body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def get_api_key():
' Get secret api key from a file on filesystem '
paren_dir = os.path.dirname(os.path.realpath(__file__))
api_path = os.path.join(paren_dir, 'weather_api.txt')
with open(api_path, 'r') as file:
api_key = file.read().replace('\n', '')
return api_key | 239,451,994,047,830,600 | Get secret api key from a file on filesystem | .config/polybar/weather/weather.py | get_api_key | NearHuscarl/dotfiles | python | def get_api_key():
' '
paren_dir = os.path.dirname(os.path.realpath(__file__))
api_path = os.path.join(paren_dir, 'weather_api.txt')
with open(api_path, 'r') as file:
api_key = file.read().replace('\n', )
return api_key |
def get_city_id():
' Workaround to get city id based on my schedule '
region_code = {'TPHCM': 1580578, 'TPHCM2': 1566083, 'Hai Duong': 1581326, 'Tan An': 1567069}
hour = int(datetime.datetime.now().strftime('%H'))
weekday = datetime.datetime.now().strftime('%a')
if (((hour >= 17) and (weekday == 'Fr... | -7,807,401,822,778,362,000 | Workaround to get city id based on my schedule | .config/polybar/weather/weather.py | get_city_id | NearHuscarl/dotfiles | python | def get_city_id():
' '
region_code = {'TPHCM': 1580578, 'TPHCM2': 1566083, 'Hai Duong': 1581326, 'Tan An': 1567069}
hour = int(datetime.datetime.now().strftime('%H'))
weekday = datetime.datetime.now().strftime('%a')
if (((hour >= 17) and (weekday == 'Fri')) or (weekday == 'Sat') or ((hour < 17) and... |
def update_weather(city_id, units, api_key):
' Update weather by using openweather api '
url = 'http://api.openweathermap.org/data/2.5/weather?id={}&appid={}&units={}'
temp_unit = ('C' if (units == 'metric') else 'K')
error_icon = color_polybar('\ue2c1', 'red')
try:
req = requests.get(url.fo... | 6,975,599,857,060,252,000 | Update weather by using openweather api | .config/polybar/weather/weather.py | update_weather | NearHuscarl/dotfiles | python | def update_weather(city_id, units, api_key):
' '
url = 'http://api.openweathermap.org/data/2.5/weather?id={}&appid={}&units={}'
temp_unit = ('C' if (units == 'metric') else 'K')
error_icon = color_polybar('\ue2c1', 'red')
try:
req = requests.get(url.format(city_id, api_key, units))
... |
def main():
' main function '
arg = get_args()
if (arg.log == 'debug'):
set_up_logging()
units = arg.unit
api_key = get_api_key()
city_id = get_city_id()
while True:
try:
update_weather(city_id, units, api_key)
except MyInternetIsShitty:
loggin... | -4,733,848,066,255,199,000 | main function | .config/polybar/weather/weather.py | main | NearHuscarl/dotfiles | python | def main():
' '
arg = get_args()
if (arg.log == 'debug'):
set_up_logging()
units = arg.unit
api_key = get_api_key()
city_id = get_city_id()
while True:
try:
update_weather(city_id, units, api_key)
except MyInternetIsShitty:
logging.info(cb('up... |
def run_server(server_port):
'Run the UDP pinger server\n '
with Socket(socket.AF_INET, socket.SOCK_DGRAM) as server_socket:
server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
server_socket.bind(('', server_port))
print('Ping server ready on port', server_port)
... | 6,592,167,236,038,547,000 | Run the UDP pinger server | ping/ping.py | run_server | akshayrb22/Kurose-and-Ross-socket-programming-exercises | python | def run_server(server_port):
'\n '
with Socket(socket.AF_INET, socket.SOCK_DGRAM) as server_socket:
server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
server_socket.bind((, server_port))
print('Ping server ready on port', server_port)
while True:
(... |
def run_client(server_address, server_port):
'Ping a UDP pinger server running at the given address\n '
raise NotImplementedError
return 0 | -7,235,053,255,711,612,000 | Ping a UDP pinger server running at the given address | ping/ping.py | run_client | akshayrb22/Kurose-and-Ross-socket-programming-exercises | python | def run_client(server_address, server_port):
'\n '
raise NotImplementedError
return 0 |
def _calculate_reciprocal_rank(self, hypothesis_ids: np.ndarray, reference_id: int) -> float:
'Calculate the reciprocal rank for a given hypothesis and reference\n \n Params:\n hypothesis_ids: Iterator of hypothesis ids (as numpy array) ordered by its relevance\n ... | -8,653,979,882,358,909,000 | Calculate the reciprocal rank for a given hypothesis and reference
Params:
hypothesis_ids: Iterator of hypothesis ids (as numpy array) ordered by its relevance
reference_id: Reference id (as a integer) of the correct id of response
Returns:
reciprocal rank | tasks/retriever/mrr.py | _calculate_reciprocal_rank | platiagro/tasks | python | def _calculate_reciprocal_rank(self, hypothesis_ids: np.ndarray, reference_id: int) -> float:
'Calculate the reciprocal rank for a given hypothesis and reference\n \n Params:\n hypothesis_ids: Iterator of hypothesis ids (as numpy array) ordered by its relevance\n ... |
def forward(self, batch_hypothesis_ids: List[np.ndarray], batch_reference_id: List[int]) -> float:
'Score the mean reciprocal rank for the batch\n \n Example from http://en.wikipedia.org/wiki/Mean_reciprocal_rank\n \n >>> batch_hypothesis_ids = [[1, 0, 2], [0, 2, 1], [1, ... | -4,246,488,428,252,922,400 | Score the mean reciprocal rank for the batch
Example from http://en.wikipedia.org/wiki/Mean_reciprocal_rank
>>> batch_hypothesis_ids = [[1, 0, 2], [0, 2, 1], [1, 0, 2]]
>>> batch_reference_id = [2, 2, 1]
>>> mrr = MRR()
>>> mrr(batch_hypothesis_ids, batch_reference_id)
0.61111111111111105
Args:
batch_hypothe... | tasks/retriever/mrr.py | forward | platiagro/tasks | python | def forward(self, batch_hypothesis_ids: List[np.ndarray], batch_reference_id: List[int]) -> float:
'Score the mean reciprocal rank for the batch\n \n Example from http://en.wikipedia.org/wiki/Mean_reciprocal_rank\n \n >>> batch_hypothesis_ids = [[1, 0, 2], [0, 2, 1], [1, ... |
def get_oof_pred_proba(self, X, normalize=None, **kwargs):
'X should be the same X passed to `.fit`'
y_oof_pred_proba = self._get_oof_pred_proba(X=X, **kwargs)
if (normalize is None):
normalize = self.normalize_pred_probas
if normalize:
y_oof_pred_proba = normalize_pred_probas(y_oof_pred... | -8,436,748,996,598,062,000 | X should be the same X passed to `.fit` | tabular/src/autogluon/tabular/models/knn/knn_model.py | get_oof_pred_proba | taesup-aws/autogluon | python | def get_oof_pred_proba(self, X, normalize=None, **kwargs):
y_oof_pred_proba = self._get_oof_pred_proba(X=X, **kwargs)
if (normalize is None):
normalize = self.normalize_pred_probas
if normalize:
y_oof_pred_proba = normalize_pred_probas(y_oof_pred_proba, self.problem_type)
y_oof_pred... |
def _fit_with_samples(self, X, y, time_limit, start_samples=10000, max_samples=None, sample_growth_factor=2, sample_time_growth_factor=8):
'\n Fit model with samples of the data repeatedly, gradually increasing the amount of data until time_limit is reached or all data is used.\n\n X and y must alread... | 7,872,693,222,056,237,000 | Fit model with samples of the data repeatedly, gradually increasing the amount of data until time_limit is reached or all data is used.
X and y must already be preprocessed.
Parameters
----------
X : np.ndarray
The training data features (preprocessed).
y : Series
The training data ground truth labels.
time_l... | tabular/src/autogluon/tabular/models/knn/knn_model.py | _fit_with_samples | taesup-aws/autogluon | python | def _fit_with_samples(self, X, y, time_limit, start_samples=10000, max_samples=None, sample_growth_factor=2, sample_time_growth_factor=8):
'\n Fit model with samples of the data repeatedly, gradually increasing the amount of data until time_limit is reached or all data is used.\n\n X and y must alread... |
def naked_twins(values):
"Eliminate values using the naked twins strategy.\n\n The naked twins strategy says that if you have two or more unallocated boxes\n in a unit and there are only two digits that can go in those two boxes, then\n those two digits can be eliminated from the possible assignments of al... | -1,691,231,728,503,389,700 | Eliminate values using the naked twins strategy.
The naked twins strategy says that if you have two or more unallocated boxes
in a unit and there are only two digits that can go in those two boxes, then
those two digits can be eliminated from the possible assignments of all other
boxes in the same unit.
Parameters
--... | Projects/1_Sudoku/solution.py | naked_twins | justinlnx/artificial-intelligence | python | def naked_twins(values):
"Eliminate values using the naked twins strategy.\n\n The naked twins strategy says that if you have two or more unallocated boxes\n in a unit and there are only two digits that can go in those two boxes, then\n those two digits can be eliminated from the possible assignments of al... |
def eliminate(values):
"Apply the eliminate strategy to a Sudoku puzzle\n\n The eliminate strategy says that if a box has a value assigned, then none\n of the peers of that box can have the same value.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '12345678... | 1,745,120,404,089,232,000 | Apply the eliminate strategy to a Sudoku puzzle
The eliminate strategy says that if a box has a value assigned, then none
of the peers of that box can have the same value.
Parameters
----------
values(dict)
a dictionary of the form {'box_name': '123456789', ...}
Returns
-------
dict
The values dictionary wit... | Projects/1_Sudoku/solution.py | eliminate | justinlnx/artificial-intelligence | python | def eliminate(values):
"Apply the eliminate strategy to a Sudoku puzzle\n\n The eliminate strategy says that if a box has a value assigned, then none\n of the peers of that box can have the same value.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '12345678... |
def only_choice(values):
"Apply the only choice strategy to a Sudoku puzzle\n\n The only choice strategy says that if only one box in a unit allows a certain\n digit, then that box must be assigned that digit.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '... | -4,383,931,250,168,897,500 | Apply the only choice strategy to a Sudoku puzzle
The only choice strategy says that if only one box in a unit allows a certain
digit, then that box must be assigned that digit.
Parameters
----------
values(dict)
a dictionary of the form {'box_name': '123456789', ...}
Returns
-------
dict
The values dictiona... | Projects/1_Sudoku/solution.py | only_choice | justinlnx/artificial-intelligence | python | def only_choice(values):
"Apply the only choice strategy to a Sudoku puzzle\n\n The only choice strategy says that if only one box in a unit allows a certain\n digit, then that box must be assigned that digit.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '... |
def reduce_puzzle(values):
"Reduce a Sudoku puzzle by repeatedly applying all constraint strategies\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict or False\n The values dictionary after continued appli... | -3,851,804,040,853,470,000 | Reduce a Sudoku puzzle by repeatedly applying all constraint strategies
Parameters
----------
values(dict)
a dictionary of the form {'box_name': '123456789', ...}
Returns
-------
dict or False
The values dictionary after continued application of the constraint strategies
no longer produces any changes, or... | Projects/1_Sudoku/solution.py | reduce_puzzle | justinlnx/artificial-intelligence | python | def reduce_puzzle(values):
"Reduce a Sudoku puzzle by repeatedly applying all constraint strategies\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict or False\n The values dictionary after continued appli... |
def search(values):
"Apply depth first search to solve Sudoku puzzles in order to solve puzzles\n that cannot be solved by repeated reduction alone.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict or False\... | -5,391,375,916,073,540,000 | Apply depth first search to solve Sudoku puzzles in order to solve puzzles
that cannot be solved by repeated reduction alone.
Parameters
----------
values(dict)
a dictionary of the form {'box_name': '123456789', ...}
Returns
-------
dict or False
The values dictionary with all boxes assigned or False
Notes
-... | Projects/1_Sudoku/solution.py | search | justinlnx/artificial-intelligence | python | def search(values):
"Apply depth first search to solve Sudoku puzzles in order to solve puzzles\n that cannot be solved by repeated reduction alone.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict or False\... |
def solve(grid):
"Find the solution to a Sudoku puzzle using search and constraint propagation\n\n Parameters\n ----------\n grid(string)\n a string representing a sudoku grid.\n \n Ex. '2.............62....1....7...6..8...3...9...7...6..4...4....8....52.............3'\n\n Returns\n... | 7,617,055,493,705,177,000 | Find the solution to a Sudoku puzzle using search and constraint propagation
Parameters
----------
grid(string)
a string representing a sudoku grid.
Ex. '2.............62....1....7...6..8...3...9...7...6..4...4....8....52.............3'
Returns
-------
dict or False
The dictionary representation of t... | Projects/1_Sudoku/solution.py | solve | justinlnx/artificial-intelligence | python | def solve(grid):
"Find the solution to a Sudoku puzzle using search and constraint propagation\n\n Parameters\n ----------\n grid(string)\n a string representing a sudoku grid.\n \n Ex. '2.............62....1....7...6..8...3...9...7...6..4...4....8....52.............3'\n\n Returns\n... |
def basic_argument_parser(distributed=True, requires_config_file=True, requires_output_dir=True):
' Basic cli tool parser for Detectron2Go binaries '
parser = argparse.ArgumentParser(description='PyTorch Object Detection Training')
parser.add_argument('--runner', type=str, default='d2go.runner.GeneralizedRC... | -3,745,655,481,647,895,000 | Basic cli tool parser for Detectron2Go binaries | d2go/setup.py | basic_argument_parser | Dinesh101041/d2go | python | def basic_argument_parser(distributed=True, requires_config_file=True, requires_output_dir=True):
' '
parser = argparse.ArgumentParser(description='PyTorch Object Detection Training')
parser.add_argument('--runner', type=str, default='d2go.runner.GeneralizedRCNNRunner', help='Full class name, i.e. (package... |
def create_cfg_from_cli_args(args, default_cfg):
"\n Instead of loading from defaults.py, this binary only includes necessary\n configs building from scratch, and overrides them from args. There're two\n levels of config:\n _C: the config system used by this binary, which is a sub-set of training\n ... | 1,567,503,064,963,738,400 | Instead of loading from defaults.py, this binary only includes necessary
configs building from scratch, and overrides them from args. There're two
levels of config:
_C: the config system used by this binary, which is a sub-set of training
config, override by configurable_cfg. It can also be override by
... | d2go/setup.py | create_cfg_from_cli_args | Dinesh101041/d2go | python | def create_cfg_from_cli_args(args, default_cfg):
"\n Instead of loading from defaults.py, this binary only includes necessary\n configs building from scratch, and overrides them from args. There're two\n levels of config:\n _C: the config system used by this binary, which is a sub-set of training\n ... |
def prepare_for_launch(args):
'\n Load config, figure out working directory, create runner.\n - when args.config_file is empty, returned cfg will be the default one\n - returned output_dir will always be non empty, args.output_dir has higher\n priority than cfg.OUTPUT_DIR.\n '
pri... | 8,141,107,573,497,229,000 | Load config, figure out working directory, create runner.
- when args.config_file is empty, returned cfg will be the default one
- returned output_dir will always be non empty, args.output_dir has higher
priority than cfg.OUTPUT_DIR. | d2go/setup.py | prepare_for_launch | Dinesh101041/d2go | python | def prepare_for_launch(args):
'\n Load config, figure out working directory, create runner.\n - when args.config_file is empty, returned cfg will be the default one\n - returned output_dir will always be non empty, args.output_dir has higher\n priority than cfg.OUTPUT_DIR.\n '
pri... |
def setup_after_launch(cfg, output_dir, runner):
'\n Set things up after entering DDP, including\n - creating working directory\n - setting up logger\n - logging environment\n - initializing runner\n '
create_dir_on_global_main_process(output_dir)
comm.synchronize()
set... | -8,754,067,670,595,316,000 | Set things up after entering DDP, including
- creating working directory
- setting up logger
- logging environment
- initializing runner | d2go/setup.py | setup_after_launch | Dinesh101041/d2go | python | def setup_after_launch(cfg, output_dir, runner):
'\n Set things up after entering DDP, including\n - creating working directory\n - setting up logger\n - logging environment\n - initializing runner\n '
create_dir_on_global_main_process(output_dir)
comm.synchronize()
set... |
def __init__(self, main_window, palette):
"\n Creates a new window for user to input\n which regions to add to scene.\n\n Arguments:\n ----------\n\n main_window: reference to the App's main window\n palette: main_window's palette, used to style widg... | 2,832,295,261,314,470,000 | Creates a new window for user to input
which regions to add to scene.
Arguments:
----------
main_window: reference to the App's main window
palette: main_window's palette, used to style widgets | brainrender_gui/widgets/add_regions.py | __init__ | brainglobe/bg-brainrender-gui | python | def __init__(self, main_window, palette):
"\n Creates a new window for user to input\n which regions to add to scene.\n\n Arguments:\n ----------\n\n main_window: reference to the App's main window\n palette: main_window's palette, used to style widg... |
def ui(self):
"\n Define UI's elements\n "
self.setGeometry(self.left, self.top, self.width, self.height)
layout = QVBoxLayout()
label = QLabel(self)
label.setObjectName('PopupLabel')
label.setText(self.label_msg)
self.textbox = QLineEdit(self)
alpha_label = QLabel(self... | -7,489,549,448,365,388,000 | Define UI's elements | brainrender_gui/widgets/add_regions.py | ui | brainglobe/bg-brainrender-gui | python | def ui(self):
"\n \n "
self.setGeometry(self.left, self.top, self.width, self.height)
layout = QVBoxLayout()
label = QLabel(self)
label.setObjectName('PopupLabel')
label.setText(self.label_msg)
self.textbox = QLineEdit(self)
alpha_label = QLabel(self)
alpha_label.se... |
def on_click(self):
"\n On click or 'Enter' get the regions\n from the input and call the add_regions\n method of the main window\n "
regions = self.textbox.text().split(' ')
self.main_window.add_regions(regions, self.alpha_textbox.text(), self.color_textbox.text())
... | -1,581,329,918,703,527,000 | On click or 'Enter' get the regions
from the input and call the add_regions
method of the main window | brainrender_gui/widgets/add_regions.py | on_click | brainglobe/bg-brainrender-gui | python | def on_click(self):
"\n On click or 'Enter' get the regions\n from the input and call the add_regions\n method of the main window\n "
regions = self.textbox.text().split(' ')
self.main_window.add_regions(regions, self.alpha_textbox.text(), self.color_textbox.text())
... |
def update_user_data():
'Update user_data to enable or disable Telemetry.\n\n If employment data has been changed Telemetry might be switched on\n automatically. The opt-in decision is taken for the new employee. Non employees\n will have an option to enable data collection.\n '
is_employee_changed ... | 4,639,693,185,802,704,000 | Update user_data to enable or disable Telemetry.
If employment data has been changed Telemetry might be switched on
automatically. The opt-in decision is taken for the new employee. Non employees
will have an option to enable data collection. | mozphab/telemetry.py | update_user_data | cgsheeh/review | python | def update_user_data():
'Update user_data to enable or disable Telemetry.\n\n If employment data has been changed Telemetry might be switched on\n automatically. The opt-in decision is taken for the new employee. Non employees\n will have an option to enable data collection.\n '
is_employee_changed ... |
def __init__(self):
'Initiate Glean, load pings and metrics.'
import glean
logging.getLogger('glean').setLevel(logging.DEBUG)
logger.debug('Initializing Glean...')
glean.Glean.initialize(application_id='MozPhab', application_version=MOZPHAB_VERSION, upload_enabled=True, configuration=glean.Configura... | -7,231,570,573,987,132,000 | Initiate Glean, load pings and metrics. | mozphab/telemetry.py | __init__ | cgsheeh/review | python | def __init__(self):
import glean
logging.getLogger('glean').setLevel(logging.DEBUG)
logger.debug('Initializing Glean...')
glean.Glean.initialize(application_id='MozPhab', application_version=MOZPHAB_VERSION, upload_enabled=True, configuration=glean.Configuration(), data_dir=(Path(environment.MOZBUI... |
def _set_os(self):
'Collect human readable information about the OS version.\n\n For Linux it is setting a distribution name and version.\n '
(system, node, release, version, machine, processor) = platform.uname()
if (system == 'Linux'):
(distribution_name, distribution_number, _) = di... | -6,868,257,696,406,971,000 | Collect human readable information about the OS version.
For Linux it is setting a distribution name and version. | mozphab/telemetry.py | _set_os | cgsheeh/review | python | def _set_os(self):
'Collect human readable information about the OS version.\n\n For Linux it is setting a distribution name and version.\n '
(system, node, release, version, machine, processor) = platform.uname()
if (system == 'Linux'):
(distribution_name, distribution_number, _) = di... |
def set_metrics(self, args):
'Sets metrics common to all commands.'
self.usage.command.set(args.command)
self._set_os()
self._set_python()
self.usage.override_switch.set((getattr(args, 'force_vcs', False) or getattr(args, 'force', False)))
self.usage.command_time.start()
self.user.installati... | -1,575,089,079,134,722,300 | Sets metrics common to all commands. | mozphab/telemetry.py | set_metrics | cgsheeh/review | python | def set_metrics(self, args):
self.usage.command.set(args.command)
self._set_os()
self._set_python()
self.usage.override_switch.set((getattr(args, 'force_vcs', False) or getattr(args, 'force', False)))
self.usage.command_time.start()
self.user.installation.set(user_data.installation_id)
... |
def binary_image_to_lut_indices(x):
'\n Convert a binary image to an index image that can be used with a lookup table\n to perform morphological operations. Non-zero elements in the image are interpreted\n as 1, zero elements as 0\n\n :param x: a 2D NumPy array.\n :return: a 2D NumPy array, same shap... | -7,441,921,039,338,985,000 | Convert a binary image to an index image that can be used with a lookup table
to perform morphological operations. Non-zero elements in the image are interpreted
as 1, zero elements as 0
:param x: a 2D NumPy array.
:return: a 2D NumPy array, same shape as x | Benchmarking/bsds500/bsds/thin.py | binary_image_to_lut_indices | CipiOrhei/eecvf | python | def binary_image_to_lut_indices(x):
'\n Convert a binary image to an index image that can be used with a lookup table\n to perform morphological operations. Non-zero elements in the image are interpreted\n as 1, zero elements as 0\n\n :param x: a 2D NumPy array.\n :return: a 2D NumPy array, same shap... |
def apply_lut(x, lut):
'\n Perform a morphological operation on the binary image x using the supplied lookup table\n :param x:\n :param lut:\n :return:\n '
if (lut.ndim != 1):
raise ValueError('lut should have 1 dimension, not {}'.format(lut.ndim))
if (lut.shape[0] != 512):
ra... | -4,490,145,918,969,152,000 | Perform a morphological operation on the binary image x using the supplied lookup table
:param x:
:param lut:
:return: | Benchmarking/bsds500/bsds/thin.py | apply_lut | CipiOrhei/eecvf | python | def apply_lut(x, lut):
'\n Perform a morphological operation on the binary image x using the supplied lookup table\n :param x:\n :param lut:\n :return:\n '
if (lut.ndim != 1):
raise ValueError('lut should have 1 dimension, not {}'.format(lut.ndim))
if (lut.shape[0] != 512):
ra... |
def identity_lut():
'\n Create identity lookup tablef\n :return:\n '
lut = np.zeros((512,), dtype=bool)
inds = np.arange(512)
lut[((inds & NEIGH_MASK_CENTRE) != 0)] = True
return lut | -3,448,551,723,326,318,600 | Create identity lookup tablef
:return: | Benchmarking/bsds500/bsds/thin.py | identity_lut | CipiOrhei/eecvf | python | def identity_lut():
'\n Create identity lookup tablef\n :return:\n '
lut = np.zeros((512,), dtype=bool)
inds = np.arange(512)
lut[((inds & NEIGH_MASK_CENTRE) != 0)] = True
return lut |
def _lut_mutate_mask(lut):
'\n Get a mask that shows which neighbourhood shapes result in changes to the image\n :param lut: lookup table\n :return: mask indicating which lookup indices result in changes\n '
return (lut != identity_lut()) | -1,491,527,051,737,313,000 | Get a mask that shows which neighbourhood shapes result in changes to the image
:param lut: lookup table
:return: mask indicating which lookup indices result in changes | Benchmarking/bsds500/bsds/thin.py | _lut_mutate_mask | CipiOrhei/eecvf | python | def _lut_mutate_mask(lut):
'\n Get a mask that shows which neighbourhood shapes result in changes to the image\n :param lut: lookup table\n :return: mask indicating which lookup indices result in changes\n '
return (lut != identity_lut()) |
def lut_masks_zero(neigh):
'\n Create a LUT index mask for which the specified neighbour is 0\n :param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour\n :return: a LUT index mask\n '
if (neigh > 8):
neigh -= 8
return ((_LUT_INDS & MASKS[neigh]) == 0) | 7,111,937,062,312,660,000 | Create a LUT index mask for which the specified neighbour is 0
:param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | lut_masks_zero | CipiOrhei/eecvf | python | def lut_masks_zero(neigh):
'\n Create a LUT index mask for which the specified neighbour is 0\n :param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour\n :return: a LUT index mask\n '
if (neigh > 8):
neigh -= 8
return ((_LUT_INDS & MASKS[neigh]) == 0) |
def lut_masks_one(neigh):
'\n Create a LUT index mask for which the specified neighbour is 1\n :param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour\n :return: a LUT index mask\n '
if (neigh > 8):
neigh -= 8
return ((_LUT_INDS & MASKS[neigh]) != 0) | 6,568,589,080,645,123,000 | Create a LUT index mask for which the specified neighbour is 1
:param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | lut_masks_one | CipiOrhei/eecvf | python | def lut_masks_one(neigh):
'\n Create a LUT index mask for which the specified neighbour is 1\n :param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour\n :return: a LUT index mask\n '
if (neigh > 8):
neigh -= 8
return ((_LUT_INDS & MASKS[neigh]) != 0) |
def _thin_cond_g1():
'\n Thinning morphological operation; condition G1\n :return: a LUT index mask\n '
b = np.zeros(512, dtype=int)
for i in range(1, 5):
b += (lut_masks_zero(((2 * i) - 1)) & (lut_masks_one((2 * i)) | lut_masks_one(((2 * i) + 1))))
return (b == 1) | 7,932,152,981,081,950,000 | Thinning morphological operation; condition G1
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | _thin_cond_g1 | CipiOrhei/eecvf | python | def _thin_cond_g1():
'\n Thinning morphological operation; condition G1\n :return: a LUT index mask\n '
b = np.zeros(512, dtype=int)
for i in range(1, 5):
b += (lut_masks_zero(((2 * i) - 1)) & (lut_masks_one((2 * i)) | lut_masks_one(((2 * i) + 1))))
return (b == 1) |
def _thin_cond_g2():
'\n Thinning morphological operation; condition G2\n :return: a LUT index mask\n '
n1 = np.zeros(512, dtype=int)
n2 = np.zeros(512, dtype=int)
for k in range(1, 5):
n1 += (lut_masks_one(((2 * k) - 1)) | lut_masks_one((2 * k)))
n2 += (lut_masks_one((2 * k)) |... | 5,711,260,385,655,939,000 | Thinning morphological operation; condition G2
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | _thin_cond_g2 | CipiOrhei/eecvf | python | def _thin_cond_g2():
'\n Thinning morphological operation; condition G2\n :return: a LUT index mask\n '
n1 = np.zeros(512, dtype=int)
n2 = np.zeros(512, dtype=int)
for k in range(1, 5):
n1 += (lut_masks_one(((2 * k) - 1)) | lut_masks_one((2 * k)))
n2 += (lut_masks_one((2 * k)) |... |
def _thin_cond_g3():
'\n Thinning morphological operation; condition G3\n :return: a LUT index mask\n '
return ((((lut_masks_one(2) | lut_masks_one(3)) | lut_masks_zero(8)) & lut_masks_one(1)) == 0) | -1,797,199,284,587,221,000 | Thinning morphological operation; condition G3
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | _thin_cond_g3 | CipiOrhei/eecvf | python | def _thin_cond_g3():
'\n Thinning morphological operation; condition G3\n :return: a LUT index mask\n '
return ((((lut_masks_one(2) | lut_masks_one(3)) | lut_masks_zero(8)) & lut_masks_one(1)) == 0) |
def _thin_cond_g3_prime():
"\n Thinning morphological operation; condition G3'\n :return: a LUT index mask\n "
return ((((lut_masks_one(6) | lut_masks_one(7)) | lut_masks_zero(4)) & lut_masks_one(5)) == 0) | 7,209,364,479,417,253,000 | Thinning morphological operation; condition G3'
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | _thin_cond_g3_prime | CipiOrhei/eecvf | python | def _thin_cond_g3_prime():
"\n Thinning morphological operation; condition G3'\n :return: a LUT index mask\n "
return ((((lut_masks_one(6) | lut_masks_one(7)) | lut_masks_zero(4)) & lut_masks_one(5)) == 0) |
def _thin_iter_1_lut():
'\n Thinning morphological operation; lookup table for iteration 1\n :return: lookup table\n '
lut = identity_lut()
cond = ((_thin_cond_g1() & _thin_cond_g2()) & _thin_cond_g3())
lut[cond] = False
return lut | 5,085,434,141,869,963,000 | Thinning morphological operation; lookup table for iteration 1
:return: lookup table | Benchmarking/bsds500/bsds/thin.py | _thin_iter_1_lut | CipiOrhei/eecvf | python | def _thin_iter_1_lut():
'\n Thinning morphological operation; lookup table for iteration 1\n :return: lookup table\n '
lut = identity_lut()
cond = ((_thin_cond_g1() & _thin_cond_g2()) & _thin_cond_g3())
lut[cond] = False
return lut |
def _thin_iter_2_lut():
'\n Thinning morphological operation; lookup table for iteration 2\n :return: lookup table\n '
lut = identity_lut()
cond = ((_thin_cond_g1() & _thin_cond_g2()) & _thin_cond_g3_prime())
lut[cond] = False
return lut | -103,154,475,881,035,140 | Thinning morphological operation; lookup table for iteration 2
:return: lookup table | Benchmarking/bsds500/bsds/thin.py | _thin_iter_2_lut | CipiOrhei/eecvf | python | def _thin_iter_2_lut():
'\n Thinning morphological operation; lookup table for iteration 2\n :return: lookup table\n '
lut = identity_lut()
cond = ((_thin_cond_g1() & _thin_cond_g2()) & _thin_cond_g3_prime())
lut[cond] = False
return lut |
def binary_thin(x, max_iter=None):
'\n Binary thinning morphological operation\n\n :param x: a binary image, or an image that is to be converted to a binary image\n :param max_iter: maximum number of iterations; default is `None` that results in an infinite\n number of iterations (note that `binary_thin... | 3,673,415,387,885,628,400 | Binary thinning morphological operation
:param x: a binary image, or an image that is to be converted to a binary image
:param max_iter: maximum number of iterations; default is `None` that results in an infinite
number of iterations (note that `binary_thin` will automatically terminate when no more changes occur)
:re... | Benchmarking/bsds500/bsds/thin.py | binary_thin | CipiOrhei/eecvf | python | def binary_thin(x, max_iter=None):
'\n Binary thinning morphological operation\n\n :param x: a binary image, or an image that is to be converted to a binary image\n :param max_iter: maximum number of iterations; default is `None` that results in an infinite\n number of iterations (note that `binary_thin... |
def play(self):
"\n # 1. Create a deck of 52 cards\n # 2. Shuffle the deck\n # 3. Ask the Player for their bet\n # 4. Make sure that the Player's bet does not exceed their available chips\n # 5. Deal two cards to the Dealer and two cards to the Player\n # 6. Show only one o... | 7,772,900,167,371,930,000 | # 1. Create a deck of 52 cards
# 2. Shuffle the deck
# 3. Ask the Player for their bet
# 4. Make sure that the Player's bet does not exceed their available chips
# 5. Deal two cards to the Dealer and two cards to the Player
# 6. Show only one of the Dealer's cards, the other remains hidden
# 7. Show both of the Player'... | BlackJack.py | play | tse4a/Python-Challenge | python | def play(self):
"\n # 1. Create a deck of 52 cards\n # 2. Shuffle the deck\n # 3. Ask the Player for their bet\n # 4. Make sure that the Player's bet does not exceed their available chips\n # 5. Deal two cards to the Dealer and two cards to the Player\n # 6. Show only one o... |
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, account_name: Optional[pulumi.Input[str]]=None, active_directories: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ActiveDirectoryArgs']]]]]=None, location: Optional[pulumi.Input[str]]=None, resource_group_name: Optio... | -3,839,363,611,189,158,000 | NetApp account resource
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] account_name: The name of the NetApp account
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ActiveDirectoryArgs']]]] active_directories: Active... | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | __init__ | pulumi-bot/pulumi-azure-native | python | def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, account_name: Optional[pulumi.Input[str]]=None, active_directories: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ActiveDirectoryArgs']]]]]=None, location: Optional[pulumi.Input[str]]=None, resource_group_name: Optio... |
@staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'Account':
"\n Get an existing Account resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique ... | 329,630,109,003,327,500 | Get an existing Account resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for... | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | get | pulumi-bot/pulumi-azure-native | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'Account':
"\n Get an existing Account resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique ... |
@property
@pulumi.getter(name='activeDirectories')
def active_directories(self) -> pulumi.Output[Optional[Sequence['outputs.ActiveDirectoryResponse']]]:
'\n Active Directories\n '
return pulumi.get(self, 'active_directories') | 6,275,772,879,752,033,000 | Active Directories | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | active_directories | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='activeDirectories')
def active_directories(self) -> pulumi.Output[Optional[Sequence['outputs.ActiveDirectoryResponse']]]:
'\n \n '
return pulumi.get(self, 'active_directories') |
@property
@pulumi.getter
def location(self) -> pulumi.Output[str]:
'\n Resource location\n '
return pulumi.get(self, 'location') | 2,974,713,878,710,662,000 | Resource location | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | location | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def location(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'location') |
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n Resource name\n '
return pulumi.get(self, 'name') | 387,709,723,693,576,260 | Resource name | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | name | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name') |
@property
@pulumi.getter(name='provisioningState')
def provisioning_state(self) -> pulumi.Output[str]:
'\n Azure lifecycle management\n '
return pulumi.get(self, 'provisioning_state') | 5,814,604,552,307,744,000 | Azure lifecycle management | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | provisioning_state | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='provisioningState')
def provisioning_state(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'provisioning_state') |
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n Resource tags\n '
return pulumi.get(self, 'tags') | -1,239,552,863,427,208,400 | Resource tags | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | tags | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'tags') |
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n Resource type\n '
return pulumi.get(self, 'type') | 8,967,421,614,257,702,000 | Resource type | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | type | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'type') |
def parse_input(input, inflv, starttime, endtime):
'Read simulations data from input file.\n\n Arguments:\n input -- prefix of file containing neutrino fluxes\n inflv -- neutrino flavor to consider\n starttime -- start time set by user via command line option (or None)\n endtime -- end time set by us... | 6,570,633,104,090,349,000 | Read simulations data from input file.
Arguments:
input -- prefix of file containing neutrino fluxes
inflv -- neutrino flavor to consider
starttime -- start time set by user via command line option (or None)
endtime -- end time set by user via command line option (or None) | sntools/formats/warren2020.py | parse_input | arfon/sntools | python | def parse_input(input, inflv, starttime, endtime):
'Read simulations data from input file.\n\n Arguments:\n input -- prefix of file containing neutrino fluxes\n inflv -- neutrino flavor to consider\n starttime -- start time set by user via command line option (or None)\n endtime -- end time set by us... |
def testEzsignformfieldResponseCompound(self):
'Test EzsignformfieldResponseCompound'
pass | -4,861,070,669,607,094,000 | Test EzsignformfieldResponseCompound | test/test_ezsignformfield_response_compound.py | testEzsignformfieldResponseCompound | eZmaxinc/eZmax-SDK-python | python | def testEzsignformfieldResponseCompound(self):
pass |
@patch('regulations.apps.get_app_template_dirs')
def test_precompute_custom_templates(self, get_app_template_dirs):
'Verify that custom templates are found'
get_app_template_dirs.return_value = [self.tmpdir]
open(os.path.join(self.tmpdir, '123-45-a.html'), 'w').close()
open(os.path.join(self.tmpdir, 'ot... | -4,249,644,129,594,510,300 | Verify that custom templates are found | regulations/tests/apps_tests.py | test_precompute_custom_templates | CMSgov/cmcs-eregulations | python | @patch('regulations.apps.get_app_template_dirs')
def test_precompute_custom_templates(self, get_app_template_dirs):
get_app_template_dirs.return_value = [self.tmpdir]
open(os.path.join(self.tmpdir, '123-45-a.html'), 'w').close()
open(os.path.join(self.tmpdir, 'other.html'), 'w').close()
Regulations... |
def uvc_return_mapping(x_sol, data, tol=1e-08, maximum_iterations=1000):
" Implements the time integration of the updated Voce-Chaboche material model.\n\n :param np.array x_sol: Updated Voce-Chaboche model parameters.\n :param pd.DataFrame data: stress-strain data.\n :param float tol: Local Newton toleran... | -8,363,361,874,546,954,000 | Implements the time integration of the updated Voce-Chaboche material model.
:param np.array x_sol: Updated Voce-Chaboche model parameters.
:param pd.DataFrame data: stress-strain data.
:param float tol: Local Newton tolerance.
:param int maximum_iterations: maximum iterations in local Newton procedure, raises Runtime... | RESSPyLab/uvc_model.py | uvc_return_mapping | AlbanoCastroSousa/RESSPyLab | python | def uvc_return_mapping(x_sol, data, tol=1e-08, maximum_iterations=1000):
" Implements the time integration of the updated Voce-Chaboche material model.\n\n :param np.array x_sol: Updated Voce-Chaboche model parameters.\n :param pd.DataFrame data: stress-strain data.\n :param float tol: Local Newton toleran... |
def sim_curve_uvc(x_sol, test_clean):
' Returns the stress-strain approximation of the updated Voce-Chaboche material model to a given strain input.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return DataFrame: Voce-Chaboche approximation\n... | 3,410,126,839,265,906,700 | Returns the stress-strain approximation of the updated Voce-Chaboche material model to a given strain input.
:param np.array x_sol: Voce-Chaboche model parameters
:param DataFrame test_clean: stress-strain data
:return DataFrame: Voce-Chaboche approximation
The strain column in the DataFrame is labeled "e_true" and t... | RESSPyLab/uvc_model.py | sim_curve_uvc | AlbanoCastroSousa/RESSPyLab | python | def sim_curve_uvc(x_sol, test_clean):
' Returns the stress-strain approximation of the updated Voce-Chaboche material model to a given strain input.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return DataFrame: Voce-Chaboche approximation\n... |
def error_single_test_uvc(x_sol, test_clean):
' Returns the relative error between a test and its approximation using the updated Voce-Chaboche material model.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return float: relative error\n\n ... | -6,505,289,781,695,587,000 | Returns the relative error between a test and its approximation using the updated Voce-Chaboche material model.
:param np.array x_sol: Voce-Chaboche model parameters
:param DataFrame test_clean: stress-strain data
:return float: relative error
The strain column in the DataFrame is labeled "e_true" and the stress colu... | RESSPyLab/uvc_model.py | error_single_test_uvc | AlbanoCastroSousa/RESSPyLab | python | def error_single_test_uvc(x_sol, test_clean):
' Returns the relative error between a test and its approximation using the updated Voce-Chaboche material model.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return float: relative error\n\n ... |
def normalized_error_single_test_uvc(x_sol, test_clean):
' Returns the error and the total area of a test and its approximation using the updated Voce-Chaboche\n material model.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return list: (f... | 1,769,212,009,327,486,500 | Returns the error and the total area of a test and its approximation using the updated Voce-Chaboche
material model.
:param np.array x_sol: Voce-Chaboche model parameters
:param DataFrame test_clean: stress-strain data
:return list: (float) total error, (float) total area
The strain column in the DataFrame is labeled... | RESSPyLab/uvc_model.py | normalized_error_single_test_uvc | AlbanoCastroSousa/RESSPyLab | python | def normalized_error_single_test_uvc(x_sol, test_clean):
' Returns the error and the total area of a test and its approximation using the updated Voce-Chaboche\n material model.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return list: (f... |
def calc_phi_total(x, data):
' Returns the sum of the normalized relative error of the updated Voce-Chaboche material model given x.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: No... | -7,501,822,167,166,433,000 | Returns the sum of the normalized relative error of the updated Voce-Chaboche material model given x.
:param np.array x: Updated Voce-Chaboche material model parameters.
:param list data: (pd.DataFrame) Stress-strain history for each test considered.
:return float: Normalized error value expressed as a percent (raw va... | RESSPyLab/uvc_model.py | calc_phi_total | AlbanoCastroSousa/RESSPyLab | python | def calc_phi_total(x, data):
' Returns the sum of the normalized relative error of the updated Voce-Chaboche material model given x.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: No... |
def test_total_area(x, data):
' Returns the total squared area underneath all the tests.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: Total squared area.\n '
area_total = 0.... | -5,041,924,756,357,932,000 | Returns the total squared area underneath all the tests.
:param np.array x: Updated Voce-Chaboche material model parameters.
:param list data: (pd.DataFrame) Stress-strain history for each test considered.
:return float: Total squared area. | RESSPyLab/uvc_model.py | test_total_area | AlbanoCastroSousa/RESSPyLab | python | def test_total_area(x, data):
' Returns the total squared area underneath all the tests.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: Total squared area.\n '
area_total = 0.... |
def uvc_get_hessian(x, data):
' Returns the Hessian of the material model error function for a given set of test data evaluated at x.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return np.array... | -5,182,262,053,579,384,000 | Returns the Hessian of the material model error function for a given set of test data evaluated at x.
:param np.array x: Updated Voce-Chaboche material model parameters.
:param list data: (pd.DataFrame) Stress-strain history for each test considered.
:return np.array: Hessian matrix of the error function. | RESSPyLab/uvc_model.py | uvc_get_hessian | AlbanoCastroSousa/RESSPyLab | python | def uvc_get_hessian(x, data):
' Returns the Hessian of the material model error function for a given set of test data evaluated at x.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return np.array... |
def uvc_consistency_metric(x_base, x_sample, data):
' Returns the xi_2 consistency metric from de Sousa and Lignos 2019 using the updated Voce-Chaboche model.\n\n :param np.array x_base: Updated Voce-Chaboche material model parameters from the base case.\n :param np.array x_sample: Updated Voce-Chaboche mater... | 7,123,153,927,627,399,000 | Returns the xi_2 consistency metric from de Sousa and Lignos 2019 using the updated Voce-Chaboche model.
:param np.array x_base: Updated Voce-Chaboche material model parameters from the base case.
:param np.array x_sample: Updated Voce-Chaboche material model parameters from the sample case.
:param list data: (pd.Data... | RESSPyLab/uvc_model.py | uvc_consistency_metric | AlbanoCastroSousa/RESSPyLab | python | def uvc_consistency_metric(x_base, x_sample, data):
' Returns the xi_2 consistency metric from de Sousa and Lignos 2019 using the updated Voce-Chaboche model.\n\n :param np.array x_base: Updated Voce-Chaboche material model parameters from the base case.\n :param np.array x_sample: Updated Voce-Chaboche mater... |
def uvc_tangent_modulus(x_sol, data, tol=1e-08, maximum_iterations=1000):
' Returns the tangent modulus at each strain step.\n\n :param np.array x_sol: Updated Voce-Chaboche model parameters.\n :param pd.DataFrame data: stress-strain data.\n :param float tol: Local Newton tolerance.\n :param int maximum... | 5,687,772,783,232,525,000 | Returns the tangent modulus at each strain step.
:param np.array x_sol: Updated Voce-Chaboche model parameters.
:param pd.DataFrame data: stress-strain data.
:param float tol: Local Newton tolerance.
:param int maximum_iterations: maximum iterations in local Newton procedure, raises RuntimeError if exceeded.
:return n... | RESSPyLab/uvc_model.py | uvc_tangent_modulus | AlbanoCastroSousa/RESSPyLab | python | def uvc_tangent_modulus(x_sol, data, tol=1e-08, maximum_iterations=1000):
' Returns the tangent modulus at each strain step.\n\n :param np.array x_sol: Updated Voce-Chaboche model parameters.\n :param pd.DataFrame data: stress-strain data.\n :param float tol: Local Newton tolerance.\n :param int maximum... |
def get_wiki_references(url, outfile=None):
'get_wiki_references.\n Extracts references from predefined sections of wiki page\n Uses `urlscan`, `refextract`, `doi`, `wikipedia`, and `re` (for ArXiv URLs)\n\n :param url: URL of wiki article to scrape\n :param outfile: File to write extracted references t... | 1,990,428,418,421,912,600 | get_wiki_references.
Extracts references from predefined sections of wiki page
Uses `urlscan`, `refextract`, `doi`, `wikipedia`, and `re` (for ArXiv URLs)
:param url: URL of wiki article to scrape
:param outfile: File to write extracted references to | scraper/apis/wikipedia.py | get_wiki_references | antimike/citation-scraper | python | def get_wiki_references(url, outfile=None):
'get_wiki_references.\n Extracts references from predefined sections of wiki page\n Uses `urlscan`, `refextract`, `doi`, `wikipedia`, and `re` (for ArXiv URLs)\n\n :param url: URL of wiki article to scrape\n :param outfile: File to write extracted references t... |
def asm_and_link_one_file(asm_path: str, work_dir: str) -> str:
'Assemble and link file at asm_path in work_dir.\n\n Returns the path to the resulting ELF\n\n '
otbn_as = os.path.join(UTIL_DIR, 'otbn-as')
otbn_ld = os.path.join(UTIL_DIR, 'otbn-ld')
obj_path = os.path.join(work_dir, 'tst.o')
el... | -372,252,728,031,894,140 | Assemble and link file at asm_path in work_dir.
Returns the path to the resulting ELF | hw/ip/otbn/dv/otbnsim/test/testutil.py | asm_and_link_one_file | OneToughMonkey/opentitan | python | def asm_and_link_one_file(asm_path: str, work_dir: str) -> str:
'Assemble and link file at asm_path in work_dir.\n\n Returns the path to the resulting ELF\n\n '
otbn_as = os.path.join(UTIL_DIR, 'otbn-as')
otbn_ld = os.path.join(UTIL_DIR, 'otbn-ld')
obj_path = os.path.join(work_dir, 'tst.o')
el... |
def find_two_smallest(L: List[float]) -> Tuple[(int, int)]:
' (see above) '
smallest = min(L)
min1 = L.index(smallest)
L.remove(smallest)
next_smallest = min(L)
min2 = L.index(next_smallest)
L.insert(min1, smallest)
if (min1 <= min2):
min2 += 1
return (min1, min2) | -1,861,280,632,368,825,900 | (see above) | chapter12/examples/example02.py | find_two_smallest | YordanIH/Intro_to_CS_w_Python | python | def find_two_smallest(L: List[float]) -> Tuple[(int, int)]:
' '
smallest = min(L)
min1 = L.index(smallest)
L.remove(smallest)
next_smallest = min(L)
min2 = L.index(next_smallest)
L.insert(min1, smallest)
if (min1 <= min2):
min2 += 1
return (min1, min2) |
def paddedInt(i):
"\n return a string that contains `i`, left-padded with 0's up to PAD_LEN digits\n "
i_str = str(i)
pad = (PAD_LEN - len(i_str))
return ((pad * '0') + i_str) | -4,372,382,450,324,855,300 | return a string that contains `i`, left-padded with 0's up to PAD_LEN digits | credstash.py | paddedInt | traveloka/credstash | python | def paddedInt(i):
"\n \n "
i_str = str(i)
pad = (PAD_LEN - len(i_str))
return ((pad * '0') + i_str) |
def getHighestVersion(name, region='us-east-1', table='credential-store'):
'\n Return the highest version of `name` in the table\n '
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
response = secrets.query(Limit=1, ScanIndexForward=False, ConsistentRead=Tr... | 6,380,276,000,185,197,000 | Return the highest version of `name` in the table | credstash.py | getHighestVersion | traveloka/credstash | python | def getHighestVersion(name, region='us-east-1', table='credential-store'):
'\n \n '
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
response = secrets.query(Limit=1, ScanIndexForward=False, ConsistentRead=True, KeyConditionExpression=boto3.dynamodb.conditi... |
def listSecrets(region='us-east-1', table='credential-store'):
'\n do a full-table scan of the credential-store,\n and return the names and versions of every credential\n '
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
response = secrets.scan(Projecti... | -3,835,120,575,174,796,300 | do a full-table scan of the credential-store,
and return the names and versions of every credential | credstash.py | listSecrets | traveloka/credstash | python | def listSecrets(region='us-east-1', table='credential-store'):
'\n do a full-table scan of the credential-store,\n and return the names and versions of every credential\n '
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
response = secrets.scan(Projecti... |
def putSecret(name, secret, version, kms_key='alias/credstash', region='us-east-1', table='credential-store', context=None):
'\n put a secret called `name` into the secret-store,\n protected by the key kms_key\n '
if (not context):
context = {}
kms = boto3.client('kms', region_name=region)
... | -7,699,812,481,823,265,000 | put a secret called `name` into the secret-store,
protected by the key kms_key | credstash.py | putSecret | traveloka/credstash | python | def putSecret(name, secret, version, kms_key='alias/credstash', region='us-east-1', table='credential-store', context=None):
'\n put a secret called `name` into the secret-store,\n protected by the key kms_key\n '
if (not context):
context = {}
kms = boto3.client('kms', region_name=region)
... |
def getAllSecrets(version='', region='us-east-1', table='credential-store', context=None):
'\n fetch and decrypt all secrets\n '
output = {}
secrets = listSecrets(region, table)
for credential in set([x['name'] for x in secrets]):
try:
output[credential] = getSecret(credential,... | 7,797,601,393,189,596,000 | fetch and decrypt all secrets | credstash.py | getAllSecrets | traveloka/credstash | python | def getAllSecrets(version=, region='us-east-1', table='credential-store', context=None):
'\n \n '
output = {}
secrets = listSecrets(region, table)
for credential in set([x['name'] for x in secrets]):
try:
output[credential] = getSecret(credential, version, region, table, contex... |
def getSecret(name, version='', region='us-east-1', table='credential-store', context=None):
'\n fetch and decrypt the secret called `name`\n '
if (not context):
context = {}
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
if (version == ''):
... | 622,606,273,363,065,900 | fetch and decrypt the secret called `name` | credstash.py | getSecret | traveloka/credstash | python | def getSecret(name, version=, region='us-east-1', table='credential-store', context=None):
'\n \n '
if (not context):
context = {}
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
if (version == ):
response = secrets.query(Limit=1, ScanI... |
def createDdbTable(region='us-east-1', table='credential-store'):
'\n create the secret store table in DDB in the specified region\n '
dynamodb = boto3.resource('dynamodb', region_name=region)
if (table in (t.name for t in dynamodb.tables.all())):
print('Credential Store table already exists')... | 5,070,826,915,824,553,000 | create the secret store table in DDB in the specified region | credstash.py | createDdbTable | traveloka/credstash | python | def createDdbTable(region='us-east-1', table='credential-store'):
'\n \n '
dynamodb = boto3.resource('dynamodb', region_name=region)
if (table in (t.name for t in dynamodb.tables.all())):
print('Credential Store table already exists')
return
print('Creating table...')
response ... |
def make_layer(self, out_channels, num_blocks, stride, expand_ratio):
'Stack InvertedResidual blocks to build a layer for MobileNetV2.\n\n Args:\n out_channels (int): out_channels of block.\n num_blocks (int): number of blocks.\n stride (int): stride of the first block. Defau... | 6,643,845,954,223,003,000 | Stack InvertedResidual blocks to build a layer for MobileNetV2.
Args:
out_channels (int): out_channels of block.
num_blocks (int): number of blocks.
stride (int): stride of the first block. Default: 1
expand_ratio (int): Expand the number of channels of the
hidden layer in InvertedResidual by t... | mmcls/models/backbones/mobilenet_v2.py | make_layer | ChaseMonsterAway/mmclassification | python | def make_layer(self, out_channels, num_blocks, stride, expand_ratio):
'Stack InvertedResidual blocks to build a layer for MobileNetV2.\n\n Args:\n out_channels (int): out_channels of block.\n num_blocks (int): number of blocks.\n stride (int): stride of the first block. Defau... |
def __init__(self, code):
"Initialize a PDBFile object with a pdb file of interest\n\n Parameters\n ----------\n code : the pdb code if interest\n Any valid PDB code can be passed into PDBFile.\n\n Examples\n --------\n >>> pdb_file = PDBFile('1rcy') \n \... | 835,532,312,311,867,000 | Initialize a PDBFile object with a pdb file of interest
Parameters
----------
code : the pdb code if interest
Any valid PDB code can be passed into PDBFile.
Examples
--------
>>> pdb_file = PDBFile('1rcy') | scalene-triangle/libs/PDB_filegetter.py | __init__ | dsw7/BridgingInteractions | python | def __init__(self, code):
"Initialize a PDBFile object with a pdb file of interest\n\n Parameters\n ----------\n code : the pdb code if interest\n Any valid PDB code can be passed into PDBFile.\n\n Examples\n --------\n >>> pdb_file = PDBFile('1rcy') \n \... |
def fetch_from_PDB(self):
"\n Connects to PDB FTP server, downloads a .gz file of interest,\n decompresses the .gz file into .ent and then dumps a copy of\n the pdb{code}.ent file into cwd.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n \n... | 5,381,435,870,021,593,000 | Connects to PDB FTP server, downloads a .gz file of interest,
decompresses the .gz file into .ent and then dumps a copy of
the pdb{code}.ent file into cwd.
Parameters
----------
None
Examples
--------
>>> inst = PDBFile('1rcy')
>>> path_to_file = inst.fetch_from_PDB()
>>> print(path_to_file) | scalene-triangle/libs/PDB_filegetter.py | fetch_from_PDB | dsw7/BridgingInteractions | python | def fetch_from_PDB(self):
"\n Connects to PDB FTP server, downloads a .gz file of interest,\n decompresses the .gz file into .ent and then dumps a copy of\n the pdb{code}.ent file into cwd.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n \n... |
def clear(self):
"\n Deletes file from current working directory after the file has\n been processed by some algorithm.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n >>> inst = PDBFile('1rcy')\n >>> path_to_file = inst.fetch_from_PDB()\n ... | 8,477,879,807,243,158,000 | Deletes file from current working directory after the file has
been processed by some algorithm.
Parameters
----------
None
Examples
--------
>>> inst = PDBFile('1rcy')
>>> path_to_file = inst.fetch_from_PDB()
>>> print(path_to_file) # process the file using some algorithm
>>> inst.clear() | scalene-triangle/libs/PDB_filegetter.py | clear | dsw7/BridgingInteractions | python | def clear(self):
"\n Deletes file from current working directory after the file has\n been processed by some algorithm.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n >>> inst = PDBFile('1rcy')\n >>> path_to_file = inst.fetch_from_PDB()\n ... |
def gen_captcha_text_image(self, img_name):
'\n 返回一个验证码的array形式和对应的字符串标签\n :return:tuple (str, numpy.array)\n '
label = img_name.split('_')[0]
img_file = os.path.join(self.img_path, img_name)
captcha_image = Image.open(img_file)
captcha_array = np.array(captcha_image)
return... | 7,944,805,907,609,061,000 | 返回一个验证码的array形式和对应的字符串标签
:return:tuple (str, numpy.array) | train_model.py | gen_captcha_text_image | shineyjg/cnn_captcha | python | def gen_captcha_text_image(self, img_name):
'\n 返回一个验证码的array形式和对应的字符串标签\n :return:tuple (str, numpy.array)\n '
label = img_name.split('_')[0]
img_file = os.path.join(self.img_path, img_name)
captcha_image = Image.open(img_file)
captcha_array = np.array(captcha_image)
return... |
@staticmethod
def convert2gray(img):
'\n 图片转为灰度图，如果是3通道图则计算，单通道图则直接返回\n :param img:\n :return:\n '
if (len(img.shape) > 2):
(r, g, b) = (img[:, :, 0], img[:, :, 1], img[:, :, 2])
gray = (((0.2989 * r) + (0.587 * g)) + (0.114 * b))
return gray
else:
... | 611,634,753,502,825,900 | 图片转为灰度图，如果是3通道图则计算，单通道图则直接返回
:param img:
:return: | train_model.py | convert2gray | shineyjg/cnn_captcha | python | @staticmethod
def convert2gray(img):
'\n 图片转为灰度图，如果是3通道图则计算，单通道图则直接返回\n :param img:\n :return:\n '
if (len(img.shape) > 2):
(r, g, b) = (img[:, :, 0], img[:, :, 1], img[:, :, 2])
gray = (((0.2989 * r) + (0.587 * g)) + (0.114 * b))
return gray
else:
... |
def text2vec(self, text):
'\n 转标签为oneHot编码\n :param text: str\n :return: numpy.array\n '
text_len = len(text)
if (text_len > self.max_captcha):
raise ValueError('验证码最长{}个字符'.format(self.max_captcha))
vector = np.zeros((self.max_captcha * self.char_set_len))
for (i... | -1,980,550,115,108,716,800 | 转标签为oneHot编码
:param text: str
:return: numpy.array | train_model.py | text2vec | shineyjg/cnn_captcha | python | def text2vec(self, text):
'\n 转标签为oneHot编码\n :param text: str\n :return: numpy.array\n '
text_len = len(text)
if (text_len > self.max_captcha):
raise ValueError('验证码最长{}个字符'.format(self.max_captcha))
vector = np.zeros((self.max_captcha * self.char_set_len))
for (i... |
def get_converter(from_unit, to_unit):
'Like Unit._get_converter, except returns None if no scaling is needed,\n i.e., if the inferred scale is unity.'
try:
scale = from_unit._to(to_unit)
except UnitsError:
return from_unit._apply_equivalencies(from_unit, to_unit, get_current_unit_registr... | 6,356,987,915,934,134,000 | Like Unit._get_converter, except returns None if no scaling is needed,
i.e., if the inferred scale is unity. | astropy/units/quantity_helper/helpers.py | get_converter | PriyankaH21/astropy | python | def get_converter(from_unit, to_unit):
'Like Unit._get_converter, except returns None if no scaling is needed,\n i.e., if the inferred scale is unity.'
try:
scale = from_unit._to(to_unit)
except UnitsError:
return from_unit._apply_equivalencies(from_unit, to_unit, get_current_unit_registr... |
def _raw_fetch(url, logger):
'\n Fetch remote data and return the text output.\n\n :param url: The URL to fetch the data from\n :param logger: A logger instance to use.\n :return: Raw text data, None otherwise\n '
ret_data = None
try:
req = requests.get(url)
if (req.status_cod... | -894,493,403,224,933,800 | Fetch remote data and return the text output.
:param url: The URL to fetch the data from
:param logger: A logger instance to use.
:return: Raw text data, None otherwise | atkinson/dlrn/http_data.py | _raw_fetch | jpichon/atkinson | python | def _raw_fetch(url, logger):
'\n Fetch remote data and return the text output.\n\n :param url: The URL to fetch the data from\n :param logger: A logger instance to use.\n :return: Raw text data, None otherwise\n '
ret_data = None
try:
req = requests.get(url)
if (req.status_cod... |
def _fetch_yaml(url, logger):
'\n Fetch remote data and process the text as yaml.\n\n :param url: The URL to fetch the data from\n :param logger: A logger instance to use.\n :return: Parsed yaml data in the form of a dictionary\n '
ret_data = None
raw_data = _raw_fetch(url, logger)
if (ra... | -3,088,369,978,945,365,500 | Fetch remote data and process the text as yaml.
:param url: The URL to fetch the data from
:param logger: A logger instance to use.
:return: Parsed yaml data in the form of a dictionary | atkinson/dlrn/http_data.py | _fetch_yaml | jpichon/atkinson | python | def _fetch_yaml(url, logger):
'\n Fetch remote data and process the text as yaml.\n\n :param url: The URL to fetch the data from\n :param logger: A logger instance to use.\n :return: Parsed yaml data in the form of a dictionary\n '
ret_data = None
raw_data = _raw_fetch(url, logger)
if (ra... |
def dlrn_http_factory(host, config_file=None, link_name=None, logger=getLogger()):
'\n Create a DlrnData instance based on a host.\n\n :param host: A host name string to build instances\n :param config_file: A dlrn config file(s) to use in addition to\n the default.\n :param link_... | -4,437,842,762,096,356,400 | Create a DlrnData instance based on a host.
:param host: A host name string to build instances
:param config_file: A dlrn config file(s) to use in addition to
the default.
:param link_name: A dlrn symlink to use. This overrides the config files
link parameter.
:param logger: An at... | atkinson/dlrn/http_data.py | dlrn_http_factory | jpichon/atkinson | python | def dlrn_http_factory(host, config_file=None, link_name=None, logger=getLogger()):
'\n Create a DlrnData instance based on a host.\n\n :param host: A host name string to build instances\n :param config_file: A dlrn config file(s) to use in addition to\n the default.\n :param link_... |
def __init__(self, url, release, link_name='current', logger=getLogger()):
'\n Class constructor\n\n :param url: The URL to the host to obtain data.\n :param releases: The release name to use for lookup.\n :param link_name: The name of the dlrn symlink to fetch data from.\n :param... | -1,853,492,324,126,466,600 | Class constructor
:param url: The URL to the host to obtain data.
:param releases: The release name to use for lookup.
:param link_name: The name of the dlrn symlink to fetch data from.
:param logger: An atkinson logger to use. Default is the base logger. | atkinson/dlrn/http_data.py | __init__ | jpichon/atkinson | python | def __init__(self, url, release, link_name='current', logger=getLogger()):
'\n Class constructor\n\n :param url: The URL to the host to obtain data.\n :param releases: The release name to use for lookup.\n :param link_name: The name of the dlrn symlink to fetch data from.\n :param... |
def _fetch_commit(self):
'\n Fetch the commit data from dlrn\n '
full_url = os.path.join(self.url, self._link_name, 'commit.yaml')
data = _fetch_yaml(full_url, self._logger)
if ((data is not None) and ('commits' in data)):
pkg = data['commits'][0]
if (pkg['status'] == 'SUCC... | 6,997,459,630,592,828,000 | Fetch the commit data from dlrn | atkinson/dlrn/http_data.py | _fetch_commit | jpichon/atkinson | python | def _fetch_commit(self):
'\n \n '
full_url = os.path.join(self.url, self._link_name, 'commit.yaml')
data = _fetch_yaml(full_url, self._logger)
if ((data is not None) and ('commits' in data)):
pkg = data['commits'][0]
if (pkg['status'] == 'SUCCESS'):
self._commit... |
def _build_url(self):
'\n Generate a url given a commit hash and distgit hash to match the format\n base/AB/CD/ABCD123_XYZ987 where ABCD123 is the commit hash and XYZ987\n is a portion of the distgit hash.\n\n :return: A string with the full URL.\n '
first = self._commit_data[... | -3,125,452,940,105,935,000 | Generate a url given a commit hash and distgit hash to match the format
base/AB/CD/ABCD123_XYZ987 where ABCD123 is the commit hash and XYZ987
is a portion of the distgit hash.
:return: A string with the full URL. | atkinson/dlrn/http_data.py | _build_url | jpichon/atkinson | python | def _build_url(self):
'\n Generate a url given a commit hash and distgit hash to match the format\n base/AB/CD/ABCD123_XYZ987 where ABCD123 is the commit hash and XYZ987\n is a portion of the distgit hash.\n\n :return: A string with the full URL.\n '
first = self._commit_data[... |
@property
def commit(self):
'\n Get the dlrn commit information\n\n :return: A dictionary of name, dist-git hash, commit hash and\n extended hash.\n An empty dictionary is returned otherwise.\n '
return self._commit_data | -1,729,170,792,126,949,000 | Get the dlrn commit information
:return: A dictionary of name, dist-git hash, commit hash and
extended hash.
An empty dictionary is returned otherwise. | atkinson/dlrn/http_data.py | commit | jpichon/atkinson | python | @property
def commit(self):
'\n Get the dlrn commit information\n\n :return: A dictionary of name, dist-git hash, commit hash and\n extended hash.\n An empty dictionary is returned otherwise.\n '
return self._commit_data |
@property
def versions(self):
'\n Get the version data for the versions.csv file and return the\n data in a dictionary\n\n :return: A dictionary of packages with commit and dist-git hashes\n '
ret_dict = {}
full_url = os.path.join(self._build_url(), 'versions.csv')
data = _ra... | -7,811,259,190,884,229,000 | Get the version data for the versions.csv file and return the
data in a dictionary
:return: A dictionary of packages with commit and dist-git hashes | atkinson/dlrn/http_data.py | versions | jpichon/atkinson | python | @property
def versions(self):
'\n Get the version data for the versions.csv file and return the\n data in a dictionary\n\n :return: A dictionary of packages with commit and dist-git hashes\n '
ret_dict = {}
full_url = os.path.join(self._build_url(), 'versions.csv')
data = _ra... |
def compute_train_val_test(X, y, model, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True):
"\n Compute the training-validation-test scores for the given model on the given dataset.\n\n The training and test scores are simply computed by splitting the dataset into the train... | -5,066,045,042,697,431,000 | Compute the training-validation-test scores for the given model on the given dataset.
The training and test scores are simply computed by splitting the dataset into the training and test sets. The validation
score is performed applying the cross validation on the training set.
Parameters
----------
X: np.array
Tw... | model_selection.py | compute_train_val_test | EnricoPittini/model-selection | python | def compute_train_val_test(X, y, model, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True):
"\n Compute the training-validation-test scores for the given model on the given dataset.\n\n The training and test scores are simply computed by splitting the dataset into the train... |
def compute_bias_variance_error(X, y, model, scale=False, N_TESTS=20, sample_size=0.67):
'\n Compute the bias^2-variance-error scores for the given model on the given dataset.\n\n These measures are computed in an approximate way, using `N_TESTS` random samples of size `sample_size` from the\n dataset.\n\n... | 1,135,176,463,303,326,600 | Compute the bias^2-variance-error scores for the given model on the given dataset.
These measures are computed in an approximate way, using `N_TESTS` random samples of size `sample_size` from the
dataset.
Parameters
----------
X: np.array
Two-dimensional np.array, containing the explanatory features of the datase... | model_selection.py | compute_bias_variance_error | EnricoPittini/model-selection | python | def compute_bias_variance_error(X, y, model, scale=False, N_TESTS=20, sample_size=0.67):
'\n Compute the bias^2-variance-error scores for the given model on the given dataset.\n\n These measures are computed in an approximate way, using `N_TESTS` random samples of size `sample_size` from the\n dataset.\n\n... |
def plot_predictions(X, y, model, scale=False, test_size=0.2, plot_type=0, xvalues=None, xlabel='Index', title='Actual vs Predicted values', figsize=(6, 6)):
"\n Plot the predictions made by the given model on the given dataset, versus its actual values.\n\n The dataset is split into training-test sets: the f... | 6,549,853,644,879,781,000 | Plot the predictions made by the given model on the given dataset, versus its actual values.
The dataset is split into training-test sets: the former is used to train the `model`, on the latter the predictions are
made.
Parameters
----------
X: np.array
Two-dimensional np.array, containing the explanatory feature... | model_selection.py | plot_predictions | EnricoPittini/model-selection | python | def plot_predictions(X, y, model, scale=False, test_size=0.2, plot_type=0, xvalues=None, xlabel='Index', title='Actual vs Predicted values', figsize=(6, 6)):
"\n Plot the predictions made by the given model on the given dataset, versus its actual values.\n\n The dataset is split into training-test sets: the f... |
def _plot_TrainVal_values(xvalues, train_val_scores, plot_train, xlabel, title, figsize=(6, 6), bar=False):
"\n Plot the given list of training-validation scores.\n\n This function is an auxiliary function for the model selection functions. It's meant to be private in the\n module.\n\n Parameters\n -... | -2,627,312,043,539,120,600 | Plot the given list of training-validation scores.
This function is an auxiliary function for the model selection functions. It's meant to be private in the
module.
Parameters
----------
xvalues: list (in general iterable)
Values to put in the x axis of the plot.
train_val_scores: np.array
Two dimensional np.... | model_selection.py | _plot_TrainVal_values | EnricoPittini/model-selection | python | def _plot_TrainVal_values(xvalues, train_val_scores, plot_train, xlabel, title, figsize=(6, 6), bar=False):
"\n Plot the given list of training-validation scores.\n\n This function is an auxiliary function for the model selection functions. It's meant to be private in the\n module.\n\n Parameters\n -... |
def hyperparameter_validation(X, y, model, hyperparameter, hyperparameter_values, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel=None, title='Hyperparameter validation', figsize=(6, 6)):
"\n Select the best value for the s... | -3,417,247,821,585,341,400 | Select the best value for the specified hyperparameter of the specified model on the given dataset.
In other words, perform the tuning of the `hyperparameter` among the values in `hyperparameter_values`.
This selection is made using the validation score (i.e. the best hyperparameter value is the one with the best val... | model_selection.py | hyperparameter_validation | EnricoPittini/model-selection | python | def hyperparameter_validation(X, y, model, hyperparameter, hyperparameter_values, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel=None, title='Hyperparameter validation', figsize=(6, 6)):
"\n Select the best value for the s... |
def hyperparameters_validation(X, y, model, param_grid, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True):
"\n Select the best combination of values for the specified hyperparameters of the specified model on the given dataset.\n\n In other words, perform the tuning of mul... | -5,705,085,024,780,375,000 | Select the best combination of values for the specified hyperparameters of the specified model on the given dataset.
In other words, perform the tuning of multiple hyperparameters.
The parameter `param_grid` is a dictionary that indicates which are the specified hyperparameters and what are the
associated values to te... | model_selection.py | hyperparameters_validation | EnricoPittini/model-selection | python | def hyperparameters_validation(X, y, model, param_grid, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True):
"\n Select the best combination of values for the specified hyperparameters of the specified model on the given dataset.\n\n In other words, perform the tuning of mul... |
def models_validation(X, y, model_paramGrid_list, scale_list=None, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Models', title='Models validation', figsize=(6, 6)):
"\n Select the best model on the given dataset.\n\n The paramete... | -7,523,235,934,046,416,000 | Select the best model on the given dataset.
The parameter `model_paramGrid_list` is the list of the models to test. It also contains, for each model, the grid of
hyperparameters that have to be tested on that model (i.e. the grid which contains the values to test for each
specified hyperparameter of the model).
(That ... | model_selection.py | models_validation | EnricoPittini/model-selection | python | def models_validation(X, y, model_paramGrid_list, scale_list=None, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Models', title='Models validation', figsize=(6, 6)):
"\n Select the best model on the given dataset.\n\n The paramete... |
def datasets_hyperparameter_validation(dataset_list, model, hyperparameter, hyperparameter_values, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6), verbose=False, figsize_ver... | -8,298,506,111,670,680,000 | Select the best dataset and the best value for the specified hyperparameter of the specified model (i.e. select the best
couple dataset-hyperparameter value).
For each dataset in `dataset_list`, all the specified values `hyperparameter_values` are tested for the specified
`hyperparameter` of `model`.
In other words, o... | model_selection.py | datasets_hyperparameter_validation | EnricoPittini/model-selection | python | def datasets_hyperparameter_validation(dataset_list, model, hyperparameter, hyperparameter_values, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6), verbose=False, figsize_ver... |
def datasets_hyperparameters_validation(dataset_list, model, param_grid, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6)):
"\n Select the best dataset and the best com... | -182,712,746,719,899,140 | Select the best dataset and the best combination of values for the specified hyperparameters of the specified model (i.e.
select the best couple dataset-combination of hyperparameters values).
For each dataset in `dataset_list`, all the possible combinations of the hyperparameters values for `model` (specified
with `p... | model_selection.py | datasets_hyperparameters_validation | EnricoPittini/model-selection | python | def datasets_hyperparameters_validation(dataset_list, model, param_grid, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6)):
"\n Select the best dataset and the best com... |
def datasets_models_validation(dataset_list, model_paramGrid_list, scale_list=None, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6), verbose=False, figsize_verbose=(6, 6)):
"\n Sel... | 2,050,898,115,793,827,300 | Select the best dataset and the best model (i.e. select the best couple dataset-model).
For each dataset in `dataset_list`, all the models in `model_paramGrid_list` are tested: each model is tested performing
an exhaustive tuning of the specified hyperparameters. In fact, `model_paramGrid_list` also contains, for each... | model_selection.py | datasets_models_validation | EnricoPittini/model-selection | python | def datasets_models_validation(dataset_list, model_paramGrid_list, scale_list=None, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6), verbose=False, figsize_verbose=(6, 6)):
"\n Sel... |
def append(self, other):
' append the recarrays from one MfList to another\n Parameters\n ----------\n other: variable: an item that can be cast in to an MfList\n that corresponds with self\n Returns\n -------\n dict of {kper:recarray}\n '
... | 3,458,584,039,420,723,000 | append the recarrays from one MfList to another
Parameters
----------
other: variable: an item that can be cast in to an MfList
that corresponds with self
Returns
-------
dict of {kper:recarray} | flopy/utils/util_list.py | append | aleaf/flopy | python | def append(self, other):
' append the recarrays from one MfList to another\n Parameters\n ----------\n other: variable: an item that can be cast in to an MfList\n that corresponds with self\n Returns\n -------\n dict of {kper:recarray}\n '
... |
def drop(self, fields):
'drop fields from an MfList\n\n Parameters\n ----------\n fields : list or set of field names to drop\n\n Returns\n -------\n dropped : MfList without the dropped fields\n '
if (not isinstance(fields, list)):
fields = [fields]
... | 1,912,237,700,778,697,200 | drop fields from an MfList
Parameters
----------
fields : list or set of field names to drop
Returns
-------
dropped : MfList without the dropped fields | flopy/utils/util_list.py | drop | aleaf/flopy | python | def drop(self, fields):
'drop fields from an MfList\n\n Parameters\n ----------\n fields : list or set of field names to drop\n\n Returns\n -------\n dropped : MfList without the dropped fields\n '
if (not isinstance(fields, list)):
fields = [fields]
... |
@property
def fmt_string(self):
'Returns a C-style fmt string for numpy savetxt that corresponds to\n the dtype'
if (self.list_free_format is not None):
use_free = self.list_free_format
else:
use_free = True
if self.package.parent.has_package('bas6'):
use_free = se... | 8,351,358,389,882,369,000 | Returns a C-style fmt string for numpy savetxt that corresponds to
the dtype | flopy/utils/util_list.py | fmt_string | aleaf/flopy | python | @property
def fmt_string(self):
'Returns a C-style fmt string for numpy savetxt that corresponds to\n the dtype'
if (self.list_free_format is not None):
use_free = self.list_free_format
else:
use_free = True
if self.package.parent.has_package('bas6'):
use_free = se... |
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