._color_scheme_data.background_color
fixed_color = colors.background_colo... | 237 | 237 | 791 | 10 | 226 | EatBreatheCode/ColorHighlighter | color_scheme_color_highlighter.py | Python | ColorSchemeColorHighlighter | ColorSchemeColorHighlighter | 125 | 212 | 125 | 125 | 029d2cc17fb7407ca9e658e7209fa1fc3881acfa | bigcode/the-stack | train |
55cf65870c78d903f9afe8b5 | train | class | class BadBlockBlock(Block):
def __init__(self, blkdev, blk_num=0):
Block.__init__(self, blkdev, blk_num, chk_loc=2, is_type=Block.BADB)
def create(self, block_pairs, host_id, size=128, next=0):
Block.create(self)
self.size = size
self.host_id = host_id
self.next = next
self.block_pairs = ... | class BadBlockBlock(Block):
| def __init__(self, blkdev, blk_num=0):
Block.__init__(self, blkdev, blk_num, chk_loc=2, is_type=Block.BADB)
def create(self, block_pairs, host_id, size=128, next=0):
Block.create(self)
self.size = size
self.host_id = host_id
self.next = next
self.block_pairs = block_pairs
def wri... | from __future__ import absolute_import
from __future__ import print_function
from ..Block import Block
class BadBlockBlock(Block):
| 28 | 127 | 424 | 6 | 21 | limi/AGSImager | dependencies/amitools-0.1.0/amitools/fs/block/rdb/BadBlocksBlock.py | Python | BadBlockBlock | BadBlockBlock | 6 | 61 | 6 | 6 | dd15eb5c07e0aa1288b9355a0f6f4c38334a8353 | bigcode/the-stack | train |
c8fa2fb8dc3c451c79b4304c | train | function | def try_simplify_traceback(tb: TracebackType) -> Optional[TracebackType]:
"""
Simplify the traceback. It removes the tracebacks in the current package, and only
shows the traceback that is related to the thirdparty and user-specified codes.
Returns
-------
TracebackType or None
Simplified... | def try_simplify_traceback(tb: TracebackType) -> Optional[TracebackType]:
| """
Simplify the traceback. It removes the tracebacks in the current package, and only
shows the traceback that is related to the thirdparty and user-specified codes.
Returns
-------
TracebackType or None
Simplified traceback instance. It returns None if it fails to simplify.
Notes
... | d+)(\..*)?$", sparkVersion)
if m is not None:
return (int(m.group(1)), int(m.group(2)))
else:
raise ValueError(
"Spark tried to parse '%s' as a Spark" % sparkVersion
+ " version string, but it could not find the major and minor"
+ "... | 256 | 256 | 1,206 | 20 | 235 | yangwwei/spark | python/pyspark/util.py | Python | try_simplify_traceback | try_simplify_traceback | 96 | 226 | 96 | 96 | a28b51bbc311c9d61956dca920e2b6ada8bf0572 | bigcode/the-stack | train |
ed71ec70812930f655184021 | train | function | def walk_tb(tb: Optional[TracebackType]) -> Iterator[TracebackType]:
while tb is not None:
yield tb
tb = tb.tb_next
| def walk_tb(tb: Optional[TracebackType]) -> Iterator[TracebackType]:
| while tb is not None:
yield tb
tb = tb.tb_next
| try:
return f(*args, **kwargs)
except StopIteration as exc:
raise RuntimeError(
"Caught StopIteration thrown from user's code; failing the task", exc
)
return wrapper
def walk_tb(tb: Optional[TracebackType]) -> Iterator[TracebackType]:
| 64 | 64 | 36 | 18 | 45 | yangwwei/spark | python/pyspark/util.py | Python | walk_tb | walk_tb | 90 | 93 | 90 | 90 | 47d6a52ec6d9ef93b40a58677881770bd9f958d8 | bigcode/the-stack | train |
74409a5471fcc4d9ce556f2d | train | function | def _parse_memory(s: str) -> int:
"""
Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
return the value in MiB
Examples
--------
>>> _parse_memory("256m")
256
>>> _parse_memory("2g")
2048
"""
units = {"g": 1024, "m": 1, "t": 1 << 20, "k": 1.0 / 1024}... | def _parse_memory(s: str) -> int:
| """
Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
return the value in MiB
Examples
--------
>>> _parse_memory("256m")
256
>>> _parse_memory("2g")
2048
"""
units = {"g": 1024, "m": 1, "t": 1 << 20, "k": 1.0 / 1024}
if s[-1].lower() not in units... | <spark-%(jar_name)s.jar> ...
________________________________________________________________________________________________
"""
% {
"lib_name": lib_name,
"pkg_name": pkg_name,
"jar_name": jar_name,
"spark_version": spark_version,
}
)
def _parse_me... | 64 | 64 | 158 | 11 | 53 | yangwwei/spark | python/pyspark/util.py | Python | _parse_memory | _parse_memory | 259 | 274 | 259 | 259 | 116dfb01e860d6694d0bd0a3ad711cc3989955aa | bigcode/the-stack | train |
a3d958db5cb129f7e934b731 | train | function | def inheritable_thread_target(f: Callable) -> Callable:
"""
Return thread target wrapper which is recommended to be used in PySpark when the
pinned thread mode is enabled. The wrapper function, before calling original
thread target, it inherits the inheritable properties specific
to JVM thread such ... | def inheritable_thread_target(f: Callable) -> Callable:
| """
Return thread target wrapper which is recommended to be used in PySpark when the
pinned thread mode is enabled. The wrapper function, before calling original
thread target, it inherits the inheritable properties specific
to JVM thread such as ``InheritableThreadLocal``.
Also, note that pinn... | }
)
def _parse_memory(s: str) -> int:
"""
Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
return the value in MiB
Examples
--------
>>> _parse_memory("256m")
256
>>> _parse_memory("2g")
2048
"""
units = {"g": 1024, "m": 1, "t": 1 << 20, "k": ... | 173 | 174 | 581 | 12 | 162 | yangwwei/spark | python/pyspark/util.py | Python | inheritable_thread_target | inheritable_thread_target | 277 | 344 | 277 | 277 | d03d099115ffd4730648223071d7c62b6b5c410e | bigcode/the-stack | train |
079281a96f33b66b0903eed2 | train | function | def _print_missing_jar(lib_name: str, pkg_name: str, jar_name: str, spark_version: str) -> None:
print(
"""
________________________________________________________________________________________________
Spark %(lib_name)s libraries not found in class path. Try one of the following.
1. Include the %(... | def _print_missing_jar(lib_name: str, pkg_name: str, jar_name: str, spark_version: str) -> None:
| print(
"""
________________________________________________________________________________________________
Spark %(lib_name)s libraries not found in class path. Try one of the following.
1. Include the %(lib_name)s library and its dependencies with in the
spark-submit command as
$ bin/spar... | tb_lineno=cur_tb.tb_frame.f_lineno if cur_tb.tb_frame.f_lineno is not None else -1,
)
tb_next = new_tb
return new_tb
def _print_missing_jar(lib_name: str, pkg_name: str, jar_name: str, spark_version: str) -> None:
| 67 | 67 | 224 | 29 | 37 | yangwwei/spark | python/pyspark/util.py | Python | _print_missing_jar | _print_missing_jar | 229 | 256 | 229 | 229 | b8c69eded07aa37ffae7455b50f0cb3aa18a8367 | bigcode/the-stack | train |
161e37815e1af06678563571 | train | class | class InheritableThread(threading.Thread):
"""
Thread that is recommended to be used in PySpark instead of :class:`threading.Thread`
when the pinned thread mode is enabled. The usage of this class is exactly same as
:class:`threading.Thread` but correctly inherits the inheritable properties specific
... | class InheritableThread(threading.Thread):
| """
Thread that is recommended to be used in PySpark instead of :class:`threading.Thread`
when the pinned thread mode is enabled. The usage of this class is exactly same as
:class:`threading.Thread` but correctly inherits the inheritable properties specific
to JVM thread such as ``InheritableThreadL... | (PYSPARK_PIN_THREAD) is on.
# NOTICE the internal difference vs `InheritableThread`. `InheritableThread`
# copies local properties when the thread starts but `inheritable_thread_target`
# copies when the function is wrapped.
assert SparkContext._active_spark_context is not None
... | 204 | 204 | 681 | 9 | 194 | yangwwei/spark | python/pyspark/util.py | Python | InheritableThread | InheritableThread | 347 | 421 | 347 | 347 | aa2d70405a419bcf329660ee6af8aee22a7a97b4 | bigcode/the-stack | train |
eeeb6a4dbc538f00337b43a2 | train | function | def print_exec(stream: TextIO) -> None:
ei = sys.exc_info()
traceback.print_exception(ei[0], ei[1], ei[2], None, stream)
| def print_exec(stream: TextIO) -> None:
| ei = sys.exc_info()
traceback.print_exception(ei[0], ei[1], ei[2], None, stream)
| from typing import Any, Callable, Iterator, List, Optional, TextIO, Tuple
from py4j.clientserver import ClientServer # type: ignore[import]
__all__: List[str] = []
from py4j.java_gateway import JavaObject
def print_exec(stream: TextIO) -> None:
| 64 | 64 | 39 | 11 | 52 | yangwwei/spark | python/pyspark/util.py | Python | print_exec | print_exec | 37 | 39 | 37 | 37 | ec5b902f78e1f7e2a7cd7b016874ef80d337d6d8 | bigcode/the-stack | train |
bfe549bb31b5fe2fb25377df | train | function | def fail_on_stopiteration(f: Callable) -> Callable:
"""
Wraps the input function to fail on 'StopIteration' by raising a 'RuntimeError'
prevents silent loss of data when 'f' is used in a for loop in Spark code
"""
def wrapper(*args: Any, **kwargs: Any) -> Any:
try:
return f(*arg... | def fail_on_stopiteration(f: Callable) -> Callable:
| """
Wraps the input function to fail on 'StopIteration' by raising a 'RuntimeError'
prevents silent loss of data when 'f' is used in a for loop in Spark code
"""
def wrapper(*args: Any, **kwargs: Any) -> Any:
try:
return f(*args, **kwargs)
except StopIteration as exc:
... | (2)))
else:
raise ValueError(
"Spark tried to parse '%s' as a Spark" % sparkVersion
+ " version string, but it could not find the major and minor"
+ " version numbers."
)
def fail_on_stopiteration(f: Callable) -> Callable:
| 64 | 64 | 118 | 12 | 52 | yangwwei/spark | python/pyspark/util.py | Python | fail_on_stopiteration | fail_on_stopiteration | 73 | 87 | 73 | 73 | eaf5ecb49daf173bc9138a6b56c3bb2b3ee929f2 | bigcode/the-stack | train |
1f0c70256699aca1088a9913 | train | class | class VersionUtils:
"""
Provides utility method to determine Spark versions with given input string.
"""
@staticmethod
def majorMinorVersion(sparkVersion: str) -> Tuple[int, int]:
"""
Given a Spark version string, return the (major version number, minor version number).
E.g.... | class VersionUtils:
| """
Provides utility method to determine Spark versions with given input string.
"""
@staticmethod
def majorMinorVersion(sparkVersion: str) -> Tuple[int, int]:
"""
Given a Spark version string, return the (major version number, minor version number).
E.g., for 2.0.1-SNAPSHOT... | .clientserver import ClientServer # type: ignore[import]
__all__: List[str] = []
from py4j.java_gateway import JavaObject
def print_exec(stream: TextIO) -> None:
ei = sys.exc_info()
traceback.print_exception(ei[0], ei[1], ei[2], None, stream)
class VersionUtils:
| 74 | 74 | 249 | 4 | 70 | yangwwei/spark | python/pyspark/util.py | Python | VersionUtils | VersionUtils | 42 | 70 | 42 | 42 | 44570e95951a1b2140d4e8110a9c1e80f417c6dc | bigcode/the-stack | train |
0c1a1e2c6fc72a24eb018f42 | train | class | class TestUsersRealApi:
@classmethod
def setup_class(cls):
configuration = Configuration()
api_key = os.getenv('JC_API_KEY')
assert (api_key is not None),\
"The environmental variable `JC_API_KEY` must contain a valid Jumpcloud API key"
configuration.api_key['x-api-ke... | class TestUsersRealApi:
@classmethod
| def setup_class(cls):
configuration = Configuration()
api_key = os.getenv('JC_API_KEY')
assert (api_key is not None),\
"The environmental variable `JC_API_KEY` must contain a valid Jumpcloud API key"
configuration.api_key['x-api-key'] = api_key
cls.systemusers_api... | import json
import os
from jcapiv1 import ApiClient, Configuration, Systemuserslist, Systemuserputpost
from jccli import cli
from click.testing import CliRunner, Result
from jcapiv1.api.systemusers_api import SystemusersApi
class TestUsersRealApi:
@classmethod
| 65 | 196 | 656 | 10 | 54 | cascadianblue/jccli | integration_tests/test_users_real_api.py | Python | TestUsersRealApi | TestUsersRealApi | 11 | 110 | 11 | 12 | ed87d4c207e16440e20f90551289af62a82201b9 | bigcode/the-stack | train |
ce766699a4fdf727fda13878 | train | function | def transcribe_streaming(stream_file):
"""Streams transcription of the given audio file."""
import io
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
# [START speech_python_migration_streaming_request... | def transcribe_streaming(stream_file):
| """Streams transcription of the given audio file."""
import io
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
# [START speech_python_migration_streaming_request]
with io.open(stream_file, 'rb') a... | by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Google Cloud Spee... | 105 | 105 | 352 | 8 | 97 | summersab/python-docs-samples | speech/cloud-client/transcribe_streaming.py | Python | transcribe_streaming | transcribe_streaming | 27 | 66 | 27 | 27 | 44cb335f27eb1f96d6f864a0f8a22eea6095d3d7 | bigcode/the-stack | train |
21aebff1b09777aa45478621 | train | function | def view_mpl(points: list, splits: list, bounds: V, palette: int or list, titles: Tuple[str]):
numpy_images = []
bounding_box = extrude_bounds(bounds)
titles = [titles[i] if i < len(titles) else '' for i in range(len(points))]
for pts, split, color, title in zip(points, splits, palette, titles):
... | def view_mpl(points: list, splits: list, bounds: V, palette: int or list, titles: Tuple[str]):
| numpy_images = []
bounding_box = extrude_bounds(bounds)
titles = [titles[i] if i < len(titles) else '' for i in range(len(points))]
for pts, split, color, title in zip(points, splits, palette, titles):
ax, fig = init_fig(bounds, 1)
add_points(ax, bounding_box, bounds, V([0, 8], dtype=np.... | bounding_box[corner, axis] = bounds[(corner % (f * 2)) // f, axis]
f = f * 2
return bounding_box
def view_mpl(points: list, splits: list, bounds: V, palette: int or list, titles: Tuple[str]):
| 64 | 64 | 161 | 27 | 36 | haohlin/pointgmm-primitive-detection | show/viewer_mpl.py | Python | view_mpl | view_mpl | 78 | 90 | 78 | 78 | 525bbe4746badc5fd63e53c3e36760f5498d99b1 | bigcode/the-stack | train |
ae8914a137c43dbaa12c98ab | train | function | def fig2data(fig: plt.Figure):
# taken from http://www.icare.univ-lille1.fr/tutorials/convert_a_matplotlib_figure
# Thanks!
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
buf = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
buf.shape = (h, w, 3)
return buf
| def fig2data(fig: plt.Figure):
# taken from http://www.icare.univ-lille1.fr/tutorials/convert_a_matplotlib_figure
# Thanks!
| fig.canvas.draw()
w, h = fig.canvas.get_width_height()
buf = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
buf.shape = (h, w, 3)
return buf
| .set_yticks([])
ax.set_zticks([])
ax.dist = 7
return ax, fig
def fig2data(fig: plt.Figure):
# taken from http://www.icare.univ-lille1.fr/tutorials/convert_a_matplotlib_figure
# Thanks!
| 64 | 64 | 88 | 39 | 24 | haohlin/pointgmm-primitive-detection | show/viewer_mpl.py | Python | fig2data | fig2data | 58 | 65 | 58 | 60 | 44588eaba51e343dd862661ea1cb952e4b363e69 | bigcode/the-stack | train |
ad5d5f1c3f192754ca030b6a | train | function | def extrude_bounds(bounds:V) -> V:
bounding_box = np.zeros((2 ** bounds.shape[1], bounds.shape[1]))
f = 1
for axis in range(bounding_box.shape[1]):
for corner in range(bounding_box.shape[0]):
bounding_box[corner, axis] = bounds[(corner % (f * 2)) // f, axis]
f = f * 2
return ... | def extrude_bounds(bounds:V) -> V:
| bounding_box = np.zeros((2 ** bounds.shape[1], bounds.shape[1]))
f = 1
for axis in range(bounding_box.shape[1]):
for corner in range(bounding_box.shape[0]):
bounding_box[corner, axis] = bounds[(corner % (f * 2)) // f, axis]
f = f * 2
return bounding_box
| # Thanks!
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
buf = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
buf.shape = (h, w, 3)
return buf
def extrude_bounds(bounds:V) -> V:
| 64 | 64 | 97 | 11 | 52 | haohlin/pointgmm-primitive-detection | show/viewer_mpl.py | Python | extrude_bounds | extrude_bounds | 68 | 75 | 68 | 68 | 3271658308908fa20c421691fc9621121293a175 | bigcode/the-stack | train |
d69f019c9d4a41aaa55a6d92 | train | function | def add_points(container, points, bounds, split, color: int or list or tuple):
if type(color) is int or type(color) is tuple:
color = (split.shape[0] - 1) * [color]
for i in range(0, split.shape[0] - 1):
c = color[i]
if type(c) is int or type(c) is float:
c = (c, c, c)
... | def add_points(container, points, bounds, split, color: int or list or tuple):
| if type(color) is int or type(color) is tuple:
color = (split.shape[0] - 1) * [color]
for i in range(0, split.shape[0] - 1):
c = color[i]
if type(c) is int or type(c) is float:
c = (c, c, c)
if type(c[0]) is int:
c = [float(c[i]) / 255. for i in range(3)]
... | axis_mask = np.logical_and(bounds[0][i] <= points[:, i], points[:, i] <= bounds[1][i])
mask = np.logical_and(mask, axis_mask)
return points[mask]
def add_points(container, points, bounds, split, color: int or list or tuple):
| 64 | 64 | 207 | 19 | 45 | haohlin/pointgmm-primitive-detection | show/viewer_mpl.py | Python | add_points | add_points | 18 | 29 | 18 | 18 | 2fcadedeb5c2f73a845c2fe4a25bb95644fd032b | bigcode/the-stack | train |
624e28c3543dad26a3d9ef00 | train | function | def init_fig(bounds: V, num_objects:int):
fig = plt.figure(figsize=(max(num_objects * 2, 3.5), 3.5))
ax = fig.gca(projection='3d')
scale = bounds[1] - bounds[0]
scale = np.diag([scale[0], scale[1], scale[2], 1.0])
scale = scale * (1.0 / scale.max())
scale[3, 3] = 1.0
def short_proj():
... | def init_fig(bounds: V, num_objects:int):
| fig = plt.figure(figsize=(max(num_objects * 2, 3.5), 3.5))
ax = fig.gca(projection='3d')
scale = bounds[1] - bounds[0]
scale = np.diag([scale[0], scale[1], scale[2], 1.0])
scale = scale * (1.0 / scale.max())
scale[3, 3] = 1.0
def short_proj():
return np.dot(Axes3D.get_proj(ax), scale... | float(c[i]) / 255. for i in range(3)]
cur_points = clear_outside_points(points[split[i]: split[i + 1], :], bounds)
if len(cur_points) > 0:
container.scatter(cur_points[:, 0], cur_points[:, 1], cur_points[:, 2], marker='o', s=3, c=((c[0],c[1],c[2]),), alpha=.4)
def init_fig(bounds: V, num_obj... | 107 | 107 | 358 | 11 | 96 | haohlin/pointgmm-primitive-detection | show/viewer_mpl.py | Python | init_fig | init_fig | 32 | 55 | 32 | 32 | 0033ae90da00a1bd64f2f1c5d4a370fc11aeda11 | bigcode/the-stack | train |
1ac43012cbc58b90ba20614d | train | function | def clear_outside_points(points, bounds):
mask = np.ones(points.shape[0], dtype=np.bool)
for i in range(points.shape[1]):
axis_mask = np.logical_and(bounds[0][i] <= points[:, i], points[:, i] <= bounds[1][i])
mask = np.logical_and(mask, axis_mask)
return points[mask]
| def clear_outside_points(points, bounds):
| mask = np.ones(points.shape[0], dtype=np.bool)
for i in range(points.shape[1]):
axis_mask = np.logical_and(bounds[0][i] <= points[:, i], points[:, i] <= bounds[1][i])
mask = np.logical_and(mask, axis_mask)
return points[mask]
| from mpl_toolkits.mplot3d.axes3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
import matplotlib as mpl
import matplotlib.pyplot as plt
from custom_types import *
mpl.use('Agg')
def clear_outside_points(points, bounds):
| 60 | 64 | 79 | 9 | 51 | haohlin/pointgmm-primitive-detection | show/viewer_mpl.py | Python | clear_outside_points | clear_outside_points | 10 | 15 | 10 | 10 | f6ff8a0dbf95f6a737fc06215e2bc3f93770961d | bigcode/the-stack | train |
f1154bf92fdaa6fcf52cbc5d | train | function | @pytest.fixture
def graphql_client(release_test_map, retrying_requests):
dagit_host = os.environ.get("BACKCOMPAT_TESTS_DAGIT_HOST", "localhost")
dagit_version = release_test_map["dagit"]
user_code_version = release_test_map["user_code"]
with docker_service_up(
file_relative_path(__file__, "./d... | @pytest.fixture
def graphql_client(release_test_map, retrying_requests):
| dagit_host = os.environ.get("BACKCOMPAT_TESTS_DAGIT_HOST", "localhost")
dagit_version = release_test_map["dagit"]
user_code_version = release_test_map["user_code"]
with docker_service_up(
file_relative_path(__file__, "./dagit_service/docker-compose.yml"),
build_args=[dagit_version, use... | :
yield
finally:
subprocess.check_output(["docker-compose", "-f", docker_compose_file, "stop"])
subprocess.check_output(["docker-compose", "-f", docker_compose_file, "rm", "-f"])
@pytest.fixture
def graphql_client(release_test_map, retrying_requests):
| 63 | 64 | 145 | 15 | 48 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | graphql_client | graphql_client | 106 | 119 | 106 | 107 | 2674ba138fe2009eb2206e55c8d72a0fc5dc8991 | bigcode/the-stack | train |
572a40e13ec4a1091db281e2 | train | function | def test_backcompat_deployed_pipeline(graphql_client):
assert_runs_and_exists(graphql_client, "the_pipeline")
| def test_backcompat_deployed_pipeline(graphql_client):
| assert_runs_and_exists(graphql_client, "the_pipeline")
| _version],
):
result = retrying_requests.get(f"http://{dagit_host}:3000/dagit_info")
assert result.json().get("dagit_version")
yield DagsterGraphQLClient(dagit_host, port_number=3000)
def test_backcompat_deployed_pipeline(graphql_client):
| 64 | 64 | 24 | 11 | 53 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | test_backcompat_deployed_pipeline | test_backcompat_deployed_pipeline | 122 | 123 | 122 | 122 | 33df1f27338dbdcc390e2e0153ad366cf04a92dd | bigcode/the-stack | train |
eac0e1061871eea579a18f27 | train | function | @contextmanager
def docker_service_up(docker_compose_file, build_args=None):
if IS_BUILDKITE:
yield # buildkite pipeline handles the service
return
try:
subprocess.check_output(["docker-compose", "-f", docker_compose_file, "stop"])
subprocess.check_output(["docker-compose", "-f... | @contextmanager
def docker_service_up(docker_compose_file, build_args=None):
| if IS_BUILDKITE:
yield # buildkite pipeline handles the service
return
try:
subprocess.check_output(["docker-compose", "-f", docker_compose_file, "stop"])
subprocess.check_output(["docker-compose", "-f", docker_compose_file, "rm", "-f"])
except subprocess.CalledProcessError... | = dagster_most_recent_release
user_code_version = request.param[1]
if user_code_version == MOST_RECENT_RELEASE_PLACEHOLDER:
user_code_version = dagster_most_recent_release
return {"dagit": dagit_version, "user_code": user_code_version}
@contextmanager
def docker_service_up(docker_compose_file, bui... | 80 | 80 | 269 | 18 | 62 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | docker_service_up | docker_service_up | 73 | 103 | 73 | 74 | 8b355c72ba32e63861af3c57ce2d46653830d8ca | bigcode/the-stack | train |
ee150ca75074d05a285d5c9f | train | function | def test_backcompat_deployed_pipeline_subset(graphql_client):
assert_runs_and_exists(graphql_client, "the_pipeline", subset_selection=["my_solid"])
| def test_backcompat_deployed_pipeline_subset(graphql_client):
| assert_runs_and_exists(graphql_client, "the_pipeline", subset_selection=["my_solid"])
| assert result.json().get("dagit_version")
yield DagsterGraphQLClient(dagit_host, port_number=3000)
def test_backcompat_deployed_pipeline(graphql_client):
assert_runs_and_exists(graphql_client, "the_pipeline")
def test_backcompat_deployed_pipeline_subset(graphql_client):
| 64 | 64 | 32 | 12 | 52 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | test_backcompat_deployed_pipeline_subset | test_backcompat_deployed_pipeline_subset | 126 | 127 | 126 | 126 | a19e245e37c36fdb21b3369e4612df4f6f82c3c4 | bigcode/the-stack | train |
da035a3b7a6b0a2041b9ff74 | train | function | def test_backcompat_deployed_job_subset(graphql_client):
assert_runs_and_exists(graphql_client, "the_job", subset_selection=["my_op"])
| def test_backcompat_deployed_job_subset(graphql_client):
| assert_runs_and_exists(graphql_client, "the_job", subset_selection=["my_op"])
| ployed_pipeline_subset(graphql_client):
assert_runs_and_exists(graphql_client, "the_pipeline", subset_selection=["my_solid"])
def test_backcompat_deployed_job(graphql_client):
assert_runs_and_exists(graphql_client, "the_job")
def test_backcompat_deployed_job_subset(graphql_client):
| 64 | 64 | 31 | 12 | 52 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | test_backcompat_deployed_job_subset | test_backcompat_deployed_job_subset | 134 | 135 | 134 | 134 | 0af5a3c3e28c20d707af09f61928504998796467 | bigcode/the-stack | train |
01639ccdb10c318bb8f044ab | train | function | def test_backcompat_deployed_job(graphql_client):
assert_runs_and_exists(graphql_client, "the_job")
| def test_backcompat_deployed_job(graphql_client):
| assert_runs_and_exists(graphql_client, "the_job")
| _deployed_pipeline(graphql_client):
assert_runs_and_exists(graphql_client, "the_pipeline")
def test_backcompat_deployed_pipeline_subset(graphql_client):
assert_runs_and_exists(graphql_client, "the_pipeline", subset_selection=["my_solid"])
def test_backcompat_deployed_job(graphql_client):
| 63 | 64 | 24 | 11 | 52 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | test_backcompat_deployed_job | test_backcompat_deployed_job | 130 | 131 | 130 | 130 | 678e61563b02cea5bdee021e71a6f318a527ccf9 | bigcode/the-stack | train |
8388bf5a2c12deceec36c0e5 | train | function | @pytest.fixture(
params=[
pytest.param(value, marks=getattr(pytest.mark, key), id=key)
for key, value in RELEASE_TEST_MAP.items()
],
)
def release_test_map(request, dagster_most_recent_release):
dagit_version = request.param[0]
if dagit_version == MOST_RECENT_RELEASE_PLACEHOLDER:
... | @pytest.fixture(
params=[
pytest.param(value, marks=getattr(pytest.mark, key), id=key)
for key, value in RELEASE_TEST_MAP.items()
],
)
def release_test_map(request, dagster_most_recent_release):
| dagit_version = request.param[0]
if dagit_version == MOST_RECENT_RELEASE_PLACEHOLDER:
dagit_version = dagster_most_recent_release
user_code_version = request.param[1]
if user_code_version == MOST_RECENT_RELEASE_PLACEHOLDER:
user_code_version = dagster_most_recent_release
return {"da... | _version.is_prerelease:
return str(release_version)
@pytest.fixture(
params=[
pytest.param(value, marks=getattr(pytest.mark, key), id=key)
for key, value in RELEASE_TEST_MAP.items()
],
)
def release_test_map(request, dagster_most_recent_release):
| 64 | 64 | 141 | 51 | 13 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | release_test_map | release_test_map | 56 | 70 | 56 | 62 | 344e0f30b88468419b573e81d538b908fb1a9aaa | bigcode/the-stack | train |
bcf4c025f143ab0b4e1736ce | train | function | def assert_runs_and_exists(client: DagsterGraphQLClient, name, subset_selection=None):
run_id = client.submit_pipeline_execution(
pipeline_name=name,
mode="default",
run_config={},
solid_selection=subset_selection,
)
assert_run_success(client, run_id)
locations = (
... | def assert_runs_and_exists(client: DagsterGraphQLClient, name, subset_selection=None):
| run_id = client.submit_pipeline_execution(
pipeline_name=name,
mode="default",
run_config={},
solid_selection=subset_selection,
)
assert_run_success(client, run_id)
locations = (
client._get_repo_locations_and_names_with_pipeline( # pylint: disable=protected-acc... | assert_runs_and_exists(graphql_client, "the_job")
def test_backcompat_deployed_job_subset(graphql_client):
assert_runs_and_exists(graphql_client, "the_job", subset_selection=["my_op"])
def assert_runs_and_exists(client: DagsterGraphQLClient, name, subset_selection=None):
| 63 | 64 | 115 | 19 | 44 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | assert_runs_and_exists | assert_runs_and_exists | 138 | 153 | 138 | 138 | 7190ecf35ed3ea14bf1e36b3c2d187b76aed9909 | bigcode/the-stack | train |
1e3df3e0bc39d936e2cec3e4 | train | function | def assert_run_success(client, run_id: int):
start_time = time.time()
while True:
if time.time() - start_time > MAX_TIMEOUT_SECONDS:
raise Exception("Timed out waiting for launched run to complete")
status = client.get_run_status(run_id)
assert status and status != PipelineR... | def assert_run_success(client, run_id: int):
| start_time = time.time()
while True:
if time.time() - start_time > MAX_TIMEOUT_SECONDS:
raise Exception("Timed out waiting for launched run to complete")
status = client.get_run_status(run_id)
assert status and status != PipelineRunStatus.FAILURE
if status == Pipelin... | LIEST_TESTED_RELEASE],
"dagit-latest-release": [MOST_RECENT_RELEASE_PLACEHOLDER, DAGSTER_CURRENT_BRANCH],
"user-code-latest-release": [DAGSTER_CURRENT_BRANCH, MOST_RECENT_RELEASE_PLACEHOLDER],
}
def assert_run_success(client, run_id: int):
| 64 | 64 | 88 | 11 | 53 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | assert_run_success | assert_run_success | 31 | 42 | 31 | 31 | 015e887af51cb482fa17627465b50a790ed10db8 | bigcode/the-stack | train |
ec1c1a557e833973e3f12dac | train | function | @pytest.fixture(name="dagster_most_recent_release", scope="session")
def dagster_most_recent_release():
res = requests.get("https://pypi.org/pypi/dagster/json")
module_json = res.json()
releases = module_json["releases"]
release_versions = [packaging.version.parse(release) for release in releases.keys()... | @pytest.fixture(name="dagster_most_recent_release", scope="session")
def dagster_most_recent_release():
| res = requests.get("https://pypi.org/pypi/dagster/json")
module_json = res.json()
releases = module_json["releases"]
release_versions = [packaging.version.parse(release) for release in releases.keys()]
for release_version in reversed(sorted(release_versions)):
if not release_version.is_prere... | ")
status = client.get_run_status(run_id)
assert status and status != PipelineRunStatus.FAILURE
if status == PipelineRunStatus.SUCCESS:
break
time.sleep(1)
@pytest.fixture(name="dagster_most_recent_release", scope="session")
def dagster_most_recent_release():
| 64 | 64 | 105 | 23 | 41 | souterjk/dagster | integration_tests/test_suites/backcompat-test-suite/test_backcompat.py | Python | dagster_most_recent_release | dagster_most_recent_release | 45 | 53 | 45 | 46 | 820a644daa5c5c196d196449cec69b5759a2f166 | bigcode/the-stack | train |
c89956d864c52b3e4fa4bbd0 | train | class | class TestModmailConversation(IntegrationTest):
@mock.patch('time.sleep', return_value=None)
def test_archive(self, _):
self.reddit.read_only = False
conversation = self.reddit.subreddit('all').modmail('ik72')
with self.recorder.use_cassette(
'TestModmailConversation.test... | class TestModmailConversation(IntegrationTest):
@mock.patch('time.sleep', return_value=None)
| def test_archive(self, _):
self.reddit.read_only = False
conversation = self.reddit.subreddit('all').modmail('ik72')
with self.recorder.use_cassette(
'TestModmailConversation.test_archive'):
conversation.archive()
conversation = self.reddit.subreddit('... | from praw.models import ModmailMessage
import mock
from ... import IntegrationTest
class TestModmailConversation(IntegrationTest):
@mock.patch('time.sleep', return_value=None)
| 38 | 256 | 927 | 21 | 16 | Theonefoster/praw | tests/integration/models/reddit/test_modmail.py | Python | TestModmailConversation | TestModmailConversation | 7 | 96 | 7 | 8 | 989ab7fee7c72a8dedb81c6646cc7c20910263c4 | bigcode/the-stack | train |
837fc4fceb524f3ef15a816d | train | class | class Doom(object):
'''Wrapper for Doom environment. Gym-style interface'''
def __init__(self, visiable=False):
self.env = self._setup(visiable)
self.state_dim = 84 * 84 * 1
self.action_dim = 3
# Identity bool matrix, transfer action to bool one-hot
self.bool_onehot = np.... | class Doom(object):
| '''Wrapper for Doom environment. Gym-style interface'''
def __init__(self, visiable=False):
self.env = self._setup(visiable)
self.state_dim = 84 * 84 * 1
self.action_dim = 3
# Identity bool matrix, transfer action to bool one-hot
self.bool_onehot = np.identity(self.action... | from __future__ import print_function
from __future__ import division
import numpy as np
from vizdoom import *
class Doom(object):
| 29 | 134 | 449 | 4 | 25 | syd951186545/reinforce_py | algorithms/A3C/doom/env_doom.py | Python | Doom | Doom | 8 | 63 | 8 | 8 | 94b6f46a7798bd089f86c14541ae7d005bae1eef | bigcode/the-stack | train |
e150f33e23fc9ae5ea34d7aa | train | class | class RobustExchange(Exchange):
""" Exchange abstraction """
def __init__(self,
loop,
future_store,
channel,
publish_method,
name=None,
type=ExchangeType.DIRECT,
passive=False,
... | class RobustExchange(Exchange):
| """ Exchange abstraction """
def __init__(self,
loop,
future_store,
channel,
publish_method,
name=None,
type=ExchangeType.DIRECT,
passive=False,
durable=False,
... | from __future__ import absolute_import
from logging import getLogger
from tornado import gen
from . import compat
from .common import FutureStore
from .exchange import Exchange, ExchangeType
from .channel import Channel
_LOGGER = getLogger(__name__)
class RobustExchange(Exchange):
| 60 | 201 | 672 | 6 | 54 | sphuber/topika | topika/robust_exchange.py | Python | RobustExchange | RobustExchange | 13 | 104 | 13 | 13 | 97bebf62a6ea4e83dfd13a4ba8a11f74c8b4076a | bigcode/the-stack | train |
d53c97a27349c476faafb5d0 | train | class | class TestSuite:
def test(self):
# EVEX.128.66.0F38.W0 42 /r
# VGETEXPPS xmm1 {k1}{z}, xmm2/m128/m32bcst
myEVEX = EVEX('EVEX.128.66.0F38.W0')
Buffer = bytes.fromhex('{}420e'.format(myEVEX.prefix()))
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myD... | class TestSuite:
| def test(self):
# EVEX.128.66.0F38.W0 42 /r
# VGETEXPPS xmm1 {k1}{z}, xmm2/m128/m32bcst
myEVEX = EVEX('EVEX.128.66.0F38.W0')
Buffer = bytes.fromhex('{}420e'.format(myEVEX.prefix()))
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instr... | #!/usr/bin/python
# -*- coding: utf-8 -*-
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This progra... | 191 | 256 | 985 | 4 | 187 | CrackerCat/rp | src/third_party/beaengine/tests/0f3842.py | Python | TestSuite | TestSuite | 21 | 88 | 21 | 21 | 660025b04445c6d0dee9852172a9d80dad7d25e5 | bigcode/the-stack | train |
949b3e8d65b5438bd75722f4 | train | class | class strategy:
"""
携带一个函数句柄
"""
@classmethod
def setting(self):
pass
@classmethod
def predict(self, market, account, hold):
"""
一个极其简单的示例策略,随机 随机真的随机
"""
if hold == 0:
__dat = random.random()
if __dat > 0.5:
r... | class strategy:
| """
携带一个函数句柄
"""
@classmethod
def setting(self):
pass
@classmethod
def predict(self, market, account, hold):
"""
一个极其简单的示例策略,随机 随机真的随机
"""
if hold == 0:
__dat = random.random()
if __dat > 0.5:
return {'if_buy':... | # coding:utf-8
import random
import QUANTAXIS as QA
class strategy:
| 20 | 64 | 171 | 3 | 16 | danfengzi/QUANTAXIS | test/new_test/strategy.py | Python | strategy | strategy | 7 | 33 | 7 | 7 | 9088a22d1a6ff38632013c3f1ab06b9f296c6c32 | bigcode/the-stack | train |
413670e2eb810f81338d5a74 | train | class | class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.String(80), unique=True)
user_key = db.Column(db.String(120), unique=True)
user_secret = db.Column(db.String(120), unique=True)
def __init__(self, user_id, user_key, user_secret):
self.user_id = user_i... | class User(db.Model):
| id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.String(80), unique=True)
user_key = db.Column(db.String(120), unique=True)
user_secret = db.Column(db.String(120), unique=True)
def __init__(self, user_id, user_key, user_secret):
self.user_id = user_id
self.user_ke... | from app import db
# Add database model to store user_id, user_key and user_secret
# Used for accessing API
class User(db.Model):
| 31 | 64 | 118 | 5 | 25 | ctaloi/Fitboard | models.py | Python | User | User | 7 | 19 | 7 | 7 | 7da307441ac77ec8a956886304ae82faec56313d | bigcode/the-stack | train |
5d62093e6f87da4887801098 | train | class | class NextMiddleware(MiddlewareMixin): # 老版本写法，后面会废弃
@staticmethod
def process_request(request):
# 若请求的是登陆页面 则往下执行
next = request.GET.get('next', None)
if next:
request.next = next
| class NextMiddleware(MiddlewareMixin): # 老版本写法，后面会废弃
@staticmethod
| def process_request(request):
# 若请求的是登陆页面 则往下执行
next = request.GET.get('next', None)
if next:
request.next = next
| django.conf import settings
import random
from django.urls import Resolver404, resolve, reverse
import sys
import re
from django.views.debug import technical_500_response, technical_404_response
class NextMiddleware(MiddlewareMixin): # 老版本写法，后面会废弃
@staticmethod
| 64 | 64 | 64 | 24 | 39 | crazypenguin/devops | util/middleware.py | Python | NextMiddleware | NextMiddleware | 12 | 18 | 12 | 13 | 320c6b66605310e18a34925ea1b0e9379ff7c1e5 | bigcode/the-stack | train |
2a06e1ff720507f04d6c94d2 | train | class | class BlackListMiddleware:
"""
黑名单中间件，可以在 settings.py 中添加一个 BLACKLIST（全大写）列表
"""
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
if request.META['REMOTE_ADDR'] in getattr(settings, "BLACKLIST", []):
return HttpResponseForbi... | class BlackListMiddleware:
| """
黑名单中间件，可以在 settings.py 中添加一个 BLACKLIST（全大写）列表
"""
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
if request.META['REMOTE_ADDR'] in getattr(settings, "BLACKLIST", []):
return HttpResponseForbidden('<h1>该IP地址被限制访问！</h1>'... | :
if request.META.get('HTTP_X_FORWARDED_FOR'):
request.META['REMOTE_ADDR'] = request.META['HTTP_X_FORWARDED_FOR'].split(',')[0]
except Exception:
pass
response = self.get_response(request)
return response
class BlackListMiddleware:
| 64 | 64 | 109 | 5 | 58 | crazypenguin/devops | util/middleware.py | Python | BlackListMiddleware | BlackListMiddleware | 68 | 79 | 68 | 68 | 33212a76adcc9cf366749836d9cf178f5f3c7250 | bigcode/the-stack | train |
46d831d9dbc552e1a1a6f9f6 | train | class | class LockScreenMiddleware:
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
if request.path not in [reverse('user:lockscreen'), reverse('user:login'), reverse('user:logout'), reverse('scheduler_api:client_upload')]:
if request.session.... | class LockScreenMiddleware:
| def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
if request.path not in [reverse('user:lockscreen'), reverse('user:login'), reverse('user:logout'), reverse('scheduler_api:client_upload')]:
if request.session.get('locked', False):
... | def __call__(self, request):
if request.META['REMOTE_ADDR'] in getattr(settings, "BLACKLIST", []):
return HttpResponseForbidden('<h1>该IP地址被限制访问！</h1>')
response = self.get_response(request)
return response
class LockScreenMiddleware:
| 64 | 64 | 99 | 5 | 58 | crazypenguin/devops | util/middleware.py | Python | LockScreenMiddleware | LockScreenMiddleware | 82 | 91 | 82 | 82 | d7da9bd215cdd1883bec75b01fb18c7c788b1f1d | bigcode/the-stack | train |
7219efa0b90ec506e376d545 | train | class | class DebugMiddleware:
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
# 当 debug 设置为 False 时，返回 404 时
# 如果是管理员，则返回一个特殊的响应对象，也就是Debug页面
# 如果是普通用户，则返回None，交给默认的流程处理
response = self.get_response(request)
if not setting... | class DebugMiddleware:
| def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
# 当 debug 设置为 False 时，返回 404 时
# 如果是管理员，则返回一个特殊的响应对象，也就是Debug页面
# 如果是普通用户，则返回None，交给默认的流程处理
response = self.get_response(request)
if not settings.DEBUG:
if... | ):
if request.path not in [reverse('user:lockscreen'), reverse('user:login'), reverse('user:logout'), reverse('scheduler_api:client_upload')]:
if request.session.get('locked', False):
return redirect(reverse('user:lockscreen'))
response = self.get_response(request)
re... | 72 | 72 | 240 | 4 | 67 | crazypenguin/devops | util/middleware.py | Python | DebugMiddleware | DebugMiddleware | 94 | 120 | 94 | 94 | 1245cb8661d888b93955ab34bf4f8c8428a1d3db | bigcode/the-stack | train |
9a709c8dd9e92d5f06156184 | train | class | class PermissionMiddleware:
"""
根据 session 里面记录的 url, 判断当前请求是否有权限
"""
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
current_url = request.path_info
for valid in settings.VALID_URL: # 白名单直接返回
if re.match('^%s$' ... | class PermissionMiddleware:
| """
根据 session 里面记录的 url, 判断当前请求是否有权限
"""
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
current_url = request.path_info
for valid in settings.VALID_URL: # 白名单直接返回
if re.match('^%s$' % valid, current_url):
... | ):
# 当 debug 设置为 False 时，返回服务器错误时
# 如果是管理员，则返回一个特殊的响应对象，也就是Debug页面
# 如果是普通用户，则返回None，交给默认的流程处理
if not settings.DEBUG:
if request.session['issuperuser']:
return technical_500_response(request, *sys.exc_info())
class PermissionMiddleware:
| 81 | 81 | 272 | 4 | 77 | crazypenguin/devops | util/middleware.py | Python | PermissionMiddleware | PermissionMiddleware | 123 | 152 | 123 | 123 | f3c69630d1d280e66caf93345e6f336fff415ba6 | bigcode/the-stack | train |
28d3449457761d62a6518f1b | train | class | class GetRealClientMiddleware:
"""
前端有 nginx 代理时，配置：
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
"""
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
if request.META.get(... | class GetRealClientMiddleware:
| """
前端有 nginx 代理时，配置：
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
"""
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
if request.META.get('HTTP_X_REAL_IP'):
... | .get('next', None)
if next:
request.next = next
response = self.get_response(request)
# 在这里编写视图调用后需要执行的代码
# 这里其实就是旧的process_response()方法的代码
return response
class GetRealClientMiddleware:
| 64 | 64 | 182 | 6 | 57 | crazypenguin/devops | util/middleware.py | Python | GetRealClientMiddleware | GetRealClientMiddleware | 43 | 65 | 43 | 43 | ae73eb4bb63ca1a3ed56212ade305ce5dc4c52b6 | bigcode/the-stack | train |
821a47003663f5142ecf7ed6 | train | class | class NewNextMiddleware: # 新版 2.2 写法
def __init__(self, get_response):
self.get_response = get_response
# 配置和初始化
def __call__(self, request):
# 在这里编写视图和后面的中间件被调用之前需要执行的代码
# 这里其实就是旧的process_request()方法的代码
next = request.GET.get('next', None)
if nex... | class NewNextMiddleware: # 新版 2.2 写法
| def __init__(self, get_response):
self.get_response = get_response
# 配置和初始化
def __call__(self, request):
# 在这里编写视图和后面的中间件被调用之前需要执行的代码
# 这里其实就是旧的process_request()方法的代码
next = request.GET.get('next', None)
if next:
request.next = next
response =... | next = request.GET.get('next', None)
if next:
request.next = next
# def process_response(self, request, response):
# response.status_code = 500
# return response
class NewNextMiddleware: # 新版 2.2 写法
| 64 | 64 | 150 | 16 | 47 | crazypenguin/devops | util/middleware.py | Python | NewNextMiddleware | NewNextMiddleware | 25 | 40 | 25 | 25 | 2a9a2726cddd2ce1d1de94aa191a30d0b31fe71b | bigcode/the-stack | train |
00f92cd097f69c9cc4715331 | train | function | def test_resume_with_recovery():
export_dir = tempfile.mkdtemp()
grid_id = "resume_with_recovery_gbm"
print("Using directory %s" % export_dir)
hyper_parameters = {
"learn_rate": [0.01, 0.05],
"ntrees": [100, 110, 120, 130]
}
grid_size = 1
for p in hyper_parameters:
gr... | def test_resume_with_recovery():
| export_dir = tempfile.mkdtemp()
grid_id = "resume_with_recovery_gbm"
print("Using directory %s" % export_dir)
hyper_parameters = {
"learn_rate": [0.01, 0.05],
"ntrees": [100, 110, 120, 130]
}
grid_size = 1
for p in hyper_parameters:
grid_size *= len(hyper_parameters[p... | print("%s not trained yet after %ss" % (models, times_waited / 10))
grid.cancel()
grid = h2o.get_grid(grid_id)
old_grid_model_count = len(grid.model_ids)
print("Grid has %d models" % old_grid_model_count)
assert old_grid_model_count < grid_size, "The full grid should not have finished yet."
h2o... | 180 | 180 | 602 | 7 | 173 | vishalbelsare/h2o-3 | h2o-py/tests/testdir_algos/grid/pyunit_grid_resume_with_recovery.py | Python | test_resume_with_recovery | test_resume_with_recovery | 42 | 88 | 42 | 42 | 49db96c47af3a24194972c43433b3de68671e5c2 | bigcode/the-stack | train |
ae5abe85608f3d3718cf5b7c | train | function | def _wait_for_grid_models(grid, grid_id, models, grid_size):
grid_in_progress = None
times_waited = 0
while (times_waited < 3000) and (grid_in_progress is None or len(grid_in_progress.model_ids) < models):
time.sleep(0.1) # give it tome to train some models
times_waited += 1
try:
... | def _wait_for_grid_models(grid, grid_id, models, grid_size):
| grid_in_progress = None
times_waited = 0
while (times_waited < 3000) and (grid_in_progress is None or len(grid_in_progress.model_ids) < models):
time.sleep(0.1) # give it tome to train some models
times_waited += 1
try:
grid_in_progress = h2o.get_grid(grid_id)
ex... | ..", "..", ".."))
import h2o
from tests import pyunit_utils
from h2o.grid.grid_search import H2OGridSearch
from h2o.estimators.gbm import H2OGradientBoostingEstimator
def _wait_for_grid_models(grid, grid_id, models, grid_size):
| 66 | 66 | 222 | 16 | 49 | vishalbelsare/h2o-3 | h2o-py/tests/testdir_algos/grid/pyunit_grid_resume_with_recovery.py | Python | _wait_for_grid_models | _wait_for_grid_models | 14 | 32 | 14 | 14 | 09530afbede32ceadfdccc79ac43745a3df3193c | bigcode/the-stack | train |
2f8efbef8788b6533949e0db | train | function | def _check_grid_loaded_properly(loaded, train, old_grid_model_count):
assert loaded is not None
assert len(loaded.model_ids) == old_grid_model_count
loaded_train = h2o.H2OFrame.get_frame(train.frame_id)
assert loaded_train is not None, "Train frame was not loaded"
| def _check_grid_loaded_properly(loaded, train, old_grid_model_count):
| assert loaded is not None
assert len(loaded.model_ids) == old_grid_model_count
loaded_train = h2o.H2OFrame.get_frame(train.frame_id)
assert loaded_train is not None, "Train frame was not loaded"
| % old_grid_model_count)
assert old_grid_model_count < grid_size, "The full grid should not have finished yet."
h2o.remove_all()
time.sleep(5)
return old_grid_model_count
def _check_grid_loaded_properly(loaded, train, old_grid_model_count):
| 64 | 64 | 71 | 18 | 45 | vishalbelsare/h2o-3 | h2o-py/tests/testdir_algos/grid/pyunit_grid_resume_with_recovery.py | Python | _check_grid_loaded_properly | _check_grid_loaded_properly | 35 | 39 | 35 | 35 | 97308d3f748f5790ae77cece4226d661e5e03c80 | bigcode/the-stack | train |
e5a2f78fb3c08b1482d5a046 | train | class | class TestNode(NodeConnCB):
def __init__(self):
super().__init__()
self.getdataset = set()
def on_getdata(self, conn, message):
for inv in message.inv:
self.getdataset.add(inv.hash)
def announce_tx_and_wait_for_getdata(self, tx, timeout=60):
with mininode_lock:
... | class TestNode(NodeConnCB):
| def __init__(self):
super().__init__()
self.getdataset = set()
def on_getdata(self, conn, message):
for inv in message.inv:
self.getdataset.add(inv.hash)
def announce_tx_and_wait_for_getdata(self, tx, timeout=60):
with mininode_lock:
self.last_messag... | bit used to signal activation of SegWit
VB_WITNESS_BIT = 1
VB_PERIOD = 144
VB_ACTIVATION_THRESHOLD = 108
VB_TOP_BITS = 0x20000000
MAX_SIGOP_COST = 80000
# Calculate the virtual size of a witness block:
# (base + witness/4)
def get_virtual_size(witness_block):
base_size = len(witness_block.serialize())
total... | 147 | 147 | 492 | 7 | 139 | Arthurb101/cougarCoin | test/functional/p2p-segwit.py | Python | TestNode | TestNode | 35 | 90 | 35 | 35 | ce7997a458d9f52b57d59011d14be0efb6f0374d | bigcode/the-stack | train |
908266e916bdec8685e14801 | train | class | class SegWitTest(BitcoinTestFramework):
def __init__(self):
super().__init__()
self.setup_clean_chain = True
self.num_nodes = 3
self.extra_args = [["-whitelist=127.0.0.1"], ["-whitelist=127.0.0.1", "-acceptnonstdtxn=0"], ["-whitelist=127.0.0.1", "-vbparams=segwit:0:0"]]
def set... | class SegWitTest(BitcoinTestFramework):
| def __init__(self):
super().__init__()
self.setup_clean_chain = True
self.num_nodes = 3
self.extra_args = [["-whitelist=127.0.0.1"], ["-whitelist=127.0.0.1", "-acceptnonstdtxn=0"], ["-whitelist=127.0.0.1", "-vbparams=segwit:0:0"]]
def setup_network(self):
self.setup_node... | assert_equal(self.connection.rpc.getbestblockhash() == block.hash, accepted)
# Used to keep track of anyone-can-spend outputs that we can use in the tests
class UTXO(object):
def __init__(self, sha256, n, nValue):
self.sha256 = sha256
self.n = n
self.nValue = nValue
# Helper for ge... | 256 | 256 | 22,216 | 10 | 246 | Arthurb101/cougarCoin | test/functional/p2p-segwit.py | Python | SegWitTest | SegWitTest | 111 | 1,956 | 111 | 112 | 8847ce98e565fd59f747d2527053efc1821a6e0a | bigcode/the-stack | train |
3b7233da54fc56614ee24c54 | train | function | def get_virtual_size(witness_block):
base_size = len(witness_block.serialize())
total_size = len(witness_block.serialize(with_witness=True))
# the "+3" is so we round up
vsize = int((3*base_size + total_size + 3)/4)
return vsize
| def get_virtual_size(witness_block):
| base_size = len(witness_block.serialize())
total_size = len(witness_block.serialize(with_witness=True))
# the "+3" is so we round up
vsize = int((3*base_size + total_size + 3)/4)
return vsize
| 1
VB_PERIOD = 144
VB_ACTIVATION_THRESHOLD = 108
VB_TOP_BITS = 0x20000000
MAX_SIGOP_COST = 80000
# Calculate the virtual size of a witness block:
# (base + witness/4)
def get_virtual_size(witness_block):
| 64 | 64 | 68 | 8 | 56 | Arthurb101/cougarCoin | test/functional/p2p-segwit.py | Python | get_virtual_size | get_virtual_size | 28 | 33 | 28 | 28 | 9851ec511b95ebdb7aeaf107b9aaceb24b4f1edb | bigcode/the-stack | train |
f21203cf2a55e25ce4352083 | train | class | class UTXO(object):
def __init__(self, sha256, n, nValue):
self.sha256 = sha256
self.n = n
self.nValue = nValue
| class UTXO(object):
| def __init__(self, sha256, n, nValue):
self.sha256 = sha256
self.n = n
self.nValue = nValue
| _witness_block(block))
else:
self.send_message(msg_block(block))
self.sync_with_ping()
assert_equal(self.connection.rpc.getbestblockhash() == block.hash, accepted)
# Used to keep track of anyone-can-spend outputs that we can use in the tests
class UTXO(object):
| 64 | 64 | 43 | 6 | 57 | Arthurb101/cougarCoin | test/functional/p2p-segwit.py | Python | UTXO | UTXO | 93 | 97 | 93 | 93 | 047cf32010ac5dae4fdba3a17d78e52cc0867c75 | bigcode/the-stack | train |
1584b328eed530abd2514031 | train | function | def sign_P2PK_witness_input(script, txTo, inIdx, hashtype, value, key):
tx_hash = SegwitVersion1SignatureHash(script, txTo, inIdx, hashtype, value)
signature = key.sign(tx_hash) + chr(hashtype).encode('latin-1')
txTo.wit.vtxinwit[inIdx].scriptWitness.stack = [signature, script]
txTo.rehash()
| def sign_P2PK_witness_input(script, txTo, inIdx, hashtype, value, key):
| tx_hash = SegwitVersion1SignatureHash(script, txTo, inIdx, hashtype, value)
signature = key.sign(tx_hash) + chr(hashtype).encode('latin-1')
txTo.wit.vtxinwit[inIdx].scriptWitness.stack = [signature, script]
txTo.rehash()
| Op(OP_DUP), CScriptOp(OP_HASH160), pubkeyhash, CScriptOp(OP_EQUALVERIFY), CScriptOp(OP_CHECKSIG)])
# Add signature for a P2PK witness program.
def sign_P2PK_witness_input(script, txTo, inIdx, hashtype, value, key):
| 64 | 64 | 95 | 24 | 40 | Arthurb101/cougarCoin | test/functional/p2p-segwit.py | Python | sign_P2PK_witness_input | sign_P2PK_witness_input | 104 | 108 | 104 | 104 | 128492c48fbf8c7a6c7325ffeb037fb03a87cffd | bigcode/the-stack | train |
418ab75196bc037a30c0d2d7 | train | function | def GetP2PKHScript(pubkeyhash):
return CScript([CScriptOp(OP_DUP), CScriptOp(OP_HASH160), pubkeyhash, CScriptOp(OP_EQUALVERIFY), CScriptOp(OP_CHECKSIG)])
| def GetP2PKHScript(pubkeyhash):
| return CScript([CScriptOp(OP_DUP), CScriptOp(OP_HASH160), pubkeyhash, CScriptOp(OP_EQUALVERIFY), CScriptOp(OP_CHECKSIG)])
| (object):
def __init__(self, sha256, n, nValue):
self.sha256 = sha256
self.n = n
self.nValue = nValue
# Helper for getting the script associated with a P2PKH
def GetP2PKHScript(pubkeyhash):
| 64 | 64 | 47 | 11 | 52 | Arthurb101/cougarCoin | test/functional/p2p-segwit.py | Python | GetP2PKHScript | GetP2PKHScript | 100 | 101 | 100 | 100 | 5cb442152a5d374b99481f0aa22bdbe8e73bdc99 | bigcode/the-stack | train |
1f2a370c7249611e0b1a0dd6 | train | function | def main():
args = get_run_args()
getattr(api, args.cli, no_cli_error)(args)
| def main():
| args = get_run_args()
getattr(api, args.cli, no_cli_error)(args)
| , 'yellow'), v) for k, v in sorted(vars(args).items())])
print('usage: %s\n%s\n%s\n' % (' '.join(sys.argv), '_' * 50, param_str))
return args
else:
parser.print_help()
exit()
def main():
| 64 | 64 | 23 | 3 | 61 | awesome-archive/gnes | gnes/cli/__init__.py | Python | main | main | 42 | 44 | 42 | 42 | c63486c1c299a01c316c3791237e48e99ad71960 | bigcode/the-stack | train |
a3bd6d1675577b960e08ce8a | train | function | def no_cli_error(*args, **kwargs):
get_main_parser().print_help()
exit()
| def no_cli_error(*args, **kwargs):
| get_main_parser().print_help()
exit()
| ' % (' '.join(sys.argv), '_' * 50, param_str))
return args
else:
parser.print_help()
exit()
def main():
args = get_run_args()
getattr(api, args.cli, no_cli_error)(args)
def no_cli_error(*args, **kwargs):
| 64 | 64 | 21 | 10 | 54 | awesome-archive/gnes | gnes/cli/__init__.py | Python | no_cli_error | no_cli_error | 47 | 49 | 47 | 47 | 87a6fb5a96d4a29086ba048f508d6d8ce04338ec | bigcode/the-stack | train |
dbdc896591428700f60ac2ae | train | function | def get_run_args(parser_fn=get_main_parser, printed=True):
parser = parser_fn()
if len(sys.argv) > 1:
args = parser.parse_args()
if printed:
param_str = '\n'.join(['%20s = %s' % (colored(k, 'yellow'), v) for k, v in sorted(vars(args).items())])
print('usage: %s\n%s\n%s\n'... | def get_run_args(parser_fn=get_main_parser, printed=True):
| parser = parser_fn()
if len(sys.argv) > 1:
args = parser.parse_args()
if printed:
param_str = '\n'.join(['%20s = %s' % (colored(k, 'yellow'), v) for k, v in sorted(vars(args).items())])
print('usage: %s\n%s\n%s\n' % (' '.join(sys.argv), '_' * 50, param_str))
retur... | language governing permissions and
# limitations under the License.
# pylint: disable=low-comment-ratio
import sys
from termcolor import colored
from . import api
from .parser import get_main_parser
__all__ = ['main']
def get_run_args(parser_fn=get_main_parser, printed=True):
| 64 | 64 | 121 | 13 | 51 | awesome-archive/gnes | gnes/cli/__init__.py | Python | get_run_args | get_run_args | 29 | 39 | 29 | 29 | cbdadcfe89e063e49e2b4b65345500d1f6ff0254 | bigcode/the-stack | train |
ea912e38726de6af3a9936d7 | train | function | def emb_sz_rule(n_cat:int)->int: return min(600, round(1.6 * n_cat**0.56))
| def emb_sz_rule(n_cat:int)->int: | return min(600, round(1.6 * n_cat**0.56))
| TabularList', 'TabularProcessor', 'tabular_learner']
OptTabTfms = Optional[Collection[TabularProc]]
#def emb_sz_rule(n_cat:int)->int: return min(50, (n_cat//2)+1)
def emb_sz_rule(n_cat:int)->int: | 64 | 64 | 28 | 11 | 53 | pjarnhus/fastai | fastai/tabular/data.py | Python | emb_sz_rule | emb_sz_rule | 15 | 15 | 15 | 15 | 13ed055381f3e7b7e0c93f7d5091f480081ac885 | bigcode/the-stack | train |
209ab12a02610cec7259eeb9 | train | class | class TabularProcessor(PreProcessor):
"Regroup the `procs` in one `PreProcessor`."
def __init__(self, ds:ItemBase=None, procs=None):
procs = ifnone(procs, ds.procs if ds is not None else None)
self.procs = listify(procs)
def process_one(self, item):
df = pd.DataFrame([item,item])
... | class TabularProcessor(PreProcessor):
| "Regroup the `procs` in one `PreProcessor`."
def __init__(self, ds:ItemBase=None, procs=None):
procs = ifnone(procs, ds.procs if ds is not None else None)
self.procs = listify(procs)
def process_one(self, item):
df = pd.DataFrame([item,item])
for proc in self.procs: proc(df,... | (classes[n])
sz = sz_dict.get(n, int(emb_sz_rule(n_cat))) # rule of thumb
return n_cat,sz
class TabularLine(ItemBase):
"Basic item for tabular data."
def __init__(self, cats, conts, classes, names):
self.cats,self.conts,self.classes,self.names = cats,conts,classes,names
self.data = [te... | 189 | 189 | 631 | 8 | 180 | pjarnhus/fastai | fastai/tabular/data.py | Python | TabularProcessor | TabularProcessor | 38 | 83 | 38 | 38 | eb1f58f8d12eb34619d48f3b843eeeb039be86bd | bigcode/the-stack | train |
dbb2bb7953c71ec17b3749f1 | train | class | class TabularLine(ItemBase):
"Basic item for tabular data."
def __init__(self, cats, conts, classes, names):
self.cats,self.conts,self.classes,self.names = cats,conts,classes,names
self.data = [tensor(cats), tensor(conts)]
def __str__(self):
res = ''
for c, n in zip(self.cat... | class TabularLine(ItemBase):
| "Basic item for tabular data."
def __init__(self, cats, conts, classes, names):
self.cats,self.conts,self.classes,self.names = cats,conts,classes,names
self.data = [tensor(cats), tensor(conts)]
def __str__(self):
res = ''
for c, n in zip(self.cats, self.names[:len(self.cats)... | ` if not given in `sz_dict`."
sz_dict = ifnone(sz_dict, {})
n_cat = len(classes[n])
sz = sz_dict.get(n, int(emb_sz_rule(n_cat))) # rule of thumb
return n_cat,sz
class TabularLine(ItemBase):
| 64 | 64 | 149 | 7 | 56 | pjarnhus/fastai | fastai/tabular/data.py | Python | TabularLine | TabularLine | 24 | 36 | 24 | 24 | dcb8f6271508ad3fca6ecb9d9e5671c3a83cf0eb | bigcode/the-stack | train |
7b62f22f3b0f2d329453faa5 | train | class | class TabularList(ItemList):
"Basic `ItemList` for tabular data."
_item_cls=TabularLine
_processor=TabularProcessor
_bunch=TabularDataBunch
def __init__(self, items:Iterator, cat_names:OptStrList=None, cont_names:OptStrList=None,
procs=None, **kwargs)->'TabularList':
super()... | class TabularList(ItemList):
| "Basic `ItemList` for tabular data."
_item_cls=TabularLine
_processor=TabularProcessor
_bunch=TabularDataBunch
def __init__(self, items:Iterator, cat_names:OptStrList=None, cont_names:OptStrList=None,
procs=None, **kwargs)->'TabularList':
super().__init__(range_of(items), **... | collate_fn:Callable=data_collate, no_check:bool=False)->DataBunch:
"Create a `DataBunch` from `df` and `valid_idx` with `dep_var`. `kwargs` are passed to `DataBunch.create`."
cat_names = ifnone(cat_names, []).copy()
cont_names = ifnone(cont_names, list(set(df)-set(cat_names)-{dep_var}))
... | 256 | 256 | 903 | 7 | 249 | pjarnhus/fastai | fastai/tabular/data.py | Python | TabularList | TabularList | 104 | 169 | 104 | 104 | 40ff04f26bf88a1b84fd43f0314bd6bb7d1b0dec | bigcode/the-stack | train |
b3f06c8cef49dc8f30214bec | train | function | def tabular_learner(data:DataBunch, layers:Collection[Union[int, Tuple[int, nn.Module]]],
emb_szs:Dict[str,int]=None, metrics=None, ps:Collection[float]=None,
emb_drop:float=0., y_range:OptRange=None, use_bn:bool=True, **learn_kwargs):
"Get a `Learner` using `data`, with `met... | def tabular_learner(data:DataBunch, layers:Collection[Union[int, Tuple[int, nn.Module]]],
emb_szs:Dict[str,int]=None, metrics=None, ps:Collection[float]=None,
emb_drop:float=0., y_range:OptRange=None, use_bn:bool=True, **learn_kwargs):
| "Get a `Learner` using `data`, with `metrics`, including a `TabularModel` created using the remaining params."
emb_szs = data.get_emb_szs(ifnone(emb_szs, {}))
model = TabularModel(emb_szs, len(data.cont_names), out_sz=data.c, layers=layers, ps=ps, emb_drop=emb_drop,
y_range=y_range,... | def tabular_learner(data:DataBunch, layers:Collection[Union[int, Tuple[int, nn.Module]]],
emb_szs:Dict[str,int]=None, metrics=None, ps:Collection[float]=None,
emb_drop:float=0., y_range:OptRange=None, use_bn:bool=True, **learn_kwargs):
| 73 | 64 | 184 | 73 | 0 | pjarnhus/fastai | fastai/tabular/data.py | Python | tabular_learner | tabular_learner | 171 | 178 | 171 | 173 | 22f241861535ea781130b90d295ee33a99586a82 | bigcode/the-stack | train |
74cacc6779ad564e39961218 | train | function | def def_emb_sz(classes, n, sz_dict=None):
"Pick an embedding size for `n` depending on `classes` if not given in `sz_dict`."
sz_dict = ifnone(sz_dict, {})
n_cat = len(classes[n])
sz = sz_dict.get(n, int(emb_sz_rule(n_cat))) # rule of thumb
return n_cat,sz
| def def_emb_sz(classes, n, sz_dict=None):
| "Pick an embedding size for `n` depending on `classes` if not given in `sz_dict`."
sz_dict = ifnone(sz_dict, {})
n_cat = len(classes[n])
sz = sz_dict.get(n, int(emb_sz_rule(n_cat))) # rule of thumb
return n_cat,sz
| #def emb_sz_rule(n_cat:int)->int: return min(50, (n_cat//2)+1)
def emb_sz_rule(n_cat:int)->int: return min(600, round(1.6 * n_cat**0.56))
def def_emb_sz(classes, n, sz_dict=None):
| 64 | 64 | 83 | 12 | 52 | pjarnhus/fastai | fastai/tabular/data.py | Python | def_emb_sz | def_emb_sz | 17 | 22 | 17 | 17 | 1e09ea935b2b6ad3e9215c941cabdd4aa097a56e | bigcode/the-stack | train |
a8e49eedaa7abdd60dc9247e | train | class | class TabularDataBunch(DataBunch):
"Create a `DataBunch` suitable for tabular data."
@classmethod
def from_df(cls, path, df:DataFrame, dep_var:str, valid_idx:Collection[int], procs:OptTabTfms=None,
cat_names:OptStrList=None, cont_names:OptStrList=None, classes:Collection=None,
... | class TabularDataBunch(DataBunch):
| "Create a `DataBunch` suitable for tabular data."
@classmethod
def from_df(cls, path, df:DataFrame, dep_var:str, valid_idx:Collection[int], procs:OptTabTfms=None,
cat_names:OptStrList=None, cont_names:OptStrList=None, classes:Collection=None,
test_df=None, bs:int=64, val_bs:... | .classes,self.classes,cat_cols = None,None,None,[]
if len(ds.cont_names) != 0:
ds.conts = np.stack([c.astype('float32').values for n,c in ds.inner_df[ds.cont_names].items()], 1)
cont_cols = list(ds.inner_df[ds.cont_names].columns.values)
else: ds.conts,cont_cols = None,[]
... | 112 | 112 | 376 | 10 | 101 | pjarnhus/fastai | fastai/tabular/data.py | Python | TabularDataBunch | TabularDataBunch | 85 | 102 | 85 | 85 | 99cb33879cc52a1cac4d7f7407feeeb84c6fd171 | bigcode/the-stack | train |
6a33981a2d917f3eb4bd6e26 | train | class | class TextMelodyEncoder(TextMelodyEncoderBase):
"""Convert melody sequences (with metric timing) to integer indices."""
def __init__(self, steps_per_quarter, min_pitch, max_pitch):
super(TextMelodyEncoder, self).__init__(min_pitch, max_pitch)
self._steps_per_quarter = steps_per_quarter
def _quantize_not... | class TextMelodyEncoder(TextMelodyEncoderBase):
| """Convert melody sequences (with metric timing) to integer indices."""
def __init__(self, steps_per_quarter, min_pitch, max_pitch):
super(TextMelodyEncoder, self).__init__(min_pitch, max_pitch)
self._steps_per_quarter = steps_per_quarter
def _quantize_note_sequence(self, ns):
return note_seq.quanti... | : List of encoded melody event indices.
"""
return self._encode_melody_events([int(a) for a in s.split()])
@property
def vocab_size(self):
return self._encoding.num_classes + self.num_reserved_ids
class TextMelodyEncoder(TextMelodyEncoderBase):
| 64 | 64 | 101 | 11 | 52 | sandutsar/magenta | magenta/models/score2perf/music_encoders.py | Python | TextMelodyEncoder | TextMelodyEncoder | 339 | 347 | 339 | 339 | dd5ad2671a607af68e40ed2a3321acfeaec3cd49 | bigcode/the-stack | train |
e9e3dcae2ae147c64af69e2a | train | class | class TextMelodyEncoderBase(object):
"""Convert melody sequences to integer indices, abstract base class."""
def __init__(self, min_pitch, max_pitch):
self._encoding = note_seq.MelodyOneHotEncoding(
min_note=min_pitch, max_note=max_pitch + 1)
@property
def num_reserved_ids(self):
return text_e... | class TextMelodyEncoderBase(object):
| """Convert melody sequences to integer indices, abstract base class."""
def __init__(self, min_pitch, max_pitch):
self._encoding = note_seq.MelodyOneHotEncoding(
min_note=min_pitch, max_note=max_pitch + 1)
@property
def num_reserved_ids(self):
return text_encoder.NUM_RESERVED_TOKENS
def _en... | ord] * (qns.total_quantized_steps - current_step)
return self._encode_chord_symbols(chords)
def encode(self, s):
"""Transform a space-delimited chord symbols string into indices.
Args:
s: Space delimited string containing a chord symbol sequence, e.g.
'C C G G Am Am F F'.
Returns:
... | 131 | 131 | 439 | 8 | 122 | sandutsar/magenta | magenta/models/score2perf/music_encoders.py | Python | TextMelodyEncoderBase | TextMelodyEncoderBase | 284 | 336 | 284 | 284 | a56540263d6edf167d6681944c440e92f27707c2 | bigcode/the-stack | train |
1fa57d1f3dfc9ceeb1ebc993 | train | class | class FlattenedTextMelodyEncoderAbsolute(TextMelodyEncoderAbsolute):
"""Encodes a melody that is flattened into only rhythm (with velocity).
TextMelodyEncoderAbsolute encodes the melody as a sequence of MELODY_NO_EVENT,
MELODY_NOTE_OFF, and pitch ids. This representation contains no
MELODY_NOTE_OFF events, and... | class FlattenedTextMelodyEncoderAbsolute(TextMelodyEncoderAbsolute):
| """Encodes a melody that is flattened into only rhythm (with velocity).
TextMelodyEncoderAbsolute encodes the melody as a sequence of MELODY_NO_EVENT,
MELODY_NOTE_OFF, and pitch ids. This representation contains no
MELODY_NOTE_OFF events, and instead of pitch ids, uses velocity-bin ids. To
take advantage of ... | ):
"""Transform a MusicXML filename into a list of score event index tuples.
Args:
s: Path to the MusicXML file.
Returns:
ids: List of score event index tuples.
"""
if s:
ns = note_seq.musicxml_file_to_sequence_proto(s)
else:
ns = note_seq.NoteSequence()
return self... | 120 | 120 | 403 | 14 | 106 | sandutsar/magenta | magenta/models/score2perf/music_encoders.py | Python | FlattenedTextMelodyEncoderAbsolute | FlattenedTextMelodyEncoderAbsolute | 395 | 443 | 395 | 395 | b6ab481c7d91945d673c4f6d48c84c53732f2030 | bigcode/the-stack | train |
41920eba1e8c7368ee694326 | train | class | class MidiPerformanceEncoder(object):
"""Convert between performance event indices and (filenames of) MIDI files."""
def __init__(self, steps_per_second, num_velocity_bins, min_pitch, max_pitch,
add_eos=False, ngrams=None):
"""Initialize a MidiPerformanceEncoder object.
Encodes MIDI using a... | class MidiPerformanceEncoder(object):
| """Convert between performance event indices and (filenames of) MIDI files."""
def __init__(self, steps_per_second, num_velocity_bins, min_pitch, max_pitch,
add_eos=False, ngrams=None):
"""Initialize a MidiPerformanceEncoder object.
Encodes MIDI using a performance event encoding. Index 0 i... | # Copyright 2022 The Magenta Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | 212 | 256 | 1,461 | 6 | 205 | sandutsar/magenta | magenta/models/score2perf/music_encoders.py | Python | MidiPerformanceEncoder | MidiPerformanceEncoder | 33 | 215 | 33 | 33 | 7db537e5748fde2efd9181f89a4085ae2413b7f4 | bigcode/the-stack | train |
347a147aff1e55f1f4cd6bba | train | class | class CompositeScoreEncoder(object):
"""Convert multi-component score sequences to tuples of integer indices."""
def __init__(self, encoders):
self._encoders = encoders
@property
def num_reserved_ids(self):
return text_encoder.NUM_RESERVED_TOKENS
def encode_note_sequence(self, ns):
return zip(*... | class CompositeScoreEncoder(object):
| """Convert multi-component score sequences to tuples of integer indices."""
def __init__(self, encoders):
self._encoders = encoders
@property
def num_reserved_ids(self):
return text_encoder.NUM_RESERVED_TOKENS
def encode_note_sequence(self, ns):
return zip(*[encoder.encode_note_sequence(ns)
... | ):
super(TextMelodyEncoderAbsolute, self).__init__(min_pitch, max_pitch)
self._steps_per_second = steps_per_second
def _quantize_note_sequence(self, ns):
return note_seq.quantize_note_sequence_absolute(ns, self._steps_per_second)
class CompositeScoreEncoder(object):
| 64 | 64 | 200 | 6 | 58 | sandutsar/magenta | magenta/models/score2perf/music_encoders.py | Python | CompositeScoreEncoder | CompositeScoreEncoder | 361 | 392 | 361 | 361 | 59088cfdf24f57c1bba07e91cc4b2463e30b09df | bigcode/the-stack | train |
be14ac562dd28acdbde600a5 | train | class | class TextChordsEncoder(object):
"""Convert chord symbol sequences to integer indices."""
def __init__(self, steps_per_quarter):
"""Initialize a TextChordsEncoder object.
Encodes chord symbols using a vocabulary of triads. Indices 0 and 1 are
reserved and unused, and the remaining 48 + 1 indices repre... | class TextChordsEncoder(object):
| """Convert chord symbol sequences to integer indices."""
def __init__(self, steps_per_quarter):
"""Initialize a TextChordsEncoder object.
Encodes chord symbols using a vocabulary of triads. Indices 0 and 1 are
reserved and unused, and the remaining 48 + 1 indices represent each of 4
triad types ov... | stemp('_decode.mid')
note_seq.sequence_proto_to_midi_file(ns, tmp_file_path)
return tmp_file_path
def decode_list(self, ids):
"""Transform a sequence of event indices into a performance MIDI file.
Args:
ids: List of performance event indices.
Returns:
Single-element list containing... | 147 | 147 | 493 | 7 | 140 | sandutsar/magenta | magenta/models/score2perf/music_encoders.py | Python | TextChordsEncoder | TextChordsEncoder | 218 | 281 | 218 | 218 | f5b7b188cb83c8d33f8b3a615cde7df84e2632fd | bigcode/the-stack | train |
ce834917eefb38f1d8280d57 | train | class | class TextMelodyEncoderAbsolute(TextMelodyEncoderBase):
"""Convert melody sequences (with absolute timing) to integer indices."""
def __init__(self, steps_per_second, min_pitch, max_pitch):
super(TextMelodyEncoderAbsolute, self).__init__(min_pitch, max_pitch)
self._steps_per_second = steps_per_second
de... | class TextMelodyEncoderAbsolute(TextMelodyEncoderBase):
| """Convert melody sequences (with absolute timing) to integer indices."""
def __init__(self, steps_per_second, min_pitch, max_pitch):
super(TextMelodyEncoderAbsolute, self).__init__(min_pitch, max_pitch)
self._steps_per_second = steps_per_second
def _quantize_note_sequence(self, ns):
return note_seq... | , self).__init__(min_pitch, max_pitch)
self._steps_per_quarter = steps_per_quarter
def _quantize_note_sequence(self, ns):
return note_seq.quantize_note_sequence(ns, self._steps_per_quarter)
class TextMelodyEncoderAbsolute(TextMelodyEncoderBase):
| 64 | 64 | 100 | 12 | 52 | sandutsar/magenta | magenta/models/score2perf/music_encoders.py | Python | TextMelodyEncoderAbsolute | TextMelodyEncoderAbsolute | 350 | 358 | 350 | 350 | cacdf5a4e91c0cd6f5c6f9b927435ce3534ea626 | bigcode/the-stack | train |
acc46c74977f5d54f6c706f2 | train | function | def check_voice():
for voice in voices:
engine.setProperty('voice', voice.id)
engine.say("hello world")
print("hello wolrd",voice)
engine.runAndWait()
engine.stop
| def check_voice():
| for voice in voices:
engine.setProperty('voice', voice.id)
engine.say("hello world")
print("hello wolrd",voice)
engine.runAndWait()
engine.stop
| import pyttsx3
engine = pyttsx3.init()
voices = engine.getProperty('voices')
def check_voice():
| 26 | 64 | 46 | 4 | 22 | saishan27/Green | kural.py | Python | check_voice | check_voice | 6 | 12 | 6 | 6 | 92478100831b487c9d1c4f1e029fc0ea7a043afc | bigcode/the-stack | train |
b253ff4667bf2d4da1896d81 | train | function | def green_voice(out):
engine.setProperty('voice', "HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Speech\Voices\Tokens\TTS_MS_EN-US_ZIRA_11.0")
engine.setProperty('rate',150)
# engine.setProperty('volume',100)
engine.say(out)
engine.runAndWait()
engine.stop
| def green_voice(out):
| engine.setProperty('voice', "HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Speech\Voices\Tokens\TTS_MS_EN-US_ZIRA_11.0")
engine.setProperty('rate',150)
# engine.setProperty('volume',100)
engine.say(out)
engine.runAndWait()
engine.stop
| ttsx3.init()
voices = engine.getProperty('voices')
def check_voice():
for voice in voices:
engine.setProperty('voice', voice.id)
engine.say("hello world")
print("hello wolrd",voice)
engine.runAndWait()
engine.stop
def green_voice(out):
| 64 | 64 | 76 | 5 | 58 | saishan27/Green | kural.py | Python | green_voice | green_voice | 15 | 21 | 15 | 15 | 970cfeca368264877d49ff0586f6f95e5d88e2b8 | bigcode/the-stack | train |
ea98c51652d88d8f4edacc9f | train | function | @numba.njit
def points_transform_(points, centers, point_masks, loc_transform,
rot_transform, valid_mask):
"""Apply transforms to points and box centers.
Args:
points (np.ndarray): Input points.
centers (np.ndarray): Input box centers.
point_masks (np.ndarray): Mas... | @numba.njit
def points_transform_(points, centers, point_masks, loc_transform,
rot_transform, valid_mask):
| """Apply transforms to points and box centers.
Args:
points (np.ndarray): Input points.
centers (np.ndarray): Input box centers.
point_masks (np.ndarray): Mask to indicate which points need
to be transformed.
loc_transform (np.ndarray): Location transform to be appli... | == 0:
rot_mat_T[1, 1] = rot_cos
rot_mat_T[1, 2] = -rot_sin
rot_mat_T[2, 1] = rot_sin
rot_mat_T[2, 2] = rot_cos
@numba.njit
def points_transform_(points, centers, point_masks, loc_transform,
rot_transform, valid_mask):
| 90 | 90 | 302 | 27 | 62 | xiaoMrzhang/mmdetection3d | mmdet3d/datasets/pipelines/data_augment_utils.py | Python | points_transform_ | points_transform_ | 281 | 308 | 281 | 283 | 29c7c609af9aa1fd6c6ee850a76cf798c283f15d | bigcode/the-stack | train |
6ae962362a7858b3d96d0919 | train | function | @numba.njit
def box3d_transform_(boxes, loc_transform, rot_transform, valid_mask):
"""Transform 3D boxes.
Args:
boxes (np.ndarray): 3D boxes to be transformed.
loc_transform (np.ndarray): Location transform to be applied.
rot_transform (np.ndarray): Rotation transform to be applied.
... | @numba.njit
def box3d_transform_(boxes, loc_transform, rot_transform, valid_mask):
| """Transform 3D boxes.
Args:
boxes (np.ndarray): 3D boxes to be transformed.
loc_transform (np.ndarray): Location transform to be applied.
rot_transform (np.ndarray): Rotation transform to be applied.
valid_mask (np.ndarray | None): Mask to indicate which boxes are valid.
""... | _mat_T[j]
points[i, :3] += centers[j, :3]
points[i, :3] += loc_transform[j]
break # only apply first box's transform
@numba.njit
def box3d_transform_(boxes, loc_transform, rot_transform, valid_mask):
| 64 | 64 | 140 | 23 | 40 | xiaoMrzhang/mmdetection3d | mmdet3d/datasets/pipelines/data_augment_utils.py | Python | box3d_transform_ | box3d_transform_ | 311 | 325 | 311 | 312 | 7dcf44c38878c22272209e2b45f0657336ae02c6 | bigcode/the-stack | train |
31beda0684abe6a0951b6519 | train | function | def noise_per_object_v3_(gt_boxes,
points=None,
valid_mask=None,
rotation_perturb=np.pi / 4,
center_noise_std=1.0,
global_random_rot_range=np.pi / 4,
num_try=100):
""... | def noise_per_object_v3_(gt_boxes,
points=None,
valid_mask=None,
rotation_perturb=np.pi / 4,
center_noise_std=1.0,
global_random_rot_range=np.pi / 4,
num_try=100):
| """Random rotate or remove each groundtruth independently. use kitti viewer
to test this function points_transform_
Args:
gt_boxes (np.ndarray): Ground truth boxes with shape (N, 7).
points (np.ndarray | None): Input point cloud with shape (M, 4).
Default: None.
valid_ma... | 1, :3] = points[i:i + 1, :3] @ rot_mat_T[j]
points[i, :3] += centers[j, :3]
points[i, :3] += loc_transform[j]
break # only apply first box's transform
@numba.njit
def box3d_transform_(boxes, loc_transform, rot_transform, valid_mask):
"""Transform 3D bo... | 256 | 256 | 877 | 56 | 200 | xiaoMrzhang/mmdetection3d | mmdet3d/datasets/pipelines/data_augment_utils.py | Python | noise_per_object_v3_ | noise_per_object_v3_ | 328 | 408 | 328 | 334 | c7f4babf44d516ff02feb3b82551b21231802f9f | bigcode/the-stack | train |
ac168771115e605318cb9aa9 | train | function | def _select_transform(transform, indices):
"""Select transform.
Args:
transform (np.ndarray): Transforms to select from.
indices (np.ndarray): Mask to indicate which transform to select.
Returns:
np.ndarray: Selected transforms.
"""
result = np.zeros((transform.shape[0], *t... | def _select_transform(transform, indices):
| """Select transform.
Args:
transform (np.ndarray): Transforms to select from.
indices (np.ndarray): Mask to indicate which transform to select.
Returns:
np.ndarray: Selected transforms.
"""
result = np.zeros((transform.shape[0], *transform.shape[2:]),
... | orners[i] = current_corners
loc_noises[i, j, :2] += (dst_pos - boxes[i, :2])
rot_noises[i, j] += (dst_grot - current_grot)
break
return success_mask
def _select_transform(transform, indices):
| 64 | 64 | 112 | 8 | 55 | xiaoMrzhang/mmdetection3d | mmdet3d/datasets/pipelines/data_augment_utils.py | Python | _select_transform | _select_transform | 234 | 249 | 234 | 234 | 0a9eb49719a03f6a5b0bd9bc0216341c71fcb457 | bigcode/the-stack | train |
abfc8975c1b7c44e0acefa48 | train | function | @numba.njit
def noise_per_box(boxes, valid_mask, loc_noises, rot_noises):
"""Add noise to every box (only on the horizontal plane).
Args:
boxes (np.ndarray): Input boxes with shape (N, 5).
valid_mask (np.ndarray): Mask to indicate which boxes are valid
with shape (N).
loc_no... | @numba.njit
def noise_per_box(boxes, valid_mask, loc_noises, rot_noises):
| """Add noise to every box (only on the horizontal plane).
Args:
boxes (np.ndarray): Input boxes with shape (N, 5).
valid_mask (np.ndarray): Mask to indicate which boxes are valid
with shape (N).
loc_noises (np.ndarray): Location noises with shape (N, M, 3).
rot_noise... | -= vec[0] * (
qboxes[j, k, 1] - boxes[i, box_l, 1])
if cross >= 0: #
qbox_overlap_box = False
break
if qbox_overlap_box is False:
... | 121 | 121 | 406 | 24 | 96 | xiaoMrzhang/mmdetection3d | mmdet3d/datasets/pipelines/data_augment_utils.py | Python | noise_per_box | noise_per_box | 126 | 164 | 126 | 127 | 0a7ef9e0aac51170e40a4efe5904519a60754ff9 | bigcode/the-stack | train |
90a0dd7357ab013c9e3f5614 | train | function | @numba.njit
def _rotation_box2d_jit_(corners, angle, rot_mat_T):
"""Rotate 2D boxes.
Args:
corners (np.ndarray): Corners of boxes.
angle (float): Rotation angle.
rot_mat_T (np.ndarray): Transposed rotation matrix.
"""
rot_sin = np.sin(angle)
rot_cos = np.cos(angle)
rot_m... | @numba.njit
def _rotation_box2d_jit_(corners, angle, rot_mat_T):
| """Rotate 2D boxes.
Args:
corners (np.ndarray): Corners of boxes.
angle (float): Rotation angle.
rot_mat_T (np.ndarray): Transposed rotation matrix.
"""
rot_sin = np.sin(angle)
rot_cos = np.cos(angle)
rot_mat_T[0, 0] = rot_cos
rot_mat_T[0, 1] = -rot_sin
rot_mat_T... |
# from numba.errors import NumbaPerformanceWarning
from mmdet3d.core.bbox import box_np_ops
# warnings.filterwarnings('ignore', category=NumbaPerformanceWarning)
@numba.njit
def _rotation_box2d_jit_(corners, angle, rot_mat_T):
| 64 | 64 | 155 | 24 | 40 | xiaoMrzhang/mmdetection3d | mmdet3d/datasets/pipelines/data_augment_utils.py | Python | _rotation_box2d_jit_ | _rotation_box2d_jit_ | 11 | 26 | 11 | 12 | 45931ef6b14e16ad285fe4d85288bce2548efe89 | bigcode/the-stack | train |
d6e2632ecc8cbae24f16e751 | train | function | @numba.jit(nopython=True)
def box_collision_test(boxes, qboxes, clockwise=True):
"""Box collision test.
Args:
boxes (np.ndarray): Corners of current boxes.
qboxes (np.ndarray): Boxes to be avoid colliding.
clockwise (bool): Whether the corners are in clockwise order.
Default... | @numba.jit(nopython=True)
def box_collision_test(boxes, qboxes, clockwise=True):
| """Box collision test.
Args:
boxes (np.ndarray): Corners of current boxes.
qboxes (np.ndarray): Boxes to be avoid colliding.
clockwise (bool): Whether the corners are in clockwise order.
Default: True.
"""
N = boxes.shape[0]
K = qboxes.shape[0]
ret = np.zeros... | import numba
import numpy as np
import warnings
# from numba.errors import NumbaPerformanceWarning
from mmdet3d.core.bbox import box_np_ops
# warnings.filterwarnings('ignore', category=NumbaPerformanceWarning)
@numba.njit
def _rotation_box2d_jit_(corners, angle, rot_mat_T):
"""Rotate 2D boxes.
Args:
... | 229 | 256 | 1,074 | 23 | 205 | xiaoMrzhang/mmdetection3d | mmdet3d/datasets/pipelines/data_augment_utils.py | Python | box_collision_test | box_collision_test | 29 | 123 | 29 | 30 | d82249a29bcd24a5e12b0e4d8c0edc405a7f7495 | bigcode/the-stack | train |
b23a1f1acaa3a9991478d7aa | train | function | @numba.njit
def noise_per_box_v2_(boxes, valid_mask, loc_noises, rot_noises,
global_rot_noises):
"""Add noise to every box (only on the horizontal plane). Version 2 used
when enable global rotations.
Args:
boxes (np.ndarray): Input boxes with shape (N, 5).
valid_mask (... | @numba.njit
def noise_per_box_v2_(boxes, valid_mask, loc_noises, rot_noises,
global_rot_noises):
| """Add noise to every box (only on the horizontal plane). Version 2 used
when enable global rotations.
Args:
boxes (np.ndarray): Input boxes with shape (N, 5).
valid_mask (np.ndarray): Mask to indicate which boxes are valid
with shape (N).
loc_noises (np.ndarray): Locati... | 2), dtype=boxes.dtype)
rot_mat_T = np.zeros((2, 2), dtype=boxes.dtype)
success_mask = -np.ones((num_boxes, ), dtype=np.int64)
# print(valid_mask)
for i in range(num_boxes):
if valid_mask[i]:
for j in range(num_tests):
current_corners[:] = box_corners[i]
... | 249 | 249 | 833 | 32 | 216 | xiaoMrzhang/mmdetection3d | mmdet3d/datasets/pipelines/data_augment_utils.py | Python | noise_per_box_v2_ | noise_per_box_v2_ | 167 | 231 | 167 | 169 | 806a2d8d791f01b17a34504973e759bee703e157 | bigcode/the-stack | train |
0960c711316338327a917206 | train | function | @numba.njit
def _rotation_matrix_3d_(rot_mat_T, angle, axis):
"""Get the 3D rotation matrix.
Args:
rot_mat_T (np.ndarray): Transposed rotation matrix.
angle (float): Rotation angle.
axis (int): Rotation axis.
"""
rot_sin = np.sin(angle)
rot_cos = np.cos(angle)
rot_mat_T[... | @numba.njit
def _rotation_matrix_3d_(rot_mat_T, angle, axis):
| """Get the 3D rotation matrix.
Args:
rot_mat_T (np.ndarray): Transposed rotation matrix.
angle (float): Rotation angle.
axis (int): Rotation axis.
"""
rot_sin = np.sin(angle)
rot_cos = np.cos(angle)
rot_mat_T[:] = np.eye(3)
if axis == 1:
rot_mat_T[0, 0] = rot... | np.ndarray: Selected transforms.
"""
result = np.zeros((transform.shape[0], *transform.shape[2:]),
dtype=transform.dtype)
for i in range(transform.shape[0]):
if indices[i] != -1:
result[i] = transform[i, indices[i]]
return result
@numba.njit
def _rotatio... | 89 | 89 | 297 | 22 | 66 | xiaoMrzhang/mmdetection3d | mmdet3d/datasets/pipelines/data_augment_utils.py | Python | _rotation_matrix_3d_ | _rotation_matrix_3d_ | 252 | 278 | 252 | 253 | bf7a052a3492a51499a8589e3ff55aedbdaa2288 | bigcode/the-stack | train |
18034ef2309afd7d784996e4 | train | class | class VocSegmentationConverter(VocConverter):
def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.segmentation
super().__init__(*args, **kwargs)
| class VocSegmentationConverter(VocConverter):
| def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.segmentation
super().__init__(*args, **kwargs)
| _layout
super().__init__(*args, **kwargs)
class VocActionConverter(VocConverter):
def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.action_classification
super().__init__(*args, **kwargs)
class VocSegmentationConverter(VocConverter):
| 64 | 64 | 43 | 9 | 55 | IRDonch/datumaro | datumaro/plugins/voc_format/converter.py | Python | VocSegmentationConverter | VocSegmentationConverter | 701 | 704 | 701 | 701 | 9bd4186695a369b1e23ebcbdb08207d425b87ad3 | bigcode/the-stack | train |
ffa0369ac7a406fc60d4b525 | train | function | def _convert_attr(name, attributes, type_conv, default=None):
d = object()
value = attributes.get(name, d)
if value is d:
return default
try:
return type_conv(value)
except Exception as e:
log.warning("Failed to convert attribute '%s'='%s': %s" % \
(name, value, ... | def _convert_attr(name, attributes, type_conv, default=None):
| d = object()
value = attributes.get(name, d)
if value is d:
return default
try:
return type_conv(value)
except Exception as e:
log.warning("Failed to convert attribute '%s'='%s': %s" % \
(name, value, e))
return default
| aro.util.mask_tools import paint_mask, remap_mask
from .format import (
VocInstColormap, VocPath, VocTask, make_voc_categories, make_voc_label_map,
parse_label_map, write_label_map,
)
def _convert_attr(name, attributes, type_conv, default=None):
| 64 | 64 | 84 | 14 | 50 | IRDonch/datumaro | datumaro/plugins/voc_format/converter.py | Python | _convert_attr | _convert_attr | 32 | 43 | 32 | 32 | 14e6f0e37f4edbe5314c726aca8f6c3863f8c750 | bigcode/the-stack | train |
715636880e8ac839138be31c | train | class | class VocActionConverter(VocConverter):
def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.action_classification
super().__init__(*args, **kwargs)
| class VocActionConverter(VocConverter):
| def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.action_classification
super().__init__(*args, **kwargs)
| Task.detection
super().__init__(*args, **kwargs)
class VocLayoutConverter(VocConverter):
def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.person_layout
super().__init__(*args, **kwargs)
class VocActionConverter(VocConverter):
| 64 | 64 | 43 | 8 | 56 | IRDonch/datumaro | datumaro/plugins/voc_format/converter.py | Python | VocActionConverter | VocActionConverter | 696 | 699 | 696 | 696 | f57ac401696758d97e2fdc5de33630d302c62c22 | bigcode/the-stack | train |
7f8a582269c492380bde49de | train | class | class VocConverter(Converter):
DEFAULT_IMAGE_EXT = VocPath.IMAGE_EXT
BUILTIN_ATTRS = {'difficult', 'pose', 'truncated', 'occluded' }
@staticmethod
def _split_tasks_string(s):
return [VocTask[i.strip()] for i in s.split(',')]
@staticmethod
def _get_labelmap(s):
if osp.isfile(s):... | class VocConverter(Converter):
| DEFAULT_IMAGE_EXT = VocPath.IMAGE_EXT
BUILTIN_ATTRS = {'difficult', 'pose', 'truncated', 'occluded' }
@staticmethod
def _split_tasks_string(s):
return [VocTask[i.strip()] for i in s.split(',')]
@staticmethod
def _get_labelmap(s):
if osp.isfile(s):
return s
t... | .format import (
VocInstColormap, VocPath, VocTask, make_voc_categories, make_voc_label_map,
parse_label_map, write_label_map,
)
def _convert_attr(name, attributes, type_conv, default=None):
d = object()
value = attributes.get(name, d)
if value is d:
return default
try:
retur... | 256 | 256 | 5,398 | 6 | 250 | IRDonch/datumaro | datumaro/plugins/voc_format/converter.py | Python | VocConverter | VocConverter | 59 | 679 | 59 | 59 | d8286a19b8a8df16d60b4e84befbbaaccf00692e | bigcode/the-stack | train |
7356e40d4538c7087cf3545d | train | function | def _write_xml_bbox(bbox, parent_elem):
x, y, w, h = bbox
bbox_elem = ET.SubElement(parent_elem, 'bndbox')
ET.SubElement(bbox_elem, 'xmin').text = str(x)
ET.SubElement(bbox_elem, 'ymin').text = str(y)
ET.SubElement(bbox_elem, 'xmax').text = str(x + w)
ET.SubElement(bbox_elem, 'ymax').text = str(... | def _write_xml_bbox(bbox, parent_elem):
| x, y, w, h = bbox
bbox_elem = ET.SubElement(parent_elem, 'bndbox')
ET.SubElement(bbox_elem, 'xmin').text = str(x)
ET.SubElement(bbox_elem, 'ymin').text = str(y)
ET.SubElement(bbox_elem, 'xmax').text = str(x + w)
ET.SubElement(bbox_elem, 'ymax').text = str(y + h)
return bbox_elem
| is d:
return default
try:
return type_conv(value)
except Exception as e:
log.warning("Failed to convert attribute '%s'='%s': %s" % \
(name, value, e))
return default
def _write_xml_bbox(bbox, parent_elem):
| 64 | 64 | 113 | 11 | 52 | IRDonch/datumaro | datumaro/plugins/voc_format/converter.py | Python | _write_xml_bbox | _write_xml_bbox | 45 | 52 | 45 | 45 | 4096e8908422923c51701309526214600a6cd36c | bigcode/the-stack | train |
6af975c68e6fc785c576ca86 | train | class | class VocClassificationConverter(VocConverter):
def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.classification
super().__init__(*args, **kwargs)
| class VocClassificationConverter(VocConverter):
| def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.classification
super().__init__(*args, **kwargs)
| + VocPath.SEGM_EXT)
if osp.isfile(path):
os.unlink(path)
path = osp.join(save_dir, VocPath.INSTANCES_DIR,
item.id + VocPath.SEGM_EXT)
if osp.isfile(path):
os.unlink(path)
class VocClassificationConverter(VocCon... | 64 | 64 | 42 | 8 | 56 | IRDonch/datumaro | datumaro/plugins/voc_format/converter.py | Python | VocClassificationConverter | VocClassificationConverter | 681 | 684 | 681 | 681 | 851ba0a4868a9d20f3116551a907a79744696471 | bigcode/the-stack | train |
825aa5e5097bf755e4477a74 | train | class | class VocLayoutConverter(VocConverter):
def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.person_layout
super().__init__(*args, **kwargs)
| class VocLayoutConverter(VocConverter):
| def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.person_layout
super().__init__(*args, **kwargs)
| Task.classification
super().__init__(*args, **kwargs)
class VocDetectionConverter(VocConverter):
def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.detection
super().__init__(*args, **kwargs)
class VocLayoutConverter(VocConverter):
| 64 | 64 | 42 | 8 | 56 | IRDonch/datumaro | datumaro/plugins/voc_format/converter.py | Python | VocLayoutConverter | VocLayoutConverter | 691 | 694 | 691 | 691 | f9a3ad026700ee850daf599233aaa87ef426f06f | bigcode/the-stack | train |
6747383dbcd23b03978a8a67 | train | class | class VocDetectionConverter(VocConverter):
def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.detection
super().__init__(*args, **kwargs)
| class VocDetectionConverter(VocConverter):
| def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.detection
super().__init__(*args, **kwargs)
| M_EXT)
if osp.isfile(path):
os.unlink(path)
class VocClassificationConverter(VocConverter):
def __init__(self, *args, **kwargs):
kwargs['tasks'] = VocTask.classification
super().__init__(*args, **kwargs)
class VocDetectionConverter(VocConverter):
| 64 | 64 | 42 | 8 | 56 | IRDonch/datumaro | datumaro/plugins/voc_format/converter.py | Python | VocDetectionConverter | VocDetectionConverter | 686 | 689 | 686 | 686 | 79a6900b013b0d22e4d4850ed68a22e27445e6c6 | bigcode/the-stack | train |
4e65f51498c66205cba16895 | train | class | class LabelmapType(Enum):
voc = auto()
source = auto()
| class LabelmapType(Enum):
| voc = auto()
source = auto()
| .SubElement(bbox_elem, 'ymin').text = str(y)
ET.SubElement(bbox_elem, 'xmax').text = str(x + w)
ET.SubElement(bbox_elem, 'ymax').text = str(y + h)
return bbox_elem
class LabelmapType(Enum):
| 64 | 64 | 16 | 6 | 57 | IRDonch/datumaro | datumaro/plugins/voc_format/converter.py | Python | LabelmapType | LabelmapType | 55 | 57 | 55 | 55 | 97a99fa8aa56c0a8ee9f470229ad97eea0992122 | bigcode/the-stack | train |
95a4935f63c570022d285bf9 | train | class | class CustomAuthExample:
# tag::custom-auth[]
def __init__(self, uri, principal, credentials, realm, scheme, **parameters):
self._driver = GraphDatabase.driver(uri, auth=custom_auth(principal, credentials, realm, scheme, **parameters))
# end::custom-auth[]
def close(self):
self._driver.... | class CustomAuthExample:
# tag::custom-auth[]
| def __init__(self, uri, principal, credentials, realm, scheme, **parameters):
self._driver = GraphDatabase.driver(uri, auth=custom_auth(principal, credentials, realm, scheme, **parameters))
# end::custom-auth[]
def close(self):
self._driver.close()
def can_connect(self):
with s... | , either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# tag::custom-auth-import[]
from neo4j.v1 import GraphDatabase, custom_auth
# end::custom-auth-import[]
class CustomAuthExample:
# tag::custom-auth[]
| 63 | 64 | 113 | 12 | 51 | jsoref/neo4j-python-driver | test/examples/custom_auth_example.py | Python | CustomAuthExample | CustomAuthExample | 26 | 38 | 26 | 27 | b1aabf12122215c889c13780f1f66eb00e275555 | bigcode/the-stack | train |
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