body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
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571b59323833c280e77b16239e63bdcdded2b8237b795111a87ad8a655b28909 | def log_step(func=None, *, time_taken=True, shape=True, shape_delta=False, names=False, dtypes=False, print_fn=print, display_args=True):
'\n Decorates a function that transforms a pandas dataframe to add automated logging statements\n\n :param func: callable, function to log, defaults to None\n :param time_taken: bool, log the time it took to run a function, defaults to True\n :param shape: bool, log the shape of the output result, defaults to True\n :param shape_delta: bool, log the difference in shape of input and output, defaults to False\n :param names: bool, log the names of the columns of the result, defaults to False\n :param dtypes: bool, log the dtypes of the results, defaults to False\n :param print_fn: callable, print function (e.g. print or logger.info), defaults to print\n :param print_args: bool, whether or not to print the arguments given to the function.\n :returns: the result of the function\n\n :Example:\n >>> @log_step\n ... def remove_outliers(df, min_obs=5):\n ... pass\n\n >>> @log_step(print_fn=logging.info, shape_delta=True)\n ... def remove_outliers(df, min_obs=5):\n ... pass\n\n '
if (func is None):
return partial(log_step, time_taken=time_taken, shape=shape, shape_delta=shape_delta, names=names, dtypes=dtypes, print_fn=print_fn, display_args=display_args)
names = (False if dtypes else names)
@wraps(func)
def wrapper(*args, **kwargs):
if shape_delta:
old_shape = args[0].shape
tic = dt.datetime.now()
result = func(*args, **kwargs)
optional_strings = [(f'time={(dt.datetime.now() - tic)}' if time_taken else None), (f'n_obs={result.shape[0]}, n_col={result.shape[1]}' if shape else None), (_get_shape_delta(old_shape, result.shape) if shape_delta else None), (f'names={result.columns.to_list()}' if names else None), (f'dtypes={result.dtypes.to_dict()}' if dtypes else None)]
combined = ' '.join([s for s in optional_strings if s])
if display_args:
func_args = inspect.signature(func).bind(*args, **kwargs).arguments
func_args_str = ''.join((', {} = {!r}'.format(*item) for item in list(func_args.items())[1:]))
print_fn((f'[{func.__name__}(df{func_args_str})] ' + combined))
else:
print_fn((f'[{func.__name__}]' + combined))
return result
return wrapper | Decorates a function that transforms a pandas dataframe to add automated logging statements
:param func: callable, function to log, defaults to None
:param time_taken: bool, log the time it took to run a function, defaults to True
:param shape: bool, log the shape of the output result, defaults to True
:param shape_delta: bool, log the difference in shape of input and output, defaults to False
:param names: bool, log the names of the columns of the result, defaults to False
:param dtypes: bool, log the dtypes of the results, defaults to False
:param print_fn: callable, print function (e.g. print or logger.info), defaults to print
:param print_args: bool, whether or not to print the arguments given to the function.
:returns: the result of the function
:Example:
>>> @log_step
... def remove_outliers(df, min_obs=5):
... pass
>>> @log_step(print_fn=logging.info, shape_delta=True)
... def remove_outliers(df, min_obs=5):
... pass | sklego/pandas_utils.py | log_step | quendee/scikit-lego | 784 | python | def log_step(func=None, *, time_taken=True, shape=True, shape_delta=False, names=False, dtypes=False, print_fn=print, display_args=True):
'\n Decorates a function that transforms a pandas dataframe to add automated logging statements\n\n :param func: callable, function to log, defaults to None\n :param time_taken: bool, log the time it took to run a function, defaults to True\n :param shape: bool, log the shape of the output result, defaults to True\n :param shape_delta: bool, log the difference in shape of input and output, defaults to False\n :param names: bool, log the names of the columns of the result, defaults to False\n :param dtypes: bool, log the dtypes of the results, defaults to False\n :param print_fn: callable, print function (e.g. print or logger.info), defaults to print\n :param print_args: bool, whether or not to print the arguments given to the function.\n :returns: the result of the function\n\n :Example:\n >>> @log_step\n ... def remove_outliers(df, min_obs=5):\n ... pass\n\n >>> @log_step(print_fn=logging.info, shape_delta=True)\n ... def remove_outliers(df, min_obs=5):\n ... pass\n\n '
if (func is None):
return partial(log_step, time_taken=time_taken, shape=shape, shape_delta=shape_delta, names=names, dtypes=dtypes, print_fn=print_fn, display_args=display_args)
names = (False if dtypes else names)
@wraps(func)
def wrapper(*args, **kwargs):
if shape_delta:
old_shape = args[0].shape
tic = dt.datetime.now()
result = func(*args, **kwargs)
optional_strings = [(f'time={(dt.datetime.now() - tic)}' if time_taken else None), (f'n_obs={result.shape[0]}, n_col={result.shape[1]}' if shape else None), (_get_shape_delta(old_shape, result.shape) if shape_delta else None), (f'names={result.columns.to_list()}' if names else None), (f'dtypes={result.dtypes.to_dict()}' if dtypes else None)]
combined = ' '.join([s for s in optional_strings if s])
if display_args:
func_args = inspect.signature(func).bind(*args, **kwargs).arguments
func_args_str = .join((', {} = {!r}'.format(*item) for item in list(func_args.items())[1:]))
print_fn((f'[{func.__name__}(df{func_args_str})] ' + combined))
else:
print_fn((f'[{func.__name__}]' + combined))
return result
return wrapper | def log_step(func=None, *, time_taken=True, shape=True, shape_delta=False, names=False, dtypes=False, print_fn=print, display_args=True):
'\n Decorates a function that transforms a pandas dataframe to add automated logging statements\n\n :param func: callable, function to log, defaults to None\n :param time_taken: bool, log the time it took to run a function, defaults to True\n :param shape: bool, log the shape of the output result, defaults to True\n :param shape_delta: bool, log the difference in shape of input and output, defaults to False\n :param names: bool, log the names of the columns of the result, defaults to False\n :param dtypes: bool, log the dtypes of the results, defaults to False\n :param print_fn: callable, print function (e.g. print or logger.info), defaults to print\n :param print_args: bool, whether or not to print the arguments given to the function.\n :returns: the result of the function\n\n :Example:\n >>> @log_step\n ... def remove_outliers(df, min_obs=5):\n ... pass\n\n >>> @log_step(print_fn=logging.info, shape_delta=True)\n ... def remove_outliers(df, min_obs=5):\n ... pass\n\n '
if (func is None):
return partial(log_step, time_taken=time_taken, shape=shape, shape_delta=shape_delta, names=names, dtypes=dtypes, print_fn=print_fn, display_args=display_args)
names = (False if dtypes else names)
@wraps(func)
def wrapper(*args, **kwargs):
if shape_delta:
old_shape = args[0].shape
tic = dt.datetime.now()
result = func(*args, **kwargs)
optional_strings = [(f'time={(dt.datetime.now() - tic)}' if time_taken else None), (f'n_obs={result.shape[0]}, n_col={result.shape[1]}' if shape else None), (_get_shape_delta(old_shape, result.shape) if shape_delta else None), (f'names={result.columns.to_list()}' if names else None), (f'dtypes={result.dtypes.to_dict()}' if dtypes else None)]
combined = ' '.join([s for s in optional_strings if s])
if display_args:
func_args = inspect.signature(func).bind(*args, **kwargs).arguments
func_args_str = .join((', {} = {!r}'.format(*item) for item in list(func_args.items())[1:]))
print_fn((f'[{func.__name__}(df{func_args_str})] ' + combined))
else:
print_fn((f'[{func.__name__}]' + combined))
return result
return wrapper<|docstring|>Decorates a function that transforms a pandas dataframe to add automated logging statements
:param func: callable, function to log, defaults to None
:param time_taken: bool, log the time it took to run a function, defaults to True
:param shape: bool, log the shape of the output result, defaults to True
:param shape_delta: bool, log the difference in shape of input and output, defaults to False
:param names: bool, log the names of the columns of the result, defaults to False
:param dtypes: bool, log the dtypes of the results, defaults to False
:param print_fn: callable, print function (e.g. print or logger.info), defaults to print
:param print_args: bool, whether or not to print the arguments given to the function.
:returns: the result of the function
:Example:
>>> @log_step
... def remove_outliers(df, min_obs=5):
... pass
>>> @log_step(print_fn=logging.info, shape_delta=True)
... def remove_outliers(df, min_obs=5):
... pass<|endoftext|> |
d132f35d1d6e8faaf988753d606eb8248be16cace6b6601e51a0bdab1c067f43 | def log_step_extra(*log_functions, print_fn=print, **log_func_kwargs):
'\n Decorates a function that transforms a pandas dataframe to add automated logging statements\n\n :param *log_functions: callable(s), functions that take the output of the decorated function and turn it into a log.\n Note that the output of each log_function is casted to string using `str()`\n :param print_fn: callable, print function (e.g. print or logger.info), defaults to print\n :param **log_func_kwargs: keyword arguments to be passed to log_functions\n :returns: the result of the function\n\n :Example:\n >>> @log_step_extra(lambda d: d["some_column"].value_counts())\n ... def remove_outliers(df, min_obs=5):\n ... pass\n\n '
if (not log_functions):
raise ValueError('Supply at least one log_function for log_step_extra')
def _log_step_extra(func):
@wraps(func)
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
func_args = inspect.signature(func).bind(*args, **kwargs).arguments
func_args_str = ''.join((', {} = {!r}'.format(*item) for item in list(func_args.items())[1:]))
try:
extra_logs = ' '.join([str(log_f(result, **log_func_kwargs)) for log_f in log_functions])
except TypeError:
raise ValueError(f'All log functions should be callable, got {[type(log_f) for log_f in log_functions]}')
print_fn((f'[{func.__name__}(df{func_args_str})] ' + extra_logs))
return result
return wrapper
return _log_step_extra | Decorates a function that transforms a pandas dataframe to add automated logging statements
:param *log_functions: callable(s), functions that take the output of the decorated function and turn it into a log.
Note that the output of each log_function is casted to string using `str()`
:param print_fn: callable, print function (e.g. print or logger.info), defaults to print
:param **log_func_kwargs: keyword arguments to be passed to log_functions
:returns: the result of the function
:Example:
>>> @log_step_extra(lambda d: d["some_column"].value_counts())
... def remove_outliers(df, min_obs=5):
... pass | sklego/pandas_utils.py | log_step_extra | quendee/scikit-lego | 784 | python | def log_step_extra(*log_functions, print_fn=print, **log_func_kwargs):
'\n Decorates a function that transforms a pandas dataframe to add automated logging statements\n\n :param *log_functions: callable(s), functions that take the output of the decorated function and turn it into a log.\n Note that the output of each log_function is casted to string using `str()`\n :param print_fn: callable, print function (e.g. print or logger.info), defaults to print\n :param **log_func_kwargs: keyword arguments to be passed to log_functions\n :returns: the result of the function\n\n :Example:\n >>> @log_step_extra(lambda d: d["some_column"].value_counts())\n ... def remove_outliers(df, min_obs=5):\n ... pass\n\n '
if (not log_functions):
raise ValueError('Supply at least one log_function for log_step_extra')
def _log_step_extra(func):
@wraps(func)
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
func_args = inspect.signature(func).bind(*args, **kwargs).arguments
func_args_str = .join((', {} = {!r}'.format(*item) for item in list(func_args.items())[1:]))
try:
extra_logs = ' '.join([str(log_f(result, **log_func_kwargs)) for log_f in log_functions])
except TypeError:
raise ValueError(f'All log functions should be callable, got {[type(log_f) for log_f in log_functions]}')
print_fn((f'[{func.__name__}(df{func_args_str})] ' + extra_logs))
return result
return wrapper
return _log_step_extra | def log_step_extra(*log_functions, print_fn=print, **log_func_kwargs):
'\n Decorates a function that transforms a pandas dataframe to add automated logging statements\n\n :param *log_functions: callable(s), functions that take the output of the decorated function and turn it into a log.\n Note that the output of each log_function is casted to string using `str()`\n :param print_fn: callable, print function (e.g. print or logger.info), defaults to print\n :param **log_func_kwargs: keyword arguments to be passed to log_functions\n :returns: the result of the function\n\n :Example:\n >>> @log_step_extra(lambda d: d["some_column"].value_counts())\n ... def remove_outliers(df, min_obs=5):\n ... pass\n\n '
if (not log_functions):
raise ValueError('Supply at least one log_function for log_step_extra')
def _log_step_extra(func):
@wraps(func)
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
func_args = inspect.signature(func).bind(*args, **kwargs).arguments
func_args_str = .join((', {} = {!r}'.format(*item) for item in list(func_args.items())[1:]))
try:
extra_logs = ' '.join([str(log_f(result, **log_func_kwargs)) for log_f in log_functions])
except TypeError:
raise ValueError(f'All log functions should be callable, got {[type(log_f) for log_f in log_functions]}')
print_fn((f'[{func.__name__}(df{func_args_str})] ' + extra_logs))
return result
return wrapper
return _log_step_extra<|docstring|>Decorates a function that transforms a pandas dataframe to add automated logging statements
:param *log_functions: callable(s), functions that take the output of the decorated function and turn it into a log.
Note that the output of each log_function is casted to string using `str()`
:param print_fn: callable, print function (e.g. print or logger.info), defaults to print
:param **log_func_kwargs: keyword arguments to be passed to log_functions
:returns: the result of the function
:Example:
>>> @log_step_extra(lambda d: d["some_column"].value_counts())
... def remove_outliers(df, min_obs=5):
... pass<|endoftext|> |
10c29218950658761c2747209a14f760386b1ff27630bf0f795bb03706984e26 | def add_lags(X, cols, lags, drop_na=True):
"\n Appends lag column(s).\n\n :param X: array-like, shape=(n_columns, n_samples,) training data.\n :param cols: column name(s) or index (indices).\n :param lags: the amount of lag for each col.\n :param drop_na: remove rows that contain NA values.\n :returns: ``pd.DataFrame | np.ndarray`` with only the selected cols.\n\n :Example:\n\n >>> import pandas as pd\n >>> df = pd.DataFrame([[1, 2, 3],\n ... [4, 5, 6],\n ... [7, 8, 9]],\n ... columns=['a', 'b', 'c'],\n ... index=[1, 2, 3])\n\n >>> add_lags(df, 'a', [1]) # doctest: +NORMALIZE_WHITESPACE\n a b c a1\n 1 1 2 3 4.0\n 2 4 5 6 7.0\n\n >>> add_lags(df, ['a', 'b'], 2) # doctest: +NORMALIZE_WHITESPACE\n a b c a2 b2\n 1 1 2 3 7.0 8.0\n\n >>> import numpy as np\n >>> X = np.array([[1, 2, 3],\n ... [4, 5, 6],\n ... [7, 8, 9]])\n\n >>> add_lags(X, 0, [1])\n array([[1, 2, 3, 4],\n [4, 5, 6, 7]])\n\n >>> add_lags(X, 1, [-1, 1])\n array([[4, 5, 6, 2, 8]])\n "
lags = as_list(lags)
if (not all((isinstance(x, int) for x in lags))):
raise ValueError(('lags must be a list of type: ' + str(int)))
allowed_inputs = {pd.core.frame.DataFrame: _add_lagged_pandas_columns, np.ndarray: _add_lagged_numpy_columns}
for (allowed_input, handler) in allowed_inputs.items():
if isinstance(X, allowed_input):
return handler(X, cols, lags, drop_na)
allowed_input_names = list(allowed_inputs.keys())
raise ValueError('X type should be one of:', allowed_input_names) | Appends lag column(s).
:param X: array-like, shape=(n_columns, n_samples,) training data.
:param cols: column name(s) or index (indices).
:param lags: the amount of lag for each col.
:param drop_na: remove rows that contain NA values.
:returns: ``pd.DataFrame | np.ndarray`` with only the selected cols.
:Example:
>>> import pandas as pd
>>> df = pd.DataFrame([[1, 2, 3],
... [4, 5, 6],
... [7, 8, 9]],
... columns=['a', 'b', 'c'],
... index=[1, 2, 3])
>>> add_lags(df, 'a', [1]) # doctest: +NORMALIZE_WHITESPACE
a b c a1
1 1 2 3 4.0
2 4 5 6 7.0
>>> add_lags(df, ['a', 'b'], 2) # doctest: +NORMALIZE_WHITESPACE
a b c a2 b2
1 1 2 3 7.0 8.0
>>> import numpy as np
>>> X = np.array([[1, 2, 3],
... [4, 5, 6],
... [7, 8, 9]])
>>> add_lags(X, 0, [1])
array([[1, 2, 3, 4],
[4, 5, 6, 7]])
>>> add_lags(X, 1, [-1, 1])
array([[4, 5, 6, 2, 8]]) | sklego/pandas_utils.py | add_lags | quendee/scikit-lego | 784 | python | def add_lags(X, cols, lags, drop_na=True):
"\n Appends lag column(s).\n\n :param X: array-like, shape=(n_columns, n_samples,) training data.\n :param cols: column name(s) or index (indices).\n :param lags: the amount of lag for each col.\n :param drop_na: remove rows that contain NA values.\n :returns: ``pd.DataFrame | np.ndarray`` with only the selected cols.\n\n :Example:\n\n >>> import pandas as pd\n >>> df = pd.DataFrame([[1, 2, 3],\n ... [4, 5, 6],\n ... [7, 8, 9]],\n ... columns=['a', 'b', 'c'],\n ... index=[1, 2, 3])\n\n >>> add_lags(df, 'a', [1]) # doctest: +NORMALIZE_WHITESPACE\n a b c a1\n 1 1 2 3 4.0\n 2 4 5 6 7.0\n\n >>> add_lags(df, ['a', 'b'], 2) # doctest: +NORMALIZE_WHITESPACE\n a b c a2 b2\n 1 1 2 3 7.0 8.0\n\n >>> import numpy as np\n >>> X = np.array([[1, 2, 3],\n ... [4, 5, 6],\n ... [7, 8, 9]])\n\n >>> add_lags(X, 0, [1])\n array([[1, 2, 3, 4],\n [4, 5, 6, 7]])\n\n >>> add_lags(X, 1, [-1, 1])\n array([[4, 5, 6, 2, 8]])\n "
lags = as_list(lags)
if (not all((isinstance(x, int) for x in lags))):
raise ValueError(('lags must be a list of type: ' + str(int)))
allowed_inputs = {pd.core.frame.DataFrame: _add_lagged_pandas_columns, np.ndarray: _add_lagged_numpy_columns}
for (allowed_input, handler) in allowed_inputs.items():
if isinstance(X, allowed_input):
return handler(X, cols, lags, drop_na)
allowed_input_names = list(allowed_inputs.keys())
raise ValueError('X type should be one of:', allowed_input_names) | def add_lags(X, cols, lags, drop_na=True):
"\n Appends lag column(s).\n\n :param X: array-like, shape=(n_columns, n_samples,) training data.\n :param cols: column name(s) or index (indices).\n :param lags: the amount of lag for each col.\n :param drop_na: remove rows that contain NA values.\n :returns: ``pd.DataFrame | np.ndarray`` with only the selected cols.\n\n :Example:\n\n >>> import pandas as pd\n >>> df = pd.DataFrame([[1, 2, 3],\n ... [4, 5, 6],\n ... [7, 8, 9]],\n ... columns=['a', 'b', 'c'],\n ... index=[1, 2, 3])\n\n >>> add_lags(df, 'a', [1]) # doctest: +NORMALIZE_WHITESPACE\n a b c a1\n 1 1 2 3 4.0\n 2 4 5 6 7.0\n\n >>> add_lags(df, ['a', 'b'], 2) # doctest: +NORMALIZE_WHITESPACE\n a b c a2 b2\n 1 1 2 3 7.0 8.0\n\n >>> import numpy as np\n >>> X = np.array([[1, 2, 3],\n ... [4, 5, 6],\n ... [7, 8, 9]])\n\n >>> add_lags(X, 0, [1])\n array([[1, 2, 3, 4],\n [4, 5, 6, 7]])\n\n >>> add_lags(X, 1, [-1, 1])\n array([[4, 5, 6, 2, 8]])\n "
lags = as_list(lags)
if (not all((isinstance(x, int) for x in lags))):
raise ValueError(('lags must be a list of type: ' + str(int)))
allowed_inputs = {pd.core.frame.DataFrame: _add_lagged_pandas_columns, np.ndarray: _add_lagged_numpy_columns}
for (allowed_input, handler) in allowed_inputs.items():
if isinstance(X, allowed_input):
return handler(X, cols, lags, drop_na)
allowed_input_names = list(allowed_inputs.keys())
raise ValueError('X type should be one of:', allowed_input_names)<|docstring|>Appends lag column(s).
:param X: array-like, shape=(n_columns, n_samples,) training data.
:param cols: column name(s) or index (indices).
:param lags: the amount of lag for each col.
:param drop_na: remove rows that contain NA values.
:returns: ``pd.DataFrame | np.ndarray`` with only the selected cols.
:Example:
>>> import pandas as pd
>>> df = pd.DataFrame([[1, 2, 3],
... [4, 5, 6],
... [7, 8, 9]],
... columns=['a', 'b', 'c'],
... index=[1, 2, 3])
>>> add_lags(df, 'a', [1]) # doctest: +NORMALIZE_WHITESPACE
a b c a1
1 1 2 3 4.0
2 4 5 6 7.0
>>> add_lags(df, ['a', 'b'], 2) # doctest: +NORMALIZE_WHITESPACE
a b c a2 b2
1 1 2 3 7.0 8.0
>>> import numpy as np
>>> X = np.array([[1, 2, 3],
... [4, 5, 6],
... [7, 8, 9]])
>>> add_lags(X, 0, [1])
array([[1, 2, 3, 4],
[4, 5, 6, 7]])
>>> add_lags(X, 1, [-1, 1])
array([[4, 5, 6, 2, 8]])<|endoftext|> |
936436f385a6e55885f282e43809d08976e9a47982f0b9704f8cc9dcf24b9dfb | def _add_lagged_numpy_columns(X, cols, lags, drop_na):
'\n Append a lag columns.\n\n :param df: the input ``np.ndarray``.\n :param cols: column index / indices.\n :param drop_na: remove rows that contain NA values.\n :returns: ``np.ndarray`` with the concatenated lagged cols.\n '
cols = as_list(cols)
if (not all([isinstance(col, int) for col in cols])):
raise ValueError('Matrix columns are indexed by integers')
if (not all([(col < X.shape[1]) for col in cols])):
raise KeyError('The column does not exist')
combos = (shift(X[(:, col)], (- lag), cval=np.NaN) for col in cols for lag in lags)
original_type = X.dtype
X = np.asarray(X, dtype=float)
answer = np.column_stack((X, *combos))
if drop_na:
answer = answer[(~ np.isnan(answer).any(axis=1))]
answer = np.asarray(answer, dtype=original_type)
return answer | Append a lag columns.
:param df: the input ``np.ndarray``.
:param cols: column index / indices.
:param drop_na: remove rows that contain NA values.
:returns: ``np.ndarray`` with the concatenated lagged cols. | sklego/pandas_utils.py | _add_lagged_numpy_columns | quendee/scikit-lego | 784 | python | def _add_lagged_numpy_columns(X, cols, lags, drop_na):
'\n Append a lag columns.\n\n :param df: the input ``np.ndarray``.\n :param cols: column index / indices.\n :param drop_na: remove rows that contain NA values.\n :returns: ``np.ndarray`` with the concatenated lagged cols.\n '
cols = as_list(cols)
if (not all([isinstance(col, int) for col in cols])):
raise ValueError('Matrix columns are indexed by integers')
if (not all([(col < X.shape[1]) for col in cols])):
raise KeyError('The column does not exist')
combos = (shift(X[(:, col)], (- lag), cval=np.NaN) for col in cols for lag in lags)
original_type = X.dtype
X = np.asarray(X, dtype=float)
answer = np.column_stack((X, *combos))
if drop_na:
answer = answer[(~ np.isnan(answer).any(axis=1))]
answer = np.asarray(answer, dtype=original_type)
return answer | def _add_lagged_numpy_columns(X, cols, lags, drop_na):
'\n Append a lag columns.\n\n :param df: the input ``np.ndarray``.\n :param cols: column index / indices.\n :param drop_na: remove rows that contain NA values.\n :returns: ``np.ndarray`` with the concatenated lagged cols.\n '
cols = as_list(cols)
if (not all([isinstance(col, int) for col in cols])):
raise ValueError('Matrix columns are indexed by integers')
if (not all([(col < X.shape[1]) for col in cols])):
raise KeyError('The column does not exist')
combos = (shift(X[(:, col)], (- lag), cval=np.NaN) for col in cols for lag in lags)
original_type = X.dtype
X = np.asarray(X, dtype=float)
answer = np.column_stack((X, *combos))
if drop_na:
answer = answer[(~ np.isnan(answer).any(axis=1))]
answer = np.asarray(answer, dtype=original_type)
return answer<|docstring|>Append a lag columns.
:param df: the input ``np.ndarray``.
:param cols: column index / indices.
:param drop_na: remove rows that contain NA values.
:returns: ``np.ndarray`` with the concatenated lagged cols.<|endoftext|> |
946e0be359113958377f3e3cf677f9ebfed9869aa260c324780807d63da6da8d | def _add_lagged_pandas_columns(df, cols, lags, drop_na):
'\n Append a lag columns.\n\n :param df: the input ``pd.DataFrame``.\n :param cols: column name(s).\n :param drop_na: remove rows that contain NA values.\n :returns: ``pd.DataFrame`` with the concatenated lagged cols.\n '
cols = as_list(cols)
if (not all([(col in df.columns.values) for col in cols])):
raise KeyError('The column does not exist')
combos = (df[col].shift((- lag)).rename((col + str(lag))) for col in cols for lag in lags)
answer = pd.concat([df, *combos], axis=1)
if drop_na:
answer = answer.dropna()
return answer | Append a lag columns.
:param df: the input ``pd.DataFrame``.
:param cols: column name(s).
:param drop_na: remove rows that contain NA values.
:returns: ``pd.DataFrame`` with the concatenated lagged cols. | sklego/pandas_utils.py | _add_lagged_pandas_columns | quendee/scikit-lego | 784 | python | def _add_lagged_pandas_columns(df, cols, lags, drop_na):
'\n Append a lag columns.\n\n :param df: the input ``pd.DataFrame``.\n :param cols: column name(s).\n :param drop_na: remove rows that contain NA values.\n :returns: ``pd.DataFrame`` with the concatenated lagged cols.\n '
cols = as_list(cols)
if (not all([(col in df.columns.values) for col in cols])):
raise KeyError('The column does not exist')
combos = (df[col].shift((- lag)).rename((col + str(lag))) for col in cols for lag in lags)
answer = pd.concat([df, *combos], axis=1)
if drop_na:
answer = answer.dropna()
return answer | def _add_lagged_pandas_columns(df, cols, lags, drop_na):
'\n Append a lag columns.\n\n :param df: the input ``pd.DataFrame``.\n :param cols: column name(s).\n :param drop_na: remove rows that contain NA values.\n :returns: ``pd.DataFrame`` with the concatenated lagged cols.\n '
cols = as_list(cols)
if (not all([(col in df.columns.values) for col in cols])):
raise KeyError('The column does not exist')
combos = (df[col].shift((- lag)).rename((col + str(lag))) for col in cols for lag in lags)
answer = pd.concat([df, *combos], axis=1)
if drop_na:
answer = answer.dropna()
return answer<|docstring|>Append a lag columns.
:param df: the input ``pd.DataFrame``.
:param cols: column name(s).
:param drop_na: remove rows that contain NA values.
:returns: ``pd.DataFrame`` with the concatenated lagged cols.<|endoftext|> |
39f9bee3cf3510ceeb7655b86801c064e2647bdb3293adc249ecc467ced7c9d2 | def test_simple(self):
'\n This test reads in a small number of particles and verifies the result of one of the particles.\n '
with open(os.path.join(RESOURCE_PATH, 'first_data.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
particles = parser.get_records(4)
self.assert_particles(particles, 'first_four.yml', RESOURCE_PATH) | This test reads in a small number of particles and verifies the result of one of the particles. | mi/dataset/parser/test/test_vel3d_a_mmp_cds.py | test_simple | krosburg/mi-instrument | 1 | python | def test_simple(self):
'\n \n '
with open(os.path.join(RESOURCE_PATH, 'first_data.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
particles = parser.get_records(4)
self.assert_particles(particles, 'first_four.yml', RESOURCE_PATH) | def test_simple(self):
'\n \n '
with open(os.path.join(RESOURCE_PATH, 'first_data.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
particles = parser.get_records(4)
self.assert_particles(particles, 'first_four.yml', RESOURCE_PATH)<|docstring|>This test reads in a small number of particles and verifies the result of one of the particles.<|endoftext|> |
0efc9a7703f8e9dd52b68ec233946074b9bcbd3a8092f6fdb5f1975e52c0329f | def test_get_many(self):
'\n This test exercises retrieving 20 particles, verifying the particles, then retrieves 30 particles\n and verifies the 30 particles.\n '
with open(os.path.join(RESOURCE_PATH, 'first_data.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
particles = parser.get_records(193)
self.assertTrue((len(particles) == 193))
self.assert_particles(particles, 'first_data.yml', RESOURCE_PATH) | This test exercises retrieving 20 particles, verifying the particles, then retrieves 30 particles
and verifies the 30 particles. | mi/dataset/parser/test/test_vel3d_a_mmp_cds.py | test_get_many | krosburg/mi-instrument | 1 | python | def test_get_many(self):
'\n This test exercises retrieving 20 particles, verifying the particles, then retrieves 30 particles\n and verifies the 30 particles.\n '
with open(os.path.join(RESOURCE_PATH, 'first_data.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
particles = parser.get_records(193)
self.assertTrue((len(particles) == 193))
self.assert_particles(particles, 'first_data.yml', RESOURCE_PATH) | def test_get_many(self):
'\n This test exercises retrieving 20 particles, verifying the particles, then retrieves 30 particles\n and verifies the 30 particles.\n '
with open(os.path.join(RESOURCE_PATH, 'first_data.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
particles = parser.get_records(193)
self.assertTrue((len(particles) == 193))
self.assert_particles(particles, 'first_data.yml', RESOURCE_PATH)<|docstring|>This test exercises retrieving 20 particles, verifying the particles, then retrieves 30 particles
and verifies the 30 particles.<|endoftext|> |
6c71e0967e626fcc329d789db0a67c18faad95be22bbe33ab9544439d804484c | def test_long_stream(self):
'\n This test exercises retrieve approximately 200 particles.\n '
with open(os.path.join(RESOURCE_PATH, 'acm_concat.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
particles = parser.get_records(400)
self.assertTrue((len(particles) == 386)) | This test exercises retrieve approximately 200 particles. | mi/dataset/parser/test/test_vel3d_a_mmp_cds.py | test_long_stream | krosburg/mi-instrument | 1 | python | def test_long_stream(self):
'\n \n '
with open(os.path.join(RESOURCE_PATH, 'acm_concat.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
particles = parser.get_records(400)
self.assertTrue((len(particles) == 386)) | def test_long_stream(self):
'\n \n '
with open(os.path.join(RESOURCE_PATH, 'acm_concat.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
particles = parser.get_records(400)
self.assertTrue((len(particles) == 386))<|docstring|>This test exercises retrieve approximately 200 particles.<|endoftext|> |
3d7057d518119a3d48304efdd9456ee31d2b1d529c751067af7af110fe0ddc5b | def test_bad_data_one(self):
'\n This test verifies that a SampleException is raised when msgpack data is malformed.\n '
with open(os.path.join(RESOURCE_PATH, 'acm_1_20131124T005004_458-BAD.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
parser.get_records(1)
self.assertEqual(len(self.exception_callback_value), 1)
self.assert_(isinstance(self.exception_callback_value[0], SampleException)) | This test verifies that a SampleException is raised when msgpack data is malformed. | mi/dataset/parser/test/test_vel3d_a_mmp_cds.py | test_bad_data_one | krosburg/mi-instrument | 1 | python | def test_bad_data_one(self):
'\n \n '
with open(os.path.join(RESOURCE_PATH, 'acm_1_20131124T005004_458-BAD.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
parser.get_records(1)
self.assertEqual(len(self.exception_callback_value), 1)
self.assert_(isinstance(self.exception_callback_value[0], SampleException)) | def test_bad_data_one(self):
'\n \n '
with open(os.path.join(RESOURCE_PATH, 'acm_1_20131124T005004_458-BAD.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
parser.get_records(1)
self.assertEqual(len(self.exception_callback_value), 1)
self.assert_(isinstance(self.exception_callback_value[0], SampleException))<|docstring|>This test verifies that a SampleException is raised when msgpack data is malformed.<|endoftext|> |
bcaa6e969cca6d7a8929cd67d0a495ecbe5b81a50402ce6a2e04199b0466471a | def test_bad_data_two(self):
'\n This test verifies that a SampleException is raised when an entire msgpack buffer is not msgpack.\n '
with open(os.path.join(RESOURCE_PATH, 'not-msg-pack.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
parser.get_records(1)
self.assertTrue((len(self.exception_callback_value) >= 1))
self.assert_(isinstance(self.exception_callback_value[0], SampleException)) | This test verifies that a SampleException is raised when an entire msgpack buffer is not msgpack. | mi/dataset/parser/test/test_vel3d_a_mmp_cds.py | test_bad_data_two | krosburg/mi-instrument | 1 | python | def test_bad_data_two(self):
'\n \n '
with open(os.path.join(RESOURCE_PATH, 'not-msg-pack.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
parser.get_records(1)
self.assertTrue((len(self.exception_callback_value) >= 1))
self.assert_(isinstance(self.exception_callback_value[0], SampleException)) | def test_bad_data_two(self):
'\n \n '
with open(os.path.join(RESOURCE_PATH, 'not-msg-pack.mpk'), 'rb') as stream_handle:
parser = MmpCdsParser(self.config, stream_handle, self.exception_callback)
parser.get_records(1)
self.assertTrue((len(self.exception_callback_value) >= 1))
self.assert_(isinstance(self.exception_callback_value[0], SampleException))<|docstring|>This test verifies that a SampleException is raised when an entire msgpack buffer is not msgpack.<|endoftext|> |
ef26aa11fcbf953bec35573720a746617a46b7eae190f642f2f2693af385e854 | def calculateBearing(coord1, coord2):
'\n calculates the azimuth in degrees from start point to end point\n\n :param coord1: start point [lat, lng]\n :param coord2: end point [lat, lng]\n :rtype: azimuth in degrees\n '
startLat = math.radians(coord1[0])
startLong = math.radians(coord1[1])
endLat = math.radians(coord2[0])
endLong = math.radians(coord2[1])
dLong = (endLong - startLong)
dPhi = math.log((math.tan(((endLat / 2.0) + (math.pi / 4.0))) / math.tan(((startLat / 2.0) + (math.pi / 4.0)))))
if (abs(dLong) > math.pi):
if (dLong > 0.0):
dLong = (- ((2.0 * math.pi) - dLong))
else:
dLong = ((2.0 * math.pi) + dLong)
bearing = ((math.degrees(math.atan2(dLong, dPhi)) + 360.0) % 360.0)
return bearing | calculates the azimuth in degrees from start point to end point
:param coord1: start point [lat, lng]
:param coord2: end point [lat, lng]
:rtype: azimuth in degrees | loutilities/geo.py | calculateBearing | louking/loutilities | 1 | python | def calculateBearing(coord1, coord2):
'\n calculates the azimuth in degrees from start point to end point\n\n :param coord1: start point [lat, lng]\n :param coord2: end point [lat, lng]\n :rtype: azimuth in degrees\n '
startLat = math.radians(coord1[0])
startLong = math.radians(coord1[1])
endLat = math.radians(coord2[0])
endLong = math.radians(coord2[1])
dLong = (endLong - startLong)
dPhi = math.log((math.tan(((endLat / 2.0) + (math.pi / 4.0))) / math.tan(((startLat / 2.0) + (math.pi / 4.0)))))
if (abs(dLong) > math.pi):
if (dLong > 0.0):
dLong = (- ((2.0 * math.pi) - dLong))
else:
dLong = ((2.0 * math.pi) + dLong)
bearing = ((math.degrees(math.atan2(dLong, dPhi)) + 360.0) % 360.0)
return bearing | def calculateBearing(coord1, coord2):
'\n calculates the azimuth in degrees from start point to end point\n\n :param coord1: start point [lat, lng]\n :param coord2: end point [lat, lng]\n :rtype: azimuth in degrees\n '
startLat = math.radians(coord1[0])
startLong = math.radians(coord1[1])
endLat = math.radians(coord2[0])
endLong = math.radians(coord2[1])
dLong = (endLong - startLong)
dPhi = math.log((math.tan(((endLat / 2.0) + (math.pi / 4.0))) / math.tan(((startLat / 2.0) + (math.pi / 4.0)))))
if (abs(dLong) > math.pi):
if (dLong > 0.0):
dLong = (- ((2.0 * math.pi) - dLong))
else:
dLong = ((2.0 * math.pi) + dLong)
bearing = ((math.degrees(math.atan2(dLong, dPhi)) + 360.0) % 360.0)
return bearing<|docstring|>calculates the azimuth in degrees from start point to end point
:param coord1: start point [lat, lng]
:param coord2: end point [lat, lng]
:rtype: azimuth in degrees<|endoftext|> |
b089094e7bddc0c57608e72ae2ad5748bdb38468de6b6740da585932b3c67374 | def elevation_gain(elevations, isMiles=True, upthreshold=8, downthreshold=8, debug=False):
'\n calculate elevation gain over a series of elevation points\n\n NOTE: thresholds of 8 meters approximately matches strava\n\n :param elevations: list of elevation points in meters\n :param isMiles: if True return feet, if False return m\n :param upthreshold: threshold of increase when to decide climbing, meters\n :param downthreshold: threshold of decrease when to decide descending, meters\n :param debug: if True, return tuple with gain, debuginfo\n :rtype: gain[, debuginfo] - gain in meters or feet depending on isMiles\n '
if debug:
debuginfo = ['ele,state,highup,lowdown,totclimb\n']
state = 'unknown'
thisel = elevations[0]
highup = thisel
lowdown = thisel
totclimb = 0.0
if debug:
debuginfo.append('{},{},{},{},{}\n'.format(thisel, state, highup, lowdown, totclimb))
for thisel in elevations[1:]:
if (state == 'unknown'):
if (thisel >= (highup + upthreshold)):
state = 'climbing'
highup = thisel
elif (thisel <= (lowdown - downthreshold)):
state = 'descending'
lowdown = thisel
elif (state == 'climbing'):
if (thisel > highup):
highup = thisel
elif (thisel <= (highup - downthreshold)):
state = 'descending'
totclimb += (highup - lowdown)
lowdown = thisel
elif (state == 'descending'):
if (thisel < lowdown):
lowdown = thisel
elif (thisel >= (lowdown + upthreshold)):
state = 'climbing'
highup = thisel
if debug:
debuginfo.append('{},{},{},{},{}\n'.format(thisel, state, highup, lowdown, totclimb))
if (state == 'climbing'):
totclimb += (highup - lowdown)
if debug:
debuginfo.pop()
debuginfo.append('{},{},{},{},{}\n'.format(thisel, state, highup, lowdown, totclimb))
if isMiles:
totclimb = ((totclimb / 1609.344) * 5280)
if debug:
return (totclimb, debuginfo)
return totclimb | calculate elevation gain over a series of elevation points
NOTE: thresholds of 8 meters approximately matches strava
:param elevations: list of elevation points in meters
:param isMiles: if True return feet, if False return m
:param upthreshold: threshold of increase when to decide climbing, meters
:param downthreshold: threshold of decrease when to decide descending, meters
:param debug: if True, return tuple with gain, debuginfo
:rtype: gain[, debuginfo] - gain in meters or feet depending on isMiles | loutilities/geo.py | elevation_gain | louking/loutilities | 1 | python | def elevation_gain(elevations, isMiles=True, upthreshold=8, downthreshold=8, debug=False):
'\n calculate elevation gain over a series of elevation points\n\n NOTE: thresholds of 8 meters approximately matches strava\n\n :param elevations: list of elevation points in meters\n :param isMiles: if True return feet, if False return m\n :param upthreshold: threshold of increase when to decide climbing, meters\n :param downthreshold: threshold of decrease when to decide descending, meters\n :param debug: if True, return tuple with gain, debuginfo\n :rtype: gain[, debuginfo] - gain in meters or feet depending on isMiles\n '
if debug:
debuginfo = ['ele,state,highup,lowdown,totclimb\n']
state = 'unknown'
thisel = elevations[0]
highup = thisel
lowdown = thisel
totclimb = 0.0
if debug:
debuginfo.append('{},{},{},{},{}\n'.format(thisel, state, highup, lowdown, totclimb))
for thisel in elevations[1:]:
if (state == 'unknown'):
if (thisel >= (highup + upthreshold)):
state = 'climbing'
highup = thisel
elif (thisel <= (lowdown - downthreshold)):
state = 'descending'
lowdown = thisel
elif (state == 'climbing'):
if (thisel > highup):
highup = thisel
elif (thisel <= (highup - downthreshold)):
state = 'descending'
totclimb += (highup - lowdown)
lowdown = thisel
elif (state == 'descending'):
if (thisel < lowdown):
lowdown = thisel
elif (thisel >= (lowdown + upthreshold)):
state = 'climbing'
highup = thisel
if debug:
debuginfo.append('{},{},{},{},{}\n'.format(thisel, state, highup, lowdown, totclimb))
if (state == 'climbing'):
totclimb += (highup - lowdown)
if debug:
debuginfo.pop()
debuginfo.append('{},{},{},{},{}\n'.format(thisel, state, highup, lowdown, totclimb))
if isMiles:
totclimb = ((totclimb / 1609.344) * 5280)
if debug:
return (totclimb, debuginfo)
return totclimb | def elevation_gain(elevations, isMiles=True, upthreshold=8, downthreshold=8, debug=False):
'\n calculate elevation gain over a series of elevation points\n\n NOTE: thresholds of 8 meters approximately matches strava\n\n :param elevations: list of elevation points in meters\n :param isMiles: if True return feet, if False return m\n :param upthreshold: threshold of increase when to decide climbing, meters\n :param downthreshold: threshold of decrease when to decide descending, meters\n :param debug: if True, return tuple with gain, debuginfo\n :rtype: gain[, debuginfo] - gain in meters or feet depending on isMiles\n '
if debug:
debuginfo = ['ele,state,highup,lowdown,totclimb\n']
state = 'unknown'
thisel = elevations[0]
highup = thisel
lowdown = thisel
totclimb = 0.0
if debug:
debuginfo.append('{},{},{},{},{}\n'.format(thisel, state, highup, lowdown, totclimb))
for thisel in elevations[1:]:
if (state == 'unknown'):
if (thisel >= (highup + upthreshold)):
state = 'climbing'
highup = thisel
elif (thisel <= (lowdown - downthreshold)):
state = 'descending'
lowdown = thisel
elif (state == 'climbing'):
if (thisel > highup):
highup = thisel
elif (thisel <= (highup - downthreshold)):
state = 'descending'
totclimb += (highup - lowdown)
lowdown = thisel
elif (state == 'descending'):
if (thisel < lowdown):
lowdown = thisel
elif (thisel >= (lowdown + upthreshold)):
state = 'climbing'
highup = thisel
if debug:
debuginfo.append('{},{},{},{},{}\n'.format(thisel, state, highup, lowdown, totclimb))
if (state == 'climbing'):
totclimb += (highup - lowdown)
if debug:
debuginfo.pop()
debuginfo.append('{},{},{},{},{}\n'.format(thisel, state, highup, lowdown, totclimb))
if isMiles:
totclimb = ((totclimb / 1609.344) * 5280)
if debug:
return (totclimb, debuginfo)
return totclimb<|docstring|>calculate elevation gain over a series of elevation points
NOTE: thresholds of 8 meters approximately matches strava
:param elevations: list of elevation points in meters
:param isMiles: if True return feet, if False return m
:param upthreshold: threshold of increase when to decide climbing, meters
:param downthreshold: threshold of decrease when to decide descending, meters
:param debug: if True, return tuple with gain, debuginfo
:rtype: gain[, debuginfo] - gain in meters or feet depending on isMiles<|endoftext|> |
01d133b565789475a74810868492c8336199aa422e33c98d89a366197a4185f4 | def haversineDistance(self, coords1, coords2, isMiles=True):
'\n calculate the distance between two lat,lng,ele coordinates\n\n Note: the ele item in the coordinate tuple is optional\n\n :param coords1: [lat, lng, ele] or [lat, lng] lat,lng dec degrees, ele meters\n :param coords2: [lat, lng, ele] or [lat, lng]\n :param isMiles: if True return miles, if False return km\n :rtype: distance between the points\n '
lat1 = coords1[0]
lon1 = coords1[1]
ele1 = (coords1[2] if (len(coords1) >= 3) else 0.0)
lat2 = coords2[0]
lon2 = coords2[1]
ele2 = (coords2[2] if (len(coords2) >= 3) else 0.0)
x1 = (lat2 - lat1)
dLat = math.radians(x1)
x2 = (lon2 - lon1)
dLon = math.radians(x2)
a = ((math.sin((dLat / 2)) * math.sin((dLat / 2))) + (((math.cos(math.radians(lat1)) * math.cos(math.radians(lat2))) * math.sin((dLon / 2))) * math.sin((dLon / 2))))
c = (2 * math.atan2(math.sqrt(a), math.sqrt((1 - a))))
d = (self.R * c)
if isMiles:
d /= 1.609344
if isMiles:
ele1 /= 5280
ele2 /= 5280
else:
ele1 /= 1000
ele2 /= 1000
de = (ele2 - ele1)
d = math.sqrt(((d * d) + (de * de)))
return d | calculate the distance between two lat,lng,ele coordinates
Note: the ele item in the coordinate tuple is optional
:param coords1: [lat, lng, ele] or [lat, lng] lat,lng dec degrees, ele meters
:param coords2: [lat, lng, ele] or [lat, lng]
:param isMiles: if True return miles, if False return km
:rtype: distance between the points | loutilities/geo.py | haversineDistance | louking/loutilities | 1 | python | def haversineDistance(self, coords1, coords2, isMiles=True):
'\n calculate the distance between two lat,lng,ele coordinates\n\n Note: the ele item in the coordinate tuple is optional\n\n :param coords1: [lat, lng, ele] or [lat, lng] lat,lng dec degrees, ele meters\n :param coords2: [lat, lng, ele] or [lat, lng]\n :param isMiles: if True return miles, if False return km\n :rtype: distance between the points\n '
lat1 = coords1[0]
lon1 = coords1[1]
ele1 = (coords1[2] if (len(coords1) >= 3) else 0.0)
lat2 = coords2[0]
lon2 = coords2[1]
ele2 = (coords2[2] if (len(coords2) >= 3) else 0.0)
x1 = (lat2 - lat1)
dLat = math.radians(x1)
x2 = (lon2 - lon1)
dLon = math.radians(x2)
a = ((math.sin((dLat / 2)) * math.sin((dLat / 2))) + (((math.cos(math.radians(lat1)) * math.cos(math.radians(lat2))) * math.sin((dLon / 2))) * math.sin((dLon / 2))))
c = (2 * math.atan2(math.sqrt(a), math.sqrt((1 - a))))
d = (self.R * c)
if isMiles:
d /= 1.609344
if isMiles:
ele1 /= 5280
ele2 /= 5280
else:
ele1 /= 1000
ele2 /= 1000
de = (ele2 - ele1)
d = math.sqrt(((d * d) + (de * de)))
return d | def haversineDistance(self, coords1, coords2, isMiles=True):
'\n calculate the distance between two lat,lng,ele coordinates\n\n Note: the ele item in the coordinate tuple is optional\n\n :param coords1: [lat, lng, ele] or [lat, lng] lat,lng dec degrees, ele meters\n :param coords2: [lat, lng, ele] or [lat, lng]\n :param isMiles: if True return miles, if False return km\n :rtype: distance between the points\n '
lat1 = coords1[0]
lon1 = coords1[1]
ele1 = (coords1[2] if (len(coords1) >= 3) else 0.0)
lat2 = coords2[0]
lon2 = coords2[1]
ele2 = (coords2[2] if (len(coords2) >= 3) else 0.0)
x1 = (lat2 - lat1)
dLat = math.radians(x1)
x2 = (lon2 - lon1)
dLon = math.radians(x2)
a = ((math.sin((dLat / 2)) * math.sin((dLat / 2))) + (((math.cos(math.radians(lat1)) * math.cos(math.radians(lat2))) * math.sin((dLon / 2))) * math.sin((dLon / 2))))
c = (2 * math.atan2(math.sqrt(a), math.sqrt((1 - a))))
d = (self.R * c)
if isMiles:
d /= 1.609344
if isMiles:
ele1 /= 5280
ele2 /= 5280
else:
ele1 /= 1000
ele2 /= 1000
de = (ele2 - ele1)
d = math.sqrt(((d * d) + (de * de)))
return d<|docstring|>calculate the distance between two lat,lng,ele coordinates
Note: the ele item in the coordinate tuple is optional
:param coords1: [lat, lng, ele] or [lat, lng] lat,lng dec degrees, ele meters
:param coords2: [lat, lng, ele] or [lat, lng]
:param isMiles: if True return miles, if False return km
:rtype: distance between the points<|endoftext|> |
fbebb85c33f7f7d9a3377e8355c9fa65f14479989a639cd80e32ba9c0711e081 | def getDestinationLatLng(self, coord, azimuth, distance):
'\n returns the lat and lng of destination point \n given the start lat, long, azimuth, and distance\n\n :param coord: [lat,lng]\n :param azimuth: direction in degrees\n :param distance: distance in meters\n :rtype: [lat, lng]\n '
brng = math.radians(azimuth)
d = (distance / 1000)
lat1 = math.radians(coord[0])
lon1 = math.radians(coord[1])
lat2 = math.asin(((math.sin(lat1) * math.cos((d / self.R))) + ((math.cos(lat1) * math.sin((d / self.R))) * math.cos(brng))))
lon2 = (lon1 + math.atan2(((math.sin(brng) * math.sin((d / self.R))) * math.cos(lat1)), (math.cos((d / self.R)) - (math.sin(lat1) * math.sin(lat2)))))
lat2 = math.degrees(lat2)
lon2 = math.degrees(lon2)
return [lat2, lon2] | returns the lat and lng of destination point
given the start lat, long, azimuth, and distance
:param coord: [lat,lng]
:param azimuth: direction in degrees
:param distance: distance in meters
:rtype: [lat, lng] | loutilities/geo.py | getDestinationLatLng | louking/loutilities | 1 | python | def getDestinationLatLng(self, coord, azimuth, distance):
'\n returns the lat and lng of destination point \n given the start lat, long, azimuth, and distance\n\n :param coord: [lat,lng]\n :param azimuth: direction in degrees\n :param distance: distance in meters\n :rtype: [lat, lng]\n '
brng = math.radians(azimuth)
d = (distance / 1000)
lat1 = math.radians(coord[0])
lon1 = math.radians(coord[1])
lat2 = math.asin(((math.sin(lat1) * math.cos((d / self.R))) + ((math.cos(lat1) * math.sin((d / self.R))) * math.cos(brng))))
lon2 = (lon1 + math.atan2(((math.sin(brng) * math.sin((d / self.R))) * math.cos(lat1)), (math.cos((d / self.R)) - (math.sin(lat1) * math.sin(lat2)))))
lat2 = math.degrees(lat2)
lon2 = math.degrees(lon2)
return [lat2, lon2] | def getDestinationLatLng(self, coord, azimuth, distance):
'\n returns the lat and lng of destination point \n given the start lat, long, azimuth, and distance\n\n :param coord: [lat,lng]\n :param azimuth: direction in degrees\n :param distance: distance in meters\n :rtype: [lat, lng]\n '
brng = math.radians(azimuth)
d = (distance / 1000)
lat1 = math.radians(coord[0])
lon1 = math.radians(coord[1])
lat2 = math.asin(((math.sin(lat1) * math.cos((d / self.R))) + ((math.cos(lat1) * math.sin((d / self.R))) * math.cos(brng))))
lon2 = (lon1 + math.atan2(((math.sin(brng) * math.sin((d / self.R))) * math.cos(lat1)), (math.cos((d / self.R)) - (math.sin(lat1) * math.sin(lat2)))))
lat2 = math.degrees(lat2)
lon2 = math.degrees(lon2)
return [lat2, lon2]<|docstring|>returns the lat and lng of destination point
given the start lat, long, azimuth, and distance
:param coord: [lat,lng]
:param azimuth: direction in degrees
:param distance: distance in meters
:rtype: [lat, lng]<|endoftext|> |
fe253a5d44db9b89f808fa1def50fc3cad2a43f02c5dd9fbb2dc182c90f0bd5c | def __init__(self, url: str, browser: HandshakeBrowser):
'\n Create a page object for the given URL\n\n :param url: the url of the page to initialize\n :type url: str\n :param browser: a HandshakeBrowser that is logged in to Handshake\n :type browser: HandshakeBrowser\n '
self._validate_url(url)
self._url = url
self._browser = browser
self._browser.get(url)
self._wait_until_page_is_loaded() | Create a page object for the given URL
:param url: the url of the page to initialize
:type url: str
:param browser: a HandshakeBrowser that is logged in to Handshake
:type browser: HandshakeBrowser | autohandshake/src/Pages/Page.py | __init__ | cedwards036/autohandshake | 3 | python | def __init__(self, url: str, browser: HandshakeBrowser):
'\n Create a page object for the given URL\n\n :param url: the url of the page to initialize\n :type url: str\n :param browser: a HandshakeBrowser that is logged in to Handshake\n :type browser: HandshakeBrowser\n '
self._validate_url(url)
self._url = url
self._browser = browser
self._browser.get(url)
self._wait_until_page_is_loaded() | def __init__(self, url: str, browser: HandshakeBrowser):
'\n Create a page object for the given URL\n\n :param url: the url of the page to initialize\n :type url: str\n :param browser: a HandshakeBrowser that is logged in to Handshake\n :type browser: HandshakeBrowser\n '
self._validate_url(url)
self._url = url
self._browser = browser
self._browser.get(url)
self._wait_until_page_is_loaded()<|docstring|>Create a page object for the given URL
:param url: the url of the page to initialize
:type url: str
:param browser: a HandshakeBrowser that is logged in to Handshake
:type browser: HandshakeBrowser<|endoftext|> |
d4d0130f9ec964dde5582d586af90621f1b9ac14216dbaf5a612e9fbaafb63de | @abstractmethod
def _wait_until_page_is_loaded(self):
'Wait until the page has finished loading.\n\n For pages without complex javascript involved in the load, simply\n returning immediately is sufficient.\n '
raise NotImplementedError | Wait until the page has finished loading.
For pages without complex javascript involved in the load, simply
returning immediately is sufficient. | autohandshake/src/Pages/Page.py | _wait_until_page_is_loaded | cedwards036/autohandshake | 3 | python | @abstractmethod
def _wait_until_page_is_loaded(self):
'Wait until the page has finished loading.\n\n For pages without complex javascript involved in the load, simply\n returning immediately is sufficient.\n '
raise NotImplementedError | @abstractmethod
def _wait_until_page_is_loaded(self):
'Wait until the page has finished loading.\n\n For pages without complex javascript involved in the load, simply\n returning immediately is sufficient.\n '
raise NotImplementedError<|docstring|>Wait until the page has finished loading.
For pages without complex javascript involved in the load, simply
returning immediately is sufficient.<|endoftext|> |
c1c98963d9ddb6d40a1a2e4fdbff74d540fe750a17349d0daa7edfcf79b64597 | def validate_current_page(self):
'Ensure that the browser is on the correct page before calling a method.\n\n To be used to make sure methods on this page are not called while the\n browser is on a different page\n '
try:
self._validate_url(self._browser.current_url)
self._wait_until_page_is_loaded()
except InvalidURLError:
raise WrongPageForMethodError() | Ensure that the browser is on the correct page before calling a method.
To be used to make sure methods on this page are not called while the
browser is on a different page | autohandshake/src/Pages/Page.py | validate_current_page | cedwards036/autohandshake | 3 | python | def validate_current_page(self):
'Ensure that the browser is on the correct page before calling a method.\n\n To be used to make sure methods on this page are not called while the\n browser is on a different page\n '
try:
self._validate_url(self._browser.current_url)
self._wait_until_page_is_loaded()
except InvalidURLError:
raise WrongPageForMethodError() | def validate_current_page(self):
'Ensure that the browser is on the correct page before calling a method.\n\n To be used to make sure methods on this page are not called while the\n browser is on a different page\n '
try:
self._validate_url(self._browser.current_url)
self._wait_until_page_is_loaded()
except InvalidURLError:
raise WrongPageForMethodError()<|docstring|>Ensure that the browser is on the correct page before calling a method.
To be used to make sure methods on this page are not called while the
browser is on a different page<|endoftext|> |
f1e082befb72cecfd021eb8ea9e397cba11bfeb5160d67de942ca4c08bf48cdd | @abstractmethod
def _validate_url(self, url):
'\n Ensure that the given URL is a valid URL for this page type\n\n :param url: the url to validate\n :type url: str\n '
raise NotImplementedError | Ensure that the given URL is a valid URL for this page type
:param url: the url to validate
:type url: str | autohandshake/src/Pages/Page.py | _validate_url | cedwards036/autohandshake | 3 | python | @abstractmethod
def _validate_url(self, url):
'\n Ensure that the given URL is a valid URL for this page type\n\n :param url: the url to validate\n :type url: str\n '
raise NotImplementedError | @abstractmethod
def _validate_url(self, url):
'\n Ensure that the given URL is a valid URL for this page type\n\n :param url: the url to validate\n :type url: str\n '
raise NotImplementedError<|docstring|>Ensure that the given URL is a valid URL for this page type
:param url: the url to validate
:type url: str<|endoftext|> |
1e994fc0f462ef93b59206c7c81ce3de50b9d2e495937fdbe088883e38f978e9 | @classmethod
def require_user_type(cls, user_type: UserType):
'\n Throw an error if the browser is not currently logged in as the required user type.\n\n To be used as a decorator for Page subclass methods that require the\n browser to be logged in as a specific user type.\n\n :param func: a page method\n :type func: function\n :param user_type: the user type to require\n :type user_type: UserType\n '
def require_user_type_decorator(func: Callable):
def inner_func(self, *args, **kwargs):
if (not (self._browser.user_type == user_type)):
raise InvalidUserTypeError('Invalid user type for method')
return func(self, *args, **kwargs)
inner_func.__doc__ = func.__doc__
return inner_func
return require_user_type_decorator | Throw an error if the browser is not currently logged in as the required user type.
To be used as a decorator for Page subclass methods that require the
browser to be logged in as a specific user type.
:param func: a page method
:type func: function
:param user_type: the user type to require
:type user_type: UserType | autohandshake/src/Pages/Page.py | require_user_type | cedwards036/autohandshake | 3 | python | @classmethod
def require_user_type(cls, user_type: UserType):
'\n Throw an error if the browser is not currently logged in as the required user type.\n\n To be used as a decorator for Page subclass methods that require the\n browser to be logged in as a specific user type.\n\n :param func: a page method\n :type func: function\n :param user_type: the user type to require\n :type user_type: UserType\n '
def require_user_type_decorator(func: Callable):
def inner_func(self, *args, **kwargs):
if (not (self._browser.user_type == user_type)):
raise InvalidUserTypeError('Invalid user type for method')
return func(self, *args, **kwargs)
inner_func.__doc__ = func.__doc__
return inner_func
return require_user_type_decorator | @classmethod
def require_user_type(cls, user_type: UserType):
'\n Throw an error if the browser is not currently logged in as the required user type.\n\n To be used as a decorator for Page subclass methods that require the\n browser to be logged in as a specific user type.\n\n :param func: a page method\n :type func: function\n :param user_type: the user type to require\n :type user_type: UserType\n '
def require_user_type_decorator(func: Callable):
def inner_func(self, *args, **kwargs):
if (not (self._browser.user_type == user_type)):
raise InvalidUserTypeError('Invalid user type for method')
return func(self, *args, **kwargs)
inner_func.__doc__ = func.__doc__
return inner_func
return require_user_type_decorator<|docstring|>Throw an error if the browser is not currently logged in as the required user type.
To be used as a decorator for Page subclass methods that require the
browser to be logged in as a specific user type.
:param func: a page method
:type func: function
:param user_type: the user type to require
:type user_type: UserType<|endoftext|> |
da5a793b77082491eff5a525059a9a9027aa5f4edd62604e89608a709d4b6e16 | @property
def url(self):
"Get the page's url"
return self._url | Get the page's url | autohandshake/src/Pages/Page.py | url | cedwards036/autohandshake | 3 | python | @property
def url(self):
return self._url | @property
def url(self):
return self._url<|docstring|>Get the page's url<|endoftext|> |
d4e4d7bf0feca78d1c5ac40e54cd53faa353e245fe76bd05757b2dd02f213f4c | def __str__(self):
' Return circle name '
return self.name | Return circle name | cride/circles/models/circles.py | __str__ | daecazu/platziride | 0 | python | def __str__(self):
' '
return self.name | def __str__(self):
' '
return self.name<|docstring|>Return circle name<|endoftext|> |
11f37b92d42a8e176ae6f118bbe0dd0282bf4dfa80ebf7215a6f382054bff7fc | def __init__(__self__, *, cluster_name: pulumi.Input[str], data_disk_size_gb: pulumi.Input[int], is_primary: pulumi.Input[bool], resource_group_name: pulumi.Input[str], vm_instance_count: pulumi.Input[int], application_ports: Optional[pulumi.Input['EndpointRangeDescriptionArgs']]=None, capacities: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, data_disk_type: Optional[pulumi.Input[Union[(str, 'DiskType')]]]=None, ephemeral_ports: Optional[pulumi.Input['EndpointRangeDescriptionArgs']]=None, is_stateless: Optional[pulumi.Input[bool]]=None, multiple_placement_groups: Optional[pulumi.Input[bool]]=None, node_type_name: Optional[pulumi.Input[str]]=None, placement_properties: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, vm_extensions: Optional[pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]]]=None, vm_image_offer: Optional[pulumi.Input[str]]=None, vm_image_publisher: Optional[pulumi.Input[str]]=None, vm_image_sku: Optional[pulumi.Input[str]]=None, vm_image_version: Optional[pulumi.Input[str]]=None, vm_managed_identity: Optional[pulumi.Input['VmManagedIdentityArgs']]=None, vm_secrets: Optional[pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]]]=None, vm_size: Optional[pulumi.Input[str]]=None):
"\n The set of arguments for constructing a NodeType resource.\n :param pulumi.Input[str] cluster_name: The name of the cluster resource.\n :param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.\n :param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.\n :param pulumi.Input[str] resource_group_name: The name of the resource group.\n :param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.\n :param pulumi.Input['EndpointRangeDescriptionArgs'] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.\n :param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.\n :param pulumi.Input['EndpointRangeDescriptionArgs'] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.\n :param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.\n :param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.\n :param pulumi.Input[str] node_type_name: The name of the node type.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.\n :param pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]] vm_extensions: Set of extensions that should be installed onto the virtual machines.\n :param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.\n :param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.\n :param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.\n :param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.\n :param pulumi.Input['VmManagedIdentityArgs'] vm_managed_identity: Identities for the virtual machine scale set under the node type.\n :param pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]] vm_secrets: The secrets to install in the virtual machines.\n :param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.\n "
pulumi.set(__self__, 'cluster_name', cluster_name)
pulumi.set(__self__, 'data_disk_size_gb', data_disk_size_gb)
pulumi.set(__self__, 'is_primary', is_primary)
pulumi.set(__self__, 'resource_group_name', resource_group_name)
pulumi.set(__self__, 'vm_instance_count', vm_instance_count)
if (application_ports is not None):
pulumi.set(__self__, 'application_ports', application_ports)
if (capacities is not None):
pulumi.set(__self__, 'capacities', capacities)
if (data_disk_type is not None):
pulumi.set(__self__, 'data_disk_type', data_disk_type)
if (ephemeral_ports is not None):
pulumi.set(__self__, 'ephemeral_ports', ephemeral_ports)
if (is_stateless is None):
is_stateless = False
if (is_stateless is not None):
pulumi.set(__self__, 'is_stateless', is_stateless)
if (multiple_placement_groups is None):
multiple_placement_groups = False
if (multiple_placement_groups is not None):
pulumi.set(__self__, 'multiple_placement_groups', multiple_placement_groups)
if (node_type_name is not None):
pulumi.set(__self__, 'node_type_name', node_type_name)
if (placement_properties is not None):
pulumi.set(__self__, 'placement_properties', placement_properties)
if (tags is not None):
pulumi.set(__self__, 'tags', tags)
if (vm_extensions is not None):
pulumi.set(__self__, 'vm_extensions', vm_extensions)
if (vm_image_offer is not None):
pulumi.set(__self__, 'vm_image_offer', vm_image_offer)
if (vm_image_publisher is not None):
pulumi.set(__self__, 'vm_image_publisher', vm_image_publisher)
if (vm_image_sku is not None):
pulumi.set(__self__, 'vm_image_sku', vm_image_sku)
if (vm_image_version is not None):
pulumi.set(__self__, 'vm_image_version', vm_image_version)
if (vm_managed_identity is not None):
pulumi.set(__self__, 'vm_managed_identity', vm_managed_identity)
if (vm_secrets is not None):
pulumi.set(__self__, 'vm_secrets', vm_secrets)
if (vm_size is not None):
pulumi.set(__self__, 'vm_size', vm_size) | The set of arguments for constructing a NodeType resource.
:param pulumi.Input[str] cluster_name: The name of the cluster resource.
:param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.
:param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.
:param pulumi.Input['EndpointRangeDescriptionArgs'] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.
:param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.
:param pulumi.Input['EndpointRangeDescriptionArgs'] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.
:param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.
:param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.
:param pulumi.Input[str] node_type_name: The name of the node type.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.
:param pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]] vm_extensions: Set of extensions that should be installed onto the virtual machines.
:param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.
:param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.
:param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.
:param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.
:param pulumi.Input['VmManagedIdentityArgs'] vm_managed_identity: Identities for the virtual machine scale set under the node type.
:param pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]] vm_secrets: The secrets to install in the virtual machines.
:param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | __init__ | polivbr/pulumi-azure-native | 0 | python | def __init__(__self__, *, cluster_name: pulumi.Input[str], data_disk_size_gb: pulumi.Input[int], is_primary: pulumi.Input[bool], resource_group_name: pulumi.Input[str], vm_instance_count: pulumi.Input[int], application_ports: Optional[pulumi.Input['EndpointRangeDescriptionArgs']]=None, capacities: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, data_disk_type: Optional[pulumi.Input[Union[(str, 'DiskType')]]]=None, ephemeral_ports: Optional[pulumi.Input['EndpointRangeDescriptionArgs']]=None, is_stateless: Optional[pulumi.Input[bool]]=None, multiple_placement_groups: Optional[pulumi.Input[bool]]=None, node_type_name: Optional[pulumi.Input[str]]=None, placement_properties: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, vm_extensions: Optional[pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]]]=None, vm_image_offer: Optional[pulumi.Input[str]]=None, vm_image_publisher: Optional[pulumi.Input[str]]=None, vm_image_sku: Optional[pulumi.Input[str]]=None, vm_image_version: Optional[pulumi.Input[str]]=None, vm_managed_identity: Optional[pulumi.Input['VmManagedIdentityArgs']]=None, vm_secrets: Optional[pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]]]=None, vm_size: Optional[pulumi.Input[str]]=None):
"\n The set of arguments for constructing a NodeType resource.\n :param pulumi.Input[str] cluster_name: The name of the cluster resource.\n :param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.\n :param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.\n :param pulumi.Input[str] resource_group_name: The name of the resource group.\n :param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.\n :param pulumi.Input['EndpointRangeDescriptionArgs'] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.\n :param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.\n :param pulumi.Input['EndpointRangeDescriptionArgs'] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.\n :param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.\n :param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.\n :param pulumi.Input[str] node_type_name: The name of the node type.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.\n :param pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]] vm_extensions: Set of extensions that should be installed onto the virtual machines.\n :param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.\n :param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.\n :param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.\n :param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.\n :param pulumi.Input['VmManagedIdentityArgs'] vm_managed_identity: Identities for the virtual machine scale set under the node type.\n :param pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]] vm_secrets: The secrets to install in the virtual machines.\n :param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.\n "
pulumi.set(__self__, 'cluster_name', cluster_name)
pulumi.set(__self__, 'data_disk_size_gb', data_disk_size_gb)
pulumi.set(__self__, 'is_primary', is_primary)
pulumi.set(__self__, 'resource_group_name', resource_group_name)
pulumi.set(__self__, 'vm_instance_count', vm_instance_count)
if (application_ports is not None):
pulumi.set(__self__, 'application_ports', application_ports)
if (capacities is not None):
pulumi.set(__self__, 'capacities', capacities)
if (data_disk_type is not None):
pulumi.set(__self__, 'data_disk_type', data_disk_type)
if (ephemeral_ports is not None):
pulumi.set(__self__, 'ephemeral_ports', ephemeral_ports)
if (is_stateless is None):
is_stateless = False
if (is_stateless is not None):
pulumi.set(__self__, 'is_stateless', is_stateless)
if (multiple_placement_groups is None):
multiple_placement_groups = False
if (multiple_placement_groups is not None):
pulumi.set(__self__, 'multiple_placement_groups', multiple_placement_groups)
if (node_type_name is not None):
pulumi.set(__self__, 'node_type_name', node_type_name)
if (placement_properties is not None):
pulumi.set(__self__, 'placement_properties', placement_properties)
if (tags is not None):
pulumi.set(__self__, 'tags', tags)
if (vm_extensions is not None):
pulumi.set(__self__, 'vm_extensions', vm_extensions)
if (vm_image_offer is not None):
pulumi.set(__self__, 'vm_image_offer', vm_image_offer)
if (vm_image_publisher is not None):
pulumi.set(__self__, 'vm_image_publisher', vm_image_publisher)
if (vm_image_sku is not None):
pulumi.set(__self__, 'vm_image_sku', vm_image_sku)
if (vm_image_version is not None):
pulumi.set(__self__, 'vm_image_version', vm_image_version)
if (vm_managed_identity is not None):
pulumi.set(__self__, 'vm_managed_identity', vm_managed_identity)
if (vm_secrets is not None):
pulumi.set(__self__, 'vm_secrets', vm_secrets)
if (vm_size is not None):
pulumi.set(__self__, 'vm_size', vm_size) | def __init__(__self__, *, cluster_name: pulumi.Input[str], data_disk_size_gb: pulumi.Input[int], is_primary: pulumi.Input[bool], resource_group_name: pulumi.Input[str], vm_instance_count: pulumi.Input[int], application_ports: Optional[pulumi.Input['EndpointRangeDescriptionArgs']]=None, capacities: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, data_disk_type: Optional[pulumi.Input[Union[(str, 'DiskType')]]]=None, ephemeral_ports: Optional[pulumi.Input['EndpointRangeDescriptionArgs']]=None, is_stateless: Optional[pulumi.Input[bool]]=None, multiple_placement_groups: Optional[pulumi.Input[bool]]=None, node_type_name: Optional[pulumi.Input[str]]=None, placement_properties: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, vm_extensions: Optional[pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]]]=None, vm_image_offer: Optional[pulumi.Input[str]]=None, vm_image_publisher: Optional[pulumi.Input[str]]=None, vm_image_sku: Optional[pulumi.Input[str]]=None, vm_image_version: Optional[pulumi.Input[str]]=None, vm_managed_identity: Optional[pulumi.Input['VmManagedIdentityArgs']]=None, vm_secrets: Optional[pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]]]=None, vm_size: Optional[pulumi.Input[str]]=None):
"\n The set of arguments for constructing a NodeType resource.\n :param pulumi.Input[str] cluster_name: The name of the cluster resource.\n :param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.\n :param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.\n :param pulumi.Input[str] resource_group_name: The name of the resource group.\n :param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.\n :param pulumi.Input['EndpointRangeDescriptionArgs'] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.\n :param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.\n :param pulumi.Input['EndpointRangeDescriptionArgs'] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.\n :param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.\n :param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.\n :param pulumi.Input[str] node_type_name: The name of the node type.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.\n :param pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]] vm_extensions: Set of extensions that should be installed onto the virtual machines.\n :param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.\n :param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.\n :param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.\n :param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.\n :param pulumi.Input['VmManagedIdentityArgs'] vm_managed_identity: Identities for the virtual machine scale set under the node type.\n :param pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]] vm_secrets: The secrets to install in the virtual machines.\n :param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.\n "
pulumi.set(__self__, 'cluster_name', cluster_name)
pulumi.set(__self__, 'data_disk_size_gb', data_disk_size_gb)
pulumi.set(__self__, 'is_primary', is_primary)
pulumi.set(__self__, 'resource_group_name', resource_group_name)
pulumi.set(__self__, 'vm_instance_count', vm_instance_count)
if (application_ports is not None):
pulumi.set(__self__, 'application_ports', application_ports)
if (capacities is not None):
pulumi.set(__self__, 'capacities', capacities)
if (data_disk_type is not None):
pulumi.set(__self__, 'data_disk_type', data_disk_type)
if (ephemeral_ports is not None):
pulumi.set(__self__, 'ephemeral_ports', ephemeral_ports)
if (is_stateless is None):
is_stateless = False
if (is_stateless is not None):
pulumi.set(__self__, 'is_stateless', is_stateless)
if (multiple_placement_groups is None):
multiple_placement_groups = False
if (multiple_placement_groups is not None):
pulumi.set(__self__, 'multiple_placement_groups', multiple_placement_groups)
if (node_type_name is not None):
pulumi.set(__self__, 'node_type_name', node_type_name)
if (placement_properties is not None):
pulumi.set(__self__, 'placement_properties', placement_properties)
if (tags is not None):
pulumi.set(__self__, 'tags', tags)
if (vm_extensions is not None):
pulumi.set(__self__, 'vm_extensions', vm_extensions)
if (vm_image_offer is not None):
pulumi.set(__self__, 'vm_image_offer', vm_image_offer)
if (vm_image_publisher is not None):
pulumi.set(__self__, 'vm_image_publisher', vm_image_publisher)
if (vm_image_sku is not None):
pulumi.set(__self__, 'vm_image_sku', vm_image_sku)
if (vm_image_version is not None):
pulumi.set(__self__, 'vm_image_version', vm_image_version)
if (vm_managed_identity is not None):
pulumi.set(__self__, 'vm_managed_identity', vm_managed_identity)
if (vm_secrets is not None):
pulumi.set(__self__, 'vm_secrets', vm_secrets)
if (vm_size is not None):
pulumi.set(__self__, 'vm_size', vm_size)<|docstring|>The set of arguments for constructing a NodeType resource.
:param pulumi.Input[str] cluster_name: The name of the cluster resource.
:param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.
:param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.
:param pulumi.Input['EndpointRangeDescriptionArgs'] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.
:param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.
:param pulumi.Input['EndpointRangeDescriptionArgs'] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.
:param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.
:param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.
:param pulumi.Input[str] node_type_name: The name of the node type.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.
:param pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]] vm_extensions: Set of extensions that should be installed onto the virtual machines.
:param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.
:param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.
:param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.
:param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.
:param pulumi.Input['VmManagedIdentityArgs'] vm_managed_identity: Identities for the virtual machine scale set under the node type.
:param pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]] vm_secrets: The secrets to install in the virtual machines.
:param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.<|endoftext|> |
91f4159503f65aa2c19cbdb220479ebe1abdbad7324c11f0317a9dec94517d30 | @property
@pulumi.getter(name='clusterName')
def cluster_name(self) -> pulumi.Input[str]:
'\n The name of the cluster resource.\n '
return pulumi.get(self, 'cluster_name') | The name of the cluster resource. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | cluster_name | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='clusterName')
def cluster_name(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'cluster_name') | @property
@pulumi.getter(name='clusterName')
def cluster_name(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'cluster_name')<|docstring|>The name of the cluster resource.<|endoftext|> |
79cc409019ca85f22cc9c09ca9897e6472090b0dc0e700bca14d70887f08c9c7 | @property
@pulumi.getter(name='dataDiskSizeGB')
def data_disk_size_gb(self) -> pulumi.Input[int]:
'\n Disk size for each vm in the node type in GBs.\n '
return pulumi.get(self, 'data_disk_size_gb') | Disk size for each vm in the node type in GBs. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | data_disk_size_gb | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='dataDiskSizeGB')
def data_disk_size_gb(self) -> pulumi.Input[int]:
'\n \n '
return pulumi.get(self, 'data_disk_size_gb') | @property
@pulumi.getter(name='dataDiskSizeGB')
def data_disk_size_gb(self) -> pulumi.Input[int]:
'\n \n '
return pulumi.get(self, 'data_disk_size_gb')<|docstring|>Disk size for each vm in the node type in GBs.<|endoftext|> |
641d6423197655fbd9fb4cdcbed921e371dc3515cb257e3aaca493fb97059063 | @property
@pulumi.getter(name='isPrimary')
def is_primary(self) -> pulumi.Input[bool]:
'\n The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.\n '
return pulumi.get(self, 'is_primary') | The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | is_primary | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='isPrimary')
def is_primary(self) -> pulumi.Input[bool]:
'\n \n '
return pulumi.get(self, 'is_primary') | @property
@pulumi.getter(name='isPrimary')
def is_primary(self) -> pulumi.Input[bool]:
'\n \n '
return pulumi.get(self, 'is_primary')<|docstring|>The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.<|endoftext|> |
98eb6c85f4a5186d9d5e838be1c375ba32ee58272931f0c5f93ad28266f15668 | @property
@pulumi.getter(name='resourceGroupName')
def resource_group_name(self) -> pulumi.Input[str]:
'\n The name of the resource group.\n '
return pulumi.get(self, 'resource_group_name') | The name of the resource group. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | resource_group_name | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='resourceGroupName')
def resource_group_name(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'resource_group_name') | @property
@pulumi.getter(name='resourceGroupName')
def resource_group_name(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'resource_group_name')<|docstring|>The name of the resource group.<|endoftext|> |
505384660705b1f96b62ac7acffc4e7d859d3c785f4eadb66711a679e292e97b | @property
@pulumi.getter(name='vmInstanceCount')
def vm_instance_count(self) -> pulumi.Input[int]:
'\n The number of nodes in the node type.\n '
return pulumi.get(self, 'vm_instance_count') | The number of nodes in the node type. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_instance_count | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmInstanceCount')
def vm_instance_count(self) -> pulumi.Input[int]:
'\n \n '
return pulumi.get(self, 'vm_instance_count') | @property
@pulumi.getter(name='vmInstanceCount')
def vm_instance_count(self) -> pulumi.Input[int]:
'\n \n '
return pulumi.get(self, 'vm_instance_count')<|docstring|>The number of nodes in the node type.<|endoftext|> |
fe3f0fa4305cbe5cf888b197e60f53f9cb2092c70284ee7c631c7d552fde373c | @property
@pulumi.getter(name='applicationPorts')
def application_ports(self) -> Optional[pulumi.Input['EndpointRangeDescriptionArgs']]:
'\n The range of ports from which cluster assigned port to Service Fabric applications.\n '
return pulumi.get(self, 'application_ports') | The range of ports from which cluster assigned port to Service Fabric applications. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | application_ports | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='applicationPorts')
def application_ports(self) -> Optional[pulumi.Input['EndpointRangeDescriptionArgs']]:
'\n \n '
return pulumi.get(self, 'application_ports') | @property
@pulumi.getter(name='applicationPorts')
def application_ports(self) -> Optional[pulumi.Input['EndpointRangeDescriptionArgs']]:
'\n \n '
return pulumi.get(self, 'application_ports')<|docstring|>The range of ports from which cluster assigned port to Service Fabric applications.<|endoftext|> |
9f0de8a27421626b8b6eeff669603b61fab0d8d9a2adb47d6abd7281f8e2c81e | @property
@pulumi.getter
def capacities(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.\n '
return pulumi.get(self, 'capacities') | The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | capacities | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter
def capacities(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n \n '
return pulumi.get(self, 'capacities') | @property
@pulumi.getter
def capacities(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n \n '
return pulumi.get(self, 'capacities')<|docstring|>The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.<|endoftext|> |
81a042ea63c84b24ea4e7b91c7a4608c0091d5c10bf1d6d104e774d8825b31a1 | @property
@pulumi.getter(name='dataDiskType')
def data_disk_type(self) -> Optional[pulumi.Input[Union[(str, 'DiskType')]]]:
'\n Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.\n '
return pulumi.get(self, 'data_disk_type') | Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | data_disk_type | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='dataDiskType')
def data_disk_type(self) -> Optional[pulumi.Input[Union[(str, 'DiskType')]]]:
'\n \n '
return pulumi.get(self, 'data_disk_type') | @property
@pulumi.getter(name='dataDiskType')
def data_disk_type(self) -> Optional[pulumi.Input[Union[(str, 'DiskType')]]]:
'\n \n '
return pulumi.get(self, 'data_disk_type')<|docstring|>Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.<|endoftext|> |
7b9b8b89c756dac4fbe2abdb15e5886ba23aef8aed731a044cb2ef102f61b133 | @property
@pulumi.getter(name='ephemeralPorts')
def ephemeral_ports(self) -> Optional[pulumi.Input['EndpointRangeDescriptionArgs']]:
'\n The range of ephemeral ports that nodes in this node type should be configured with.\n '
return pulumi.get(self, 'ephemeral_ports') | The range of ephemeral ports that nodes in this node type should be configured with. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | ephemeral_ports | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='ephemeralPorts')
def ephemeral_ports(self) -> Optional[pulumi.Input['EndpointRangeDescriptionArgs']]:
'\n \n '
return pulumi.get(self, 'ephemeral_ports') | @property
@pulumi.getter(name='ephemeralPorts')
def ephemeral_ports(self) -> Optional[pulumi.Input['EndpointRangeDescriptionArgs']]:
'\n \n '
return pulumi.get(self, 'ephemeral_ports')<|docstring|>The range of ephemeral ports that nodes in this node type should be configured with.<|endoftext|> |
a684f9d341f65e4eca61fa6373baf3f6aef5cc7b739b371c387d83079636d59d | @property
@pulumi.getter(name='isStateless')
def is_stateless(self) -> Optional[pulumi.Input[bool]]:
'\n Indicates if the node type can only host Stateless workloads.\n '
return pulumi.get(self, 'is_stateless') | Indicates if the node type can only host Stateless workloads. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | is_stateless | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='isStateless')
def is_stateless(self) -> Optional[pulumi.Input[bool]]:
'\n \n '
return pulumi.get(self, 'is_stateless') | @property
@pulumi.getter(name='isStateless')
def is_stateless(self) -> Optional[pulumi.Input[bool]]:
'\n \n '
return pulumi.get(self, 'is_stateless')<|docstring|>Indicates if the node type can only host Stateless workloads.<|endoftext|> |
a54242b267561cf04647a943dd91d62d782ebee43c586771e98eba31bc8bd3db | @property
@pulumi.getter(name='multiplePlacementGroups')
def multiple_placement_groups(self) -> Optional[pulumi.Input[bool]]:
'\n Indicates if scale set associated with the node type can be composed of multiple placement groups.\n '
return pulumi.get(self, 'multiple_placement_groups') | Indicates if scale set associated with the node type can be composed of multiple placement groups. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | multiple_placement_groups | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='multiplePlacementGroups')
def multiple_placement_groups(self) -> Optional[pulumi.Input[bool]]:
'\n \n '
return pulumi.get(self, 'multiple_placement_groups') | @property
@pulumi.getter(name='multiplePlacementGroups')
def multiple_placement_groups(self) -> Optional[pulumi.Input[bool]]:
'\n \n '
return pulumi.get(self, 'multiple_placement_groups')<|docstring|>Indicates if scale set associated with the node type can be composed of multiple placement groups.<|endoftext|> |
891934ce1055c19f8e0f71b0a42af02a6d1401cbb7f9d8dd9af34b16eddd75f6 | @property
@pulumi.getter(name='nodeTypeName')
def node_type_name(self) -> Optional[pulumi.Input[str]]:
'\n The name of the node type.\n '
return pulumi.get(self, 'node_type_name') | The name of the node type. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | node_type_name | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='nodeTypeName')
def node_type_name(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'node_type_name') | @property
@pulumi.getter(name='nodeTypeName')
def node_type_name(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'node_type_name')<|docstring|>The name of the node type.<|endoftext|> |
a744587bb29fa2e3b0226aa5e65515e385297164fe324094e35364f191d94875 | @property
@pulumi.getter(name='placementProperties')
def placement_properties(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.\n '
return pulumi.get(self, 'placement_properties') | The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | placement_properties | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='placementProperties')
def placement_properties(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n \n '
return pulumi.get(self, 'placement_properties') | @property
@pulumi.getter(name='placementProperties')
def placement_properties(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n \n '
return pulumi.get(self, 'placement_properties')<|docstring|>The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.<|endoftext|> |
d2ad48d74500be2ed95374dcf56b8b21ea5acdf9b7e1eb9474a2c3a845a3f7de | @property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n Azure resource tags.\n '
return pulumi.get(self, 'tags') | Azure resource tags. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | tags | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n \n '
return pulumi.get(self, 'tags') | @property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n \n '
return pulumi.get(self, 'tags')<|docstring|>Azure resource tags.<|endoftext|> |
6be4e233af5e9d3c2f7f98ad597c98b3e3ffc9b0966d0094ee7738edc087a5ce | @property
@pulumi.getter(name='vmExtensions')
def vm_extensions(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]]]:
'\n Set of extensions that should be installed onto the virtual machines.\n '
return pulumi.get(self, 'vm_extensions') | Set of extensions that should be installed onto the virtual machines. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_extensions | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmExtensions')
def vm_extensions(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]]]:
'\n \n '
return pulumi.get(self, 'vm_extensions') | @property
@pulumi.getter(name='vmExtensions')
def vm_extensions(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['VMSSExtensionArgs']]]]:
'\n \n '
return pulumi.get(self, 'vm_extensions')<|docstring|>Set of extensions that should be installed onto the virtual machines.<|endoftext|> |
3029b89dc22a2b31b4e7fb463827989648eec5ef66f60d761ce58128992a0b5c | @property
@pulumi.getter(name='vmImageOffer')
def vm_image_offer(self) -> Optional[pulumi.Input[str]]:
'\n The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.\n '
return pulumi.get(self, 'vm_image_offer') | The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_image_offer | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmImageOffer')
def vm_image_offer(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_offer') | @property
@pulumi.getter(name='vmImageOffer')
def vm_image_offer(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_offer')<|docstring|>The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.<|endoftext|> |
5918c58052f4581d39ed325a3dc63810e602937d9b81d20b1245df2bc87d6b76 | @property
@pulumi.getter(name='vmImagePublisher')
def vm_image_publisher(self) -> Optional[pulumi.Input[str]]:
'\n The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.\n '
return pulumi.get(self, 'vm_image_publisher') | The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_image_publisher | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmImagePublisher')
def vm_image_publisher(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_publisher') | @property
@pulumi.getter(name='vmImagePublisher')
def vm_image_publisher(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_publisher')<|docstring|>The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.<|endoftext|> |
dc6c1ee3a898075440ca5a4f57d657e17ed276caf4438f1ac01d3d840edf415d | @property
@pulumi.getter(name='vmImageSku')
def vm_image_sku(self) -> Optional[pulumi.Input[str]]:
'\n The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.\n '
return pulumi.get(self, 'vm_image_sku') | The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_image_sku | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmImageSku')
def vm_image_sku(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_sku') | @property
@pulumi.getter(name='vmImageSku')
def vm_image_sku(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_sku')<|docstring|>The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.<|endoftext|> |
69b7d7088910c346e00e615882132def15e5fd32ff6968eed5ea412e799350d2 | @property
@pulumi.getter(name='vmImageVersion')
def vm_image_version(self) -> Optional[pulumi.Input[str]]:
"\n The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.\n "
return pulumi.get(self, 'vm_image_version') | The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_image_version | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmImageVersion')
def vm_image_version(self) -> Optional[pulumi.Input[str]]:
"\n \n "
return pulumi.get(self, 'vm_image_version') | @property
@pulumi.getter(name='vmImageVersion')
def vm_image_version(self) -> Optional[pulumi.Input[str]]:
"\n \n "
return pulumi.get(self, 'vm_image_version')<|docstring|>The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.<|endoftext|> |
6ca43c36959668a402475f236bd191238f3d3da12efa22e21f6e33b8ffc175ba | @property
@pulumi.getter(name='vmManagedIdentity')
def vm_managed_identity(self) -> Optional[pulumi.Input['VmManagedIdentityArgs']]:
'\n Identities for the virtual machine scale set under the node type.\n '
return pulumi.get(self, 'vm_managed_identity') | Identities for the virtual machine scale set under the node type. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_managed_identity | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmManagedIdentity')
def vm_managed_identity(self) -> Optional[pulumi.Input['VmManagedIdentityArgs']]:
'\n \n '
return pulumi.get(self, 'vm_managed_identity') | @property
@pulumi.getter(name='vmManagedIdentity')
def vm_managed_identity(self) -> Optional[pulumi.Input['VmManagedIdentityArgs']]:
'\n \n '
return pulumi.get(self, 'vm_managed_identity')<|docstring|>Identities for the virtual machine scale set under the node type.<|endoftext|> |
acf51520c66562a15df7d8624bdd1980276107981009967001e6d58606093bfa | @property
@pulumi.getter(name='vmSecrets')
def vm_secrets(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]]]:
'\n The secrets to install in the virtual machines.\n '
return pulumi.get(self, 'vm_secrets') | The secrets to install in the virtual machines. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_secrets | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmSecrets')
def vm_secrets(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]]]:
'\n \n '
return pulumi.get(self, 'vm_secrets') | @property
@pulumi.getter(name='vmSecrets')
def vm_secrets(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['VaultSecretGroupArgs']]]]:
'\n \n '
return pulumi.get(self, 'vm_secrets')<|docstring|>The secrets to install in the virtual machines.<|endoftext|> |
63f8bff09bda1900e5623d1dc959c8c14f709783fcb6b9086ffa0b2c73a40f73 | @property
@pulumi.getter(name='vmSize')
def vm_size(self) -> Optional[pulumi.Input[str]]:
'\n The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.\n '
return pulumi.get(self, 'vm_size') | The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_size | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmSize')
def vm_size(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'vm_size') | @property
@pulumi.getter(name='vmSize')
def vm_size(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'vm_size')<|docstring|>The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.<|endoftext|> |
8fc8619722876f25f85cd4f57dfb8e17afb9f6153946d9fdcc5a2d9de9c15e8a | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, application_ports: Optional[pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']]]=None, capacities: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, cluster_name: Optional[pulumi.Input[str]]=None, data_disk_size_gb: Optional[pulumi.Input[int]]=None, data_disk_type: Optional[pulumi.Input[Union[(str, 'DiskType')]]]=None, ephemeral_ports: Optional[pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']]]=None, is_primary: Optional[pulumi.Input[bool]]=None, is_stateless: Optional[pulumi.Input[bool]]=None, multiple_placement_groups: Optional[pulumi.Input[bool]]=None, node_type_name: Optional[pulumi.Input[str]]=None, placement_properties: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, resource_group_name: Optional[pulumi.Input[str]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, vm_extensions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VMSSExtensionArgs']]]]]=None, vm_image_offer: Optional[pulumi.Input[str]]=None, vm_image_publisher: Optional[pulumi.Input[str]]=None, vm_image_sku: Optional[pulumi.Input[str]]=None, vm_image_version: Optional[pulumi.Input[str]]=None, vm_instance_count: Optional[pulumi.Input[int]]=None, vm_managed_identity: Optional[pulumi.Input[pulumi.InputType['VmManagedIdentityArgs']]]=None, vm_secrets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VaultSecretGroupArgs']]]]]=None, vm_size: Optional[pulumi.Input[str]]=None, __props__=None):
"\n Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.\n :param pulumi.Input[str] cluster_name: The name of the cluster resource.\n :param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.\n :param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.\n :param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.\n :param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.\n :param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.\n :param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.\n :param pulumi.Input[str] node_type_name: The name of the node type.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.\n :param pulumi.Input[str] resource_group_name: The name of the resource group.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VMSSExtensionArgs']]]] vm_extensions: Set of extensions that should be installed onto the virtual machines.\n :param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.\n :param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.\n :param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.\n :param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.\n :param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.\n :param pulumi.Input[pulumi.InputType['VmManagedIdentityArgs']] vm_managed_identity: Identities for the virtual machine scale set under the node type.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VaultSecretGroupArgs']]]] vm_secrets: The secrets to install in the virtual machines.\n :param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.\n "
... | Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.
:param pulumi.Input[str] cluster_name: The name of the cluster resource.
:param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.
:param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.
:param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.
:param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.
:param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.
:param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.
:param pulumi.Input[str] node_type_name: The name of the node type.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VMSSExtensionArgs']]]] vm_extensions: Set of extensions that should be installed onto the virtual machines.
:param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.
:param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.
:param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.
:param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.
:param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.
:param pulumi.Input[pulumi.InputType['VmManagedIdentityArgs']] vm_managed_identity: Identities for the virtual machine scale set under the node type.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VaultSecretGroupArgs']]]] vm_secrets: The secrets to install in the virtual machines.
:param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | __init__ | polivbr/pulumi-azure-native | 0 | python | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, application_ports: Optional[pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']]]=None, capacities: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, cluster_name: Optional[pulumi.Input[str]]=None, data_disk_size_gb: Optional[pulumi.Input[int]]=None, data_disk_type: Optional[pulumi.Input[Union[(str, 'DiskType')]]]=None, ephemeral_ports: Optional[pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']]]=None, is_primary: Optional[pulumi.Input[bool]]=None, is_stateless: Optional[pulumi.Input[bool]]=None, multiple_placement_groups: Optional[pulumi.Input[bool]]=None, node_type_name: Optional[pulumi.Input[str]]=None, placement_properties: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, resource_group_name: Optional[pulumi.Input[str]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, vm_extensions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VMSSExtensionArgs']]]]]=None, vm_image_offer: Optional[pulumi.Input[str]]=None, vm_image_publisher: Optional[pulumi.Input[str]]=None, vm_image_sku: Optional[pulumi.Input[str]]=None, vm_image_version: Optional[pulumi.Input[str]]=None, vm_instance_count: Optional[pulumi.Input[int]]=None, vm_managed_identity: Optional[pulumi.Input[pulumi.InputType['VmManagedIdentityArgs']]]=None, vm_secrets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VaultSecretGroupArgs']]]]]=None, vm_size: Optional[pulumi.Input[str]]=None, __props__=None):
"\n Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.\n :param pulumi.Input[str] cluster_name: The name of the cluster resource.\n :param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.\n :param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.\n :param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.\n :param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.\n :param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.\n :param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.\n :param pulumi.Input[str] node_type_name: The name of the node type.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.\n :param pulumi.Input[str] resource_group_name: The name of the resource group.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VMSSExtensionArgs']]]] vm_extensions: Set of extensions that should be installed onto the virtual machines.\n :param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.\n :param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.\n :param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.\n :param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.\n :param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.\n :param pulumi.Input[pulumi.InputType['VmManagedIdentityArgs']] vm_managed_identity: Identities for the virtual machine scale set under the node type.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VaultSecretGroupArgs']]]] vm_secrets: The secrets to install in the virtual machines.\n :param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.\n "
... | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, application_ports: Optional[pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']]]=None, capacities: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, cluster_name: Optional[pulumi.Input[str]]=None, data_disk_size_gb: Optional[pulumi.Input[int]]=None, data_disk_type: Optional[pulumi.Input[Union[(str, 'DiskType')]]]=None, ephemeral_ports: Optional[pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']]]=None, is_primary: Optional[pulumi.Input[bool]]=None, is_stateless: Optional[pulumi.Input[bool]]=None, multiple_placement_groups: Optional[pulumi.Input[bool]]=None, node_type_name: Optional[pulumi.Input[str]]=None, placement_properties: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, resource_group_name: Optional[pulumi.Input[str]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, vm_extensions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VMSSExtensionArgs']]]]]=None, vm_image_offer: Optional[pulumi.Input[str]]=None, vm_image_publisher: Optional[pulumi.Input[str]]=None, vm_image_sku: Optional[pulumi.Input[str]]=None, vm_image_version: Optional[pulumi.Input[str]]=None, vm_instance_count: Optional[pulumi.Input[int]]=None, vm_managed_identity: Optional[pulumi.Input[pulumi.InputType['VmManagedIdentityArgs']]]=None, vm_secrets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VaultSecretGroupArgs']]]]]=None, vm_size: Optional[pulumi.Input[str]]=None, __props__=None):
"\n Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.\n :param pulumi.Input[str] cluster_name: The name of the cluster resource.\n :param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.\n :param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.\n :param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.\n :param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.\n :param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.\n :param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.\n :param pulumi.Input[str] node_type_name: The name of the node type.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.\n :param pulumi.Input[str] resource_group_name: The name of the resource group.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VMSSExtensionArgs']]]] vm_extensions: Set of extensions that should be installed onto the virtual machines.\n :param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.\n :param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.\n :param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.\n :param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.\n :param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.\n :param pulumi.Input[pulumi.InputType['VmManagedIdentityArgs']] vm_managed_identity: Identities for the virtual machine scale set under the node type.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VaultSecretGroupArgs']]]] vm_secrets: The secrets to install in the virtual machines.\n :param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.\n "
...<|docstring|>Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] application_ports: The range of ports from which cluster assigned port to Service Fabric applications.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] capacities: The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.
:param pulumi.Input[str] cluster_name: The name of the cluster resource.
:param pulumi.Input[int] data_disk_size_gb: Disk size for each vm in the node type in GBs.
:param pulumi.Input[Union[str, 'DiskType']] data_disk_type: Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.
:param pulumi.Input[pulumi.InputType['EndpointRangeDescriptionArgs']] ephemeral_ports: The range of ephemeral ports that nodes in this node type should be configured with.
:param pulumi.Input[bool] is_primary: The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.
:param pulumi.Input[bool] is_stateless: Indicates if the node type can only host Stateless workloads.
:param pulumi.Input[bool] multiple_placement_groups: Indicates if scale set associated with the node type can be composed of multiple placement groups.
:param pulumi.Input[str] node_type_name: The name of the node type.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] placement_properties: The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Azure resource tags.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VMSSExtensionArgs']]]] vm_extensions: Set of extensions that should be installed onto the virtual machines.
:param pulumi.Input[str] vm_image_offer: The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.
:param pulumi.Input[str] vm_image_publisher: The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.
:param pulumi.Input[str] vm_image_sku: The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.
:param pulumi.Input[str] vm_image_version: The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.
:param pulumi.Input[int] vm_instance_count: The number of nodes in the node type.
:param pulumi.Input[pulumi.InputType['VmManagedIdentityArgs']] vm_managed_identity: Identities for the virtual machine scale set under the node type.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VaultSecretGroupArgs']]]] vm_secrets: The secrets to install in the virtual machines.
:param pulumi.Input[str] vm_size: The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.<|endoftext|> |
f7a46d6476023bbe6170d33ac3a817587452a3e253f04068a60ceb68c7b6ea2a | @overload
def __init__(__self__, resource_name: str, args: NodeTypeArgs, opts: Optional[pulumi.ResourceOptions]=None):
"\n Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.\n\n :param str resource_name: The name of the resource.\n :param NodeTypeArgs args: The arguments to use to populate this resource's properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
... | Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.
:param str resource_name: The name of the resource.
:param NodeTypeArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | __init__ | polivbr/pulumi-azure-native | 0 | python | @overload
def __init__(__self__, resource_name: str, args: NodeTypeArgs, opts: Optional[pulumi.ResourceOptions]=None):
"\n Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.\n\n :param str resource_name: The name of the resource.\n :param NodeTypeArgs args: The arguments to use to populate this resource's properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
... | @overload
def __init__(__self__, resource_name: str, args: NodeTypeArgs, opts: Optional[pulumi.ResourceOptions]=None):
"\n Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.\n\n :param str resource_name: The name of the resource.\n :param NodeTypeArgs args: The arguments to use to populate this resource's properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
...<|docstring|>Describes a node type in the cluster, each node type represents sub set of nodes in the cluster.
:param str resource_name: The name of the resource.
:param NodeTypeArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.<|endoftext|> |
d02e442e725031dc51aef18497aa22e64d97228db241b7847d2ff5572ce81d7b | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'NodeType':
"\n Get an existing NodeType 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 name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = NodeTypeArgs.__new__(NodeTypeArgs)
__props__.__dict__['application_ports'] = None
__props__.__dict__['capacities'] = None
__props__.__dict__['data_disk_size_gb'] = None
__props__.__dict__['data_disk_type'] = None
__props__.__dict__['ephemeral_ports'] = None
__props__.__dict__['is_primary'] = None
__props__.__dict__['is_stateless'] = None
__props__.__dict__['multiple_placement_groups'] = None
__props__.__dict__['name'] = None
__props__.__dict__['placement_properties'] = None
__props__.__dict__['provisioning_state'] = None
__props__.__dict__['system_data'] = None
__props__.__dict__['tags'] = None
__props__.__dict__['type'] = None
__props__.__dict__['vm_extensions'] = None
__props__.__dict__['vm_image_offer'] = None
__props__.__dict__['vm_image_publisher'] = None
__props__.__dict__['vm_image_sku'] = None
__props__.__dict__['vm_image_version'] = None
__props__.__dict__['vm_instance_count'] = None
__props__.__dict__['vm_managed_identity'] = None
__props__.__dict__['vm_secrets'] = None
__props__.__dict__['vm_size'] = None
return NodeType(resource_name, opts=opts, __props__=__props__) | Get an existing NodeType 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 the resource. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | get | polivbr/pulumi-azure-native | 0 | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'NodeType':
"\n Get an existing NodeType 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 name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = NodeTypeArgs.__new__(NodeTypeArgs)
__props__.__dict__['application_ports'] = None
__props__.__dict__['capacities'] = None
__props__.__dict__['data_disk_size_gb'] = None
__props__.__dict__['data_disk_type'] = None
__props__.__dict__['ephemeral_ports'] = None
__props__.__dict__['is_primary'] = None
__props__.__dict__['is_stateless'] = None
__props__.__dict__['multiple_placement_groups'] = None
__props__.__dict__['name'] = None
__props__.__dict__['placement_properties'] = None
__props__.__dict__['provisioning_state'] = None
__props__.__dict__['system_data'] = None
__props__.__dict__['tags'] = None
__props__.__dict__['type'] = None
__props__.__dict__['vm_extensions'] = None
__props__.__dict__['vm_image_offer'] = None
__props__.__dict__['vm_image_publisher'] = None
__props__.__dict__['vm_image_sku'] = None
__props__.__dict__['vm_image_version'] = None
__props__.__dict__['vm_instance_count'] = None
__props__.__dict__['vm_managed_identity'] = None
__props__.__dict__['vm_secrets'] = None
__props__.__dict__['vm_size'] = None
return NodeType(resource_name, opts=opts, __props__=__props__) | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'NodeType':
"\n Get an existing NodeType 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 name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = NodeTypeArgs.__new__(NodeTypeArgs)
__props__.__dict__['application_ports'] = None
__props__.__dict__['capacities'] = None
__props__.__dict__['data_disk_size_gb'] = None
__props__.__dict__['data_disk_type'] = None
__props__.__dict__['ephemeral_ports'] = None
__props__.__dict__['is_primary'] = None
__props__.__dict__['is_stateless'] = None
__props__.__dict__['multiple_placement_groups'] = None
__props__.__dict__['name'] = None
__props__.__dict__['placement_properties'] = None
__props__.__dict__['provisioning_state'] = None
__props__.__dict__['system_data'] = None
__props__.__dict__['tags'] = None
__props__.__dict__['type'] = None
__props__.__dict__['vm_extensions'] = None
__props__.__dict__['vm_image_offer'] = None
__props__.__dict__['vm_image_publisher'] = None
__props__.__dict__['vm_image_sku'] = None
__props__.__dict__['vm_image_version'] = None
__props__.__dict__['vm_instance_count'] = None
__props__.__dict__['vm_managed_identity'] = None
__props__.__dict__['vm_secrets'] = None
__props__.__dict__['vm_size'] = None
return NodeType(resource_name, opts=opts, __props__=__props__)<|docstring|>Get an existing NodeType 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 the resource.<|endoftext|> |
8edbb27f5ab5da248ce538753d15f8067fdf0dcad09e08e52230111fdecc1bff | @property
@pulumi.getter(name='applicationPorts')
def application_ports(self) -> pulumi.Output[Optional['outputs.EndpointRangeDescriptionResponse']]:
'\n The range of ports from which cluster assigned port to Service Fabric applications.\n '
return pulumi.get(self, 'application_ports') | The range of ports from which cluster assigned port to Service Fabric applications. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | application_ports | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='applicationPorts')
def application_ports(self) -> pulumi.Output[Optional['outputs.EndpointRangeDescriptionResponse']]:
'\n \n '
return pulumi.get(self, 'application_ports') | @property
@pulumi.getter(name='applicationPorts')
def application_ports(self) -> pulumi.Output[Optional['outputs.EndpointRangeDescriptionResponse']]:
'\n \n '
return pulumi.get(self, 'application_ports')<|docstring|>The range of ports from which cluster assigned port to Service Fabric applications.<|endoftext|> |
4acdf9c15d3b307d5460bd2304c1d4b078115c7208206dfac918ea283fac2df3 | @property
@pulumi.getter
def capacities(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.\n '
return pulumi.get(self, 'capacities') | The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | capacities | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter
def capacities(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'capacities') | @property
@pulumi.getter
def capacities(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'capacities')<|docstring|>The capacity tags applied to the nodes in the node type, the cluster resource manager uses these tags to understand how much resource a node has.<|endoftext|> |
84f3a767f698bdd6ab77411d80bdb238739c21585b418f4901ce6f992439fb5b | @property
@pulumi.getter(name='dataDiskSizeGB')
def data_disk_size_gb(self) -> pulumi.Output[int]:
'\n Disk size for each vm in the node type in GBs.\n '
return pulumi.get(self, 'data_disk_size_gb') | Disk size for each vm in the node type in GBs. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | data_disk_size_gb | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='dataDiskSizeGB')
def data_disk_size_gb(self) -> pulumi.Output[int]:
'\n \n '
return pulumi.get(self, 'data_disk_size_gb') | @property
@pulumi.getter(name='dataDiskSizeGB')
def data_disk_size_gb(self) -> pulumi.Output[int]:
'\n \n '
return pulumi.get(self, 'data_disk_size_gb')<|docstring|>Disk size for each vm in the node type in GBs.<|endoftext|> |
b8f8d2f0ffb45eb36c89af837ace3f50f6511fe61c78b6e20fdffd89a6b202ae | @property
@pulumi.getter(name='dataDiskType')
def data_disk_type(self) -> pulumi.Output[Optional[str]]:
'\n Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.\n '
return pulumi.get(self, 'data_disk_type') | Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | data_disk_type | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='dataDiskType')
def data_disk_type(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'data_disk_type') | @property
@pulumi.getter(name='dataDiskType')
def data_disk_type(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'data_disk_type')<|docstring|>Managed data disk type. IOPS and throughput are given by the disk size, to see more information go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.<|endoftext|> |
df7c5037daf4e73db536995eb83bae04c0cf0fd94d129117c27e511f5a4247ac | @property
@pulumi.getter(name='ephemeralPorts')
def ephemeral_ports(self) -> pulumi.Output[Optional['outputs.EndpointRangeDescriptionResponse']]:
'\n The range of ephemeral ports that nodes in this node type should be configured with.\n '
return pulumi.get(self, 'ephemeral_ports') | The range of ephemeral ports that nodes in this node type should be configured with. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | ephemeral_ports | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='ephemeralPorts')
def ephemeral_ports(self) -> pulumi.Output[Optional['outputs.EndpointRangeDescriptionResponse']]:
'\n \n '
return pulumi.get(self, 'ephemeral_ports') | @property
@pulumi.getter(name='ephemeralPorts')
def ephemeral_ports(self) -> pulumi.Output[Optional['outputs.EndpointRangeDescriptionResponse']]:
'\n \n '
return pulumi.get(self, 'ephemeral_ports')<|docstring|>The range of ephemeral ports that nodes in this node type should be configured with.<|endoftext|> |
ec9433a1dff92a3849979e5271b125f46198b3fedbd47227fd1c719594046e4b | @property
@pulumi.getter(name='isPrimary')
def is_primary(self) -> pulumi.Output[bool]:
'\n The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.\n '
return pulumi.get(self, 'is_primary') | The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | is_primary | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='isPrimary')
def is_primary(self) -> pulumi.Output[bool]:
'\n \n '
return pulumi.get(self, 'is_primary') | @property
@pulumi.getter(name='isPrimary')
def is_primary(self) -> pulumi.Output[bool]:
'\n \n '
return pulumi.get(self, 'is_primary')<|docstring|>The node type on which system services will run. Only one node type should be marked as primary. Primary node type cannot be deleted or changed for existing clusters.<|endoftext|> |
7423ae33d09dc294dce63a3ea6476b0765c4f4649d252967bb39355b4034e74e | @property
@pulumi.getter(name='isStateless')
def is_stateless(self) -> pulumi.Output[Optional[bool]]:
'\n Indicates if the node type can only host Stateless workloads.\n '
return pulumi.get(self, 'is_stateless') | Indicates if the node type can only host Stateless workloads. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | is_stateless | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='isStateless')
def is_stateless(self) -> pulumi.Output[Optional[bool]]:
'\n \n '
return pulumi.get(self, 'is_stateless') | @property
@pulumi.getter(name='isStateless')
def is_stateless(self) -> pulumi.Output[Optional[bool]]:
'\n \n '
return pulumi.get(self, 'is_stateless')<|docstring|>Indicates if the node type can only host Stateless workloads.<|endoftext|> |
488ba4369fa9b3041b52a7794259d85c1244de1ba8bd3b775413c6ba4004b863 | @property
@pulumi.getter(name='multiplePlacementGroups')
def multiple_placement_groups(self) -> pulumi.Output[Optional[bool]]:
'\n Indicates if scale set associated with the node type can be composed of multiple placement groups.\n '
return pulumi.get(self, 'multiple_placement_groups') | Indicates if scale set associated with the node type can be composed of multiple placement groups. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | multiple_placement_groups | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='multiplePlacementGroups')
def multiple_placement_groups(self) -> pulumi.Output[Optional[bool]]:
'\n \n '
return pulumi.get(self, 'multiple_placement_groups') | @property
@pulumi.getter(name='multiplePlacementGroups')
def multiple_placement_groups(self) -> pulumi.Output[Optional[bool]]:
'\n \n '
return pulumi.get(self, 'multiple_placement_groups')<|docstring|>Indicates if scale set associated with the node type can be composed of multiple placement groups.<|endoftext|> |
721c782f20ef337abdf9d991847b51dec0a2b625da009bd6b13fc31a3e4def2c | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n Azure resource name.\n '
return pulumi.get(self, 'name') | Azure resource name. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | name | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>Azure resource name.<|endoftext|> |
5d581ab538c20150462d627bc644d44f3c44802d7f87e205e413694a88bba688 | @property
@pulumi.getter(name='placementProperties')
def placement_properties(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.\n '
return pulumi.get(self, 'placement_properties') | The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | placement_properties | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='placementProperties')
def placement_properties(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'placement_properties') | @property
@pulumi.getter(name='placementProperties')
def placement_properties(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'placement_properties')<|docstring|>The placement tags applied to nodes in the node type, which can be used to indicate where certain services (workload) should run.<|endoftext|> |
73cbe380fa9be9eb1083fe2e96416ce5b7fb4d6baa3095d4508bcf2f90753a5b | @property
@pulumi.getter(name='provisioningState')
def provisioning_state(self) -> pulumi.Output[str]:
'\n The provisioning state of the managed cluster resource.\n '
return pulumi.get(self, 'provisioning_state') | The provisioning state of the managed cluster resource. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | provisioning_state | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='provisioningState')
def provisioning_state(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'provisioning_state') | @property
@pulumi.getter(name='provisioningState')
def provisioning_state(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'provisioning_state')<|docstring|>The provisioning state of the managed cluster resource.<|endoftext|> |
77ff99fdf31084d6af0c9b83478af9b5fb69a279c25c4cddc7c5a4234772fe27 | @property
@pulumi.getter(name='systemData')
def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']:
'\n Metadata pertaining to creation and last modification of the resource.\n '
return pulumi.get(self, 'system_data') | Metadata pertaining to creation and last modification of the resource. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | system_data | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='systemData')
def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']:
'\n \n '
return pulumi.get(self, 'system_data') | @property
@pulumi.getter(name='systemData')
def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']:
'\n \n '
return pulumi.get(self, 'system_data')<|docstring|>Metadata pertaining to creation and last modification of the resource.<|endoftext|> |
6d1a452a09c56b6d2867db287f8270888723c247484107467350fd171695df4a | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n Azure resource tags.\n '
return pulumi.get(self, 'tags') | Azure resource tags. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | tags | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'tags') | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'tags')<|docstring|>Azure resource tags.<|endoftext|> |
a6fb1638d2453686fcfbc8b0f024490117759c5d17387bfce58d44117d9f21d5 | @property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n Azure resource type.\n '
return pulumi.get(self, 'type') | Azure resource type. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | type | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'type') | @property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'type')<|docstring|>Azure resource type.<|endoftext|> |
f0e05de2e9ee77db2b043a61527207c5df020e591dc48dccc4720791c0828c85 | @property
@pulumi.getter(name='vmExtensions')
def vm_extensions(self) -> pulumi.Output[Optional[Sequence['outputs.VMSSExtensionResponse']]]:
'\n Set of extensions that should be installed onto the virtual machines.\n '
return pulumi.get(self, 'vm_extensions') | Set of extensions that should be installed onto the virtual machines. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_extensions | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmExtensions')
def vm_extensions(self) -> pulumi.Output[Optional[Sequence['outputs.VMSSExtensionResponse']]]:
'\n \n '
return pulumi.get(self, 'vm_extensions') | @property
@pulumi.getter(name='vmExtensions')
def vm_extensions(self) -> pulumi.Output[Optional[Sequence['outputs.VMSSExtensionResponse']]]:
'\n \n '
return pulumi.get(self, 'vm_extensions')<|docstring|>Set of extensions that should be installed onto the virtual machines.<|endoftext|> |
268b2360b366a90cc9a53afc747dfe656ef65a62154aa78d675eedf8dbec28bd | @property
@pulumi.getter(name='vmImageOffer')
def vm_image_offer(self) -> pulumi.Output[Optional[str]]:
'\n The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.\n '
return pulumi.get(self, 'vm_image_offer') | The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_image_offer | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmImageOffer')
def vm_image_offer(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_offer') | @property
@pulumi.getter(name='vmImageOffer')
def vm_image_offer(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_offer')<|docstring|>The offer type of the Azure Virtual Machines Marketplace image. For example, UbuntuServer or WindowsServer.<|endoftext|> |
002ddf16b7d1beda811fa9bb429d40ceef6130201d05c75869db64775c37409d | @property
@pulumi.getter(name='vmImagePublisher')
def vm_image_publisher(self) -> pulumi.Output[Optional[str]]:
'\n The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.\n '
return pulumi.get(self, 'vm_image_publisher') | The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_image_publisher | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmImagePublisher')
def vm_image_publisher(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_publisher') | @property
@pulumi.getter(name='vmImagePublisher')
def vm_image_publisher(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_publisher')<|docstring|>The publisher of the Azure Virtual Machines Marketplace image. For example, Canonical or MicrosoftWindowsServer.<|endoftext|> |
18af5fc915148f7538691f0fe541d5d6b70790f6996769736d69a0aa0290c606 | @property
@pulumi.getter(name='vmImageSku')
def vm_image_sku(self) -> pulumi.Output[Optional[str]]:
'\n The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.\n '
return pulumi.get(self, 'vm_image_sku') | The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_image_sku | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmImageSku')
def vm_image_sku(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_sku') | @property
@pulumi.getter(name='vmImageSku')
def vm_image_sku(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'vm_image_sku')<|docstring|>The SKU of the Azure Virtual Machines Marketplace image. For example, 14.04.0-LTS or 2012-R2-Datacenter.<|endoftext|> |
9d0410b38206de81f19338a8bc648f037b40420564c33246569c7e0b7c75ea45 | @property
@pulumi.getter(name='vmImageVersion')
def vm_image_version(self) -> pulumi.Output[Optional[str]]:
"\n The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.\n "
return pulumi.get(self, 'vm_image_version') | The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_image_version | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmImageVersion')
def vm_image_version(self) -> pulumi.Output[Optional[str]]:
"\n \n "
return pulumi.get(self, 'vm_image_version') | @property
@pulumi.getter(name='vmImageVersion')
def vm_image_version(self) -> pulumi.Output[Optional[str]]:
"\n \n "
return pulumi.get(self, 'vm_image_version')<|docstring|>The version of the Azure Virtual Machines Marketplace image. A value of 'latest' can be specified to select the latest version of an image. If omitted, the default is 'latest'.<|endoftext|> |
753364731bff2de105fd6f64de1dd8a46047700085ea0614ca3d01bdaa01ad40 | @property
@pulumi.getter(name='vmInstanceCount')
def vm_instance_count(self) -> pulumi.Output[int]:
'\n The number of nodes in the node type.\n '
return pulumi.get(self, 'vm_instance_count') | The number of nodes in the node type. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_instance_count | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmInstanceCount')
def vm_instance_count(self) -> pulumi.Output[int]:
'\n \n '
return pulumi.get(self, 'vm_instance_count') | @property
@pulumi.getter(name='vmInstanceCount')
def vm_instance_count(self) -> pulumi.Output[int]:
'\n \n '
return pulumi.get(self, 'vm_instance_count')<|docstring|>The number of nodes in the node type.<|endoftext|> |
9bf67215d714adafd0092942069552e607c90bb51b2c2579765152093c951dfe | @property
@pulumi.getter(name='vmManagedIdentity')
def vm_managed_identity(self) -> pulumi.Output[Optional['outputs.VmManagedIdentityResponse']]:
'\n Identities for the virtual machine scale set under the node type.\n '
return pulumi.get(self, 'vm_managed_identity') | Identities for the virtual machine scale set under the node type. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_managed_identity | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmManagedIdentity')
def vm_managed_identity(self) -> pulumi.Output[Optional['outputs.VmManagedIdentityResponse']]:
'\n \n '
return pulumi.get(self, 'vm_managed_identity') | @property
@pulumi.getter(name='vmManagedIdentity')
def vm_managed_identity(self) -> pulumi.Output[Optional['outputs.VmManagedIdentityResponse']]:
'\n \n '
return pulumi.get(self, 'vm_managed_identity')<|docstring|>Identities for the virtual machine scale set under the node type.<|endoftext|> |
487dc44b3119c62f2e3b282224c6cda87443c94b55fe6863d98bc00374e5d26f | @property
@pulumi.getter(name='vmSecrets')
def vm_secrets(self) -> pulumi.Output[Optional[Sequence['outputs.VaultSecretGroupResponse']]]:
'\n The secrets to install in the virtual machines.\n '
return pulumi.get(self, 'vm_secrets') | The secrets to install in the virtual machines. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_secrets | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmSecrets')
def vm_secrets(self) -> pulumi.Output[Optional[Sequence['outputs.VaultSecretGroupResponse']]]:
'\n \n '
return pulumi.get(self, 'vm_secrets') | @property
@pulumi.getter(name='vmSecrets')
def vm_secrets(self) -> pulumi.Output[Optional[Sequence['outputs.VaultSecretGroupResponse']]]:
'\n \n '
return pulumi.get(self, 'vm_secrets')<|docstring|>The secrets to install in the virtual machines.<|endoftext|> |
d1bcf9ca15f7c05f302337b4803022528afcb3e4b35ca0f6f861257513ea7781 | @property
@pulumi.getter(name='vmSize')
def vm_size(self) -> pulumi.Output[Optional[str]]:
'\n The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.\n '
return pulumi.get(self, 'vm_size') | The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3. | sdk/python/pulumi_azure_native/servicefabric/v20210501/node_type.py | vm_size | polivbr/pulumi-azure-native | 0 | python | @property
@pulumi.getter(name='vmSize')
def vm_size(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'vm_size') | @property
@pulumi.getter(name='vmSize')
def vm_size(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'vm_size')<|docstring|>The size of virtual machines in the pool. All virtual machines in a pool are the same size. For example, Standard_D3.<|endoftext|> |
aeb25c9b78bdd85754f3a32c59843f229f7bc6e982effebde5fba27501a4b335 | def centerize(src, shape, margin_color=None, return_mask=False):
'Centerize image for specified image size\n Parameters\n ----------\n src: numpy.ndarray\n Image to centerize\n shape: tuple of int\n Image shape (height, width) or (height, width, channel)\n margin_color: numpy.ndarray\n Color to be filled in the blank.\n return_mask: numpy.ndarray\n Mask for centerized image.\n '
if (src.shape[:2] == shape[:2]):
if return_mask:
return (src, np.ones(shape[:2], dtype=bool))
else:
return src
if (len(shape) != src.ndim):
shape = (list(shape) + [src.shape[2]])
centerized = np.zeros(shape, dtype=src.dtype)
if margin_color:
centerized[(:, :)] = margin_color
(src_h, src_w) = src.shape[:2]
(scale_h, scale_w) = (((1.0 * shape[0]) / src_h), ((1.0 * shape[1]) / src_w))
scale = min(scale_h, scale_w)
dtype = src.dtype
src = skimage.transform.rescale(src, scale, preserve_range=True)
src = src.astype(dtype)
(ph, pw) = (0, 0)
(h, w) = src.shape[:2]
(dst_h, dst_w) = shape[:2]
if (h < dst_h):
ph = ((dst_h - h) // 2)
if (w < dst_w):
pw = ((dst_w - w) // 2)
mask = np.zeros(shape[:2], dtype=bool)
mask[(ph:(ph + h), pw:(pw + w))] = True
centerized[(ph:(ph + h), pw:(pw + w))] = src
if return_mask:
return (centerized, mask)
else:
return centerized | Centerize image for specified image size
Parameters
----------
src: numpy.ndarray
Image to centerize
shape: tuple of int
Image shape (height, width) or (height, width, channel)
margin_color: numpy.ndarray
Color to be filled in the blank.
return_mask: numpy.ndarray
Mask for centerized image. | jsk_arc2017_common/python/jsk_arc2017_common/utils.py | centerize | pazeshun/jsk_apc | 0 | python | def centerize(src, shape, margin_color=None, return_mask=False):
'Centerize image for specified image size\n Parameters\n ----------\n src: numpy.ndarray\n Image to centerize\n shape: tuple of int\n Image shape (height, width) or (height, width, channel)\n margin_color: numpy.ndarray\n Color to be filled in the blank.\n return_mask: numpy.ndarray\n Mask for centerized image.\n '
if (src.shape[:2] == shape[:2]):
if return_mask:
return (src, np.ones(shape[:2], dtype=bool))
else:
return src
if (len(shape) != src.ndim):
shape = (list(shape) + [src.shape[2]])
centerized = np.zeros(shape, dtype=src.dtype)
if margin_color:
centerized[(:, :)] = margin_color
(src_h, src_w) = src.shape[:2]
(scale_h, scale_w) = (((1.0 * shape[0]) / src_h), ((1.0 * shape[1]) / src_w))
scale = min(scale_h, scale_w)
dtype = src.dtype
src = skimage.transform.rescale(src, scale, preserve_range=True)
src = src.astype(dtype)
(ph, pw) = (0, 0)
(h, w) = src.shape[:2]
(dst_h, dst_w) = shape[:2]
if (h < dst_h):
ph = ((dst_h - h) // 2)
if (w < dst_w):
pw = ((dst_w - w) // 2)
mask = np.zeros(shape[:2], dtype=bool)
mask[(ph:(ph + h), pw:(pw + w))] = True
centerized[(ph:(ph + h), pw:(pw + w))] = src
if return_mask:
return (centerized, mask)
else:
return centerized | def centerize(src, shape, margin_color=None, return_mask=False):
'Centerize image for specified image size\n Parameters\n ----------\n src: numpy.ndarray\n Image to centerize\n shape: tuple of int\n Image shape (height, width) or (height, width, channel)\n margin_color: numpy.ndarray\n Color to be filled in the blank.\n return_mask: numpy.ndarray\n Mask for centerized image.\n '
if (src.shape[:2] == shape[:2]):
if return_mask:
return (src, np.ones(shape[:2], dtype=bool))
else:
return src
if (len(shape) != src.ndim):
shape = (list(shape) + [src.shape[2]])
centerized = np.zeros(shape, dtype=src.dtype)
if margin_color:
centerized[(:, :)] = margin_color
(src_h, src_w) = src.shape[:2]
(scale_h, scale_w) = (((1.0 * shape[0]) / src_h), ((1.0 * shape[1]) / src_w))
scale = min(scale_h, scale_w)
dtype = src.dtype
src = skimage.transform.rescale(src, scale, preserve_range=True)
src = src.astype(dtype)
(ph, pw) = (0, 0)
(h, w) = src.shape[:2]
(dst_h, dst_w) = shape[:2]
if (h < dst_h):
ph = ((dst_h - h) // 2)
if (w < dst_w):
pw = ((dst_w - w) // 2)
mask = np.zeros(shape[:2], dtype=bool)
mask[(ph:(ph + h), pw:(pw + w))] = True
centerized[(ph:(ph + h), pw:(pw + w))] = src
if return_mask:
return (centerized, mask)
else:
return centerized<|docstring|>Centerize image for specified image size
Parameters
----------
src: numpy.ndarray
Image to centerize
shape: tuple of int
Image shape (height, width) or (height, width, channel)
margin_color: numpy.ndarray
Color to be filled in the blank.
return_mask: numpy.ndarray
Mask for centerized image.<|endoftext|> |
c3159eec1a9d2427f44ccb150373f7c80778d18b9ee9b04937c09aff852300e9 | def _tile(imgs, shape, dst):
'Tile images which have same size.\n Parameters\n ----------\n imgs: numpy.ndarray\n Image list which should be tiled.\n shape: tuple of int\n Tile shape.\n dst:\n Image to put the tile on.\n '
(y_num, x_num) = shape
tile_w = imgs[0].shape[1]
tile_h = imgs[0].shape[0]
if (dst is None):
if (len(imgs[0].shape) == 3):
dst = np.zeros(((tile_h * y_num), (tile_w * x_num), 3), dtype=np.uint8)
else:
dst = np.zeros(((tile_h * y_num), (tile_w * x_num)), dtype=np.uint8)
for y in range(y_num):
for x in range(x_num):
i = (x + (y * x_num))
if (i < len(imgs)):
y1 = (y * tile_h)
y2 = ((y + 1) * tile_h)
x1 = (x * tile_w)
x2 = ((x + 1) * tile_w)
dst[(y1:y2, x1:x2)] = imgs[i]
return dst | Tile images which have same size.
Parameters
----------
imgs: numpy.ndarray
Image list which should be tiled.
shape: tuple of int
Tile shape.
dst:
Image to put the tile on. | jsk_arc2017_common/python/jsk_arc2017_common/utils.py | _tile | pazeshun/jsk_apc | 0 | python | def _tile(imgs, shape, dst):
'Tile images which have same size.\n Parameters\n ----------\n imgs: numpy.ndarray\n Image list which should be tiled.\n shape: tuple of int\n Tile shape.\n dst:\n Image to put the tile on.\n '
(y_num, x_num) = shape
tile_w = imgs[0].shape[1]
tile_h = imgs[0].shape[0]
if (dst is None):
if (len(imgs[0].shape) == 3):
dst = np.zeros(((tile_h * y_num), (tile_w * x_num), 3), dtype=np.uint8)
else:
dst = np.zeros(((tile_h * y_num), (tile_w * x_num)), dtype=np.uint8)
for y in range(y_num):
for x in range(x_num):
i = (x + (y * x_num))
if (i < len(imgs)):
y1 = (y * tile_h)
y2 = ((y + 1) * tile_h)
x1 = (x * tile_w)
x2 = ((x + 1) * tile_w)
dst[(y1:y2, x1:x2)] = imgs[i]
return dst | def _tile(imgs, shape, dst):
'Tile images which have same size.\n Parameters\n ----------\n imgs: numpy.ndarray\n Image list which should be tiled.\n shape: tuple of int\n Tile shape.\n dst:\n Image to put the tile on.\n '
(y_num, x_num) = shape
tile_w = imgs[0].shape[1]
tile_h = imgs[0].shape[0]
if (dst is None):
if (len(imgs[0].shape) == 3):
dst = np.zeros(((tile_h * y_num), (tile_w * x_num), 3), dtype=np.uint8)
else:
dst = np.zeros(((tile_h * y_num), (tile_w * x_num)), dtype=np.uint8)
for y in range(y_num):
for x in range(x_num):
i = (x + (y * x_num))
if (i < len(imgs)):
y1 = (y * tile_h)
y2 = ((y + 1) * tile_h)
x1 = (x * tile_w)
x2 = ((x + 1) * tile_w)
dst[(y1:y2, x1:x2)] = imgs[i]
return dst<|docstring|>Tile images which have same size.
Parameters
----------
imgs: numpy.ndarray
Image list which should be tiled.
shape: tuple of int
Tile shape.
dst:
Image to put the tile on.<|endoftext|> |
8e7d2b2cd1e0f7cc65b028f05428fcbb2c5e917c4251108363044e4b64e90ad5 | def tile(imgs, shape=None, dst=None, margin_color=None):
'Tile images which have different size.\n Parameters\n ----------\n imgs:\n Image list which should be tiled.\n shape:\n The tile shape.\n dst:\n Image to put the tile on.\n margin_color: numpy.ndarray\n Color to be filled in the blank.\n '
if (shape is None):
shape = get_tile_shape(len(imgs))
(max_h, max_w) = (np.inf, np.inf)
for img in imgs:
max_h = min(max_h, img.shape[0])
max_w = min(max_w, img.shape[1])
is_color = False
for (i, img) in enumerate(imgs):
if (img.ndim >= 3):
is_color = True
if (is_color and (img.ndim == 2)):
img = skimage.color.gray2rgb(img)
if (is_color and (img.shape[2] == 4)):
img = img[(:, :, :3)]
img = skimage.util.img_as_ubyte(img)
img = centerize(img, (max_h, max_w, 3), margin_color)
imgs[i] = img
return _tile(imgs, shape, dst) | Tile images which have different size.
Parameters
----------
imgs:
Image list which should be tiled.
shape:
The tile shape.
dst:
Image to put the tile on.
margin_color: numpy.ndarray
Color to be filled in the blank. | jsk_arc2017_common/python/jsk_arc2017_common/utils.py | tile | pazeshun/jsk_apc | 0 | python | def tile(imgs, shape=None, dst=None, margin_color=None):
'Tile images which have different size.\n Parameters\n ----------\n imgs:\n Image list which should be tiled.\n shape:\n The tile shape.\n dst:\n Image to put the tile on.\n margin_color: numpy.ndarray\n Color to be filled in the blank.\n '
if (shape is None):
shape = get_tile_shape(len(imgs))
(max_h, max_w) = (np.inf, np.inf)
for img in imgs:
max_h = min(max_h, img.shape[0])
max_w = min(max_w, img.shape[1])
is_color = False
for (i, img) in enumerate(imgs):
if (img.ndim >= 3):
is_color = True
if (is_color and (img.ndim == 2)):
img = skimage.color.gray2rgb(img)
if (is_color and (img.shape[2] == 4)):
img = img[(:, :, :3)]
img = skimage.util.img_as_ubyte(img)
img = centerize(img, (max_h, max_w, 3), margin_color)
imgs[i] = img
return _tile(imgs, shape, dst) | def tile(imgs, shape=None, dst=None, margin_color=None):
'Tile images which have different size.\n Parameters\n ----------\n imgs:\n Image list which should be tiled.\n shape:\n The tile shape.\n dst:\n Image to put the tile on.\n margin_color: numpy.ndarray\n Color to be filled in the blank.\n '
if (shape is None):
shape = get_tile_shape(len(imgs))
(max_h, max_w) = (np.inf, np.inf)
for img in imgs:
max_h = min(max_h, img.shape[0])
max_w = min(max_w, img.shape[1])
is_color = False
for (i, img) in enumerate(imgs):
if (img.ndim >= 3):
is_color = True
if (is_color and (img.ndim == 2)):
img = skimage.color.gray2rgb(img)
if (is_color and (img.shape[2] == 4)):
img = img[(:, :, :3)]
img = skimage.util.img_as_ubyte(img)
img = centerize(img, (max_h, max_w, 3), margin_color)
imgs[i] = img
return _tile(imgs, shape, dst)<|docstring|>Tile images which have different size.
Parameters
----------
imgs:
Image list which should be tiled.
shape:
The tile shape.
dst:
Image to put the tile on.
margin_color: numpy.ndarray
Color to be filled in the blank.<|endoftext|> |
d99c6d5a1748a2d1c5db23681f2db25698d3396f273604cac9bae3eb292f7b09 | def ui_to_py(filename: str, filepath: str=os.path.dirname(__file__), outputpath: str=os.path.dirname(__file__)) -> None:
'\n Converts a .ui file from Qt Designer to a python script.\n\n Args:\n filename (str): The .ui file name to be converted.\n filepath (str): The .ui file path to be converted.\n outputpath (str): The output directory to the python script.\n\n Returns:\n None.\n '
if (isinstance(filename, str) and isinstance(filepath, str)):
if (not (' ' in filename)):
if os.path.isfile('{}\\{}.ui'.format(filepath, filename)):
filepath = filepath
filename = filename
chk_py = os.path.isfile('{}\\{}.py'.format(filepath, filename))
os.system('cd {0} & pyuic5 -x {1}.ui -o {1}.py'.format(filepath, filename))
shutil.move('{}\\{}.py'.format(filepath, filename), '{}\\{}.py'.format(outputpath, filename))
if chk_py:
print('File Converter Info: {}.py file updated.'.format(filename))
else:
print('File Converter Info: {}.py file created.'.format(filename))
else:
print("File Converter Alert: The {}.ui file doesn't exist.".format(filename))
else:
print('File Converter Error: The filename contains spaces.')
else:
print('File Converter Error: Arguments are not string.') | Converts a .ui file from Qt Designer to a python script.
Args:
filename (str): The .ui file name to be converted.
filepath (str): The .ui file path to be converted.
outputpath (str): The output directory to the python script.
Returns:
None. | models/toolscontext/fileconverter.py | ui_to_py | vinirossa/password_generator_test | 2 | python | def ui_to_py(filename: str, filepath: str=os.path.dirname(__file__), outputpath: str=os.path.dirname(__file__)) -> None:
'\n Converts a .ui file from Qt Designer to a python script.\n\n Args:\n filename (str): The .ui file name to be converted.\n filepath (str): The .ui file path to be converted.\n outputpath (str): The output directory to the python script.\n\n Returns:\n None.\n '
if (isinstance(filename, str) and isinstance(filepath, str)):
if (not (' ' in filename)):
if os.path.isfile('{}\\{}.ui'.format(filepath, filename)):
filepath = filepath
filename = filename
chk_py = os.path.isfile('{}\\{}.py'.format(filepath, filename))
os.system('cd {0} & pyuic5 -x {1}.ui -o {1}.py'.format(filepath, filename))
shutil.move('{}\\{}.py'.format(filepath, filename), '{}\\{}.py'.format(outputpath, filename))
if chk_py:
print('File Converter Info: {}.py file updated.'.format(filename))
else:
print('File Converter Info: {}.py file created.'.format(filename))
else:
print("File Converter Alert: The {}.ui file doesn't exist.".format(filename))
else:
print('File Converter Error: The filename contains spaces.')
else:
print('File Converter Error: Arguments are not string.') | def ui_to_py(filename: str, filepath: str=os.path.dirname(__file__), outputpath: str=os.path.dirname(__file__)) -> None:
'\n Converts a .ui file from Qt Designer to a python script.\n\n Args:\n filename (str): The .ui file name to be converted.\n filepath (str): The .ui file path to be converted.\n outputpath (str): The output directory to the python script.\n\n Returns:\n None.\n '
if (isinstance(filename, str) and isinstance(filepath, str)):
if (not (' ' in filename)):
if os.path.isfile('{}\\{}.ui'.format(filepath, filename)):
filepath = filepath
filename = filename
chk_py = os.path.isfile('{}\\{}.py'.format(filepath, filename))
os.system('cd {0} & pyuic5 -x {1}.ui -o {1}.py'.format(filepath, filename))
shutil.move('{}\\{}.py'.format(filepath, filename), '{}\\{}.py'.format(outputpath, filename))
if chk_py:
print('File Converter Info: {}.py file updated.'.format(filename))
else:
print('File Converter Info: {}.py file created.'.format(filename))
else:
print("File Converter Alert: The {}.ui file doesn't exist.".format(filename))
else:
print('File Converter Error: The filename contains spaces.')
else:
print('File Converter Error: Arguments are not string.')<|docstring|>Converts a .ui file from Qt Designer to a python script.
Args:
filename (str): The .ui file name to be converted.
filepath (str): The .ui file path to be converted.
outputpath (str): The output directory to the python script.
Returns:
None.<|endoftext|> |
3633f7d8cb689d79ad4c34669701978bc9e3ccc59bcd5bd002f924b872a62a7e | def qrc_to_py(filename: str, filepath: str=os.path.dirname(__file__), outputpath: str=os.path.dirname(__file__)) -> None:
'\n Converts a .qrc file from Qt Designer to a python script.\n\n Args:\n filename (str): The .qrc file name to be converted.\n filepath (str): The .qrc file path to be converted.\n outputpath (str): The output directory to the python script.\n\n Returns:\n None.\n '
if (isinstance(filename, str) and isinstance(filepath, str)):
if (not (' ' in filename)):
if os.path.isfile('{}\\{}.qrc'.format(filepath, filename)):
filepath = filepath
filename = filename
chk_py = os.path.isfile('{}\\{}.py'.format(filepath, filename))
os.system('cd {0} & pyrcc5 {1}.qrc -o {1}.py'.format(filepath, filename))
shutil.move('{}\\{}.py'.format(filepath, filename), '{}\\{}.py'.format(outputpath, filename))
if chk_py:
print('File Converter Info: {}.py file updated.'.format(filename))
else:
print('File Converter Info: {}.py file created.'.format(filename))
else:
print("File Converter Alert: The {}.qrc file doesn't exist.".format(filename))
else:
print('File Converter Error: The filename contains spaces.')
else:
print('File Converter Error: Arguments are not string.') | Converts a .qrc file from Qt Designer to a python script.
Args:
filename (str): The .qrc file name to be converted.
filepath (str): The .qrc file path to be converted.
outputpath (str): The output directory to the python script.
Returns:
None. | models/toolscontext/fileconverter.py | qrc_to_py | vinirossa/password_generator_test | 2 | python | def qrc_to_py(filename: str, filepath: str=os.path.dirname(__file__), outputpath: str=os.path.dirname(__file__)) -> None:
'\n Converts a .qrc file from Qt Designer to a python script.\n\n Args:\n filename (str): The .qrc file name to be converted.\n filepath (str): The .qrc file path to be converted.\n outputpath (str): The output directory to the python script.\n\n Returns:\n None.\n '
if (isinstance(filename, str) and isinstance(filepath, str)):
if (not (' ' in filename)):
if os.path.isfile('{}\\{}.qrc'.format(filepath, filename)):
filepath = filepath
filename = filename
chk_py = os.path.isfile('{}\\{}.py'.format(filepath, filename))
os.system('cd {0} & pyrcc5 {1}.qrc -o {1}.py'.format(filepath, filename))
shutil.move('{}\\{}.py'.format(filepath, filename), '{}\\{}.py'.format(outputpath, filename))
if chk_py:
print('File Converter Info: {}.py file updated.'.format(filename))
else:
print('File Converter Info: {}.py file created.'.format(filename))
else:
print("File Converter Alert: The {}.qrc file doesn't exist.".format(filename))
else:
print('File Converter Error: The filename contains spaces.')
else:
print('File Converter Error: Arguments are not string.') | def qrc_to_py(filename: str, filepath: str=os.path.dirname(__file__), outputpath: str=os.path.dirname(__file__)) -> None:
'\n Converts a .qrc file from Qt Designer to a python script.\n\n Args:\n filename (str): The .qrc file name to be converted.\n filepath (str): The .qrc file path to be converted.\n outputpath (str): The output directory to the python script.\n\n Returns:\n None.\n '
if (isinstance(filename, str) and isinstance(filepath, str)):
if (not (' ' in filename)):
if os.path.isfile('{}\\{}.qrc'.format(filepath, filename)):
filepath = filepath
filename = filename
chk_py = os.path.isfile('{}\\{}.py'.format(filepath, filename))
os.system('cd {0} & pyrcc5 {1}.qrc -o {1}.py'.format(filepath, filename))
shutil.move('{}\\{}.py'.format(filepath, filename), '{}\\{}.py'.format(outputpath, filename))
if chk_py:
print('File Converter Info: {}.py file updated.'.format(filename))
else:
print('File Converter Info: {}.py file created.'.format(filename))
else:
print("File Converter Alert: The {}.qrc file doesn't exist.".format(filename))
else:
print('File Converter Error: The filename contains spaces.')
else:
print('File Converter Error: Arguments are not string.')<|docstring|>Converts a .qrc file from Qt Designer to a python script.
Args:
filename (str): The .qrc file name to be converted.
filepath (str): The .qrc file path to be converted.
outputpath (str): The output directory to the python script.
Returns:
None.<|endoftext|> |
821057e5bb466cb6c391bfbd4ed4b907f7319591974c92514b8843135c6b20ef | def assign(self, transaction_data):
' Assign data from dict\n\n :param transaction_data: Transaction data\n :type transaction_data: dict\n '
for (property_name, value) in transaction_data.items():
if hasattr(self, property_name):
self.__setattr__(property_name, value) | Assign data from dict
:param transaction_data: Transaction data
:type transaction_data: dict | django_fiobank/models.py | assign | rbas/django-fiobank | 1 | python | def assign(self, transaction_data):
' Assign data from dict\n\n :param transaction_data: Transaction data\n :type transaction_data: dict\n '
for (property_name, value) in transaction_data.items():
if hasattr(self, property_name):
self.__setattr__(property_name, value) | def assign(self, transaction_data):
' Assign data from dict\n\n :param transaction_data: Transaction data\n :type transaction_data: dict\n '
for (property_name, value) in transaction_data.items():
if hasattr(self, property_name):
self.__setattr__(property_name, value)<|docstring|>Assign data from dict
:param transaction_data: Transaction data
:type transaction_data: dict<|endoftext|> |
7e0c5eff604e722286e2163d8bac7196a989cf7aca9c713a26ceda2c26936954 | def _split_generators(self, dl_manager):
'Returns SplitGenerators.'
dl_files = dl_manager.download_and_extract(_URLs)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': dl_files['train']}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'filepath': dl_files['test']})] | Returns SplitGenerators. | datasets/trec/trec.py | _split_generators | patrickvonplaten/datasets-1 | 10,608 | python | def _split_generators(self, dl_manager):
dl_files = dl_manager.download_and_extract(_URLs)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': dl_files['train']}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'filepath': dl_files['test']})] | def _split_generators(self, dl_manager):
dl_files = dl_manager.download_and_extract(_URLs)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': dl_files['train']}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'filepath': dl_files['test']})]<|docstring|>Returns SplitGenerators.<|endoftext|> |
a9f539c04d188bf4e328620899b7b5b4e89d10aa287a70a760207ab469f6c0de | def _generate_examples(self, filepath):
'Yields examples.'
with open(filepath, 'rb') as f:
for (id_, row) in enumerate(f):
(label, _, text) = row.replace(b'\xf0', b' ').strip().decode().partition(' ')
(coarse_label, _, fine_label) = label.partition(':')
(yield (id_, {'label-coarse': coarse_label, 'label-fine': fine_label, 'text': text})) | Yields examples. | datasets/trec/trec.py | _generate_examples | patrickvonplaten/datasets-1 | 10,608 | python | def _generate_examples(self, filepath):
with open(filepath, 'rb') as f:
for (id_, row) in enumerate(f):
(label, _, text) = row.replace(b'\xf0', b' ').strip().decode().partition(' ')
(coarse_label, _, fine_label) = label.partition(':')
(yield (id_, {'label-coarse': coarse_label, 'label-fine': fine_label, 'text': text})) | def _generate_examples(self, filepath):
with open(filepath, 'rb') as f:
for (id_, row) in enumerate(f):
(label, _, text) = row.replace(b'\xf0', b' ').strip().decode().partition(' ')
(coarse_label, _, fine_label) = label.partition(':')
(yield (id_, {'label-coarse': coarse_label, 'label-fine': fine_label, 'text': text}))<|docstring|>Yields examples.<|endoftext|> |
901faf165db329d870b718623d209031e6f3a78e6221c3a34726c5d6363fc410 | def stream_copy(self, sha_iter, odb):
"Copy the streams as identified by sha's yielded by sha_iter into the given odb\n\t\tThe streams will be copied directly\n\t\t:note: the object will only be written if it did not exist in the target db\n\t\t:return: amount of streams actually copied into odb. If smaller than the amount\n\t\t\tof input shas, one or more objects did already exist in odb"
count = 0
for sha in sha_iter:
if odb.has_object(sha):
continue
ostream = self.stream(sha)
sio = StringIO(ostream.stream.data())
istream = IStream(ostream.type, ostream.size, sio, sha)
odb.store(istream)
count += 1
return count | Copy the streams as identified by sha's yielded by sha_iter into the given odb
The streams will be copied directly
:note: the object will only be written if it did not exist in the target db
:return: amount of streams actually copied into odb. If smaller than the amount
of input shas, one or more objects did already exist in odb | git/db/py/mem.py | stream_copy | swallat/GitPython | 1 | python | def stream_copy(self, sha_iter, odb):
"Copy the streams as identified by sha's yielded by sha_iter into the given odb\n\t\tThe streams will be copied directly\n\t\t:note: the object will only be written if it did not exist in the target db\n\t\t:return: amount of streams actually copied into odb. If smaller than the amount\n\t\t\tof input shas, one or more objects did already exist in odb"
count = 0
for sha in sha_iter:
if odb.has_object(sha):
continue
ostream = self.stream(sha)
sio = StringIO(ostream.stream.data())
istream = IStream(ostream.type, ostream.size, sio, sha)
odb.store(istream)
count += 1
return count | def stream_copy(self, sha_iter, odb):
"Copy the streams as identified by sha's yielded by sha_iter into the given odb\n\t\tThe streams will be copied directly\n\t\t:note: the object will only be written if it did not exist in the target db\n\t\t:return: amount of streams actually copied into odb. If smaller than the amount\n\t\t\tof input shas, one or more objects did already exist in odb"
count = 0
for sha in sha_iter:
if odb.has_object(sha):
continue
ostream = self.stream(sha)
sio = StringIO(ostream.stream.data())
istream = IStream(ostream.type, ostream.size, sio, sha)
odb.store(istream)
count += 1
return count<|docstring|>Copy the streams as identified by sha's yielded by sha_iter into the given odb
The streams will be copied directly
:note: the object will only be written if it did not exist in the target db
:return: amount of streams actually copied into odb. If smaller than the amount
of input shas, one or more objects did already exist in odb<|endoftext|> |
766d5af600b96475ebe8b9804d16f568a7e99a94526e86f368e003804b30b32a | def create_user(self, email, password=None, **extra_fields):
'create and saves a new user'
if (not email):
raise ValueError('users must have a email address.')
user = self.model(email=self.normalize_email(email), **extra_fields)
user.set_password(password)
user.save(using=self._db)
return user | create and saves a new user | apiuser/core/models.py | create_user | ngelrojas/cotizate-back | 0 | python | def create_user(self, email, password=None, **extra_fields):
if (not email):
raise ValueError('users must have a email address.')
user = self.model(email=self.normalize_email(email), **extra_fields)
user.set_password(password)
user.save(using=self._db)
return user | def create_user(self, email, password=None, **extra_fields):
if (not email):
raise ValueError('users must have a email address.')
user = self.model(email=self.normalize_email(email), **extra_fields)
user.set_password(password)
user.save(using=self._db)
return user<|docstring|>create and saves a new user<|endoftext|> |
7dc270b374b7fb41f7f09ec7a46235e25861f2fa042f21cfd1ba1ccfd3f12c83 | def create_superuser(self, email, password):
'create and saves a new super user'
user = self.create_user(email, password)
user.is_active = True
user.is_staff = True
user.is_superuser = True
user.save(using=self._db)
return user | create and saves a new super user | apiuser/core/models.py | create_superuser | ngelrojas/cotizate-back | 0 | python | def create_superuser(self, email, password):
user = self.create_user(email, password)
user.is_active = True
user.is_staff = True
user.is_superuser = True
user.save(using=self._db)
return user | def create_superuser(self, email, password):
user = self.create_user(email, password)
user.is_active = True
user.is_staff = True
user.is_superuser = True
user.save(using=self._db)
return user<|docstring|>create and saves a new super user<|endoftext|> |
2aab7518a3ee67f04ac8e8a0f051ecd562ec81d02bb90a0acab6eb01ac2b92dc | def test_pei(self):
'\n pis checked\n '
sum_value = sum(self.spaam_2006_2007.pei_attributions)
total_value = (self.spaam_2006_2007.pei - 1)
self.assertAlmostEqual(sum_value, total_value) | pis checked | PDA/test/Test_Attribution_Transport.py | test_pei | gaufung/CodeBase | 4 | python | def test_pei(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.pei_attributions)
total_value = (self.spaam_2006_2007.pei - 1)
self.assertAlmostEqual(sum_value, total_value) | def test_pei(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.pei_attributions)
total_value = (self.spaam_2006_2007.pei - 1)
self.assertAlmostEqual(sum_value, total_value)<|docstring|>pis checked<|endoftext|> |
bf0ea2c7289ab54446671d58f993607cb1c0bab8bdcb40df2a4d990abcab273f | def test_est(self):
'\n pei checked\n '
sum_value = sum(self.spaam_2006_2007.est_attributions)
total_value = (self.spaam_2006_2007.est - 1)
self.assertAlmostEqual(sum_value, total_value) | pei checked | PDA/test/Test_Attribution_Transport.py | test_est | gaufung/CodeBase | 4 | python | def test_est(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.est_attributions)
total_value = (self.spaam_2006_2007.est - 1)
self.assertAlmostEqual(sum_value, total_value) | def test_est(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.est_attributions)
total_value = (self.spaam_2006_2007.est - 1)
self.assertAlmostEqual(sum_value, total_value)<|docstring|>pei checked<|endoftext|> |
78b92da7cb5f4d170a83235afaf342a9fc5af2d90c1f93448e0b12efb13acf28 | def test_eue(self):
'\n isg checked\n '
sum_value = sum(self.spaam_2006_2007.eue_attributions)
total_value = (self.spaam_2006_2007.eue - 1)
self.assertAlmostEqual(sum_value, total_value) | isg checked | PDA/test/Test_Attribution_Transport.py | test_eue | gaufung/CodeBase | 4 | python | def test_eue(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.eue_attributions)
total_value = (self.spaam_2006_2007.eue - 1)
self.assertAlmostEqual(sum_value, total_value) | def test_eue(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.eue_attributions)
total_value = (self.spaam_2006_2007.eue - 1)
self.assertAlmostEqual(sum_value, total_value)<|docstring|>isg checked<|endoftext|> |
c4782a532df18112adf0c960eb0b938543a6d0722dcc88ed814fe9214a566198 | def test_pti(self):
'\n eue checked\n '
sum_value = sum(self.spaam_2006_2007.pti_attributions)
total_value = (self.spaam_2006_2007.pti - 1)
self.assertAlmostEqual(sum_value, total_value) | eue checked | PDA/test/Test_Attribution_Transport.py | test_pti | gaufung/CodeBase | 4 | python | def test_pti(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.pti_attributions)
total_value = (self.spaam_2006_2007.pti - 1)
self.assertAlmostEqual(sum_value, total_value) | def test_pti(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.pti_attributions)
total_value = (self.spaam_2006_2007.pti - 1)
self.assertAlmostEqual(sum_value, total_value)<|docstring|>eue checked<|endoftext|> |
d357ff93486953e4413b73245b6597ed7fa7ebf32cc964366dbcc0d324be6233 | def test_yoe(self):
'\n est checked\n '
sum_value = sum(self.spaam_2006_2007.yoe_attributions)
total_value = (self.spaam_2006_2007.yoe - 1)
self.assertAlmostEqual(sum_value, total_value) | est checked | PDA/test/Test_Attribution_Transport.py | test_yoe | gaufung/CodeBase | 4 | python | def test_yoe(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.yoe_attributions)
total_value = (self.spaam_2006_2007.yoe - 1)
self.assertAlmostEqual(sum_value, total_value) | def test_yoe(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.yoe_attributions)
total_value = (self.spaam_2006_2007.yoe - 1)
self.assertAlmostEqual(sum_value, total_value)<|docstring|>est checked<|endoftext|> |
0a62b1a1c03ec315913a769157bc202b239e6deab41794ec37d73085f152e32d | def test_yct(self):
'\n yoe checked\n '
sum_value = sum(self.spaam_2006_2007.yct_attributions)
total_value = (self.spaam_2006_2007.yct - 1)
self.assertAlmostEqual(sum_value, total_value) | yoe checked | PDA/test/Test_Attribution_Transport.py | test_yct | gaufung/CodeBase | 4 | python | def test_yct(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.yct_attributions)
total_value = (self.spaam_2006_2007.yct - 1)
self.assertAlmostEqual(sum_value, total_value) | def test_yct(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.yct_attributions)
total_value = (self.spaam_2006_2007.yct - 1)
self.assertAlmostEqual(sum_value, total_value)<|docstring|>yoe checked<|endoftext|> |
f0e63013eb236c5b187153224cf427da2f34dd8867b44d97f23cf95cdf998788 | def test_rts(self):
'\n yct checked\n '
sum_value = sum(self.spaam_2006_2007.rts_attributions)
total_value = (self.spaam_2006_2007.rts - 1)
self.assertAlmostEqual(sum_value, total_value) | yct checked | PDA/test/Test_Attribution_Transport.py | test_rts | gaufung/CodeBase | 4 | python | def test_rts(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.rts_attributions)
total_value = (self.spaam_2006_2007.rts - 1)
self.assertAlmostEqual(sum_value, total_value) | def test_rts(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.rts_attributions)
total_value = (self.spaam_2006_2007.rts - 1)
self.assertAlmostEqual(sum_value, total_value)<|docstring|>yct checked<|endoftext|> |
9c491bcc67a62879767f7c3b25bc97fe718e4362b19a5b04392953d6761203bd | def test_cef(self):
'\n cef test\n '
sum_value = sum(self.spaam_2006_2007.cef_attributions)
total_value = (self.spaam_2006_2007.cef - 1)
self.assertAlmostEqual(sum_value, total_value) | cef test | PDA/test/Test_Attribution_Transport.py | test_cef | gaufung/CodeBase | 4 | python | def test_cef(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.cef_attributions)
total_value = (self.spaam_2006_2007.cef - 1)
self.assertAlmostEqual(sum_value, total_value) | def test_cef(self):
'\n \n '
sum_value = sum(self.spaam_2006_2007.cef_attributions)
total_value = (self.spaam_2006_2007.cef - 1)
self.assertAlmostEqual(sum_value, total_value)<|docstring|>cef test<|endoftext|> |
2096e3cf9c41ccba165123a0ce9bbdc55977a6433b4991adf05c35335ecc6712 | @abc.abstractproperty
def package(self):
'\n The name of the package for which this reader loads resources.\n ' | The name of the package for which this reader loads resources. | pipenv/vendor/importlib_resources/simple.py | package | sweco/pipenv | 52,316 | python | @abc.abstractproperty
def package(self):
'\n \n ' | @abc.abstractproperty
def package(self):
'\n \n '<|docstring|>The name of the package for which this reader loads resources.<|endoftext|> |
1ce88e8fccef2e87bbd9c14be440efe08928c44d28a7a83f35d551013dfb7fc8 | @abc.abstractmethod
def children(self):
'\n Obtain an iterable of SimpleReader for available\n child containers (e.g. directories).\n ' | Obtain an iterable of SimpleReader for available
child containers (e.g. directories). | pipenv/vendor/importlib_resources/simple.py | children | sweco/pipenv | 52,316 | python | @abc.abstractmethod
def children(self):
'\n Obtain an iterable of SimpleReader for available\n child containers (e.g. directories).\n ' | @abc.abstractmethod
def children(self):
'\n Obtain an iterable of SimpleReader for available\n child containers (e.g. directories).\n '<|docstring|>Obtain an iterable of SimpleReader for available
child containers (e.g. directories).<|endoftext|> |
6247b7b8da02ef3a86f900684bbe54881ae5f85007d985a8471b8ff0d732616d | @abc.abstractmethod
def resources(self):
'\n Obtain available named resources for this virtual package.\n ' | Obtain available named resources for this virtual package. | pipenv/vendor/importlib_resources/simple.py | resources | sweco/pipenv | 52,316 | python | @abc.abstractmethod
def resources(self):
'\n \n ' | @abc.abstractmethod
def resources(self):
'\n \n '<|docstring|>Obtain available named resources for this virtual package.<|endoftext|> |
6a503dab41fd020dbc323803cbe5a25f91a9d1e724eccbee0f7f3407639b574b | @abc.abstractmethod
def open_binary(self, resource):
'\n Obtain a File-like for a named resource.\n ' | Obtain a File-like for a named resource. | pipenv/vendor/importlib_resources/simple.py | open_binary | sweco/pipenv | 52,316 | python | @abc.abstractmethod
def open_binary(self, resource):
'\n \n ' | @abc.abstractmethod
def open_binary(self, resource):
'\n \n '<|docstring|>Obtain a File-like for a named resource.<|endoftext|> |
1b9359a9bd60b242d7e2cf881fa40ee4b95ab932ac8665777d102c1d93ffd16e | def to_dict(self):
'Return JSON-serializable representation of the object.'
out = {'addr': self.addr, 'no_ack': self.no_ack, 'rts_cts': self.rts_cts, 'max_amsdu_len': self._max_amsdu_len, 'mcast': TX_MCAST[self.mcast], 'mcs': sorted(self.mcs), 'ht_mcs': sorted(self.ht_mcs), 'ur_count': self.ur_count}
return out | Return JSON-serializable representation of the object. | empower/core/txpolicy.py | to_dict | EstefaniaCC/empower-runtime-5g-essence-multicast | 0 | python | def to_dict(self):
out = {'addr': self.addr, 'no_ack': self.no_ack, 'rts_cts': self.rts_cts, 'max_amsdu_len': self._max_amsdu_len, 'mcast': TX_MCAST[self.mcast], 'mcs': sorted(self.mcs), 'ht_mcs': sorted(self.ht_mcs), 'ur_count': self.ur_count}
return out | def to_dict(self):
out = {'addr': self.addr, 'no_ack': self.no_ack, 'rts_cts': self.rts_cts, 'max_amsdu_len': self._max_amsdu_len, 'mcast': TX_MCAST[self.mcast], 'mcs': sorted(self.mcs), 'ht_mcs': sorted(self.ht_mcs), 'ur_count': self.ur_count}
return out<|docstring|>Return JSON-serializable representation of the object.<|endoftext|> |
6f12bfaa91ee8e4eadf957287b6c4c3c75a70fe9a6e704ba59c9ee9203046a1b | @property
def ur_count(self):
'Get ur_count.'
return self._ur_count | Get ur_count. | empower/core/txpolicy.py | ur_count | EstefaniaCC/empower-runtime-5g-essence-multicast | 0 | python | @property
def ur_count(self):
return self._ur_count | @property
def ur_count(self):
return self._ur_count<|docstring|>Get ur_count.<|endoftext|> |
441eabe579e996dcc1d938a82652728fe953204689f757d1599d3d6f03bca3ff | @ur_count.setter
def ur_count(self, ur_count):
'Set ur_count.'
self.set_ur_count(ur_count)
self.block.wtp.connection.send_set_tx_policy(self) | Set ur_count. | empower/core/txpolicy.py | ur_count | EstefaniaCC/empower-runtime-5g-essence-multicast | 0 | python | @ur_count.setter
def ur_count(self, ur_count):
self.set_ur_count(ur_count)
self.block.wtp.connection.send_set_tx_policy(self) | @ur_count.setter
def ur_count(self, ur_count):
self.set_ur_count(ur_count)
self.block.wtp.connection.send_set_tx_policy(self)<|docstring|>Set ur_count.<|endoftext|> |
64fe28e7313f6ae5ff80c835c1d658d9af5a4de0376aeb3c2b694a867fc438d2 | def set_ur_count(self, ur_count):
'Set ur_count without sending anything.'
self._ur_count = int(ur_count) | Set ur_count without sending anything. | empower/core/txpolicy.py | set_ur_count | EstefaniaCC/empower-runtime-5g-essence-multicast | 0 | python | def set_ur_count(self, ur_count):
self._ur_count = int(ur_count) | def set_ur_count(self, ur_count):
self._ur_count = int(ur_count)<|docstring|>Set ur_count without sending anything.<|endoftext|> |
f3581533336f7ed8a13feca7843c26aa3e9f417130ade44311c276af4c7b5dd5 | @property
def mcast(self):
'Get mcast mode.'
return self._mcast | Get mcast mode. | empower/core/txpolicy.py | mcast | EstefaniaCC/empower-runtime-5g-essence-multicast | 0 | python | @property
def mcast(self):
return self._mcast | @property
def mcast(self):
return self._mcast<|docstring|>Get mcast mode.<|endoftext|> |
f0ab151871e9d1c06a8f6e2f826403e8e90b1bccfc6bdef68d7f218193f952d3 | @mcast.setter
def mcast(self, mcast):
'Set the mcast mode.'
self.set_mcast(mcast)
self.block.wtp.connection.send_set_tx_policy(self) | Set the mcast mode. | empower/core/txpolicy.py | mcast | EstefaniaCC/empower-runtime-5g-essence-multicast | 0 | python | @mcast.setter
def mcast(self, mcast):
self.set_mcast(mcast)
self.block.wtp.connection.send_set_tx_policy(self) | @mcast.setter
def mcast(self, mcast):
self.set_mcast(mcast)
self.block.wtp.connection.send_set_tx_policy(self)<|docstring|>Set the mcast mode.<|endoftext|> |
523943bc82a73620014e5146a0596e2af369ce278ab88b962b8518ca33ff212f | def set_mcast(self, mcast):
'Set the mcast mode without sending anything.'
self._mcast = (int(mcast) if (int(mcast) in TX_MCAST) else TX_MCAST_LEGACY) | Set the mcast mode without sending anything. | empower/core/txpolicy.py | set_mcast | EstefaniaCC/empower-runtime-5g-essence-multicast | 0 | python | def set_mcast(self, mcast):
self._mcast = (int(mcast) if (int(mcast) in TX_MCAST) else TX_MCAST_LEGACY) | def set_mcast(self, mcast):
self._mcast = (int(mcast) if (int(mcast) in TX_MCAST) else TX_MCAST_LEGACY)<|docstring|>Set the mcast mode without sending anything.<|endoftext|> |
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