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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f8748ac0bc202d105e89fae8 | train | class | class Upsample2xBlock(torch.nn.Module):
def __init__(self, input_size, output_size, bias=True, upsample='rnc', activation='relu', norm='batch'):
super(Upsample2xBlock, self).__init__()
scale_factor = 2
self.upsample = torch.nn.Sequential(
torch.nn.Upsample(scale_factor=s... | class Upsample2xBlock(torch.nn.Module):
| def __init__(self, input_size, output_size, bias=True, upsample='rnc', activation='relu', norm='batch'):
super(Upsample2xBlock, self).__init__()
scale_factor = 2
self.upsample = torch.nn.Sequential(
torch.nn.Upsample(scale_factor=scale_factor, mode='nearest'),
... | def forward(self, x):
if self.norm is not None:
out = self.bn(self.conv(x))
else:
out = self.conv(x)
if self.activation is not None:
return self.act(out)
else:
return out
class Upsample2xBlock(torch.nn.Module):
| 64 | 64 | 144 | 10 | 53 | Siyeong-Lee/Deep_Recursive_HDRI | block.py | Python | Upsample2xBlock | Upsample2xBlock | 94 | 108 | 94 | 94 | 951f02117d8303d5ebb268938b40962ad4875ed4 | bigcode/the-stack | train |
849a597838d110196e5cc0de | train | function | def geometry(left_fitx, right_fitx, ploty, leftx, rightx):
# Radius
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####
... | def geometry(left_fitx, right_fitx, ploty, leftx, rightx):
# Radius
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
| y_eval = np.max(ploty)
##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####
left_curverad = (1+(2*left_fit[0]*y_eval+left_fit[1])**2)**(3.0/2)/abs(2*left_fit[0])
right_curverad = (1+(2*right_fit[0]*y_eval+right_fit[1])**2)**(3.0/2)/abs(2*right_fit[0])
# Meters
# Defi... | = cv2.warpPerspective(color_warp, Minv, (binary_warped.shape[1], binary_warped.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
return result
def geometry(left_fitx, right_fitx, ploty, leftx, rightx):
# Radius
# Define y-value where we wa... | 122 | 122 | 409 | 52 | 69 | lianghu83/CarND-Advanced-Lane-Lines-master | pipeline.py | Python | geometry | geometry | 313 | 330 | 313 | 316 | 41878f12ed77d44c91220569a2c5a3bbb7875082 | bigcode/the-stack | train |
4f4a45309a26ab743caaab50 | train | function | def search_around_poly_refit(binary_warped):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
margin = 100
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
### Set the area ... | def search_around_poly_refit(binary_warped):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
| margin = 100
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
### Set the area of search based on activated x-values ###
### within the +/- margin of our polynomial function ###
### consider the window areas for the... | 255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# Plot the polynomial lines onto the image
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
## End visualization steps ##
# ... | 153 | 153 | 512 | 35 | 118 | lianghu83/CarND-Advanced-Lane-Lines-master | pipeline.py | Python | search_around_poly_refit | search_around_poly_refit | 263 | 294 | 263 | 265 | 9399e9d32816df52d558fd05c97ce73d6eb2d1e7 | bigcode/the-stack | train |
7e3a11643df72c4cc4dac43f | train | function | def undistort_image(img, mtx, dist):
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
| def undistort_image(img, mtx, dist):
| undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
| # Read in the saved camera matrix and distortion coefficients
dist_pickle = pickle.load( open( "wide_dist_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
# Undistort image
def undistort_image(img, mtx, dist):
| 64 | 64 | 39 | 12 | 51 | lianghu83/CarND-Advanced-Lane-Lines-master | pipeline.py | Python | undistort_image | undistort_image | 19 | 21 | 19 | 19 | 6cb2592a916ff0d27fa6ea70465338b72e11da22 | bigcode/the-stack | train |
5bc60a9d1e21282958aa0396 | train | function | def color_lane_region(undist, binary_warped, left_fitx, right_fitx, ploty, Minv):
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
... | def color_lane_region(undist, binary_warped, left_fitx, right_fitx, ploty, Minv):
# Create an image to draw the lines on
| warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vst... | _fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
#return
return left_fitx, right_fitx, ploty
def color_lane_region(undist, binary_warped, left_fitx, right_fitx, ploty, Minv):
# Create an image to draw the lines on
| 81 | 81 | 272 | 38 | 42 | lianghu83/CarND-Advanced-Lane-Lines-master | pipeline.py | Python | color_lane_region | color_lane_region | 296 | 310 | 296 | 297 | 661203f3aa29a45f4cc1145b9b66782ce00626b1 | bigcode/the-stack | train |
a92be9647508b30f4af7fba1 | train | function | def warper(img, src, dst):
# Compute and apply perpective transform
# The img should be an undistorted image
img_size = (img.shape[1], img.shape[0])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST) # keep same size as input image
... | def warper(img, src, dst):
# Compute and apply perpective transform
# The img should be an undistorted image
| img_size = (img.shape[1], img.shape[0])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST) # keep same size as input image
return warped, M
| ],
[320, 720],
[960, 720],
[960, 0]])
Minv = cv2.getPerspectiveTransform(dst, src)
def warper(img, src, dst):
# Compute and apply perpective transform
# The img should be an undistorted image
| 64 | 64 | 94 | 30 | 34 | lianghu83/CarND-Advanced-Lane-Lines-master | pipeline.py | Python | warper | warper | 58 | 64 | 58 | 60 | efd53047e43767f79f7d300bcedc62b7d5cceae9 | bigcode/the-stack | train |
084bf87056ec4c6fb7eabd8c | train | class | class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
... | class Line():
| def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial co... | and right polynomials on the lane lines
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
# Return output image
return out_img
"""
# Define a class to receive the characteristics of each line detection
class Line():
| 65 | 65 | 219 | 3 | 61 | lianghu83/CarND-Advanced-Lane-Lines-master | pipeline.py | Python | Line | Line | 170 | 191 | 170 | 170 | 0fa7c65bfacf9578dfb56a06cc25230e94bca471 | bigcode/the-stack | train |
870502a5d56bbebb54a5c305 | train | function | def create_binary(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
img = np.copy(img)
# Convert to HLS color space and separate the V channel
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
l_channel = hls[:,:,1]
s_channel = hls[:,:,2]
# Sobel x
sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Ta... | def create_binary(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
| img = np.copy(img)
# Convert to HLS color space and separate the V channel
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
l_channel = hls[:,:,1]
s_channel = hls[:,:,2]
# Sobel x
sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Abso... | ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
# Undistort image
def undistort_image(img, mtx, dist):
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
# Get binary image
def create_binary(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
| 89 | 89 | 299 | 21 | 67 | lianghu83/CarND-Advanced-Lane-Lines-master | pipeline.py | Python | create_binary | create_binary | 24 | 44 | 24 | 24 | c692c029b532928018f65c41c9b9ba7b2483aaeb | bigcode/the-stack | train |
35bb57288adc49f46e675215 | train | function | def command( iArgs, iFiles, iConfig, iDirs, iKeys ):
ProjectDependencies.utils.notify_ignore_args( iArgs )
ProjectDependencies.utils.smart_gather_wtree_resolve_all_hash_inconsistencies( iDirs, iFiles )
# Bake utility strings from gathered information
src = iConfig["remote"] + iConfig["file"]
dst =... | def command( iArgs, iFiles, iConfig, iDirs, iKeys ):
| ProjectDependencies.utils.notify_ignore_args( iArgs )
ProjectDependencies.utils.smart_gather_wtree_resolve_all_hash_inconsistencies( iDirs, iFiles )
# Bake utility strings from gathered information
src = iConfig["remote"] + iConfig["file"]
dst = iDirs["tmp"] + iConfig["file"]
# Create tm... |
#:: furnished to do so, subject to the following conditions:
#::
#:: The above copyright notice and this permission notice shall be included in all
#:: copies or substantial portions of the Software.
#::
#:: THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#:: IMPLIED, INCLUDING BUT NOT LIMIT... | 225 | 225 | 751 | 18 | 207 | Robot-Fromage/ProjectDependencies | ProjectDependencies/download.py | Python | command | command | 40 | 126 | 40 | 40 | 04803086c42f61d62c6737ce3ee36653f3c7e7a8 | bigcode/the-stack | train |
01a6e81dc2c5a99a1de6b20f | train | function | def validate_collection_change(obj):
"""Validates collection change.
Args:
obj: dict. Data that needs to be validated.
"""
# No explicit call to validate_dict method is necessary, because
# CollectionChange calls validate method while initialization.
collection_domain.CollectionChange(o... | def validate_collection_change(obj):
| """Validates collection change.
Args:
obj: dict. Data that needs to be validated.
"""
# No explicit call to validate_dict method is necessary, because
# CollectionChange calls validate method while initialization.
collection_domain.CollectionChange(obj) # type: ignore[no-untyped-call]
| # type: ignore[no-untyped-call]
'list_of_default_tags_for_blog_post').value
if not all(tag in list_of_default_tags for tag in change_dict['tags']):
raise Exception(
'Invalid tags provided. Tags not in default tags list.')
def validate_collection_change(obj):
| 63 | 64 | 71 | 6 | 57 | nbaddam/oppia | core/controllers/domain_objects_validator.py | Python | validate_collection_change | validate_collection_change | 87 | 95 | 87 | 87 | 9a97209cb98c3fe13556ec7d059e1a13ae909226 | bigcode/the-stack | train |
deee3617c9178f66bc068376 | train | function | def validate_change_dict_for_blog_post(change_dict):
"""Validates change_dict required for updating values of blog post.
Args:
change_dict: dict. Data that needs to be validated.
"""
if 'title' in change_dict:
blog_domain.BlogPost.require_valid_title( # type: ignore[no-untyped-call]
... | def validate_change_dict_for_blog_post(change_dict):
| """Validates change_dict required for updating values of blog post.
Args:
change_dict: dict. Data that needs to be validated.
"""
if 'title' in change_dict:
blog_domain.BlogPost.require_valid_title( # type: ignore[no-untyped-call]
change_dict['title'], True)
if 'thumbnai... | should be a string, received'
': %s' % name)
config_property = config_domain.Registry.get_config_property(name) # type: ignore[no-untyped-call]
if config_property is None:
raise Exception('%s do not have any schema.' % name)
config_property.normalize(value)
def vali... | 74 | 74 | 249 | 10 | 64 | nbaddam/oppia | core/controllers/domain_objects_validator.py | Python | validate_change_dict_for_blog_post | validate_change_dict_for_blog_post | 63 | 84 | 63 | 63 | 75e5ec3a20df8c569b4ca4ffb9fba8a8133e6cc2 | bigcode/the-stack | train |
e9469af4fa3f7c0c438d0d62 | train | function | def validate_aggregated_stats(aggregated_stats):
"""Validates the attribute stats dict.
Args:
aggregated_stats: dict. Data that needs to be validated.
Raises:
InvalidInputException. Property not in aggregated stats dict.
"""
exploration_stats_properties = [
'num_starts',
... | def validate_aggregated_stats(aggregated_stats):
| """Validates the attribute stats dict.
Args:
aggregated_stats: dict. Data that needs to be validated.
Raises:
InvalidInputException. Property not in aggregated stats dict.
"""
exploration_stats_properties = [
'num_starts',
'num_actual_starts',
'num_completio... | ')
target_id = task_entries.get('target_id', None)
if target_id is None:
raise base.BaseHandler.InvalidInputException('No target_id provided')
status = task_entries.get('status', None)
if status is None:
raise base.BaseHandler.InvalidInputException('No status provided')
def validate_aggr... | 76 | 76 | 254 | 11 | 65 | nbaddam/oppia | core/controllers/domain_objects_validator.py | Python | validate_aggregated_stats | validate_aggregated_stats | 148 | 180 | 148 | 148 | 38846edbc4c2c62fbbe04289304ae7d6db3d40ac | bigcode/the-stack | train |
30684d4548f135e1c4745554 | train | function | def validate_task_entries(task_entries):
"""Validates the task entry dict.
Args:
task_entries: dict. Data that needs to be validated.
"""
entity_version = task_entries.get('entity_version', None)
if entity_version is None:
raise base.BaseHandler.InvalidInputException(
'N... | def validate_task_entries(task_entries):
| """Validates the task entry dict.
Args:
task_entries: dict. Data that needs to be validated.
"""
entity_version = task_entries.get('entity_version', None)
if entity_version is None:
raise base.BaseHandler.InvalidInputException(
'No entity_version provided')
task_type... | '] for predicate in predicates]
for key, value in data.items():
if value is None:
continue
if key not in possible_keys:
# Raise exception if key is not one of the allowed keys.
raise Exception('400 Invalid input for query.')
def validate_task_entries(task_entries... | 63 | 64 | 168 | 7 | 56 | nbaddam/oppia | core/controllers/domain_objects_validator.py | Python | validate_task_entries | validate_task_entries | 127 | 145 | 127 | 127 | 2ab73a68b8173ce001951a37936683813cf6961d | bigcode/the-stack | train |
0fadeba24011a6c876b42c7d | train | function | def validate_exploration_change(obj):
"""Validates exploration change.
Args:
obj: dict. Data that needs to be validated.
"""
# No explicit call to validate_dict method is necessary, because
# ExplorationChange calls validate method while initialization.
exp_domain.ExplorationChange(obj)... | def validate_exploration_change(obj):
| """Validates exploration change.
Args:
obj: dict. Data that needs to be validated.
"""
# No explicit call to validate_dict method is necessary, because
# ExplorationChange calls validate method while initialization.
exp_domain.ExplorationChange(obj) # type: ignore[no-untyped-call]
| from core.constants import constants
from core.controllers import base
from core.domain import blog_domain
from core.domain import collection_domain
from core.domain import config_domain
from core.domain import exp_domain
from core.domain import state_domain
from typing import Dict, Optional, Union
def validate_explor... | 64 | 64 | 75 | 8 | 55 | nbaddam/oppia | core/controllers/domain_objects_validator.py | Python | validate_exploration_change | validate_exploration_change | 34 | 42 | 34 | 34 | e17e87c9f33cd22597a838b24a2a7eb98026ae87 | bigcode/the-stack | train |
dbef847586c5b1358795e3ae | train | function | def validate_state_dict(state_dict):
"""Validates state dict.
Args:
state_dict: dict. Data that needs to be validated.
"""
validation_class = state_domain.State.from_dict(state_dict) # type: ignore[no-untyped-call]
validation_class.validate(None, True)
| def validate_state_dict(state_dict):
| """Validates state dict.
Args:
state_dict: dict. Data that needs to be validated.
"""
validation_class = state_domain.State.from_dict(state_dict) # type: ignore[no-untyped-call]
validation_class.validate(None, True)
| Args:
obj: dict. Data that needs to be validated.
"""
# No explicit call to validate_dict method is necessary, because
# CollectionChange calls validate method while initialization.
collection_domain.CollectionChange(obj) # type: ignore[no-untyped-call]
def validate_state_dict(state_dict):
| 64 | 64 | 62 | 7 | 57 | nbaddam/oppia | core/controllers/domain_objects_validator.py | Python | validate_state_dict | validate_state_dict | 98 | 105 | 98 | 98 | f62d46493c40ff086c592e06d44f170688880204 | bigcode/the-stack | train |
0cf019410e4e076328b17dce | train | function | def validate_email_dashboard_data(
data: Dict[str, Optional[Union[bool, int]]]
) -> None:
"""Validates email dashboard data.
Args:
data: dict. Data that needs to be validated.
"""
predicates = constants.EMAIL_DASHBOARD_PREDICATE_DEFINITION
possible_keys = [predicate['backend_attr'] ... | def validate_email_dashboard_data(
data: Dict[str, Optional[Union[bool, int]]]
) -> None:
| """Validates email dashboard data.
Args:
data: dict. Data that needs to be validated.
"""
predicates = constants.EMAIL_DASHBOARD_PREDICATE_DEFINITION
possible_keys = [predicate['backend_attr'] for predicate in predicates]
for key, value in data.items():
if value is None:
... | Data that needs to be validated.
"""
validation_class = state_domain.State.from_dict(state_dict) # type: ignore[no-untyped-call]
validation_class.validate(None, True)
def validate_email_dashboard_data(
data: Dict[str, Optional[Union[bool, int]]]
) -> None:
| 64 | 64 | 130 | 25 | 39 | nbaddam/oppia | core/controllers/domain_objects_validator.py | Python | validate_email_dashboard_data | validate_email_dashboard_data | 108 | 124 | 108 | 110 | 0a31ab9b06d6ec2d47eb56ec4162297a06e0b297 | bigcode/the-stack | train |
b49ba335e0f7325392a5eb91 | train | function | def validate_new_config_property_values(obj):
"""Validates new config property values.
Args:
obj: dict. Data that needs to be validated.
"""
for (name, value) in obj.items():
if not isinstance(name, str):
raise Exception(
'config property name should be a str... | def validate_new_config_property_values(obj):
| """Validates new config property values.
Args:
obj: dict. Data that needs to be validated.
"""
for (name, value) in obj.items():
if not isinstance(name, str):
raise Exception(
'config property name should be a string, received'
': %s' % name)
... | obj: dict. Data that needs to be validated.
"""
# No explicit call to validate_dict method is necessary, because
# ExplorationChange calls validate method while initialization.
exp_domain.ExplorationChange(obj) # type: ignore[no-untyped-call]
def validate_new_config_property_values(obj):
| 64 | 64 | 126 | 8 | 56 | nbaddam/oppia | core/controllers/domain_objects_validator.py | Python | validate_new_config_property_values | validate_new_config_property_values | 45 | 60 | 45 | 45 | 56417191cf73efa162053bb3af964dbc3e72b0dd | bigcode/the-stack | train |
cbfc94ec0d7bb12f7bb2efa5 | train | function | def print_help():
"""
Print the manual of the program
"""
print("merge_files.py allow you to easily merge .ins files\nTo use it:>\n\npython merge_files.py <directory_to_save_output> <fileA> <fileB> <fileC> ... \n\nFor all the files:\n\npython merge_files.py <directory_to_save_output> <directory/*.ins>"... | def print_help():
| """
Print the manual of the program
"""
print("merge_files.py allow you to easily merge .ins files\nTo use it:>\n\npython merge_files.py <directory_to_save_output> <fileA> <fileB> <fileC> ... \n\nFor all the files:\n\npython merge_files.py <directory_to_save_output> <directory/*.ins>")
| #!/usr/bin/env python
import os
import sys
import collections
def print_help():
| 19 | 64 | 86 | 4 | 14 | SMV818VMS/pyutils | merge_ins_files.py | Python | print_help | print_help | 8 | 13 | 8 | 8 | 863938a55e198fec40ada76506a4e9f668df25e1 | bigcode/the-stack | train |
a23559f0930a049e7775fc75 | train | function | def main():
""" Main function to run the merge_files function """
# Evaluate arguments and if help required:
if len(sys.argv) <= 2:
print_help()
else:
merge_files()
| def main():
| """ Main function to run the merge_files function """
# Evaluate arguments and if help required:
if len(sys.argv) <= 2:
print_help()
else:
merge_files()
| (line[1])
# Export to a file:
fo = open(outFile, 'w')
od = collections.OrderedDict(sorted(results_dic.items()))
for k, v in od.iteritems():
fo.write(str(k)+'\t'+str(v)+'\n')
fo.close()
def main():
| 64 | 64 | 44 | 3 | 61 | SMV818VMS/pyutils | merge_ins_files.py | Python | main | main | 49 | 56 | 49 | 49 | ff815ce18a19d1885ffb4f0ea452f4e32ad64110 | bigcode/the-stack | train |
64c6dd9d313e95f06e8c0890 | train | function | def merge_files():
"""
Given a list of files where position\treads\nposition\treads...\n
Returns a merged file containing the position and the reads, if the position between two files
is the same, sums the reads.
"""
# Ste the variables
outFile = sys.argv[1]
inFile = sys.argv[2:] # Ta... | def merge_files():
| """
Given a list of files where position\treads\nposition\treads...\n
Returns a merged file containing the position and the reads, if the position between two files
is the same, sums the reads.
"""
# Ste the variables
outFile = sys.argv[1]
inFile = sys.argv[2:] # Takes the second argu... |
"""
print("merge_files.py allow you to easily merge .ins files\nTo use it:>\n\npython merge_files.py <directory_to_save_output> <fileA> <fileB> <fileC> ... \n\nFor all the files:\n\npython merge_files.py <directory_to_save_output> <directory/*.ins>")
def merge_files():
| 77 | 78 | 260 | 4 | 74 | SMV818VMS/pyutils | merge_ins_files.py | Python | merge_files | merge_files | 16 | 46 | 16 | 16 | 8033d4a173771a1cd710fc3caef68fa867b14867 | bigcode/the-stack | train |
182bcee11b6ae5f1fac3a748 | train | function | def x_length_words(sentence, x):
sentence_split = sentence.split()
for word in sentence_split:
if len(word) < x:
return False
else:
return True
| def x_length_words(sentence, x):
| sentence_split = sentence.split()
for word in sentence_split:
if len(word) < x:
return False
else:
return True
| # Write your x_length_words function here:
def x_length_words(sentence, x):
| 17 | 64 | 41 | 8 | 9 | orby2002/learn-python | 6-strings/code-challenge/x-length.py | Python | x_length_words | x_length_words | 2 | 8 | 2 | 2 | 30e4b6e239b41aec4bce7057bd930db11151758a | bigcode/the-stack | train |
5121082516ab3d7632dc8f1f | train | class | class TestNAT44EDMW(TestNAT44ED):
""" NAT44ED MW Test Case """
vpp_worker_count = 4
max_sessions = 5000
def test_dynamic(self):
""" NAT44ED dynamic translation test """
pkt_count = 1500
tcp_port_offset = 20
udp_port_offset = 20
icmp_id_offset = 20
self.n... | class TestNAT44EDMW(TestNAT44ED):
| """ NAT44ED MW Test Case """
vpp_worker_count = 4
max_sessions = 5000
def test_dynamic(self):
""" NAT44ED dynamic translation test """
pkt_count = 1500
tcp_port_offset = 20
udp_port_offset = 20
icmp_id_offset = 20
self.nat_add_address(self.nat_addr)
... | port=7000+i, dport=8000+i) /
Raw(payload))
info.data = p
pkts.append(p)
self.pg0.add_stream(pkts)
self.pg_enable_capture(self.pg_interfaces)
self.pg_start()
recvd = self.pg1.get_capture(len(pkts))
for p_recvd in recvd:
payload... | 256 | 256 | 16,710 | 13 | 243 | caijie2020/vpp | test/test_nat44_ed.py | Python | TestNAT44EDMW | TestNAT44EDMW | 2,155 | 3,838 | 2,155 | 2,155 | 3fbdc1750e4366fc2a952f7f710ad978a737f47d | bigcode/the-stack | train |
a50c9585f2606cf4ef2c60fd | train | class | class TestNAT44ED(VppTestCase):
""" NAT44ED Test Case """
nat_addr = '10.0.0.3'
tcp_port_in = 6303
tcp_port_out = 6303
udp_port_in = 6304
udp_port_out = 6304
icmp_id_in = 6305
icmp_id_out = 6305
tcp_external_port = 80
max_sessions = 100
def setUp(self):
super()... | class TestNAT44ED(VppTestCase):
| """ NAT44ED Test Case """
nat_addr = '10.0.0.3'
tcp_port_in = 6303
tcp_port_out = 6303
udp_port_in = 6304
udp_port_out = 6304
icmp_id_in = 6305
icmp_id_out = 6305
tcp_external_port = 80
max_sessions = 100
def setUp(self):
super().setUp()
self.plugin_ena... | #!/usr/bin/env python3
import unittest
from io import BytesIO
from random import randint, shuffle, choice
import scapy.compat
from framework import VppTestCase, VppTestRunner
from scapy.data import IP_PROTOS
from scapy.layers.inet import IP, TCP, UDP, ICMP, GRE
from scapy.layers.inet import IPerror, TCPerror
from sca... | 195 | 256 | 19,311 | 11 | 183 | caijie2020/vpp | test/test_nat44_ed.py | Python | TestNAT44ED | TestNAT44ED | 22 | 2,152 | 22 | 22 | cda64c30f3696cbaaca0521c3df0b8f235e630f5 | bigcode/the-stack | train |
129b5a9fdd9ffdb96a9ce528 | train | class | class gdrive(object):
def __init__(self):
self.initialize = ee.Initialize()
self.credentials = ee.Credentials()
self.service = discovery.build(
serviceName="drive",
version="v3",
cache_discovery=False,
credentials=self.credentials,
)
... | class gdrive(object):
| def __init__(self):
self.initialize = ee.Initialize()
self.credentials = ee.Credentials()
self.service = discovery.build(
serviceName="drive",
version="v3",
cache_discovery=False,
credentials=self.credentials,
)
def tasks_list(sel... | from pathlib import Path
import ee
import io
from googleapiclient.http import MediaIoBaseDownload
from apiclient import discovery
from component.message import ms
from .gee import search_task
import logging
logging.getLogger("googleapiclient.discovery_cache").setLevel(logging.ERROR)
class gdrive(object):
| 67 | 256 | 987 | 5 | 62 | 12rambau/coverage_analysis | component/scripts/gdrive.py | Python | gdrive | gdrive | 16 | 163 | 16 | 16 | 282b1f1f40b111b25334d2b68d321a0f59762f7a | bigcode/the-stack | train |
9cea67ab5a48ba5c52cc16d5 | train | function | @exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def callback(event, context):
logger.info(f"received event from verification for callback {event}")
payload = event["body"]
path_parameters = event["queryStringParameters"]
if "verification_id" not in ... | @exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def callback(event, context):
| logger.info(f"received event from verification for callback {event}")
payload = event["body"]
path_parameters = event["queryStringParameters"]
if "verification_id" not in path_parameters and "entity_id" not in path_parameters:
raise BadRequestException()
entity_id = path_parameters.get("enti... | Code.CREATED,
{"status": ResponseStatus.SUCCESS, "data": response, "error": {}},
cors_enabled=True)
@exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def callback(event, context):
| 64 | 64 | 170 | 38 | 26 | DhivakharVenkatachalam/snet-marketplace-service | verification/application/handlers/verification_handlers.py | Python | callback | callback | 31 | 43 | 31 | 32 | 858f64690a6f4da9f8933c33672a2a96cad30bd5 | bigcode/the-stack | train |
81478ad6c06f293d455f6635 | train | function | @exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def get_status(event, context):
query_parameters = event["queryStringParameters"]
if "type" not in query_parameters:
raise BadRequestException()
verification_type = query_parameters["type"]
if ... | @exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def get_status(event, context):
| query_parameters = event["queryStringParameters"]
if "type" not in query_parameters:
raise BadRequestException()
verification_type = query_parameters["type"]
if verification_type == VerificationType.INDIVIDUAL.value:
entity_id = event["requestContext"]["authorizer"]["claims"]["email"]
... | .CREATED,
{"status": ResponseStatus.SUCCESS, "data": response, "error": {}},
cors_enabled=True)
@exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def get_status(event, context):
| 64 | 64 | 181 | 39 | 25 | DhivakharVenkatachalam/snet-marketplace-service | verification/application/handlers/verification_handlers.py | Python | get_status | get_status | 46 | 60 | 46 | 47 | 3fe33583316a644a483ea49c4281d064bffcc9dd | bigcode/the-stack | train |
fb7a9a479c23eb4e181759df | train | function | @exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def initiate(event, context):
payload = json.loads(event["body"])
username = event["requestContext"]["authorizer"]["claims"]["email"]
required_keys = ["type"]
if not validate_dict(payload, required_key... | @exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def initiate(event, context):
| payload = json.loads(event["body"])
username = event["requestContext"]["authorizer"]["claims"]["email"]
required_keys = ["type"]
if not validate_dict(payload, required_keys):
raise BadRequestException()
response = VerificationManager().initiate_verification(payload, username)
return gene... | import VerificationType
from verification.exceptions import EXCEPTIONS, BadRequestException
patch_all()
logger = get_logger(__name__)
@exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def initiate(event, context):
| 64 | 64 | 131 | 38 | 26 | DhivakharVenkatachalam/snet-marketplace-service | verification/application/handlers/verification_handlers.py | Python | initiate | initiate | 18 | 28 | 18 | 19 | 994b87b47b3a1fa0354c76cb8fefea14afdbf214 | bigcode/the-stack | train |
0ea62687aac2779f0d8e437b | train | function | @exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def get_verifications(event, context):
query_parameters = event["queryStringParameters"]
if "type" not in query_parameters:
raise BadRequestException()
response = VerificationManager().get_verifica... | @exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def get_verifications(event, context):
| query_parameters = event["queryStringParameters"]
if "type" not in query_parameters:
raise BadRequestException()
response = VerificationManager().get_verifications(query_parameters)
return generate_lambda_response(StatusCode.OK, {"status": ResponseStatus.SUCCESS, "data": response, "error": {}},
... | .OK, {"status": ResponseStatus.SUCCESS, "data": response, "error": {}},
cors_enabled=True)
@exception_handler(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger, EXCEPTIONS=EXCEPTIONS)
def get_verifications(event, context):
| 64 | 64 | 109 | 40 | 24 | DhivakharVenkatachalam/snet-marketplace-service | verification/application/handlers/verification_handlers.py | Python | get_verifications | get_verifications | 63 | 70 | 63 | 64 | a75a30e205b77e91b08afa39e7036123a521bd68 | bigcode/the-stack | train |
eee6b7347f6e9f2244f20abf | train | function | def setup_path() -> None:
if os.path.basename(sys.prefix) != "zulip-py3-venv":
BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
venv = os.path.join(BASE_DIR, "zulip-py3-venv")
activate_this = os.path.join(venv, "bin", "activate_this.py")
activat... | def setup_path() -> None:
| if os.path.basename(sys.prefix) != "zulip-py3-venv":
BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
venv = os.path.join(BASE_DIR, "zulip-py3-venv")
activate_this = os.path.join(venv, "bin", "activate_this.py")
activate_locals = dict(__file__=a... | """
Use libraries from a virtualenv (by modifying sys.path) in production.
"""
import os
import sys
def setup_path() -> None:
| 30 | 64 | 194 | 7 | 22 | cozyrohan/zulip | scripts/lib/setup_path.py | Python | setup_path | setup_path | 8 | 20 | 8 | 8 | b597f631f392f63434fe01ca9af9e714d43f6551 | bigcode/the-stack | train |
1e8af6f955e3c7d0893f9a18 | train | class | class Migration(migrations.Migration):
dependencies = [
('timewebapp', '0021_auto_20210418_1303'),
]
operations = [
migrations.AlterField(
model_name='settingsmodel',
name='show_info_buttons',
field=models.BooleanField(default=True, verbose_name='Show In... | class Migration(migrations.Migration):
| dependencies = [
('timewebapp', '0021_auto_20210418_1303'),
]
operations = [
migrations.AlterField(
model_name='settingsmodel',
name='show_info_buttons',
field=models.BooleanField(default=True, verbose_name='Show Info Buttons'),
),
]
| # Generated by Django 3.1.8 on 2021-04-18 20:23
from django.db import migrations, models
class Migration(migrations.Migration):
| 38 | 64 | 73 | 7 | 30 | snapsnap123/TimeWeb | timeweb/timewebapp/migrations/0022_auto_20210418_1323.py | Python | Migration | Migration | 6 | 18 | 6 | 7 | dbd9582811b4211d9ab8a250a17fc58cfa3251ec | bigcode/the-stack | train |
0fd18aa40e678fab5025be97 | train | function | def assert_xyz_img_dir(xyz_dir, ids):
""" Asserts XYZ images in the xyz_dir directory.
Args:
xyz_dir: directory of the XYZ images for data augmentation.
ids: a list of scene IDs.
"""
for i in range(len(ids)):
if not os.path.exists(os.path.join(xyz_dir, '%04d.png' % (ids[i] + 1))):
print('Imag... | def assert_xyz_img_dir(xyz_dir, ids):
| """ Asserts XYZ images in the xyz_dir directory.
Args:
xyz_dir: directory of the XYZ images for data augmentation.
ids: a list of scene IDs.
"""
for i in range(len(ids)):
if not os.path.exists(os.path.join(xyz_dir, '%04d.png' % (ids[i] + 1))):
print('Image %s not found!' % os.path.join(xyz_di... | name.
Args:
camera_models: a list of camera model names.
"""
for c in range(len(camera_models)):
target_cam = camera_models[c]
assert (target_cam.lower() in cameras or target_cam.lower() == 'all')
def assert_xyz_img_dir(xyz_dir, ids):
| 64 | 64 | 124 | 11 | 53 | manipopopo/C5 | src/aug_ops.py | Python | assert_xyz_img_dir | assert_xyz_img_dir | 543 | 554 | 543 | 543 | dc6c9dfbcc44ec7539462c8e3df8d41a0862b58b | bigcode/the-stack | train |
dad50421b9196d72089560f0 | train | function | def assert_target_camera(camera_models):
""" Asserts target camera model name.
Args:
camera_models: a list of camera model names.
"""
for c in range(len(camera_models)):
target_cam = camera_models[c]
assert (target_cam.lower() in cameras or target_cam.lower() == 'all')
| def assert_target_camera(camera_models):
| """ Asserts target camera model name.
Args:
camera_models: a list of camera model names.
"""
for c in range(len(camera_models)):
target_cam = camera_models[c]
assert (target_cam.lower() in cameras or target_cam.lower() == 'all')
| result[:, :, c] = (cst[c, 0] * im[:, :, 0] + cst[c, 1] * im[:, :, 1] +
cst[c, 2] * im[:, :, 2])
return result
def assert_target_camera(camera_models):
| 64 | 64 | 67 | 7 | 56 | manipopopo/C5 | src/aug_ops.py | Python | assert_target_camera | assert_target_camera | 532 | 540 | 532 | 532 | 89a960b8c0959bff6abb72d8396463c97a0c5172 | bigcode/the-stack | train |
d3c51e2347840dd6149fbe40 | train | function | def sampling(im, t_camera_data, output_dir, filename,
c_temp, baseline_exposure, baseline_noise, ISO, aperture,
aperture_norm, exposure_time, transfer_intensity,
remove_saturated_pixels, save_as_16_bits,
output_image_size, saturation_level, cropping, rotated,
... | def sampling(im, t_camera_data, output_dir, filename,
c_temp, baseline_exposure, baseline_noise, ISO, aperture,
aperture_norm, exposure_time, transfer_intensity,
remove_saturated_pixels, save_as_16_bits,
output_image_size, saturation_level, cropping, rotated,
... | """ Samples from target camera set's settings and maps input image (im) to
the sampled setting.
Args:
im: source image.
t_camera_data: metadata of the target camera model.
output_dir: output directory to save mapped images and metadata.
filename: filename of output image.
c_temp: color temper... | (%d/%d)...', camera_i,
len(target_cameras))
for image_i in range(images_per_scene):
filename = 'image_%07d_sensorname_%s.png' % (
counter, target_cameras[camera_i].lower().replace(' ', '_'))
status = sampling(copy.deepcopy(image), target_cameras_data[camera... | 256 | 256 | 2,001 | 76 | 179 | manipopopo/C5 | src/aug_ops.py | Python | sampling | sampling | 266 | 446 | 266 | 271 | ce2335963b73dea0efea0f3207ef6ea736886f19 | bigcode/the-stack | train |
f661ef73ebb107e61a933d11 | train | function | def softmax(x):
""" Applies softmax function: softmax(x) = np.exp(x)/sum(np.exp(x)).
"""
return np.exp(x) / sum(np.exp(x))
| def softmax(x):
| """ Applies softmax function: softmax(x) = np.exp(x)/sum(np.exp(x)).
"""
return np.exp(x) / sum(np.exp(x))
| 0) > 0.4 * num_pixels or
np.sum(im[:, :, 2] < 0) > 0.4 * num_pixels or
np.sum(np.isnan(ill)) >= 1):
return False
else:
return True
def softmax(x):
| 64 | 64 | 40 | 5 | 58 | manipopopo/C5 | src/aug_ops.py | Python | softmax | softmax | 468 | 471 | 468 | 468 | cd2e78d5bb4ce771a8bdc3a9e97b00bcf457f8ea | bigcode/the-stack | train |
7605909d1585a1200218e1d8 | train | function | def knnsearch(query, data, k):
""" Finds the nearest K data points.
Args:
query: input vector (1 x d).
data: training vectors (n x d).
k: number of nearest data points.
Returns:
indices: indices of the k nearest data points.
d: distances between the query data point and the k n... | def knnsearch(query, data, k):
| """ Finds the nearest K data points.
Args:
query: input vector (1 x d).
data: training vectors (n x d).
k: number of nearest data points.
Returns:
indices: indices of the k nearest data points.
d: distances between the query data point and the k nearest data points.
"""
d ... | 1):
return False
else:
return True
def softmax(x):
""" Applies softmax function: softmax(x) = np.exp(x)/sum(np.exp(x)).
"""
return np.exp(x) / sum(np.exp(x))
def knnsearch(query, data, k):
| 64 | 64 | 139 | 10 | 54 | manipopopo/C5 | src/aug_ops.py | Python | knnsearch | knnsearch | 474 | 492 | 474 | 474 | 8e00623045e6e64500f8867f8b5e699e0c5f557b | bigcode/the-stack | train |
826bd70913e1f9be05fa53a3 | train | function | def set_sampling_params(im_per_scene_per_camera=1, intensity_transfer=False,
target_aug_im_num=5000, excluded_camera_models=None,
excluded_datasets=None, save_as_16_bits=True,
remove_saturated_pixels=False, saturation_level=0.97,
... | def set_sampling_params(im_per_scene_per_camera=1, intensity_transfer=False,
target_aug_im_num=5000, excluded_camera_models=None,
excluded_datasets=None, save_as_16_bits=True,
remove_saturated_pixels=False, saturation_level=0.97,
... | """ Sets sampling parameters.
Args:
im_per_scene_per_camera: number of sampled images per scene per camera
model; the default is 1.
intensity_transfer: transfer the intensity of target image into the
source image. This is useful for methods that are not relying on the
log-chroma space as ... |
import random
import cv2
import ops
import copy
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
rand = np.random.rand
cameras = ['canon eos 550d', 'canon eos 5d', 'canon eos-1ds',
'canon eos-1ds mark iii', 'fujifilm x-m1', 'nikon d40',
'nikon d5200', 'olympus e-pl6',... | 218 | 218 | 727 | 91 | 127 | manipopopo/C5 | src/aug_ops.py | Python | set_sampling_params | set_sampling_params | 34 | 98 | 34 | 40 | 4cef8ec62f6c9bcadf67a9c9f695dcb80c5d04f5 | bigcode/the-stack | train |
59d75a9df338f5b640626105 | train | function | def map_raw_images(xyz_img_dir, target_cameras, output_dir, params):
""" Maps raw images to target camera models.
Args:
xyz_img_dir: directory of XYZ images.
target_cameras: target camera model name is a list of one or more
of the following models:
'Canon EOS 550D', 'Canon EOS 5D', 'Canon EOS... | def map_raw_images(xyz_img_dir, target_cameras, output_dir, params):
| """ Maps raw images to target camera models.
Args:
xyz_img_dir: directory of XYZ images.
target_cameras: target camera model name is a list of one or more
of the following models:
'Canon EOS 550D', 'Canon EOS 5D', 'Canon EOS-1DS',
'Canon EOS-1Ds Mark III', 'Fujifilm X-M1', 'Nikon D40'... | channel in sampling (see the paper for more info.); default is 1.2.
k: number of nearest neighbors; default is 15.
Returns:
params: a dict of sampling parameters.
"""
if excluded_camera_models is None:
excluded_camera_models = []
if excluded_datasets is None:
excluded_datasets = []
if o... | 256 | 256 | 1,398 | 18 | 237 | manipopopo/C5 | src/aug_ops.py | Python | map_raw_images | map_raw_images | 101 | 263 | 101 | 101 | 1c1e7c7f080149f6b0a5f1b5de88e5e3600744d6 | bigcode/the-stack | train |
2dde68236448b0f64a74bdaf | train | function | def check_sampled_data(im, ill, cst):
""" Checks the mapped image, illuminant value, and the inverse of CST matrix.
"""
h, w, c = im.shape
num_pixels = h * w
if (np.sum(np.isnan(cst)) >= 1 or np.sum(np.isnan(im)) >= 1 or
np.sum(np.isinf(im)) >= 1 or np.mean(im) < 0.009 or
np.sum(im[:, :, 0] > 1) >... | def check_sampled_data(im, ill, cst):
| """ Checks the mapped image, illuminant value, and the inverse of CST matrix.
"""
h, w, c = im.shape
num_pixels = h * w
if (np.sum(np.isnan(cst)) >= 1 or np.sum(np.isnan(im)) >= 1 or
np.sum(np.isinf(im)) >= 1 or np.mean(im) < 0.009 or
np.sum(im[:, :, 0] > 1) > 0.4 * num_pixels or
np.sum(im[:... | ops.from_rgb2bgr(im)
cv2.imwrite(os.path.join(output_dir, filename), im)
with open(os.path.join(
output_dir, filename.lower().replace('.png', '') + '_metadata.json'),
'w') as outfile:
json.dump(output_metadata, outfile)
return True
def check_sampled_data(im, ill, cst):
| 77 | 77 | 259 | 12 | 64 | manipopopo/C5 | src/aug_ops.py | Python | check_sampled_data | check_sampled_data | 449 | 465 | 449 | 449 | 221a6797829441a89b6e2249d9ddd18dd56d4f28 | bigcode/the-stack | train |
62e8ecb99aad6e09c7487865 | train | function | def predict(x, w):
""" Predicts a response y given a value x in a linear regression model with
polynomial basis function.
Args:
x: input data point
w: (d x 1) vector contains the weights of the basis functions.
Returns:
y: predicted value.
"""
d = len(w)
phi = np.zeros((1, d))
for i in ... | def predict(x, w):
| """ Predicts a response y given a value x in a linear regression model with
polynomial basis function.
Args:
x: input data point
w: (d x 1) vector contains the weights of the basis functions.
Returns:
y: predicted value.
"""
d = len(w)
phi = np.zeros((1, d))
for i in range(d):
phi[0... | """
d = np.sqrt(np.sum((query - data) ** 2, axis=1))
indices = np.argsort(d)
d.sort()
indices = indices[0: k]
d = d[0: k]
return indices, d
def predict(x, w):
| 64 | 64 | 114 | 6 | 57 | manipopopo/C5 | src/aug_ops.py | Python | predict | predict | 495 | 511 | 495 | 495 | 102bc40839ebe24e651f68dd079af9d07b2c9c39 | bigcode/the-stack | train |
1e086f3b57d599a854e08cf8 | train | function | def apply_cst(im, cst):
""" Applies CST matrix to image.
Args:
im: input ndarray image ((height * width) x channel).
cst: a 3x3 CST matrix.
Returns:
transformed image.
"""
result = im
for c in range(3):
result[:, :, c] = (cst[c, 0] * im[:, :, 0] + cst[c, 1] * im[:, :, 1] +
... | def apply_cst(im, cst):
| """ Applies CST matrix to image.
Args:
im: input ndarray image ((height * width) x channel).
cst: a 3x3 CST matrix.
Returns:
transformed image.
"""
result = im
for c in range(3):
result[:, :, c] = (cst[c, 0] * im[:, :, 0] + cst[c, 1] * im[:, :, 1] +
cst[c, 2] * im[:... | :
y: predicted value.
"""
d = len(w)
phi = np.zeros((1, d))
for i in range(d):
phi[0, i] = x ** (i)
return np.matmul(phi, w)[0]
def apply_cst(im, cst):
| 64 | 64 | 125 | 9 | 55 | manipopopo/C5 | src/aug_ops.py | Python | apply_cst | apply_cst | 514 | 529 | 514 | 514 | 2e1b137088122ace3a3d4f7b2bd64ee9321aa16c | bigcode/the-stack | train |
b82f2a0cc95be584847eeef5 | train | class | class PCNN_Loss(nn.Module):
def __init__(self, one_d):
super(PCNN_Loss, self).__init__()
self.size_average = True
self.log2 = np.log(2.0)
if one_d:
self.loss_func = discretized_mix_logistic_loss_1d
else:
self.loss_func = discretized_mix_logisti... | class PCNN_Loss(nn.Module):
| def __init__(self, one_d):
super(PCNN_Loss, self).__init__()
self.size_average = True
self.log2 = np.log(2.0)
if one_d:
self.loss_func = discretized_mix_logistic_loss_1d
else:
self.loss_func = discretized_mix_logistic_loss
def forward(sel... | torch.sum(log_sum_exp(log_probs))
else:
lse_val = log_sum_exp(log_probs)
lse_val = lse_val.view(lse_val.size(0), -1).mean(dim=1, keepdim=True)
return -lse_val
class PCNN_Loss(nn.Module):
| 64 | 64 | 195 | 8 | 55 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | PCNN_Loss | PCNN_Loss | 154 | 173 | 154 | 154 | 1ceffc1a627abd77b745c1e5e3e875b6bda5bcf4 | bigcode/the-stack | train |
1573e098f5ee1f4fcfe29a9c | train | function | def to_one_hot(tensor, n, fill_with=1.):
# we perform one hot encore with respect to the last axis
one_hot = torch.FloatTensor(tensor.size() + (n,)).zero_()
if tensor.is_cuda : one_hot = one_hot.cuda()
one_hot.scatter_(len(tensor.size()), tensor.unsqueeze(-1), fill_with)
return one_hot
| def to_one_hot(tensor, n, fill_with=1.):
# we perform one hot encore with respect to the last axis
| one_hot = torch.FloatTensor(tensor.size() + (n,)).zero_()
if tensor.is_cuda : one_hot = one_hot.cuda()
one_hot.scatter_(len(tensor.size()), tensor.unsqueeze(-1), fill_with)
return one_hot
| deno = self.log2
if do_reduce:
obs = Y.numel()
deno = deno * obs
return loss / deno
def to_one_hot(tensor, n, fill_with=1.):
# we perform one hot encore with respect to the last axis
| 64 | 64 | 84 | 29 | 34 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | to_one_hot | to_one_hot | 175 | 180 | 175 | 176 | 9e1a7fcc2be762e014f337dcf4ee7ad2abf67b24 | bigcode/the-stack | train |
a9aedcf0e2053fe4a8bd759f | train | function | def down_shift(x, pad=None):
# Pytorch ordering
xs = [int(y) for y in x.size()]
# when downshifting, the last row is removed
x = x[:, :, :xs[2] - 1, :]
# padding left, padding right, padding top, padding bottom
pad = nn.ZeroPad2d((0, 0, 1, 0)) if pad is None else pad
return pad(x)
| def down_shift(x, pad=None):
# Pytorch ordering
| xs = [int(y) for y in x.size()]
# when downshifting, the last row is removed
x = x[:, :, :xs[2] - 1, :]
# padding left, padding right, padding top, padding bottom
pad = nn.ZeroPad2d((0, 0, 1, 0)) if pad is None else pad
return pad(x)
| dim=3)
# put back in Pytorch ordering
out = out.permute(0, 3, 1, 2)
return out
''' utilities for shifting the image around, efficient alternative to masking convolutions '''
def down_shift(x, pad=None):
# Pytorch ordering
| 64 | 64 | 104 | 14 | 50 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | down_shift | down_shift | 259 | 266 | 259 | 260 | 57275e6889cf147a5d9a7f648414682f8ec9dd7d | bigcode/the-stack | train |
a06d1a2c07545c9abb74f2eb | train | function | def log_prob_from_logits(x):
""" numerically stable log_softmax implementation that prevents overflow """
# TF ordering
axis = len(x.size()) - 1
m, _ = torch.max(x, dim=axis, keepdim=True)
return x - m - torch.log(torch.sum(torch.exp(x - m), dim=axis, keepdim=True))
| def log_prob_from_logits(x):
| """ numerically stable log_softmax implementation that prevents overflow """
# TF ordering
axis = len(x.size()) - 1
m, _ = torch.max(x, dim=axis, keepdim=True)
return x - m - torch.log(torch.sum(torch.exp(x - m), dim=axis, keepdim=True))
| ()) - 1
m, _ = torch.max(x, dim=axis)
m2, _ = torch.max(x, dim=axis, keepdim=True)
return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))
def log_prob_from_logits(x):
| 63 | 64 | 77 | 7 | 56 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | log_prob_from_logits | log_prob_from_logits | 24 | 29 | 24 | 24 | ec7b6550f11740e98e378b1b25c355ad278c85d0 | bigcode/the-stack | train |
05693010ef2de261a573d3c0 | train | function | def sample_from_discretized_mix_logistic(l, nr_mix):
# Pytorch ordering
l = l.permute(0, 2, 3, 1)
ls = [int(y) for y in l.size()]
xs = ls[:-1] + [3]
# unpack parameters
logit_probs = l[:, :, :, :nr_mix]
l = l[:, :, :, nr_mix:].contiguous().view(xs + [nr_mix * 3])
# sample mixtu... | def sample_from_discretized_mix_logistic(l, nr_mix):
# Pytorch ordering
| l = l.permute(0, 2, 3, 1)
ls = [int(y) for y in l.size()]
xs = ls[:-1] + [3]
# unpack parameters
logit_probs = l[:, :, :, :nr_mix]
l = l[:, :, :, nr_mix:].contiguous().view(xs + [nr_mix * 3])
# sample mixture indicator from softmax
temp = torch.FloatTensor(logit_probs.size())
... | (l[:, :, :, :, :nr_mix] * sel, dim=4)
log_scales = torch.clamp(torch.sum(
l[:, :, :, :, nr_mix:2 * nr_mix] * sel, dim=4), min=-7.)
u = torch.FloatTensor(means.size())
if l.is_cuda : u = u.cuda()
u.uniform_(1e-5, 1. - 1e-5)
x = means + torch.exp(log_scales) * (torch.log(u) - torch.log(... | 179 | 179 | 597 | 20 | 158 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | sample_from_discretized_mix_logistic | sample_from_discretized_mix_logistic | 215 | 254 | 215 | 216 | 8b15b6eff75c3163f87ef7df1709ffd4e4de3867 | bigcode/the-stack | train |
5d87ef6af929eeee7b4cbb20 | train | function | def concat_elu(x):
""" like concatenated ReLU (http://arxiv.org/abs/1603.05201), but then with ELU """
# Pytorch ordering
axis = len(x.size()) - 3
return F.elu(torch.cat([x, -x], dim=axis))
| def concat_elu(x):
| """ like concatenated ReLU (http://arxiv.org/abs/1603.05201), but then with ELU """
# Pytorch ordering
axis = len(x.size()) - 3
return F.elu(torch.cat([x, -x], dim=axis))
| import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm as wn
import numpy as np
def concat_elu(x):
| 37 | 64 | 68 | 6 | 30 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | concat_elu | concat_elu | 8 | 12 | 8 | 8 | c54d8b2e082fb7025852b59dbb3309f10b481c36 | bigcode/the-stack | train |
76af1a7e1b7b16eec9c7a421 | train | function | def log_sum_exp(x):
""" numerically stable log_sum_exp implementation that prevents overflow """
# TF ordering
axis = len(x.size()) - 1
m, _ = torch.max(x, dim=axis)
m2, _ = torch.max(x, dim=axis, keepdim=True)
return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))
| def log_sum_exp(x):
| """ numerically stable log_sum_exp implementation that prevents overflow """
# TF ordering
axis = len(x.size()) - 1
m, _ = torch.max(x, dim=axis)
m2, _ = torch.max(x, dim=axis, keepdim=True)
return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))
| ReLU (http://arxiv.org/abs/1603.05201), but then with ELU """
# Pytorch ordering
axis = len(x.size()) - 3
return F.elu(torch.cat([x, -x], dim=axis))
def log_sum_exp(x):
| 63 | 64 | 87 | 6 | 57 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | log_sum_exp | log_sum_exp | 15 | 21 | 15 | 15 | 3c562325790e76ff360167f5ef29ff90193bd062 | bigcode/the-stack | train |
c1785e2ea18edcdad9954649 | train | function | def discretized_mix_logistic_loss(x, l, do_reduce=True):
""" log-likelihood for mixture of discretized logistics, assumes the data has been rescaled to [-1,1] interval """
# Pytorch ordering
x = x.permute(0, 2, 3, 1)
l = l.permute(0, 2, 3, 1)
xs = [int(y) for y in x.size()]
ls = [int(y) fo... | def discretized_mix_logistic_loss(x, l, do_reduce=True):
| """ log-likelihood for mixture of discretized logistics, assumes the data has been rescaled to [-1,1] interval """
# Pytorch ordering
x = x.permute(0, 2, 3, 1)
l = l.permute(0, 2, 3, 1)
xs = [int(y) for y in x.size()]
ls = [int(y) for y in l.size()]
# here and below: unpacking the... |
import numpy as np
def concat_elu(x):
""" like concatenated ReLU (http://arxiv.org/abs/1603.05201), but then with ELU """
# Pytorch ordering
axis = len(x.size()) - 3
return F.elu(torch.cat([x, -x], dim=axis))
def log_sum_exp(x):
""" numerically stable log_sum_exp implementation that... | 255 | 256 | 1,192 | 15 | 240 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | discretized_mix_logistic_loss | discretized_mix_logistic_loss | 32 | 102 | 32 | 32 | 492a7e9051ee1e05ea63206fb6bbe4c34316f9e5 | bigcode/the-stack | train |
ff4016fdd673c294d19e4b21 | train | function | def load_part_of_model(model, path):
params = torch.load(path)
added = 0
for name, param in params.items():
if name in model.state_dict().keys():
try :
model.state_dict()[name].copy_(param)
added += 1
except Exception as e:
... | def load_part_of_model(model, path):
| params = torch.load(path)
added = 0
for name, param in params.items():
if name in model.state_dict().keys():
try :
model.state_dict()[name].copy_(param)
added += 1
except Exception as e:
print(e)
pass
... | :xs[3] - 1]
# padding left, padding right, padding top, padding bottom
pad = nn.ZeroPad2d((1, 0, 0, 0)) if pad is None else pad
return pad(x)
def load_part_of_model(model, path):
| 64 | 64 | 97 | 9 | 55 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | load_part_of_model | load_part_of_model | 279 | 290 | 279 | 279 | 1f1e51a8a3adbddcc8d5ced97c5f10e6a347dfde | bigcode/the-stack | train |
4c716dd0334ee08254b88cab | train | function | def discretized_mix_logistic_loss_1d(x, l, do_reduce=True):
""" log-likelihood for mixture of discretized logistics, assumes the data has been rescaled to [-1,1] interval """
# Pytorch ordering
x = x.permute(0, 2, 3, 1)
l = l.permute(0, 2, 3, 1)
xs = [int(y) for y in x.size()]
ls = [int(y)... | def discretized_mix_logistic_loss_1d(x, l, do_reduce=True):
| """ log-likelihood for mixture of discretized logistics, assumes the data has been rescaled to [-1,1] interval """
# Pytorch ordering
x = x.permute(0, 2, 3, 1)
l = l.permute(0, 2, 3, 1)
xs = [int(y) for y in x.size()]
ls = [int(y) for y in l.size()]
# here and below: unpacking the pa... | = inner_inner_cond * torch.log(torch.clamp(cdf_delta, min=1e-12)) + (1. - inner_inner_cond) * (log_pdf_mid - np.log(127.5))
inner_cond = (x > 0.999).float()
inner_out = inner_cond * log_one_minus_cdf_min + (1. - inner_cond) * inner_inner_out
cond = (x < -0.999).float()
log... | 228 | 228 | 761 | 18 | 209 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | discretized_mix_logistic_loss_1d | discretized_mix_logistic_loss_1d | 104 | 152 | 104 | 104 | 405471d2e4880752cfb2fa6311f4b49a34bc0792 | bigcode/the-stack | train |
0b0aeba40a60604bd8caccdf | train | function | def right_shift(x, pad=None):
# Pytorch ordering
xs = [int(y) for y in x.size()]
# when righshifting, the last column is removed
x = x[:, :, :, :xs[3] - 1]
# padding left, padding right, padding top, padding bottom
pad = nn.ZeroPad2d((1, 0, 0, 0)) if pad is None else pad
return pad(x... | def right_shift(x, pad=None):
# Pytorch ordering
| xs = [int(y) for y in x.size()]
# when righshifting, the last column is removed
x = x[:, :, :, :xs[3] - 1]
# padding left, padding right, padding top, padding bottom
pad = nn.ZeroPad2d((1, 0, 0, 0)) if pad is None else pad
return pad(x)
| 1, :]
# padding left, padding right, padding top, padding bottom
pad = nn.ZeroPad2d((0, 0, 1, 0)) if pad is None else pad
return pad(x)
def right_shift(x, pad=None):
# Pytorch ordering
| 64 | 64 | 104 | 14 | 50 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | right_shift | right_shift | 269 | 276 | 269 | 270 | 2b77a5e5c33b158462cc4bc6c446a3ccdb3f3f44 | bigcode/the-stack | train |
bd7950e7a1a8be46f815b0b3 | train | function | def sample_from_discretized_mix_logistic_1d(l, nr_mix):
# Pytorch ordering
l = l.permute(0, 2, 3, 1)
ls = [int(y) for y in l.size()]
xs = ls[:-1] + [1] #[3]
# unpack parameters
logit_probs = l[:, :, :, :nr_mix]
l = l[:, :, :, nr_mix:].contiguous().view(xs + [nr_mix * 2]) # for mean,... | def sample_from_discretized_mix_logistic_1d(l, nr_mix):
# Pytorch ordering
| l = l.permute(0, 2, 3, 1)
ls = [int(y) for y in l.size()]
xs = ls[:-1] + [1] #[3]
# unpack parameters
logit_probs = l[:, :, :, :nr_mix]
l = l[:, :, :, nr_mix:].contiguous().view(xs + [nr_mix * 2]) # for mean, scale
# sample mixture indicator from softmax
temp = torch.FloatTens... | * obs
return loss / deno
def to_one_hot(tensor, n, fill_with=1.):
# we perform one hot encore with respect to the last axis
one_hot = torch.FloatTensor(tensor.size() + (n,)).zero_()
if tensor.is_cuda : one_hot = one_hot.cuda()
one_hot.scatter_(len(tensor.size()), tensor.unsqueeze(-1), f... | 117 | 117 | 393 | 23 | 93 | BartlomiejOlber/od-test | models/pixelcnn/utils.py | Python | sample_from_discretized_mix_logistic_1d | sample_from_discretized_mix_logistic_1d | 183 | 212 | 183 | 184 | 26b5903e36b467b72df2bba7955707e7ef7e5185 | bigcode/the-stack | train |
7f287191b5f1b38223611147 | train | function | def file_to_b64(filename):
"""Converts image file ti b64 string
Args:
filename: string variable containing the path and name of the
image file on computer
new_filename: filepath to save file as
Returns:
b64_string: string variable containing the image bytes encoded
... | def file_to_b64(filename):
| """Converts image file ti b64 string
Args:
filename: string variable containing the path and name of the
image file on computer
new_filename: filepath to save file as
Returns:
b64_string: string variable containing the image bytes encoded
as a bas... | on the local computer with the path and name
contained in the new_filename variable
"""
image_bytes = base64.b64decode(b64_string)
with open(new_filename, "wb") as out_file:
out_file.write(image_bytes)
return None
def file_to_b64(filename):
| 64 | 64 | 123 | 7 | 56 | braden2447/BME547_Final_Project | image_toolbox.py | Python | file_to_b64 | file_to_b64 | 27 | 43 | 27 | 27 | 14211b7a4cf7adf364a9d90cc86537c4ddfd6c7d | bigcode/the-stack | train |
b7aa9ce0526e894c30335305 | train | function | def b64_to_file(b64_string, new_filename):
"""Converts b64 string to image file, saved
as new_filename
Args:
b64_string: string variable containing the image bytes
encoded as a base64 string
new_filename: filepath to save file as
Returns:
an image file on the... | def b64_to_file(b64_string, new_filename):
| """Converts b64 string to image file, saved
as new_filename
Args:
b64_string: string variable containing the image bytes
encoded as a base64 string
new_filename: filepath to save file as
Returns:
an image file on the local computer with the path and name
... | import base64
import io
import matplotlib.image as mpimg
from matplotlib import pyplot as plt
from skimage.io import imsave
def b64_to_file(b64_string, new_filename):
| 41 | 64 | 127 | 12 | 28 | braden2447/BME547_Final_Project | image_toolbox.py | Python | b64_to_file | b64_to_file | 8 | 24 | 8 | 8 | a51a2dd6f04e44a97a8d89b01228071348c0535e | bigcode/the-stack | train |
6b73659259b6b6a5f7396ccf | train | function | def plot_image(img_ndarray):
"""Converts b64 string to numpy ndarray
Args:
img_ndarray: variable containing an ndarray with image data
Returns:
A `matplotlib` window with an image
"""
plt.imshow(img_ndarray, interpolation='nearest')
plt.show()
| def plot_image(img_ndarray):
| """Converts b64 string to numpy ndarray
Args:
img_ndarray: variable containing an ndarray with image data
Returns:
A `matplotlib` window with an image
"""
plt.imshow(img_ndarray, interpolation='nearest')
plt.show()
| 64 string
"""
f = io.BytesIO()
imsave(f, img_ndarray, plugin='pil')
y = base64.b64encode(f.getvalue())
b64_string = str(y, encoding='utf-8')
return b64_string
def plot_image(img_ndarray):
| 64 | 64 | 65 | 7 | 56 | braden2447/BME547_Final_Project | image_toolbox.py | Python | plot_image | plot_image | 79 | 89 | 79 | 79 | deda15a0cabcb963d8c51c58db05e6653c01b0ad | bigcode/the-stack | train |
5c2d429f884bd114579b3799 | train | function | def ndarray_to_b64(img_ndarray):
"""Converts b64 string to numpy ndarray
Args:
img_ndarray: variable containing an ndarray with image data
Returns:
b64_string: string variable containing image bytes encoded
as a base64 string
"""
f = io.BytesIO()
imsave(f, im... | def ndarray_to_b64(img_ndarray):
| """Converts b64 string to numpy ndarray
Args:
img_ndarray: variable containing an ndarray with image data
Returns:
b64_string: string variable containing image bytes encoded
as a base64 string
"""
f = io.BytesIO()
imsave(f, img_ndarray, plugin='pil')
y = ... | variable containing an ndarray with image data
"""
image_bytes = base64.b64decode(b64_string)
image_buf = io.BytesIO(image_bytes)
img_ndarray = mpimg.imread(image_buf, format='JPG')
return img_ndarray
def ndarray_to_b64(img_ndarray):
| 64 | 64 | 113 | 9 | 54 | braden2447/BME547_Final_Project | image_toolbox.py | Python | ndarray_to_b64 | ndarray_to_b64 | 62 | 76 | 62 | 62 | 600dbe3dacd3cee7e8cb9672ef0dd70d5086b449 | bigcode/the-stack | train |
0760e328adad86f3c0f74adb | train | function | def b64_to_ndarray(b64_string):
"""Converts b64 string to numpy ndarray
Args:
b64_string: string variable containing the image bytes
encoded as a base64 string
Returns:
img_ndarray: variable containing an ndarray with image data
"""
image_bytes = base64.b64decode(... | def b64_to_ndarray(b64_string):
| """Converts b64 string to numpy ndarray
Args:
b64_string: string variable containing the image bytes
encoded as a base64 string
Returns:
img_ndarray: variable containing an ndarray with image data
"""
image_bytes = base64.b64decode(b64_string)
image_buf = io.B... | base64 string
"""
with open(filename, "rb") as image_file:
b64_bytes = base64.b64encode(image_file.read())
b64_string = str(b64_bytes, encoding='utf-8')
return b64_string
def b64_to_ndarray(b64_string):
| 64 | 64 | 107 | 10 | 53 | braden2447/BME547_Final_Project | image_toolbox.py | Python | b64_to_ndarray | b64_to_ndarray | 46 | 59 | 46 | 46 | 944e25a3ad4a7b85ceb130de2c133958706aa022 | bigcode/the-stack | train |
ae45b2149866024887f39a6a | train | function | def get_access_token(client_id, auto=True):
cache = msal.SerializableTokenCache()
if os.path.exists(RefreshTokenFile):
cache.deserialize(open(RefreshTokenFile, "r").read())
atexit.register(lambda:
open(RefreshTokenFile, "w").write(cache.serialize())
# Hint: The following optional lin... | def get_access_token(client_id, auto=True):
| cache = msal.SerializableTokenCache()
if os.path.exists(RefreshTokenFile):
cache.deserialize(open(RefreshTokenFile, "r").read())
atexit.register(lambda:
open(RefreshTokenFile, "w").write(cache.serialize())
# Hint: The following optional line persists only when state changed
i... | """helper functions for Microsoft Graph"""
# Copyright (c) Microsoft. All rights reserved. Licensed under the MIT license.
# See LICENSE in the project root for license information.
import base64
import mimetypes
import os
import urllib
import webbrowser
import logging
import sys
import json
import msal
import atexit
... | 133 | 222 | 740 | 10 | 123 | poiriersimon/PythonTeamsPresence | helpers.py | Python | get_access_token | get_access_token | 26 | 102 | 26 | 26 | 413ee4ec2aff05856ae15b41f8b79b9999589670 | bigcode/the-stack | train |
9bca27809ce9c8880c2e5312 | train | function | def api_endpoint(url):
"""Convert a relative path such as /me/photo/$value to a full URI based
on the current RESOURCE and API_VERSION settings in config.py.
"""
if urllib.parse.urlparse(url).scheme in ['http', 'https']:
return url # url is already complete
return urllib.parse.urljoin(f'{con... | def api_endpoint(url):
| """Convert a relative path such as /me/photo/$value to a full URI based
on the current RESOURCE and API_VERSION settings in config.py.
"""
if urllib.parse.urlparse(url).scheme in ['http', 'https']:
return url # url is already complete
return urllib.parse.urljoin(f'{config.RESOURCE}/{config.A... | -python-msal',
'x-client-SKU': 'sample-python-msal'})
return session
else:
print(result.get("error"))
print(result.get("error_description"))
print(result.get("correlation_id")) # You may need this when reporting a bug
def api_endpoint(url):
| 64 | 64 | 87 | 5 | 58 | poiriersimon/PythonTeamsPresence | helpers.py | Python | api_endpoint | api_endpoint | 104 | 111 | 104 | 104 | fe6bdc1e9ea4debf8935fac34f37f159b57ddc77 | bigcode/the-stack | train |
a43a2190c965a59af7fedf0c | train | class | class NNet(object):
def __init__(self, n_input, netDims, n_iter=50, learn = 0.1, tf = sigmoid, dtf = dsigmoid):
"""
netSizes: network sizes array: output size is in position N
input: number of input neurons
hidden: number of hidden neurons
output: number of output neuron... | class NNet(object):
| def __init__(self, n_input, netDims, n_iter=50, learn = 0.1, tf = sigmoid, dtf = dsigmoid):
"""
netSizes: network sizes array: output size is in position N
input: number of input neurons
hidden: number of hidden neurons
output: number of output neurons
n_iter: how man... | import time
import random
import numpy as np
from utils import *
from transfer_functions import *
class NNet(object):
| 26 | 256 | 1,541 | 6 | 20 | lucabenedetto/DeepLearning | 1_NeuralNetwork_MNIST/NNet.py | Python | NNet | NNet | 9 | 155 | 9 | 10 | 56b1d89522dbada2a77a90dc5fac2b701c1683af | bigcode/the-stack | train |
1a037d1f3ec66fda6a06bc67 | train | function | def parse_args():
parser = argparse.ArgumentParser(description='Inference on an image')
parser.add_argument(
'--im', dest='im_file', help='input image', default=None, type=str
)
parser.add_argument(
'--rpn-pkl',
dest='rpn_pkl',
help='rpn model file (pkl)',
default... | def parse_args():
| parser = argparse.ArgumentParser(description='Inference on an image')
parser.add_argument(
'--im', dest='im_file', help='input image', default=None, type=str
)
parser.add_argument(
'--rpn-pkl',
dest='rpn_pkl',
help='rpn model file (pkl)',
default=None,
typ... | --im [path/to/image.jpg] \
# --rpn-model [path/to/rpn/model.pkl] \
# --rpn-cfg [path/to/rpn/config.yaml] \
# --output-dir [path/to/output/dir] \
# [model1] [config1] [model2] [config2] ...
def parse_args():
| 76 | 76 | 254 | 4 | 72 | summer1719/CBNet | tools/infer.py | Python | parse_args | parse_args | 66 | 101 | 66 | 66 | 53fab843466d019b55ffc6fe7b998742c0cadaaf | bigcode/the-stack | train |
e4cf2fbac91afd0d3514eacd | train | function | def main(args):
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(yaml.dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.Res... | def main(args):
| logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(yaml.dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.ResetWorkspace()
... | def get_rpn_box_proposals(im, args):
cfg.immutable(False)
merge_cfg_from_file(args.rpn_cfg)
cfg.NUM_GPUS = 1
cfg.MODEL.RPN_ONLY = True
cfg.TEST.RPN_PRE_NMS_TOP_N = 10000
cfg.TEST.RPN_POST_NMS_TOP_N = 2000
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_... | 137 | 137 | 457 | 4 | 132 | summer1719/CBNet | tools/infer.py | Python | main | main | 119 | 170 | 119 | 119 | 5d0d63aa4c05a025d337e3e42c865911d04ad8ef | bigcode/the-stack | train |
4c0d56b8c9576b02bda1a6e7 | train | function | def check_args(args):
assert (
(args.rpn_pkl is not None and args.rpn_cfg is not None) or
(args.rpn_pkl is None and args.rpn_cfg is None)
)
if args.rpn_pkl is not None:
args.rpn_pkl = cache_url(args.rpn_pkl, cfg.DOWNLOAD_CACHE)
assert os.path.exists(args.rpn_pkl)
asse... | def check_args(args):
| assert (
(args.rpn_pkl is not None and args.rpn_cfg is not None) or
(args.rpn_pkl is None and args.rpn_cfg is None)
)
if args.rpn_pkl is not None:
args.rpn_pkl = cache_url(args.rpn_pkl, cfg.DOWNLOAD_CACHE)
assert os.path.exists(args.rpn_pkl)
assert os.path.exists(args... | args.im_file,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
def check_args(args):
| 64 | 64 | 204 | 5 | 59 | summer1719/CBNet | tools/infer.py | Python | check_args | check_args | 173 | 190 | 173 | 173 | c8cfbfff8df9e66bf0021e2a69bb349c6a4aaa54 | bigcode/the-stack | train |
ad439bd3de896f23c6af3a30 | train | function | def get_rpn_box_proposals(im, args):
cfg.immutable(False)
merge_cfg_from_file(args.rpn_cfg)
cfg.NUM_GPUS = 1
cfg.MODEL.RPN_ONLY = True
cfg.TEST.RPN_PRE_NMS_TOP_N = 10000
cfg.TEST.RPN_POST_NMS_TOP_N = 2000
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_... | def get_rpn_box_proposals(im, args):
| cfg.immutable(False)
merge_cfg_from_file(args.rpn_cfg)
cfg.NUM_GPUS = 1
cfg.MODEL.RPN_ONLY = True
cfg.TEST.RPN_PRE_NMS_TOP_N = 10000
cfg.TEST.RPN_POST_NMS_TOP_N = 2000
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(args.rpn_pkl)
with c2_utils.N... | [pkl2] [yaml2] ...',
default=None,
nargs=argparse.REMAINDER
)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def get_rpn_box_proposals(im, args):
| 64 | 64 | 133 | 11 | 53 | summer1719/CBNet | tools/infer.py | Python | get_rpn_box_proposals | get_rpn_box_proposals | 104 | 116 | 104 | 104 | a1499d7e60260a13580c6ee474083c582d66b05b | bigcode/the-stack | train |
3dee57cad9f3c9f87280bb5f | train | function | def sample(n_samps, n_steps, epsilon, path):
if path is not None:
dirname = os.path.dirname(path)
if not os.path.exists(dirname):
os.makedirs(dirname)
start = time()
samples = hmc(lnpdf, x0=np.random.randn(10), n_samples=int(n_samps), n_steps=n_steps, epsilon=epsilon)
end = t... | def sample(n_samps, n_steps, epsilon, path):
| if path is not None:
dirname = os.path.dirname(path)
if not os.path.exists(dirname):
os.makedirs(dirname)
start = time()
samples = hmc(lnpdf, x0=np.random.randn(10), n_samples=int(n_samps), n_steps=n_steps, epsilon=epsilon)
end = time()
np.savez(path, samples=samples, wal... | (theta):
input = np.atleast_2d(theta)
lnpdf.counter += len(input)
return np.squeeze(tmp_lnpdf(input)), np.squeeze(elementwise_grad(tmp_lnpdf)(input))
lnpdf.counter = 0
def sample(n_samps, n_steps, epsilon, path):
| 64 | 64 | 121 | 13 | 50 | DrawZeroPoint/VIPS | python/experiments/HMC/planar_robot_10.py | Python | sample | sample | 20 | 30 | 20 | 20 | b14163d8ca825785c567f3563afc05a3b575f3d1 | bigcode/the-stack | train |
dfe24835f125e52af9dcf8b8 | train | function | def sample_with_progress(repeats, n_samps, n_steps, epsilon, path=None):
if path is not None:
dirname = os.path.dirname(path)
if not os.path.exists(dirname):
os.makedirs(dirname)
last = np.random.randn(10)
timestamps = []
all_samples = []
nfevals = []
start = time()
... | def sample_with_progress(repeats, n_samps, n_steps, epsilon, path=None):
| if path is not None:
dirname = os.path.dirname(path)
if not os.path.exists(dirname):
os.makedirs(dirname)
last = np.random.randn(10)
timestamps = []
all_samples = []
nfevals = []
start = time()
for i in range(repeats):
timestamps.append(time())
sam... | epsilon=epsilon)
end = time()
np.savez(path, samples=samples, wallclocktime=end-start)
#samples = np.vstack([c[0] for c in chain])
print("done")
def sample_with_progress(repeats, n_samps, n_steps, epsilon, path=None):
| 66 | 66 | 221 | 20 | 46 | DrawZeroPoint/VIPS | python/experiments/HMC/planar_robot_10.py | Python | sample_with_progress | sample_with_progress | 32 | 52 | 32 | 32 | d3c1eeff0ef39e80e3109f0b122f763f8246697a | bigcode/the-stack | train |
d1ccc64baaf3edad98d4737b | train | function | def lnpdf(theta):
input = np.atleast_2d(theta)
lnpdf.counter += len(input)
return np.squeeze(tmp_lnpdf(input)), np.squeeze(elementwise_grad(tmp_lnpdf)(input))
| def lnpdf(theta):
| input = np.atleast_2d(theta)
lnpdf.counter += len(input)
return np.squeeze(tmp_lnpdf(input)), np.squeeze(elementwise_grad(tmp_lnpdf)(input))
| 4e-2 * np.ones(num_dimensions)
conf_likelihood_var[0] = 1
cart_likelihood_var = np.array([1e-4, 1e-4])
tmp_lnpdf = build_target_likelihood_planar_autograd(num_dimensions)[0]
def lnpdf(theta):
| 64 | 64 | 48 | 6 | 58 | DrawZeroPoint/VIPS | python/experiments/HMC/planar_robot_10.py | Python | lnpdf | lnpdf | 14 | 17 | 14 | 14 | c91df028ecb37813850de9f10c86fe7c6a65f360 | bigcode/the-stack | train |
5963ad3acc0d84274197b16e | train | class | class MUSDBDataset(torch.utils.data.Dataset):
def __init__(
self,
target='vocals',
root=None,
download=False,
is_wav=False,
subsets='train',
split='train',
seq_duration=6.0,
samples_per_track=64,
source_augmentations=lambda audio: audio... | class MUSDBDataset(torch.utils.data.Dataset):
| def __init__(
self,
target='vocals',
root=None,
download=False,
is_wav=False,
subsets='train',
split='train',
seq_duration=6.0,
samples_per_track=64,
source_augmentations=lambda audio: audio,
random_track_mix=False,
dtyp... | ations(y)
x += y
# Use silence if target does not exist
else:
y = torch.zeros(audio.shape)
return x, y
def __len__(self):
return len(self.tracks)
def get_tracks(self):
p = Path(self.root, self.split)
for track_path in tqdm.tqdm(p.iterdi... | 256 | 256 | 1,045 | 9 | 247 | 1uka/open-unmix-pytorch | data.py | Python | MUSDBDataset | MUSDBDataset | 679 | 817 | 679 | 679 | 99f1bcf2a7fd7bf80b4690b92297a61c5c2d4e34 | bigcode/the-stack | train |
9ebaf871c98d60559184406d | train | function | def _augment_channelswap(audio):
"""Swap channels of stereo signals with a probability of p=0.5"""
if audio.shape[0] == 2 and torch.FloatTensor(1).uniform_() < 0.5:
return torch.flip(audio, [0])
else:
return audio
| def _augment_channelswap(audio):
| """Swap channels of stereo signals with a probability of p=0.5"""
if audio.shape[0] == 2 and torch.FloatTensor(1).uniform_() < 0.5:
return torch.flip(audio, [0])
else:
return audio
|
def _augment_gain(audio, low=0.25, high=1.25):
"""Applies a random gain between `low` and `high`"""
g = low + torch.rand(1) * (high - low)
return audio * g
def _augment_channelswap(audio):
| 64 | 64 | 66 | 7 | 56 | 1uka/open-unmix-pytorch | data.py | Python | _augment_channelswap | _augment_channelswap | 32 | 37 | 32 | 32 | 1e3f809c71bd986b5a7346f61169007652ce7877 | bigcode/the-stack | train |
9260eafbfe436a1324b55ab4 | train | function | def _augment_gain(audio, low=0.25, high=1.25):
"""Applies a random gain between `low` and `high`"""
g = low + torch.rand(1) * (high - low)
return audio * g
| def _augment_gain(audio, low=0.25, high=1.25):
| """Applies a random gain between `low` and `high`"""
g = low + torch.rand(1) * (high - low)
return audio * g
| to compose.
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, audio):
for t in self.transforms:
audio = t(audio)
return audio
def _augment_gain(audio, low=0.25, high=1.25):
| 64 | 64 | 56 | 18 | 45 | 1uka/open-unmix-pytorch | data.py | Python | _augment_gain | _augment_gain | 26 | 29 | 26 | 26 | f7bbf0f7137dab22bdbdd2c292fa6479176e6c28 | bigcode/the-stack | train |
091ce2676c843fd0d73fdeaf | train | class | class VariableSourcesTrackFolderDataset(torch.utils.data.Dataset):
def __init__(
self,
root,
split='train',
target_file='vocals.wav',
ext='.wav',
seq_duration=None,
random_chunks=False,
random_interferer_mix=False,
sample_rate=44100,
so... | class VariableSourcesTrackFolderDataset(torch.utils.data.Dataset):
| def __init__(
self,
root,
split='train',
target_file='vocals.wav',
ext='.wav',
seq_duration=None,
random_chunks=False,
random_interferer_mix=False,
sample_rate=44100,
source_augmentations=lambda audio: audio,
silence_missing_tar... | audio_sources.append(audio)
stems = torch.stack(audio_sources)
# # apply linear mix over source index=0
x = stems.sum(0)
# target is always the first element in the list
y = stems[0]
return x, y
def __len__(self):
return len(self.tracks)
def get_tracks... | 256 | 256 | 1,049 | 11 | 245 | 1uka/open-unmix-pytorch | data.py | Python | VariableSourcesTrackFolderDataset | VariableSourcesTrackFolderDataset | 536 | 676 | 536 | 536 | 8755ee8f09101468d0848ac70e6b4d3a65da6a2b | bigcode/the-stack | train |
5fe4769ef82591f41fbf25e7 | train | class | class AlignedDataset(torch.utils.data.Dataset):
def __init__(
self,
root,
split='train',
input_file='mixture.wav',
output_file='vocals.wav',
seq_duration=None,
random_chunks=False,
sample_rate=44100
):
"""A dataset of that assumes multiple ... | class AlignedDataset(torch.utils.data.Dataset):
| def __init__(
self,
root,
split='train',
input_file='mixture.wav',
output_file='vocals.wav',
seq_duration=None,
random_chunks=False,
sample_rate=44100
):
"""A dataset of that assumes multiple track folders
where each track includes ... | parser.parse_args()
dataset_kwargs = {
'root': args.root,
'is_wav': args.is_wav,
'subsets': 'train',
'target': args.target,
'download': args.root is None,
'seed': args.seed
}
source_augmentations = Compose(
[gl... | 192 | 192 | 642 | 9 | 182 | 1uka/open-unmix-pytorch | data.py | Python | AlignedDataset | AlignedDataset | 237 | 317 | 237 | 237 | 708a398d429f0ebccc5c7f84f2acb8391009bff4 | bigcode/the-stack | train |
e3a1272a76b9a0ae0549d1b2 | train | class | class FixedSourcesTrackFolderDataset(torch.utils.data.Dataset):
def __init__(
self,
root,
split='train',
target_file='vocals.wav',
interferer_files=['bass.wav', 'drums.wav'],
seq_duration=None,
random_chunks=False,
random_track_mix=False,
sourc... | class FixedSourcesTrackFolderDataset(torch.utils.data.Dataset):
| def __init__(
self,
root,
split='train',
target_file='vocals.wav',
interferer_files=['bass.wav', 'drums.wav'],
seq_duration=None,
random_chunks=False,
random_track_mix=False,
source_augmentations=lambda audio: audio,
sample_rate=44100,
... | 0
audio = load_audio(
source_path, start=start, dur=self.seq_duration
)
audio = self.source_augmentations(audio)
audio_sources.append(audio)
stems = torch.stack(audio_sources)
# # apply linear mix over source index=0
x = stems.sum... | 256 | 256 | 988 | 11 | 244 | 1uka/open-unmix-pytorch | data.py | Python | FixedSourcesTrackFolderDataset | FixedSourcesTrackFolderDataset | 414 | 533 | 414 | 414 | 8e2c4a977577bfde3e6d960eef207afcb0b77bca | bigcode/the-stack | train |
6e3f00019e43d2667146310f | train | class | class SourceFolderDataset(torch.utils.data.Dataset):
def __init__(
self,
root,
split='train',
target_dir='vocals',
interferer_dirs=['bass', 'drums'],
ext='.flac',
nb_samples=1000,
seq_duration=None,
random_chunks=False,
sample_rate=4410... | class SourceFolderDataset(torch.utils.data.Dataset):
| def __init__(
self,
root,
split='train',
target_dir='vocals',
interferer_dirs=['bass', 'drums'],
ext='.flac',
nb_samples=1000,
seq_duration=None,
random_chunks=False,
sample_rate=44100,
source_augmentations=lambda audio: audio,
... | tensors
return X_audio, Y_audio
def __len__(self):
return len(self.tuple_paths)
def _get_paths(self):
"""Loads input and output tracks"""
p = Path(self.root, self.split)
for track_path in tqdm.tqdm(p.iterdir()):
if track_path.is_dir():
input... | 210 | 210 | 701 | 9 | 201 | 1uka/open-unmix-pytorch | data.py | Python | SourceFolderDataset | SourceFolderDataset | 320 | 411 | 320 | 320 | 2f0cd33197c4277bd1704b28d83d2df7c466bff9 | bigcode/the-stack | train |
d7a1db6f225169c13745f64d | train | class | class Compose(object):
"""Composes several augmentation transforms.
Args:
augmentations: list of augmentations to compose.
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, audio):
for t in self.transforms:
audio = t(audio)
... | class Compose(object):
| """Composes several augmentation transforms.
Args:
augmentations: list of augmentations to compose.
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, audio):
for t in self.transforms:
audio = t(audio)
return audio
| from utils import load_audio, load_info
from pathlib import Path
import torch.utils.data
import argparse
import random
import musdb
import torch
import tqdm
class Compose(object):
| 39 | 64 | 69 | 4 | 34 | 1uka/open-unmix-pytorch | data.py | Python | Compose | Compose | 11 | 23 | 11 | 11 | 4879d2c68cc8169215b2485c3021fe4f19d40604 | bigcode/the-stack | train |
cfaa2925361640d56cd71717 | train | function | def load_datasets(parser, args):
"""Loads the specified dataset from commandline arguments
Returns:
train_dataset, validation_dataset
"""
if args.dataset == 'aligned':
parser.add_argument('--input-file', type=str)
parser.add_argument('--output-file', type=str)
args = pa... | def load_datasets(parser, args):
| """Loads the specified dataset from commandline arguments
Returns:
train_dataset, validation_dataset
"""
if args.dataset == 'aligned':
parser.add_argument('--input-file', type=str)
parser.add_argument('--output-file', type=str)
args = parser.parse_args()
# set o... | from utils import load_audio, load_info
from pathlib import Path
import torch.utils.data
import argparse
import random
import musdb
import torch
import tqdm
class Compose(object):
"""Composes several augmentation transforms.
Args:
augmentations: list of augmentations to compose.
"""
def __ini... | 234 | 256 | 1,323 | 8 | 225 | 1uka/open-unmix-pytorch | data.py | Python | load_datasets | load_datasets | 40 | 234 | 40 | 40 | 2df80bba1167cd4d9716bf5788ba28aa69fc500a | bigcode/the-stack | train |
d7ea8e0e2bce8a862b292d7c | train | function | @mock_kinesis
def test_get_records_millis_behind_latest():
conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
conn.put_record(StreamName=stream_name, Data="0", PartitionKey="0")
time.sleep(1.0)
conn.put_reco... | @mock_kinesis
def test_get_records_millis_behind_latest():
| conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
conn.put_record(StreamName=stream_name, Data="0", PartitionKey="0")
time.sleep(1.0)
conn.put_record(StreamName=stream_name, Data="1", PartitionKey="1")
... | )
response["Records"][0]["PartitionKey"].should.equal("1")
response["Records"][0]["ApproximateArrivalTimestamp"].should.be.greater_than(
timestamp
)
response["MillisBehindLatest"].should.equal(0)
@mock_kinesis
def test_get_records_millis_behind_latest():
| 66 | 66 | 223 | 15 | 51 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_millis_behind_latest | test_get_records_millis_behind_latest | 402 | 421 | 402 | 403 | 67218d87c0f73e1fa6caa99d09ca3e8b767d50f9 | bigcode/the-stack | train |
94571664e62e0caa5b9ee419 | train | function | @mock_kinesis_deprecated
def test_add_tags():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
conn.describe_stream(stream_name)
conn.add_tags_to_stream(stream_name, {"tag1": "val1"})
conn.add_tags_to_stream(stream_name, {"tag2": "v... | @mock_kinesis_deprecated
def test_add_tags():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
conn.describe_stream(stream_name)
conn.add_tags_to_stream(stream_name, {"tag1": "val1"})
conn.add_tags_to_stream(stream_name, {"tag2": "val2"})
conn.add_tags_to_stream(stream_name... | (stream_name)
shard_id = response["StreamDescription"]["Shards"][0]["ShardId"]
response = conn.get_shard_iterator.when.called_with(
stream_name, shard_id, "invalid-type"
).should.throw(InvalidArgumentException)
@mock_kinesis_deprecated
def test_add_tags():
| 64 | 64 | 120 | 12 | 52 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_add_tags | test_add_tags | 492 | 502 | 492 | 493 | eb52f3b8934aff5b13e8f309d6d6d8ab02c4391a | bigcode/the-stack | train |
88899d8b8661def7a1e66492 | train | function | @mock_kinesis_deprecated
def test_basic_shard_iterator():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
response = conn.describe_stream(stream_name)
shard_id = response["StreamDescription"]["Shards"][0]["ShardId"]
response = co... | @mock_kinesis_deprecated
def test_basic_shard_iterator():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
response = conn.describe_stream(stream_name)
shard_id = response["StreamDescription"]["Shards"][0]["ShardId"]
response = conn.get_shard_iterator(stream_name, shard_id, "TRIM_HORIZON... | .equal(shard_count)
stream["StreamARN"].should.equal(
"arn:aws:kinesis:us-west-2:{}:{}".format(ACCOUNT_ID, stream_name)
)
stream["StreamStatus"].should.equal("ACTIVE")
@mock_kinesis_deprecated
def test_basic_shard_iterator():
| 64 | 64 | 143 | 14 | 50 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_basic_shard_iterator | test_basic_shard_iterator | 98 | 114 | 98 | 99 | 861e13c24d5bd26f3bf4e11d89cd5812182e1b84 | bigcode/the-stack | train |
b27ca7134edcc249b1263d52 | train | function | @mock_kinesis_deprecated
def test_get_records_after_sequence_number():
# AFTER_SEQUENCE_NUMBER - Start reading right after the position denoted
# by a specific sequence number.
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
# Crea... | @mock_kinesis_deprecated
def test_get_records_after_sequence_number():
# AFTER_SEQUENCE_NUMBER - Start reading right after the position denoted
# by a specific sequence number.
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
# Create some data
for index in range(1, 5):
conn.put_record(stream_name, str(index), str(index))
# Get a shard iterator
response = conn.describe_stream(stream_name)
... | = conn.get_records(shard_iterator)
# And the first result returned should be the second item
response["Records"][0]["SequenceNumber"].should.equal(second_sequence_id)
response["Records"][0]["Data"].should.equal("2")
@mock_kinesis_deprecated
def test_get_records_after_sequence_number():
# AFTER_SEQUENCE... | 89 | 89 | 297 | 38 | 51 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_after_sequence_number | test_get_records_after_sequence_number | 224 | 255 | 224 | 227 | ddc2b1600f71e6aae9cd39c2bb660391b48aeecb | bigcode/the-stack | train |
34b291c1134efb15186cf2b7 | train | function | @mock_kinesis_deprecated
def test_get_records_at_sequence_number():
# AT_SEQUENCE_NUMBER - Start reading exactly from the position denoted by
# a specific sequence number.
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
# Create so... | @mock_kinesis_deprecated
def test_get_records_at_sequence_number():
# AT_SEQUENCE_NUMBER - Start reading exactly from the position denoted by
# a specific sequence number.
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
# Create some data
for index in range(1, 5):
conn.put_record(stream_name, str(index), str(index))
# Get a shard iterator
response = conn.describe_stream(stream_name)
... | should.have.length_of(3)
# Then get the rest of the results
next_shard_iterator = response["NextShardIterator"]
response = conn.get_records(next_shard_iterator)
response["Records"].should.have.length_of(2)
@mock_kinesis_deprecated
def test_get_records_at_sequence_number():
# AT_SEQUENCE_NUMBER - St... | 90 | 90 | 300 | 38 | 52 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_at_sequence_number | test_get_records_at_sequence_number | 190 | 221 | 190 | 193 | 4c9889e97b571d6e4862b839b91833241bfa3633 | bigcode/the-stack | train |
0ef86adeef0f9208e9e363a6 | train | function | @mock_kinesis_deprecated
def test_get_invalid_shard_iterator():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
conn.get_shard_iterator.when.called_with(
stream_name, "123", "TRIM_HORIZON"
).should.throw(ResourceNotFoundExcept... | @mock_kinesis_deprecated
def test_get_invalid_shard_iterator():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
conn.get_shard_iterator.when.called_with(
stream_name, "123", "TRIM_HORIZON"
).should.throw(ResourceNotFoundException)
| shard_iterator = response["ShardIterator"]
response = conn.get_records(shard_iterator)
shard_iterator = response["NextShardIterator"]
response["Records"].should.equal([])
response["MillisBehindLatest"].should.equal(0)
@mock_kinesis_deprecated
def test_get_invalid_shard_iterator():
| 64 | 64 | 81 | 15 | 49 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_invalid_shard_iterator | test_get_invalid_shard_iterator | 117 | 126 | 117 | 118 | ae59fba057e99bd2682a2a0af6ae76c1b49986ad | bigcode/the-stack | train |
44488e49895675d302f1e464 | train | function | @mock_kinesis
def test_list_many_streams():
conn = boto3.client("kinesis", region_name="us-west-2")
for i in range(11):
conn.create_stream(StreamName="stream%d" % i, ShardCount=1)
resp = conn.list_streams()
stream_names = resp["StreamNames"]
has_more_streams = resp["HasMoreStreams"]
st... | @mock_kinesis
def test_list_many_streams():
| conn = boto3.client("kinesis", region_name="us-west-2")
for i in range(11):
conn.create_stream(StreamName="stream%d" % i, ShardCount=1)
resp = conn.list_streams()
stream_names = resp["StreamNames"]
has_more_streams = resp["HasMoreStreams"]
stream_names.should.have.length_of(10)
has... | conn.delete_stream("stream2")
conn.list_streams()["StreamNames"].should.have.length_of(1)
# Delete invalid id
conn.delete_stream.when.called_with("not-a-stream").should.throw(
ResourceNotFoundException
)
@mock_kinesis
def test_list_many_streams():
| 64 | 64 | 166 | 12 | 52 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_list_many_streams | test_list_many_streams | 61 | 77 | 61 | 62 | 558af402eb98e928ed1339e8d61323f8d7c8ac98 | bigcode/the-stack | train |
ff345a8bfe201b7b7ab807fc | train | function | @mock_kinesis_deprecated
def test_list_tags():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
conn.describe_stream(stream_name)
conn.add_tags_to_stream(stream_name, {"tag1": "val1"})
tags = dict(
[
(tag["Key"],... | @mock_kinesis_deprecated
def test_list_tags():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
conn.describe_stream(stream_name)
conn.add_tags_to_stream(stream_name, {"tag1": "val1"})
tags = dict(
[
(tag["Key"], tag["Value"])
for tag in conn.list... | _name, 1)
conn.describe_stream(stream_name)
conn.add_tags_to_stream(stream_name, {"tag1": "val1"})
conn.add_tags_to_stream(stream_name, {"tag2": "val2"})
conn.add_tags_to_stream(stream_name, {"tag1": "val3"})
conn.add_tags_to_stream(stream_name, {"tag2": "val4"})
@mock_kinesis_deprecated
def test_l... | 92 | 93 | 312 | 12 | 80 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_list_tags | test_list_tags | 505 | 543 | 505 | 506 | 8879239aeb02b436076e46ff994aaa1d574a81c8 | bigcode/the-stack | train |
4e0ccd549c6806746e5657e4 | train | function | @mock_kinesis_deprecated
def test_merge_shards():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 4)
# Create some data
for index in range(1, 100):
conn.put_record(stream_name, str(index), str(index))
stream_response = conn.... | @mock_kinesis_deprecated
def test_merge_shards():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 4)
# Create some data
for index in range(1, 100):
conn.put_record(stream_name, str(index), str(index))
stream_response = conn.describe_stream(stream_name)
stream = stream_... | ["EndingHashKey"]) + int(shard_range["StartingHashKey"])
) // 2
conn.split_shard("my_stream", shards[2]["ShardId"], str(new_starting_hash))
stream_response = conn.describe_stream(stream_name)
stream = stream_response["StreamDescription"]
shards = stream["Shards"]
shards.should.have.length_of(4... | 120 | 120 | 402 | 13 | 107 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_merge_shards | test_merge_shards | 639 | 688 | 639 | 640 | 7047fb155b2c6d0a83556936b595b4ff5fc4f7fe | bigcode/the-stack | train |
12af1d14164074caf2cf81ce | train | function | @mock_kinesis_deprecated
def test_describe_non_existant_stream():
conn = boto.kinesis.connect_to_region("us-east-1")
conn.describe_stream.when.called_with("not-a-stream").should.throw(
ResourceNotFoundException
)
| @mock_kinesis_deprecated
def test_describe_non_existant_stream():
| conn = boto.kinesis.connect_to_region("us-east-1")
conn.describe_stream.when.called_with("not-a-stream").should.throw(
ResourceNotFoundException
)
| aws:kinesis:us-west-2:{}:my_stream".format(ACCOUNT_ID)
)
stream["StreamStatus"].should.equal("ACTIVE")
shards = stream["Shards"]
shards.should.have.length_of(3)
@mock_kinesis_deprecated
def test_describe_non_existant_stream():
| 64 | 64 | 54 | 16 | 48 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_describe_non_existant_stream | test_describe_non_existant_stream | 34 | 39 | 34 | 35 | 0bfd86e1e2c1e48ecd0a0d3606f32e1d8201a61d | bigcode/the-stack | train |
04360b062ad5c81823cd6ccb | train | function | @mock_kinesis_deprecated
def test_invalid_shard_iterator_type():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
response = conn.describe_stream(stream_name)
shard_id = response["StreamDescription"]["Shards"][0]["ShardId"]
response... | @mock_kinesis_deprecated
def test_invalid_shard_iterator_type():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
response = conn.describe_stream(stream_name)
shard_id = response["StreamDescription"]["Shards"][0]["ShardId"]
response = conn.get_shard_iterator.when.called_with(
stream_name,... | )
shard_iterator = response["ShardIterator"]
response = conn.get_records(ShardIterator=shard_iterator)
response["Records"].should.have.length_of(0)
response["MillisBehindLatest"].should.equal(0)
@mock_kinesis_deprecated
def test_invalid_shard_iterator_type():
| 64 | 64 | 106 | 15 | 49 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_invalid_shard_iterator_type | test_invalid_shard_iterator_type | 479 | 489 | 479 | 480 | 9b611235bf6aad38a83b81e7a90c47736b26fba2 | bigcode/the-stack | train |
b80e5d8b1ef9208c0029fda8 | train | function | @mock_kinesis_deprecated
def test_split_shard():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 2)
# Create some data
for index in range(1, 100):
conn.put_record(stream_name, str(index), str(index))
stream_response = conn.d... | @mock_kinesis_deprecated
def test_split_shard():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 2)
# Create some data
for index in range(1, 100):
conn.put_record(stream_name, str(index), str(index))
stream_response = conn.describe_stream(stream_name)
stream = stream_... | tags = dict(
[
(tag["Key"], tag["Value"])
for tag in conn.list_tags_for_stream(stream_name)["Tags"]
]
)
tags.get("tag2").should.equal("val2")
conn.remove_tags_from_stream(stream_name, ["tag2"])
tags = dict(
[
(tag["Key"], tag["Value"])
... | 120 | 120 | 402 | 13 | 107 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_split_shard | test_split_shard | 588 | 636 | 588 | 589 | 6e3222784b36a614c4fe822648c713c0e97e2a1f | bigcode/the-stack | train |
7cecdf13d44f408391d8a338 | train | function | @mock_kinesis_deprecated
def test_remove_tags():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
conn.describe_stream(stream_name)
conn.add_tags_to_stream(stream_name, {"tag1": "val1"})
tags = dict(
[
(tag["Key"... | @mock_kinesis_deprecated
def test_remove_tags():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
conn.describe_stream(stream_name)
conn.add_tags_to_stream(stream_name, {"tag1": "val1"})
tags = dict(
[
(tag["Key"], tag["Value"])
for tag in conn.list... | tags.get("tag1").should.equal("val3")
conn.add_tags_to_stream(stream_name, {"tag2": "val4"})
tags = dict(
[
(tag["Key"], tag["Value"])
for tag in conn.list_tags_for_stream(stream_name)["Tags"]
]
)
tags.get("tag2").should.equal("val4")
@mock_kinesis_deprecated
... | 90 | 90 | 300 | 12 | 78 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_remove_tags | test_remove_tags | 546 | 585 | 546 | 547 | c7168f621e0f00ed94b82c0e19d9a0d3a99bab9d | bigcode/the-stack | train |
a06ca208a081a3e29e5ad4cc | train | function | @mock_kinesis
def test_get_records_at_timestamp():
# AT_TIMESTAMP - Read the first record at or after the specified timestamp
conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
# Create some data
for index i... | @mock_kinesis
def test_get_records_at_timestamp():
# AT_TIMESTAMP - Read the first record at or after the specified timestamp
| conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
# Create some data
for index in range(1, 5):
conn.put_record(
StreamName=stream_name, Data=str(index), PartitionKey=str(index)
)
... | _record(stream_name, "last_record", "last_record")
response = conn.get_records(shard_iterator)
# And the only result returned should be the new item
response["Records"].should.have.length_of(1)
response["Records"][0]["PartitionKey"].should.equal("last_record")
response["Records"][0]["Data"].should.... | 118 | 118 | 394 | 28 | 90 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_at_timestamp | test_get_records_at_timestamp | 297 | 338 | 297 | 299 | 846ffa3ad4ed40330aba29acb3462adf4f6f0bef | bigcode/the-stack | train |
6ff42c86a8b94fa1fef6a065 | train | function | @mock_kinesis_deprecated
def test_put_records():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
data = "hello world"
partition_key = "1234"
conn.put_record.when.called_with(stream_name, data, 1234).should.throw(
InvalidA... | @mock_kinesis_deprecated
def test_put_records():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
data = "hello world"
partition_key = "1234"
conn.put_record.when.called_with(stream_name, data, 1234).should.throw(
InvalidArgumentException
)
conn.put_record(strea... | us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
conn.get_shard_iterator.when.called_with(
stream_name, "123", "TRIM_HORIZON"
).should.throw(ResourceNotFoundException)
@mock_kinesis_deprecated
def test_put_records():
| 68 | 68 | 229 | 12 | 56 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_put_records | test_put_records | 129 | 158 | 129 | 130 | 11e6c894f4b8c307d71e03401cfb3bc048c69ecf | bigcode/the-stack | train |
4ba320d6443f2ac1945350d8 | train | function | @mock_kinesis_deprecated
def test_list_and_delete_stream():
conn = boto.kinesis.connect_to_region("us-west-2")
conn.create_stream("stream1", 1)
conn.create_stream("stream2", 1)
conn.list_streams()["StreamNames"].should.have.length_of(2)
conn.delete_stream("stream2")
conn.list_streams()["Stre... | @mock_kinesis_deprecated
def test_list_and_delete_stream():
| conn = boto.kinesis.connect_to_region("us-west-2")
conn.create_stream("stream1", 1)
conn.create_stream("stream2", 1)
conn.list_streams()["StreamNames"].should.have.length_of(2)
conn.delete_stream("stream2")
conn.list_streams()["StreamNames"].should.have.length_of(1)
# Delete invalid id
... | _deprecated
def test_describe_non_existant_stream():
conn = boto.kinesis.connect_to_region("us-east-1")
conn.describe_stream.when.called_with("not-a-stream").should.throw(
ResourceNotFoundException
)
@mock_kinesis_deprecated
def test_list_and_delete_stream():
| 64 | 64 | 120 | 14 | 50 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_list_and_delete_stream | test_list_and_delete_stream | 42 | 58 | 42 | 43 | cea1c935277e0096816df6cecc4f6d0ae25f8da4 | bigcode/the-stack | train |
3af803ef0fa3b98e17c62683 | train | function | @mock_kinesis_deprecated
def test_get_records_limit():
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
# Create some data
data = "hello world"
for index in range(5):
conn.put_record(stream_name, data, str(index))
# G... | @mock_kinesis_deprecated
def test_get_records_limit():
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
# Create some data
data = "hello world"
for index in range(5):
conn.put_record(stream_name, data, str(index))
# Get a shard iterator
response = conn.describe_stream... | ["Records"].should.have.length_of(1)
record = response["Records"][0]
record["Data"].should.equal("hello world")
record["PartitionKey"].should.equal("1234")
record["SequenceNumber"].should.equal("1")
@mock_kinesis_deprecated
def test_get_records_limit():
| 66 | 66 | 221 | 13 | 53 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_limit | test_get_records_limit | 161 | 187 | 161 | 162 | eedd5a9f8fa42360a3308b694fac62b5709a3fdd | bigcode/the-stack | train |
e3d048f4439d67674267f41c | train | function | @mock_kinesis_deprecated
def test_create_cluster():
conn = boto.kinesis.connect_to_region("us-west-2")
conn.create_stream("my_stream", 3)
stream_response = conn.describe_stream("my_stream")
stream = stream_response["StreamDescription"]
stream["StreamName"].should.equal("my_stream")
stream["Ha... | @mock_kinesis_deprecated
def test_create_cluster():
| conn = boto.kinesis.connect_to_region("us-west-2")
conn.create_stream("my_stream", 3)
stream_response = conn.describe_stream("my_stream")
stream = stream_response["StreamDescription"]
stream["StreamName"].should.equal("my_stream")
stream["HasMoreShards"].should.equal(False)
stream["Stream... | _literals
import datetime
import time
import boto.kinesis
import boto3
from boto.kinesis.exceptions import ResourceNotFoundException, InvalidArgumentException
from moto import mock_kinesis, mock_kinesis_deprecated
from moto.core import ACCOUNT_ID
@mock_kinesis_deprecated
def test_create_cluster():
| 64 | 64 | 143 | 12 | 51 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_create_cluster | test_create_cluster | 14 | 31 | 14 | 15 | b90e657161d9a4999fbba03c9594fe92504db042 | bigcode/the-stack | train |
b4c2121fd33fd1d6ad2be0c0 | train | function | @mock_kinesis
def test_get_records_at_very_old_timestamp():
conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
# Create some data
keys = [str(i) for i in range(1, 5)]
for k in keys:
conn.put_record(S... | @mock_kinesis
def test_get_records_at_very_old_timestamp():
| conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
# Create some data
keys = [str(i) for i in range(1, 5)]
for k in keys:
conn.put_record(StreamName=stream_name, Data=k, PartitionKey=k)
# Get a ... | conn.get_records(ShardIterator=shard_iterator)
response["Records"].should.have.length_of(len(keys))
partition_keys = [r["PartitionKey"] for r in response["Records"]]
partition_keys.should.equal(keys)
response["MillisBehindLatest"].should.equal(0)
@mock_kinesis
def test_get_records_at_very_old_timestam... | 75 | 75 | 252 | 15 | 60 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_at_very_old_timestamp | test_get_records_at_very_old_timestamp | 341 | 367 | 341 | 342 | 21ec32dfa4b75c61816eb8c117f83c81306adbfb | bigcode/the-stack | train |
9eeda36aa89aa9b98e286857 | train | function | @mock_kinesis_deprecated
def test_get_records_latest():
# LATEST - Start reading just after the most recent record in the shard,
# so that you always read the most recent data in the shard.
conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1... | @mock_kinesis_deprecated
def test_get_records_latest():
# LATEST - Start reading just after the most recent record in the shard,
# so that you always read the most recent data in the shard.
| conn = boto.kinesis.connect_to_region("us-west-2")
stream_name = "my_stream"
conn.create_stream(stream_name, 1)
# Create some data
for index in range(1, 5):
conn.put_record(stream_name, str(index), str(index))
# Get a shard iterator
response = conn.describe_stream(stream_name)
... | )
shard_iterator = response["ShardIterator"]
response = conn.get_records(shard_iterator)
# And the first result returned should be the third item
response["Records"][0]["Data"].should.equal("3")
response["MillisBehindLatest"].should.equal(0)
@mock_kinesis_deprecated
def test_get_records_latest(... | 105 | 105 | 352 | 45 | 60 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_latest | test_get_records_latest | 258 | 294 | 258 | 261 | 9e95782c2a81616c7c58703b309ef9b07b402151 | bigcode/the-stack | train |
be3a899f5a3fa2a6bba0ce72 | train | function | @mock_kinesis
def test_describe_stream_summary():
conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream_summary"
shard_count = 5
conn.create_stream(StreamName=stream_name, ShardCount=shard_count)
resp = conn.describe_stream_summary(StreamName=stream_name)
stream = res... | @mock_kinesis
def test_describe_stream_summary():
| conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream_summary"
shard_count = 5
conn.create_stream(StreamName=stream_name, ShardCount=shard_count)
resp = conn.describe_stream_summary(StreamName=stream_name)
stream = resp["StreamDescriptionSummary"]
stream["StreamN... | ExclusiveStartStreamName=stream_names[-1])
stream_names = resp2["StreamNames"]
has_more_streams = resp2["HasMoreStreams"]
stream_names.should.have.length_of(1)
has_more_streams.should.equal(False)
@mock_kinesis
def test_describe_stream_summary():
| 64 | 64 | 155 | 12 | 52 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_describe_stream_summary | test_describe_stream_summary | 80 | 95 | 80 | 81 | 332ea8defd23d7b93bbf6058ac08b44f113d2d73 | bigcode/the-stack | train |
b2a4ed681f9e74f29fc238be | train | function | @mock_kinesis
def test_get_records_at_very_new_timestamp():
conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
# Create some data
keys = [str(i) for i in range(1, 5)]
for k in keys:
conn.put_record(S... | @mock_kinesis
def test_get_records_at_very_new_timestamp():
| conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
# Create some data
keys = [str(i) for i in range(1, 5)]
for k in keys:
conn.put_record(StreamName=stream_name, Data=k, PartitionKey=k)
timestam... | "
)
shard_iterator = response["ShardIterator"]
response = conn.get_records(ShardIterator=shard_iterator, Limit=1)
response["Records"].should.have.length_of(1)
response["MillisBehindLatest"].should.be.greater_than(0)
@mock_kinesis
def test_get_records_at_very_new_timestamp():
| 72 | 72 | 241 | 15 | 57 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_at_very_new_timestamp | test_get_records_at_very_new_timestamp | 424 | 451 | 424 | 425 | 4f02220a7b18c06e9f7cfab78381a95c8d0d158a | bigcode/the-stack | train |
76cd831ce24d835b208ab0ed | train | function | @mock_kinesis
def test_get_records_from_empty_stream_at_timestamp():
conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
timestamp = datetime.datetime.utcnow()
# Get a shard iterator
response = conn.describe... | @mock_kinesis
def test_get_records_from_empty_stream_at_timestamp():
| conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
timestamp = datetime.datetime.utcnow()
# Get a shard iterator
response = conn.describe_stream(StreamName=stream_name)
shard_id = response["StreamDescri... | )
shard_iterator = response["ShardIterator"]
response = conn.get_records(ShardIterator=shard_iterator)
response["Records"].should.have.length_of(0)
response["MillisBehindLatest"].should.equal(0)
@mock_kinesis
def test_get_records_from_empty_stream_at_timestamp():
| 64 | 64 | 188 | 15 | 49 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_from_empty_stream_at_timestamp | test_get_records_from_empty_stream_at_timestamp | 454 | 476 | 454 | 455 | 1f9b8d6ece5cc8b0de89689867df332fdf00c485 | bigcode/the-stack | train |
9d7484611310dee3fd5b44c7 | train | function | @mock_kinesis
def test_get_records_timestamp_filtering():
conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
conn.put_record(StreamName=stream_name, Data="0", PartitionKey="0")
time.sleep(1.0)
timestamp = d... | @mock_kinesis
def test_get_records_timestamp_filtering():
| conn = boto3.client("kinesis", region_name="us-west-2")
stream_name = "my_stream"
conn.create_stream(StreamName=stream_name, ShardCount=1)
conn.put_record(StreamName=stream_name, Data="0", PartitionKey="0")
time.sleep(1.0)
timestamp = datetime.datetime.utcnow()
conn.put_record(StreamName=... | Iterator"]
response = conn.get_records(ShardIterator=shard_iterator)
response["Records"].should.have.length_of(len(keys))
partition_keys = [r["PartitionKey"] for r in response["Records"]]
partition_keys.should.equal(keys)
response["MillisBehindLatest"].should.equal(0)
@mock_kinesis
def test_get_rec... | 78 | 78 | 263 | 13 | 65 | moseb/moto | tests/test_kinesis/test_kinesis.py | Python | test_get_records_timestamp_filtering | test_get_records_timestamp_filtering | 370 | 399 | 370 | 371 | 90935f0a1b3790179c2fc7e2ea4ff9cb1b0c3dff | bigcode/the-stack | train |
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