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def median(self, axis: Optional[Axis]=None, numeric_only: bool=None, accuracy: int=10000) -> Union[(Scalar, 'Series')]:
"\n Return the median of the values for the requested axis.\n\n .. note:: Unlike pandas', the median in pandas-on-Spark is an approximated median based upon\n approximate percentile computation because computing median across a large dataset\n is extremely expensive.\n\n Parameters\n ----------\n axis : {index (0), columns (1)}\n Axis for the function to be applied on.\n numeric_only : bool, default None\n Include only float, int, boolean columns. False is not supported. This parameter\n is mainly for pandas compatibility.\n accuracy : int, optional\n Default accuracy of approximation. Larger value means better accuracy.\n The relative error can be deduced by 1.0 / accuracy.\n\n Returns\n -------\n median : scalar or Series\n\n Examples\n --------\n >>> df = ps.DataFrame({\n ... 'a': [24., 21., 25., 33., 26.], 'b': [1, 2, 3, 4, 5]}, columns=['a', 'b'])\n >>> df\n a b\n 0 24.0 1\n 1 21.0 2\n 2 25.0 3\n 3 33.0 4\n 4 26.0 5\n\n On a DataFrame:\n\n >>> df.median()\n a 25.0\n b 3.0\n dtype: float64\n\n On a Series:\n\n >>> df['a'].median()\n 25.0\n >>> (df['b'] + 100).median()\n 103.0\n\n For multi-index columns,\n\n >>> df.columns = pd.MultiIndex.from_tuples([('x', 'a'), ('y', 'b')])\n >>> df\n x y\n a b\n 0 24.0 1\n 1 21.0 2\n 2 25.0 3\n 3 33.0 4\n 4 26.0 5\n\n On a DataFrame:\n\n >>> df.median()\n x a 25.0\n y b 3.0\n dtype: float64\n\n >>> df.median(axis=1)\n 0 12.5\n 1 11.5\n 2 14.0\n 3 18.5\n 4 15.5\n dtype: float64\n\n On a Series:\n\n >>> df[('x', 'a')].median()\n 25.0\n >>> (df[('y', 'b')] + 100).median()\n 103.0\n "
axis = validate_axis(axis)
if ((numeric_only is None) and (axis == 0)):
numeric_only = True
if (not isinstance(accuracy, int)):
raise TypeError(('accuracy must be an integer; however, got [%s]' % type(accuracy).__name__))
def median(spark_column: Column, spark_type: DataType) -> Column:
if isinstance(spark_type, (BooleanType, NumericType)):
return F.percentile_approx(spark_column.cast(DoubleType()), 0.5, accuracy)
else:
raise TypeError('Could not convert {} ({}) to numeric'.format(spark_type_to_pandas_dtype(spark_type), spark_type.simpleString()))
return self._reduce_for_stat_function(median, name='median', numeric_only=numeric_only, axis=axis) | -876,830,135,422,874,200 | Return the median of the values for the requested axis.
.. note:: Unlike pandas', the median in pandas-on-Spark is an approximated median based upon
approximate percentile computation because computing median across a large dataset
is extremely expensive.
Parameters
----------
axis : {index (0), columns (1)}
Axis for the function to be applied on.
numeric_only : bool, default None
Include only float, int, boolean columns. False is not supported. This parameter
is mainly for pandas compatibility.
accuracy : int, optional
Default accuracy of approximation. Larger value means better accuracy.
The relative error can be deduced by 1.0 / accuracy.
Returns
-------
median : scalar or Series
Examples
--------
>>> df = ps.DataFrame({
... 'a': [24., 21., 25., 33., 26.], 'b': [1, 2, 3, 4, 5]}, columns=['a', 'b'])
>>> df
a b
0 24.0 1
1 21.0 2
2 25.0 3
3 33.0 4
4 26.0 5
On a DataFrame:
>>> df.median()
a 25.0
b 3.0
dtype: float64
On a Series:
>>> df['a'].median()
25.0
>>> (df['b'] + 100).median()
103.0
For multi-index columns,
>>> df.columns = pd.MultiIndex.from_tuples([('x', 'a'), ('y', 'b')])
>>> df
x y
a b
0 24.0 1
1 21.0 2
2 25.0 3
3 33.0 4
4 26.0 5
On a DataFrame:
>>> df.median()
x a 25.0
y b 3.0
dtype: float64
>>> df.median(axis=1)
0 12.5
1 11.5
2 14.0
3 18.5
4 15.5
dtype: float64
On a Series:
>>> df[('x', 'a')].median()
25.0
>>> (df[('y', 'b')] + 100).median()
103.0 | python/pyspark/pandas/generic.py | median | XpressAI/spark | python | def median(self, axis: Optional[Axis]=None, numeric_only: bool=None, accuracy: int=10000) -> Union[(Scalar, 'Series')]:
"\n Return the median of the values for the requested axis.\n\n .. note:: Unlike pandas', the median in pandas-on-Spark is an approximated median based upon\n approximate percentile computation because computing median across a large dataset\n is extremely expensive.\n\n Parameters\n ----------\n axis : {index (0), columns (1)}\n Axis for the function to be applied on.\n numeric_only : bool, default None\n Include only float, int, boolean columns. False is not supported. This parameter\n is mainly for pandas compatibility.\n accuracy : int, optional\n Default accuracy of approximation. Larger value means better accuracy.\n The relative error can be deduced by 1.0 / accuracy.\n\n Returns\n -------\n median : scalar or Series\n\n Examples\n --------\n >>> df = ps.DataFrame({\n ... 'a': [24., 21., 25., 33., 26.], 'b': [1, 2, 3, 4, 5]}, columns=['a', 'b'])\n >>> df\n a b\n 0 24.0 1\n 1 21.0 2\n 2 25.0 3\n 3 33.0 4\n 4 26.0 5\n\n On a DataFrame:\n\n >>> df.median()\n a 25.0\n b 3.0\n dtype: float64\n\n On a Series:\n\n >>> df['a'].median()\n 25.0\n >>> (df['b'] + 100).median()\n 103.0\n\n For multi-index columns,\n\n >>> df.columns = pd.MultiIndex.from_tuples([('x', 'a'), ('y', 'b')])\n >>> df\n x y\n a b\n 0 24.0 1\n 1 21.0 2\n 2 25.0 3\n 3 33.0 4\n 4 26.0 5\n\n On a DataFrame:\n\n >>> df.median()\n x a 25.0\n y b 3.0\n dtype: float64\n\n >>> df.median(axis=1)\n 0 12.5\n 1 11.5\n 2 14.0\n 3 18.5\n 4 15.5\n dtype: float64\n\n On a Series:\n\n >>> df[('x', 'a')].median()\n 25.0\n >>> (df[('y', 'b')] + 100).median()\n 103.0\n "
axis = validate_axis(axis)
if ((numeric_only is None) and (axis == 0)):
numeric_only = True
if (not isinstance(accuracy, int)):
raise TypeError(('accuracy must be an integer; however, got [%s]' % type(accuracy).__name__))
def median(spark_column: Column, spark_type: DataType) -> Column:
if isinstance(spark_type, (BooleanType, NumericType)):
return F.percentile_approx(spark_column.cast(DoubleType()), 0.5, accuracy)
else:
raise TypeError('Could not convert {} ({}) to numeric'.format(spark_type_to_pandas_dtype(spark_type), spark_type.simpleString()))
return self._reduce_for_stat_function(median, name='median', numeric_only=numeric_only, axis=axis) |
def sem(self, axis: Optional[Axis]=None, ddof: int=1, numeric_only: bool=None) -> Union[(Scalar, 'Series')]:
'\n Return unbiased standard error of the mean over requested axis.\n\n Parameters\n ----------\n axis : {index (0), columns (1)}\n Axis for the function to be applied on.\n ddof : int, default 1\n Delta Degrees of Freedom. The divisor used in calculations is N - ddof,\n where N represents the number of elements.\n numeric_only : bool, default None\n Include only float, int, boolean columns. False is not supported. This parameter\n is mainly for pandas compatibility.\n\n Returns\n -------\n scalar(for Series) or Series(for DataFrame)\n\n Examples\n --------\n >>> psdf = ps.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})\n >>> psdf\n a b\n 0 1 4\n 1 2 5\n 2 3 6\n\n >>> psdf.sem()\n a 0.57735\n b 0.57735\n dtype: float64\n\n >>> psdf.sem(ddof=0)\n a 0.471405\n b 0.471405\n dtype: float64\n\n >>> psdf.sem(axis=1)\n 0 1.5\n 1 1.5\n 2 1.5\n dtype: float64\n\n Support for Series\n\n >>> psser = psdf.a\n >>> psser\n 0 1\n 1 2\n 2 3\n Name: a, dtype: int64\n\n >>> psser.sem()\n 0.5773502691896258\n\n >>> psser.sem(ddof=0)\n 0.47140452079103173\n '
assert (ddof in (0, 1))
axis = validate_axis(axis)
if ((numeric_only is None) and (axis == 0)):
numeric_only = True
def std(spark_column: Column, spark_type: DataType) -> Column:
if isinstance(spark_type, BooleanType):
spark_column = spark_column.cast(LongType())
elif (not isinstance(spark_type, NumericType)):
raise TypeError('Could not convert {} ({}) to numeric'.format(spark_type_to_pandas_dtype(spark_type), spark_type.simpleString()))
if (ddof == 0):
return F.stddev_pop(spark_column)
else:
return F.stddev_samp(spark_column)
def sem(spark_column: Column, spark_type: DataType) -> Column:
return (std(spark_column, spark_type) / pow(Frame._count_expr(spark_column, spark_type), 0.5))
return self._reduce_for_stat_function(sem, name='sem', numeric_only=numeric_only, axis=axis, ddof=ddof) | 4,448,271,079,385,983,500 | Return unbiased standard error of the mean over requested axis.
Parameters
----------
axis : {index (0), columns (1)}
Axis for the function to be applied on.
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the number of elements.
numeric_only : bool, default None
Include only float, int, boolean columns. False is not supported. This parameter
is mainly for pandas compatibility.
Returns
-------
scalar(for Series) or Series(for DataFrame)
Examples
--------
>>> psdf = ps.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> psdf
a b
0 1 4
1 2 5
2 3 6
>>> psdf.sem()
a 0.57735
b 0.57735
dtype: float64
>>> psdf.sem(ddof=0)
a 0.471405
b 0.471405
dtype: float64
>>> psdf.sem(axis=1)
0 1.5
1 1.5
2 1.5
dtype: float64
Support for Series
>>> psser = psdf.a
>>> psser
0 1
1 2
2 3
Name: a, dtype: int64
>>> psser.sem()
0.5773502691896258
>>> psser.sem(ddof=0)
0.47140452079103173 | python/pyspark/pandas/generic.py | sem | XpressAI/spark | python | def sem(self, axis: Optional[Axis]=None, ddof: int=1, numeric_only: bool=None) -> Union[(Scalar, 'Series')]:
'\n Return unbiased standard error of the mean over requested axis.\n\n Parameters\n ----------\n axis : {index (0), columns (1)}\n Axis for the function to be applied on.\n ddof : int, default 1\n Delta Degrees of Freedom. The divisor used in calculations is N - ddof,\n where N represents the number of elements.\n numeric_only : bool, default None\n Include only float, int, boolean columns. False is not supported. This parameter\n is mainly for pandas compatibility.\n\n Returns\n -------\n scalar(for Series) or Series(for DataFrame)\n\n Examples\n --------\n >>> psdf = ps.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})\n >>> psdf\n a b\n 0 1 4\n 1 2 5\n 2 3 6\n\n >>> psdf.sem()\n a 0.57735\n b 0.57735\n dtype: float64\n\n >>> psdf.sem(ddof=0)\n a 0.471405\n b 0.471405\n dtype: float64\n\n >>> psdf.sem(axis=1)\n 0 1.5\n 1 1.5\n 2 1.5\n dtype: float64\n\n Support for Series\n\n >>> psser = psdf.a\n >>> psser\n 0 1\n 1 2\n 2 3\n Name: a, dtype: int64\n\n >>> psser.sem()\n 0.5773502691896258\n\n >>> psser.sem(ddof=0)\n 0.47140452079103173\n '
assert (ddof in (0, 1))
axis = validate_axis(axis)
if ((numeric_only is None) and (axis == 0)):
numeric_only = True
def std(spark_column: Column, spark_type: DataType) -> Column:
if isinstance(spark_type, BooleanType):
spark_column = spark_column.cast(LongType())
elif (not isinstance(spark_type, NumericType)):
raise TypeError('Could not convert {} ({}) to numeric'.format(spark_type_to_pandas_dtype(spark_type), spark_type.simpleString()))
if (ddof == 0):
return F.stddev_pop(spark_column)
else:
return F.stddev_samp(spark_column)
def sem(spark_column: Column, spark_type: DataType) -> Column:
return (std(spark_column, spark_type) / pow(Frame._count_expr(spark_column, spark_type), 0.5))
return self._reduce_for_stat_function(sem, name='sem', numeric_only=numeric_only, axis=axis, ddof=ddof) |
@property
def size(self) -> int:
"\n Return an int representing the number of elements in this object.\n\n Return the number of rows if Series. Otherwise return the number of\n rows times number of columns if DataFrame.\n\n Examples\n --------\n >>> s = ps.Series({'a': 1, 'b': 2, 'c': None})\n >>> s.size\n 3\n\n >>> df = ps.DataFrame({'col1': [1, 2, None], 'col2': [3, 4, None]})\n >>> df.size\n 6\n\n >>> df = ps.DataFrame(index=[1, 2, None])\n >>> df.size\n 0\n "
num_columns = len(self._internal.data_spark_columns)
if (num_columns == 0):
return 0
else:
return (len(self) * num_columns) | -4,052,080,229,098,774,500 | Return an int representing the number of elements in this object.
Return the number of rows if Series. Otherwise return the number of
rows times number of columns if DataFrame.
Examples
--------
>>> s = ps.Series({'a': 1, 'b': 2, 'c': None})
>>> s.size
3
>>> df = ps.DataFrame({'col1': [1, 2, None], 'col2': [3, 4, None]})
>>> df.size
6
>>> df = ps.DataFrame(index=[1, 2, None])
>>> df.size
0 | python/pyspark/pandas/generic.py | size | XpressAI/spark | python | @property
def size(self) -> int:
"\n Return an int representing the number of elements in this object.\n\n Return the number of rows if Series. Otherwise return the number of\n rows times number of columns if DataFrame.\n\n Examples\n --------\n >>> s = ps.Series({'a': 1, 'b': 2, 'c': None})\n >>> s.size\n 3\n\n >>> df = ps.DataFrame({'col1': [1, 2, None], 'col2': [3, 4, None]})\n >>> df.size\n 6\n\n >>> df = ps.DataFrame(index=[1, 2, None])\n >>> df.size\n 0\n "
num_columns = len(self._internal.data_spark_columns)
if (num_columns == 0):
return 0
else:
return (len(self) * num_columns) |
def abs(self: FrameLike) -> FrameLike:
"\n Return a Series/DataFrame with absolute numeric value of each element.\n\n Returns\n -------\n abs : Series/DataFrame containing the absolute value of each element.\n\n Examples\n --------\n\n Absolute numeric values in a Series.\n\n >>> s = ps.Series([-1.10, 2, -3.33, 4])\n >>> s.abs()\n 0 1.10\n 1 2.00\n 2 3.33\n 3 4.00\n dtype: float64\n\n Absolute numeric values in a DataFrame.\n\n >>> df = ps.DataFrame({\n ... 'a': [4, 5, 6, 7],\n ... 'b': [10, 20, 30, 40],\n ... 'c': [100, 50, -30, -50]\n ... },\n ... columns=['a', 'b', 'c'])\n >>> df.abs()\n a b c\n 0 4 10 100\n 1 5 20 50\n 2 6 30 30\n 3 7 40 50\n "
def abs(psser: 'Series') -> Union[('Series', Column)]:
if isinstance(psser.spark.data_type, BooleanType):
return psser
elif isinstance(psser.spark.data_type, NumericType):
return psser._with_new_scol(F.abs(psser.spark.column), field=psser._internal.data_fields[0])
else:
raise TypeError('bad operand type for abs(): {} ({})'.format(spark_type_to_pandas_dtype(psser.spark.data_type), psser.spark.data_type.simpleString()))
return self._apply_series_op(abs) | -8,096,641,240,787,788,000 | Return a Series/DataFrame with absolute numeric value of each element.
Returns
-------
abs : Series/DataFrame containing the absolute value of each element.
Examples
--------
Absolute numeric values in a Series.
>>> s = ps.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0 1.10
1 2.00
2 3.33
3 4.00
dtype: float64
Absolute numeric values in a DataFrame.
>>> df = ps.DataFrame({
... 'a': [4, 5, 6, 7],
... 'b': [10, 20, 30, 40],
... 'c': [100, 50, -30, -50]
... },
... columns=['a', 'b', 'c'])
>>> df.abs()
a b c
0 4 10 100
1 5 20 50
2 6 30 30
3 7 40 50 | python/pyspark/pandas/generic.py | abs | XpressAI/spark | python | def abs(self: FrameLike) -> FrameLike:
"\n Return a Series/DataFrame with absolute numeric value of each element.\n\n Returns\n -------\n abs : Series/DataFrame containing the absolute value of each element.\n\n Examples\n --------\n\n Absolute numeric values in a Series.\n\n >>> s = ps.Series([-1.10, 2, -3.33, 4])\n >>> s.abs()\n 0 1.10\n 1 2.00\n 2 3.33\n 3 4.00\n dtype: float64\n\n Absolute numeric values in a DataFrame.\n\n >>> df = ps.DataFrame({\n ... 'a': [4, 5, 6, 7],\n ... 'b': [10, 20, 30, 40],\n ... 'c': [100, 50, -30, -50]\n ... },\n ... columns=['a', 'b', 'c'])\n >>> df.abs()\n a b c\n 0 4 10 100\n 1 5 20 50\n 2 6 30 30\n 3 7 40 50\n "
def abs(psser: 'Series') -> Union[('Series', Column)]:
if isinstance(psser.spark.data_type, BooleanType):
return psser
elif isinstance(psser.spark.data_type, NumericType):
return psser._with_new_scol(F.abs(psser.spark.column), field=psser._internal.data_fields[0])
else:
raise TypeError('bad operand type for abs(): {} ({})'.format(spark_type_to_pandas_dtype(psser.spark.data_type), psser.spark.data_type.simpleString()))
return self._apply_series_op(abs) |
def groupby(self: FrameLike, by: Union[(Any, Tuple, 'Series', List[Union[(Any, Tuple, 'Series')]])], axis: Axis=0, as_index: bool=True, dropna: bool=True) -> 'GroupBy[FrameLike]':
'\n Group DataFrame or Series using a Series of columns.\n\n A groupby operation involves some combination of splitting the\n object, applying a function, and combining the results. This can be\n used to group large amounts of data and compute operations on these\n groups.\n\n Parameters\n ----------\n by : Series, label, or list of labels\n Used to determine the groups for the groupby.\n If Series is passed, the Series or dict VALUES\n will be used to determine the groups. A label or list of\n labels may be passed to group by the columns in ``self``.\n axis : int, default 0 or \'index\'\n Can only be set to 0 at the moment.\n as_index : bool, default True\n For aggregated output, return object with group labels as the\n index. Only relevant for DataFrame input. as_index=False is\n effectively "SQL-style" grouped output.\n dropna : bool, default True\n If True, and if group keys contain NA values,\n NA values together with row/column will be dropped.\n If False, NA values will also be treated as the key in groups.\n\n Returns\n -------\n DataFrameGroupBy or SeriesGroupBy\n Depends on the calling object and returns groupby object that\n contains information about the groups.\n\n See Also\n --------\n pyspark.pandas.groupby.GroupBy\n\n Examples\n --------\n >>> df = ps.DataFrame({\'Animal\': [\'Falcon\', \'Falcon\',\n ... \'Parrot\', \'Parrot\'],\n ... \'Max Speed\': [380., 370., 24., 26.]},\n ... columns=[\'Animal\', \'Max Speed\'])\n >>> df\n Animal Max Speed\n 0 Falcon 380.0\n 1 Falcon 370.0\n 2 Parrot 24.0\n 3 Parrot 26.0\n\n >>> df.groupby([\'Animal\']).mean().sort_index() # doctest: +NORMALIZE_WHITESPACE\n Max Speed\n Animal\n Falcon 375.0\n Parrot 25.0\n\n >>> df.groupby([\'Animal\'], as_index=False).mean().sort_values(\'Animal\')\n ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE\n Animal Max Speed\n ...Falcon 375.0\n ...Parrot 25.0\n\n We can also choose to include NA in group keys or not by setting dropna parameter,\n the default setting is True:\n\n >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]\n >>> df = ps.DataFrame(l, columns=["a", "b", "c"])\n >>> df.groupby(by=["b"]).sum().sort_index() # doctest: +NORMALIZE_WHITESPACE\n a c\n b\n 1.0 2 3\n 2.0 2 5\n\n >>> df.groupby(by=["b"], dropna=False).sum().sort_index() # doctest: +NORMALIZE_WHITESPACE\n a c\n b\n 1.0 2 3\n 2.0 2 5\n NaN 1 4\n '
if isinstance(by, ps.DataFrame):
raise ValueError("Grouper for '{}' not 1-dimensional".format(type(by).__name__))
elif isinstance(by, ps.Series):
new_by = [by]
elif is_name_like_tuple(by):
if isinstance(self, ps.Series):
raise KeyError(by)
new_by = [cast(Tuple, by)]
elif is_name_like_value(by):
if isinstance(self, ps.Series):
raise KeyError(by)
new_by = [(by,)]
elif is_list_like(by):
new_by = []
for key in by:
if isinstance(key, ps.DataFrame):
raise ValueError("Grouper for '{}' not 1-dimensional".format(type(key).__name__))
elif isinstance(key, ps.Series):
new_by.append(key)
elif is_name_like_tuple(key):
if isinstance(self, ps.Series):
raise KeyError(key)
new_by.append(key)
elif is_name_like_value(key):
if isinstance(self, ps.Series):
raise KeyError(key)
new_by.append((key,))
else:
raise ValueError("Grouper for '{}' not 1-dimensional".format(type(key).__name__))
else:
raise ValueError("Grouper for '{}' not 1-dimensional".format(type(by).__name__))
if (not len(new_by)):
raise ValueError('No group keys passed!')
axis = validate_axis(axis)
if (axis != 0):
raise NotImplementedError('axis should be either 0 or "index" currently.')
return self._build_groupby(by=new_by, as_index=as_index, dropna=dropna) | 5,165,825,879,761,929,000 | Group DataFrame or Series using a Series of columns.
A groupby operation involves some combination of splitting the
object, applying a function, and combining the results. This can be
used to group large amounts of data and compute operations on these
groups.
Parameters
----------
by : Series, label, or list of labels
Used to determine the groups for the groupby.
If Series is passed, the Series or dict VALUES
will be used to determine the groups. A label or list of
labels may be passed to group by the columns in ``self``.
axis : int, default 0 or 'index'
Can only be set to 0 at the moment.
as_index : bool, default True
For aggregated output, return object with group labels as the
index. Only relevant for DataFrame input. as_index=False is
effectively "SQL-style" grouped output.
dropna : bool, default True
If True, and if group keys contain NA values,
NA values together with row/column will be dropped.
If False, NA values will also be treated as the key in groups.
Returns
-------
DataFrameGroupBy or SeriesGroupBy
Depends on the calling object and returns groupby object that
contains information about the groups.
See Also
--------
pyspark.pandas.groupby.GroupBy
Examples
--------
>>> df = ps.DataFrame({'Animal': ['Falcon', 'Falcon',
... 'Parrot', 'Parrot'],
... 'Max Speed': [380., 370., 24., 26.]},
... columns=['Animal', 'Max Speed'])
>>> df
Animal Max Speed
0 Falcon 380.0
1 Falcon 370.0
2 Parrot 24.0
3 Parrot 26.0
>>> df.groupby(['Animal']).mean().sort_index() # doctest: +NORMALIZE_WHITESPACE
Max Speed
Animal
Falcon 375.0
Parrot 25.0
>>> df.groupby(['Animal'], as_index=False).mean().sort_values('Animal')
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
Animal Max Speed
...Falcon 375.0
...Parrot 25.0
We can also choose to include NA in group keys or not by setting dropna parameter,
the default setting is True:
>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = ps.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum().sort_index() # doctest: +NORMALIZE_WHITESPACE
a c
b
1.0 2 3
2.0 2 5
>>> df.groupby(by=["b"], dropna=False).sum().sort_index() # doctest: +NORMALIZE_WHITESPACE
a c
b
1.0 2 3
2.0 2 5
NaN 1 4 | python/pyspark/pandas/generic.py | groupby | XpressAI/spark | python | def groupby(self: FrameLike, by: Union[(Any, Tuple, 'Series', List[Union[(Any, Tuple, 'Series')]])], axis: Axis=0, as_index: bool=True, dropna: bool=True) -> 'GroupBy[FrameLike]':
'\n Group DataFrame or Series using a Series of columns.\n\n A groupby operation involves some combination of splitting the\n object, applying a function, and combining the results. This can be\n used to group large amounts of data and compute operations on these\n groups.\n\n Parameters\n ----------\n by : Series, label, or list of labels\n Used to determine the groups for the groupby.\n If Series is passed, the Series or dict VALUES\n will be used to determine the groups. A label or list of\n labels may be passed to group by the columns in ``self``.\n axis : int, default 0 or \'index\'\n Can only be set to 0 at the moment.\n as_index : bool, default True\n For aggregated output, return object with group labels as the\n index. Only relevant for DataFrame input. as_index=False is\n effectively "SQL-style" grouped output.\n dropna : bool, default True\n If True, and if group keys contain NA values,\n NA values together with row/column will be dropped.\n If False, NA values will also be treated as the key in groups.\n\n Returns\n -------\n DataFrameGroupBy or SeriesGroupBy\n Depends on the calling object and returns groupby object that\n contains information about the groups.\n\n See Also\n --------\n pyspark.pandas.groupby.GroupBy\n\n Examples\n --------\n >>> df = ps.DataFrame({\'Animal\': [\'Falcon\', \'Falcon\',\n ... \'Parrot\', \'Parrot\'],\n ... \'Max Speed\': [380., 370., 24., 26.]},\n ... columns=[\'Animal\', \'Max Speed\'])\n >>> df\n Animal Max Speed\n 0 Falcon 380.0\n 1 Falcon 370.0\n 2 Parrot 24.0\n 3 Parrot 26.0\n\n >>> df.groupby([\'Animal\']).mean().sort_index() # doctest: +NORMALIZE_WHITESPACE\n Max Speed\n Animal\n Falcon 375.0\n Parrot 25.0\n\n >>> df.groupby([\'Animal\'], as_index=False).mean().sort_values(\'Animal\')\n ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE\n Animal Max Speed\n ...Falcon 375.0\n ...Parrot 25.0\n\n We can also choose to include NA in group keys or not by setting dropna parameter,\n the default setting is True:\n\n >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]\n >>> df = ps.DataFrame(l, columns=["a", "b", "c"])\n >>> df.groupby(by=["b"]).sum().sort_index() # doctest: +NORMALIZE_WHITESPACE\n a c\n b\n 1.0 2 3\n 2.0 2 5\n\n >>> df.groupby(by=["b"], dropna=False).sum().sort_index() # doctest: +NORMALIZE_WHITESPACE\n a c\n b\n 1.0 2 3\n 2.0 2 5\n NaN 1 4\n '
if isinstance(by, ps.DataFrame):
raise ValueError("Grouper for '{}' not 1-dimensional".format(type(by).__name__))
elif isinstance(by, ps.Series):
new_by = [by]
elif is_name_like_tuple(by):
if isinstance(self, ps.Series):
raise KeyError(by)
new_by = [cast(Tuple, by)]
elif is_name_like_value(by):
if isinstance(self, ps.Series):
raise KeyError(by)
new_by = [(by,)]
elif is_list_like(by):
new_by = []
for key in by:
if isinstance(key, ps.DataFrame):
raise ValueError("Grouper for '{}' not 1-dimensional".format(type(key).__name__))
elif isinstance(key, ps.Series):
new_by.append(key)
elif is_name_like_tuple(key):
if isinstance(self, ps.Series):
raise KeyError(key)
new_by.append(key)
elif is_name_like_value(key):
if isinstance(self, ps.Series):
raise KeyError(key)
new_by.append((key,))
else:
raise ValueError("Grouper for '{}' not 1-dimensional".format(type(key).__name__))
else:
raise ValueError("Grouper for '{}' not 1-dimensional".format(type(by).__name__))
if (not len(new_by)):
raise ValueError('No group keys passed!')
axis = validate_axis(axis)
if (axis != 0):
raise NotImplementedError('axis should be either 0 or "index" currently.')
return self._build_groupby(by=new_by, as_index=as_index, dropna=dropna) |
def bool(self) -> bool:
"\n Return the bool of a single element in the current object.\n\n This must be a boolean scalar value, either True or False. Raise a ValueError if\n the object does not have exactly 1 element, or that element is not boolean\n\n Returns\n --------\n bool\n\n Examples\n --------\n >>> ps.DataFrame({'a': [True]}).bool()\n True\n\n >>> ps.Series([False]).bool()\n False\n\n If there are non-boolean or multiple values exist, it raises an exception in all\n cases as below.\n\n >>> ps.DataFrame({'a': ['a']}).bool()\n Traceback (most recent call last):\n ...\n ValueError: bool cannot act on a non-boolean single element DataFrame\n\n >>> ps.DataFrame({'a': [True], 'b': [False]}).bool() # doctest: +NORMALIZE_WHITESPACE\n Traceback (most recent call last):\n ...\n ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(),\n a.item(), a.any() or a.all().\n\n >>> ps.Series([1]).bool()\n Traceback (most recent call last):\n ...\n ValueError: bool cannot act on a non-boolean single element DataFrame\n "
if isinstance(self, ps.DataFrame):
df = self
elif isinstance(self, ps.Series):
df = self.to_dataframe()
else:
raise TypeError(('bool() expects DataFrame or Series; however, got [%s]' % (self,)))
return df.head(2)._to_internal_pandas().bool() | -1,561,303,879,960,741,400 | Return the bool of a single element in the current object.
This must be a boolean scalar value, either True or False. Raise a ValueError if
the object does not have exactly 1 element, or that element is not boolean
Returns
--------
bool
Examples
--------
>>> ps.DataFrame({'a': [True]}).bool()
True
>>> ps.Series([False]).bool()
False
If there are non-boolean or multiple values exist, it raises an exception in all
cases as below.
>>> ps.DataFrame({'a': ['a']}).bool()
Traceback (most recent call last):
...
ValueError: bool cannot act on a non-boolean single element DataFrame
>>> ps.DataFrame({'a': [True], 'b': [False]}).bool() # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(),
a.item(), a.any() or a.all().
>>> ps.Series([1]).bool()
Traceback (most recent call last):
...
ValueError: bool cannot act on a non-boolean single element DataFrame | python/pyspark/pandas/generic.py | bool | XpressAI/spark | python | def bool(self) -> bool:
"\n Return the bool of a single element in the current object.\n\n This must be a boolean scalar value, either True or False. Raise a ValueError if\n the object does not have exactly 1 element, or that element is not boolean\n\n Returns\n --------\n bool\n\n Examples\n --------\n >>> ps.DataFrame({'a': [True]}).bool()\n True\n\n >>> ps.Series([False]).bool()\n False\n\n If there are non-boolean or multiple values exist, it raises an exception in all\n cases as below.\n\n >>> ps.DataFrame({'a': ['a']}).bool()\n Traceback (most recent call last):\n ...\n ValueError: bool cannot act on a non-boolean single element DataFrame\n\n >>> ps.DataFrame({'a': [True], 'b': [False]}).bool() # doctest: +NORMALIZE_WHITESPACE\n Traceback (most recent call last):\n ...\n ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(),\n a.item(), a.any() or a.all().\n\n >>> ps.Series([1]).bool()\n Traceback (most recent call last):\n ...\n ValueError: bool cannot act on a non-boolean single element DataFrame\n "
if isinstance(self, ps.DataFrame):
df = self
elif isinstance(self, ps.Series):
df = self.to_dataframe()
else:
raise TypeError(('bool() expects DataFrame or Series; however, got [%s]' % (self,)))
return df.head(2)._to_internal_pandas().bool() |
def first_valid_index(self) -> Optional[Union[(Scalar, Tuple[(Scalar, ...)])]]:
"\n Retrieves the index of the first valid value.\n\n Returns\n -------\n scalar, tuple, or None\n\n Examples\n --------\n\n Support for DataFrame\n\n >>> psdf = ps.DataFrame({'a': [None, 2, 3, 2],\n ... 'b': [None, 2.0, 3.0, 1.0],\n ... 'c': [None, 200, 400, 200]},\n ... index=['Q', 'W', 'E', 'R'])\n >>> psdf\n a b c\n Q NaN NaN NaN\n W 2.0 2.0 200.0\n E 3.0 3.0 400.0\n R 2.0 1.0 200.0\n\n >>> psdf.first_valid_index()\n 'W'\n\n Support for MultiIndex columns\n\n >>> psdf.columns = pd.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])\n >>> psdf\n a b c\n x y z\n Q NaN NaN NaN\n W 2.0 2.0 200.0\n E 3.0 3.0 400.0\n R 2.0 1.0 200.0\n\n >>> psdf.first_valid_index()\n 'W'\n\n Support for Series.\n\n >>> s = ps.Series([None, None, 3, 4, 5], index=[100, 200, 300, 400, 500])\n >>> s\n 100 NaN\n 200 NaN\n 300 3.0\n 400 4.0\n 500 5.0\n dtype: float64\n\n >>> s.first_valid_index()\n 300\n\n Support for MultiIndex\n\n >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],\n ... ['speed', 'weight', 'length']],\n ... [[0, 0, 0, 1, 1, 1, 2, 2, 2],\n ... [0, 1, 2, 0, 1, 2, 0, 1, 2]])\n >>> s = ps.Series([None, None, None, None, 250, 1.5, 320, 1, 0.3], index=midx)\n >>> s\n lama speed NaN\n weight NaN\n length NaN\n cow speed NaN\n weight 250.0\n length 1.5\n falcon speed 320.0\n weight 1.0\n length 0.3\n dtype: float64\n\n >>> s.first_valid_index()\n ('cow', 'weight')\n "
data_spark_columns = self._internal.data_spark_columns
if (len(data_spark_columns) == 0):
return None
cond = reduce((lambda x, y: (x & y)), map((lambda x: x.isNotNull()), data_spark_columns))
with sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
first_valid_row = cast(pd.DataFrame, self._internal.spark_frame.filter(cond).select(self._internal.index_spark_columns).limit(1).toPandas())
if (len(first_valid_row) == 0):
return None
first_valid_row = first_valid_row.iloc[0]
if (len(first_valid_row) == 1):
return first_valid_row.iloc[0]
else:
return tuple(first_valid_row) | -2,649,245,325,038,494,000 | Retrieves the index of the first valid value.
Returns
-------
scalar, tuple, or None
Examples
--------
Support for DataFrame
>>> psdf = ps.DataFrame({'a': [None, 2, 3, 2],
... 'b': [None, 2.0, 3.0, 1.0],
... 'c': [None, 200, 400, 200]},
... index=['Q', 'W', 'E', 'R'])
>>> psdf
a b c
Q NaN NaN NaN
W 2.0 2.0 200.0
E 3.0 3.0 400.0
R 2.0 1.0 200.0
>>> psdf.first_valid_index()
'W'
Support for MultiIndex columns
>>> psdf.columns = pd.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
>>> psdf
a b c
x y z
Q NaN NaN NaN
W 2.0 2.0 200.0
E 3.0 3.0 400.0
R 2.0 1.0 200.0
>>> psdf.first_valid_index()
'W'
Support for Series.
>>> s = ps.Series([None, None, 3, 4, 5], index=[100, 200, 300, 400, 500])
>>> s
100 NaN
200 NaN
300 3.0
400 4.0
500 5.0
dtype: float64
>>> s.first_valid_index()
300
Support for MultiIndex
>>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... [[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> s = ps.Series([None, None, None, None, 250, 1.5, 320, 1, 0.3], index=midx)
>>> s
lama speed NaN
weight NaN
length NaN
cow speed NaN
weight 250.0
length 1.5
falcon speed 320.0
weight 1.0
length 0.3
dtype: float64
>>> s.first_valid_index()
('cow', 'weight') | python/pyspark/pandas/generic.py | first_valid_index | XpressAI/spark | python | def first_valid_index(self) -> Optional[Union[(Scalar, Tuple[(Scalar, ...)])]]:
"\n Retrieves the index of the first valid value.\n\n Returns\n -------\n scalar, tuple, or None\n\n Examples\n --------\n\n Support for DataFrame\n\n >>> psdf = ps.DataFrame({'a': [None, 2, 3, 2],\n ... 'b': [None, 2.0, 3.0, 1.0],\n ... 'c': [None, 200, 400, 200]},\n ... index=['Q', 'W', 'E', 'R'])\n >>> psdf\n a b c\n Q NaN NaN NaN\n W 2.0 2.0 200.0\n E 3.0 3.0 400.0\n R 2.0 1.0 200.0\n\n >>> psdf.first_valid_index()\n 'W'\n\n Support for MultiIndex columns\n\n >>> psdf.columns = pd.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])\n >>> psdf\n a b c\n x y z\n Q NaN NaN NaN\n W 2.0 2.0 200.0\n E 3.0 3.0 400.0\n R 2.0 1.0 200.0\n\n >>> psdf.first_valid_index()\n 'W'\n\n Support for Series.\n\n >>> s = ps.Series([None, None, 3, 4, 5], index=[100, 200, 300, 400, 500])\n >>> s\n 100 NaN\n 200 NaN\n 300 3.0\n 400 4.0\n 500 5.0\n dtype: float64\n\n >>> s.first_valid_index()\n 300\n\n Support for MultiIndex\n\n >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],\n ... ['speed', 'weight', 'length']],\n ... [[0, 0, 0, 1, 1, 1, 2, 2, 2],\n ... [0, 1, 2, 0, 1, 2, 0, 1, 2]])\n >>> s = ps.Series([None, None, None, None, 250, 1.5, 320, 1, 0.3], index=midx)\n >>> s\n lama speed NaN\n weight NaN\n length NaN\n cow speed NaN\n weight 250.0\n length 1.5\n falcon speed 320.0\n weight 1.0\n length 0.3\n dtype: float64\n\n >>> s.first_valid_index()\n ('cow', 'weight')\n "
data_spark_columns = self._internal.data_spark_columns
if (len(data_spark_columns) == 0):
return None
cond = reduce((lambda x, y: (x & y)), map((lambda x: x.isNotNull()), data_spark_columns))
with sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
first_valid_row = cast(pd.DataFrame, self._internal.spark_frame.filter(cond).select(self._internal.index_spark_columns).limit(1).toPandas())
if (len(first_valid_row) == 0):
return None
first_valid_row = first_valid_row.iloc[0]
if (len(first_valid_row) == 1):
return first_valid_row.iloc[0]
else:
return tuple(first_valid_row) |
def last_valid_index(self) -> Optional[Union[(Scalar, Tuple[(Scalar, ...)])]]:
"\n Return index for last non-NA/null value.\n\n Returns\n -------\n scalar, tuple, or None\n\n Notes\n -----\n This API only works with PySpark >= 3.0.\n\n Examples\n --------\n\n Support for DataFrame\n\n >>> psdf = ps.DataFrame({'a': [1, 2, 3, None],\n ... 'b': [1.0, 2.0, 3.0, None],\n ... 'c': [100, 200, 400, None]},\n ... index=['Q', 'W', 'E', 'R'])\n >>> psdf\n a b c\n Q 1.0 1.0 100.0\n W 2.0 2.0 200.0\n E 3.0 3.0 400.0\n R NaN NaN NaN\n\n >>> psdf.last_valid_index() # doctest: +SKIP\n 'E'\n\n Support for MultiIndex columns\n\n >>> psdf.columns = pd.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])\n >>> psdf\n a b c\n x y z\n Q 1.0 1.0 100.0\n W 2.0 2.0 200.0\n E 3.0 3.0 400.0\n R NaN NaN NaN\n\n >>> psdf.last_valid_index() # doctest: +SKIP\n 'E'\n\n Support for Series.\n\n >>> s = ps.Series([1, 2, 3, None, None], index=[100, 200, 300, 400, 500])\n >>> s\n 100 1.0\n 200 2.0\n 300 3.0\n 400 NaN\n 500 NaN\n dtype: float64\n\n >>> s.last_valid_index() # doctest: +SKIP\n 300\n\n Support for MultiIndex\n\n >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],\n ... ['speed', 'weight', 'length']],\n ... [[0, 0, 0, 1, 1, 1, 2, 2, 2],\n ... [0, 1, 2, 0, 1, 2, 0, 1, 2]])\n >>> s = ps.Series([250, 1.5, 320, 1, 0.3, None, None, None, None], index=midx)\n >>> s\n lama speed 250.0\n weight 1.5\n length 320.0\n cow speed 1.0\n weight 0.3\n length NaN\n falcon speed NaN\n weight NaN\n length NaN\n dtype: float64\n\n >>> s.last_valid_index() # doctest: +SKIP\n ('cow', 'weight')\n "
data_spark_columns = self._internal.data_spark_columns
if (len(data_spark_columns) == 0):
return None
cond = reduce((lambda x, y: (x & y)), map((lambda x: x.isNotNull()), data_spark_columns))
last_valid_rows = self._internal.spark_frame.filter(cond).select(self._internal.index_spark_columns).tail(1)
if (len(last_valid_rows) == 0):
return None
last_valid_row = last_valid_rows[0]
if (len(last_valid_row) == 1):
return last_valid_row[0]
else:
return tuple(last_valid_row) | 613,440,096,527,983,700 | Return index for last non-NA/null value.
Returns
-------
scalar, tuple, or None
Notes
-----
This API only works with PySpark >= 3.0.
Examples
--------
Support for DataFrame
>>> psdf = ps.DataFrame({'a': [1, 2, 3, None],
... 'b': [1.0, 2.0, 3.0, None],
... 'c': [100, 200, 400, None]},
... index=['Q', 'W', 'E', 'R'])
>>> psdf
a b c
Q 1.0 1.0 100.0
W 2.0 2.0 200.0
E 3.0 3.0 400.0
R NaN NaN NaN
>>> psdf.last_valid_index() # doctest: +SKIP
'E'
Support for MultiIndex columns
>>> psdf.columns = pd.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
>>> psdf
a b c
x y z
Q 1.0 1.0 100.0
W 2.0 2.0 200.0
E 3.0 3.0 400.0
R NaN NaN NaN
>>> psdf.last_valid_index() # doctest: +SKIP
'E'
Support for Series.
>>> s = ps.Series([1, 2, 3, None, None], index=[100, 200, 300, 400, 500])
>>> s
100 1.0
200 2.0
300 3.0
400 NaN
500 NaN
dtype: float64
>>> s.last_valid_index() # doctest: +SKIP
300
Support for MultiIndex
>>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... [[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> s = ps.Series([250, 1.5, 320, 1, 0.3, None, None, None, None], index=midx)
>>> s
lama speed 250.0
weight 1.5
length 320.0
cow speed 1.0
weight 0.3
length NaN
falcon speed NaN
weight NaN
length NaN
dtype: float64
>>> s.last_valid_index() # doctest: +SKIP
('cow', 'weight') | python/pyspark/pandas/generic.py | last_valid_index | XpressAI/spark | python | def last_valid_index(self) -> Optional[Union[(Scalar, Tuple[(Scalar, ...)])]]:
"\n Return index for last non-NA/null value.\n\n Returns\n -------\n scalar, tuple, or None\n\n Notes\n -----\n This API only works with PySpark >= 3.0.\n\n Examples\n --------\n\n Support for DataFrame\n\n >>> psdf = ps.DataFrame({'a': [1, 2, 3, None],\n ... 'b': [1.0, 2.0, 3.0, None],\n ... 'c': [100, 200, 400, None]},\n ... index=['Q', 'W', 'E', 'R'])\n >>> psdf\n a b c\n Q 1.0 1.0 100.0\n W 2.0 2.0 200.0\n E 3.0 3.0 400.0\n R NaN NaN NaN\n\n >>> psdf.last_valid_index() # doctest: +SKIP\n 'E'\n\n Support for MultiIndex columns\n\n >>> psdf.columns = pd.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])\n >>> psdf\n a b c\n x y z\n Q 1.0 1.0 100.0\n W 2.0 2.0 200.0\n E 3.0 3.0 400.0\n R NaN NaN NaN\n\n >>> psdf.last_valid_index() # doctest: +SKIP\n 'E'\n\n Support for Series.\n\n >>> s = ps.Series([1, 2, 3, None, None], index=[100, 200, 300, 400, 500])\n >>> s\n 100 1.0\n 200 2.0\n 300 3.0\n 400 NaN\n 500 NaN\n dtype: float64\n\n >>> s.last_valid_index() # doctest: +SKIP\n 300\n\n Support for MultiIndex\n\n >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],\n ... ['speed', 'weight', 'length']],\n ... [[0, 0, 0, 1, 1, 1, 2, 2, 2],\n ... [0, 1, 2, 0, 1, 2, 0, 1, 2]])\n >>> s = ps.Series([250, 1.5, 320, 1, 0.3, None, None, None, None], index=midx)\n >>> s\n lama speed 250.0\n weight 1.5\n length 320.0\n cow speed 1.0\n weight 0.3\n length NaN\n falcon speed NaN\n weight NaN\n length NaN\n dtype: float64\n\n >>> s.last_valid_index() # doctest: +SKIP\n ('cow', 'weight')\n "
data_spark_columns = self._internal.data_spark_columns
if (len(data_spark_columns) == 0):
return None
cond = reduce((lambda x, y: (x & y)), map((lambda x: x.isNotNull()), data_spark_columns))
last_valid_rows = self._internal.spark_frame.filter(cond).select(self._internal.index_spark_columns).tail(1)
if (len(last_valid_rows) == 0):
return None
last_valid_row = last_valid_rows[0]
if (len(last_valid_row) == 1):
return last_valid_row[0]
else:
return tuple(last_valid_row) |
def rolling(self: FrameLike, window: int, min_periods: Optional[int]=None) -> 'Rolling[FrameLike]':
"\n Provide rolling transformations.\n\n .. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.\n Unlike pandas, NA is also counted as the period. This might be changed\n in the near future.\n\n Parameters\n ----------\n window : int, or offset\n Size of the moving window.\n This is the number of observations used for calculating the statistic.\n Each window will be a fixed size.\n\n min_periods : int, default None\n Minimum number of observations in window required to have a value\n (otherwise result is NA).\n For a window that is specified by an offset, min_periods will default to 1.\n Otherwise, min_periods will default to the size of the window.\n\n Returns\n -------\n a Window sub-classed for the particular operation\n "
from pyspark.pandas.window import Rolling
return Rolling(self, window=window, min_periods=min_periods) | 3,435,677,467,007,363,600 | Provide rolling transformations.
.. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.
Unlike pandas, NA is also counted as the period. This might be changed
in the near future.
Parameters
----------
window : int, or offset
Size of the moving window.
This is the number of observations used for calculating the statistic.
Each window will be a fixed size.
min_periods : int, default None
Minimum number of observations in window required to have a value
(otherwise result is NA).
For a window that is specified by an offset, min_periods will default to 1.
Otherwise, min_periods will default to the size of the window.
Returns
-------
a Window sub-classed for the particular operation | python/pyspark/pandas/generic.py | rolling | XpressAI/spark | python | def rolling(self: FrameLike, window: int, min_periods: Optional[int]=None) -> 'Rolling[FrameLike]':
"\n Provide rolling transformations.\n\n .. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.\n Unlike pandas, NA is also counted as the period. This might be changed\n in the near future.\n\n Parameters\n ----------\n window : int, or offset\n Size of the moving window.\n This is the number of observations used for calculating the statistic.\n Each window will be a fixed size.\n\n min_periods : int, default None\n Minimum number of observations in window required to have a value\n (otherwise result is NA).\n For a window that is specified by an offset, min_periods will default to 1.\n Otherwise, min_periods will default to the size of the window.\n\n Returns\n -------\n a Window sub-classed for the particular operation\n "
from pyspark.pandas.window import Rolling
return Rolling(self, window=window, min_periods=min_periods) |
def expanding(self: FrameLike, min_periods: int=1) -> 'Expanding[FrameLike]':
"\n Provide expanding transformations.\n\n .. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.\n Unlike pandas, NA is also counted as the period. This might be changed\n in the near future.\n\n Parameters\n ----------\n min_periods : int, default 1\n Minimum number of observations in window required to have a value\n (otherwise result is NA).\n\n Returns\n -------\n a Window sub-classed for the particular operation\n "
from pyspark.pandas.window import Expanding
return Expanding(self, min_periods=min_periods) | -4,089,165,445,054,864,000 | Provide expanding transformations.
.. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.
Unlike pandas, NA is also counted as the period. This might be changed
in the near future.
Parameters
----------
min_periods : int, default 1
Minimum number of observations in window required to have a value
(otherwise result is NA).
Returns
-------
a Window sub-classed for the particular operation | python/pyspark/pandas/generic.py | expanding | XpressAI/spark | python | def expanding(self: FrameLike, min_periods: int=1) -> 'Expanding[FrameLike]':
"\n Provide expanding transformations.\n\n .. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.\n Unlike pandas, NA is also counted as the period. This might be changed\n in the near future.\n\n Parameters\n ----------\n min_periods : int, default 1\n Minimum number of observations in window required to have a value\n (otherwise result is NA).\n\n Returns\n -------\n a Window sub-classed for the particular operation\n "
from pyspark.pandas.window import Expanding
return Expanding(self, min_periods=min_periods) |
def get(self, key: Any, default: Optional[Any]=None) -> Any:
"\n Get item from object for given key (DataFrame column, Panel slice,\n etc.). Returns default value if not found.\n\n Parameters\n ----------\n key : object\n\n Returns\n -------\n value : same type as items contained in object\n\n Examples\n --------\n >>> df = ps.DataFrame({'x':range(3), 'y':['a','b','b'], 'z':['a','b','b']},\n ... columns=['x', 'y', 'z'], index=[10, 20, 20])\n >>> df\n x y z\n 10 0 a a\n 20 1 b b\n 20 2 b b\n\n >>> df.get('x')\n 10 0\n 20 1\n 20 2\n Name: x, dtype: int64\n\n >>> df.get(['x', 'y'])\n x y\n 10 0 a\n 20 1 b\n 20 2 b\n\n >>> df.x.get(10)\n 0\n\n >>> df.x.get(20)\n 20 1\n 20 2\n Name: x, dtype: int64\n\n >>> df.x.get(15, -1)\n -1\n "
try:
return self[key]
except (KeyError, ValueError, IndexError):
return default | 1,690,284,315,299,788,500 | Get item from object for given key (DataFrame column, Panel slice,
etc.). Returns default value if not found.
Parameters
----------
key : object
Returns
-------
value : same type as items contained in object
Examples
--------
>>> df = ps.DataFrame({'x':range(3), 'y':['a','b','b'], 'z':['a','b','b']},
... columns=['x', 'y', 'z'], index=[10, 20, 20])
>>> df
x y z
10 0 a a
20 1 b b
20 2 b b
>>> df.get('x')
10 0
20 1
20 2
Name: x, dtype: int64
>>> df.get(['x', 'y'])
x y
10 0 a
20 1 b
20 2 b
>>> df.x.get(10)
0
>>> df.x.get(20)
20 1
20 2
Name: x, dtype: int64
>>> df.x.get(15, -1)
-1 | python/pyspark/pandas/generic.py | get | XpressAI/spark | python | def get(self, key: Any, default: Optional[Any]=None) -> Any:
"\n Get item from object for given key (DataFrame column, Panel slice,\n etc.). Returns default value if not found.\n\n Parameters\n ----------\n key : object\n\n Returns\n -------\n value : same type as items contained in object\n\n Examples\n --------\n >>> df = ps.DataFrame({'x':range(3), 'y':['a','b','b'], 'z':['a','b','b']},\n ... columns=['x', 'y', 'z'], index=[10, 20, 20])\n >>> df\n x y z\n 10 0 a a\n 20 1 b b\n 20 2 b b\n\n >>> df.get('x')\n 10 0\n 20 1\n 20 2\n Name: x, dtype: int64\n\n >>> df.get(['x', 'y'])\n x y\n 10 0 a\n 20 1 b\n 20 2 b\n\n >>> df.x.get(10)\n 0\n\n >>> df.x.get(20)\n 20 1\n 20 2\n Name: x, dtype: int64\n\n >>> df.x.get(15, -1)\n -1\n "
try:
return self[key]
except (KeyError, ValueError, IndexError):
return default |
def squeeze(self, axis: Optional[Axis]=None) -> Union[(Scalar, 'DataFrame', 'Series')]:
"\n Squeeze 1 dimensional axis objects into scalars.\n\n Series or DataFrames with a single element are squeezed to a scalar.\n DataFrames with a single column or a single row are squeezed to a\n Series. Otherwise the object is unchanged.\n\n This method is most useful when you don't know if your\n object is a Series or DataFrame, but you do know it has just a single\n column. In that case you can safely call `squeeze` to ensure you have a\n Series.\n\n Parameters\n ----------\n axis : {0 or 'index', 1 or 'columns', None}, default None\n A specific axis to squeeze. By default, all length-1 axes are\n squeezed.\n\n Returns\n -------\n DataFrame, Series, or scalar\n The projection after squeezing `axis` or all the axes.\n\n See Also\n --------\n Series.iloc : Integer-location based indexing for selecting scalars.\n DataFrame.iloc : Integer-location based indexing for selecting Series.\n Series.to_frame : Inverse of DataFrame.squeeze for a\n single-column DataFrame.\n\n Examples\n --------\n >>> primes = ps.Series([2, 3, 5, 7])\n\n Slicing might produce a Series with a single value:\n\n >>> even_primes = primes[primes % 2 == 0]\n >>> even_primes\n 0 2\n dtype: int64\n\n >>> even_primes.squeeze()\n 2\n\n Squeezing objects with more than one value in every axis does nothing:\n\n >>> odd_primes = primes[primes % 2 == 1]\n >>> odd_primes\n 1 3\n 2 5\n 3 7\n dtype: int64\n\n >>> odd_primes.squeeze()\n 1 3\n 2 5\n 3 7\n dtype: int64\n\n Squeezing is even more effective when used with DataFrames.\n\n >>> df = ps.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])\n >>> df\n a b\n 0 1 2\n 1 3 4\n\n Slicing a single column will produce a DataFrame with the columns\n having only one value:\n\n >>> df_a = df[['a']]\n >>> df_a\n a\n 0 1\n 1 3\n\n So the columns can be squeezed down, resulting in a Series:\n\n >>> df_a.squeeze('columns')\n 0 1\n 1 3\n Name: a, dtype: int64\n\n Slicing a single row from a single column will produce a single\n scalar DataFrame:\n\n >>> df_1a = df.loc[[1], ['a']]\n >>> df_1a\n a\n 1 3\n\n Squeezing the rows produces a single scalar Series:\n\n >>> df_1a.squeeze('rows')\n a 3\n Name: 1, dtype: int64\n\n Squeezing all axes will project directly into a scalar:\n\n >>> df_1a.squeeze()\n 3\n "
if (axis is not None):
axis = ('index' if (axis == 'rows') else axis)
axis = validate_axis(axis)
if isinstance(self, ps.DataFrame):
from pyspark.pandas.series import first_series
is_squeezable = (len(self.columns[:2]) == 1)
if (not is_squeezable):
return self
series_from_column = first_series(self)
has_single_value = (len(series_from_column.head(2)) == 1)
if has_single_value:
result = self._to_internal_pandas().squeeze(axis)
return (ps.Series(result) if isinstance(result, pd.Series) else result)
elif (axis == 0):
return self
else:
return series_from_column
else:
self_top_two = cast('Series', self).head(2)
has_single_value = (len(self_top_two) == 1)
return cast(Union[(Scalar, ps.Series)], (self_top_two[0] if has_single_value else self)) | -1,916,325,584,634,146,800 | Squeeze 1 dimensional axis objects into scalars.
Series or DataFrames with a single element are squeezed to a scalar.
DataFrames with a single column or a single row are squeezed to a
Series. Otherwise the object is unchanged.
This method is most useful when you don't know if your
object is a Series or DataFrame, but you do know it has just a single
column. In that case you can safely call `squeeze` to ensure you have a
Series.
Parameters
----------
axis : {0 or 'index', 1 or 'columns', None}, default None
A specific axis to squeeze. By default, all length-1 axes are
squeezed.
Returns
-------
DataFrame, Series, or scalar
The projection after squeezing `axis` or all the axes.
See Also
--------
Series.iloc : Integer-location based indexing for selecting scalars.
DataFrame.iloc : Integer-location based indexing for selecting Series.
Series.to_frame : Inverse of DataFrame.squeeze for a
single-column DataFrame.
Examples
--------
>>> primes = ps.Series([2, 3, 5, 7])
Slicing might produce a Series with a single value:
>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0 2
dtype: int64
>>> even_primes.squeeze()
2
Squeezing objects with more than one value in every axis does nothing:
>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1 3
2 5
3 7
dtype: int64
>>> odd_primes.squeeze()
1 3
2 5
3 7
dtype: int64
Squeezing is even more effective when used with DataFrames.
>>> df = ps.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
a b
0 1 2
1 3 4
Slicing a single column will produce a DataFrame with the columns
having only one value:
>>> df_a = df[['a']]
>>> df_a
a
0 1
1 3
So the columns can be squeezed down, resulting in a Series:
>>> df_a.squeeze('columns')
0 1
1 3
Name: a, dtype: int64
Slicing a single row from a single column will produce a single
scalar DataFrame:
>>> df_1a = df.loc[[1], ['a']]
>>> df_1a
a
1 3
Squeezing the rows produces a single scalar Series:
>>> df_1a.squeeze('rows')
a 3
Name: 1, dtype: int64
Squeezing all axes will project directly into a scalar:
>>> df_1a.squeeze()
3 | python/pyspark/pandas/generic.py | squeeze | XpressAI/spark | python | def squeeze(self, axis: Optional[Axis]=None) -> Union[(Scalar, 'DataFrame', 'Series')]:
"\n Squeeze 1 dimensional axis objects into scalars.\n\n Series or DataFrames with a single element are squeezed to a scalar.\n DataFrames with a single column or a single row are squeezed to a\n Series. Otherwise the object is unchanged.\n\n This method is most useful when you don't know if your\n object is a Series or DataFrame, but you do know it has just a single\n column. In that case you can safely call `squeeze` to ensure you have a\n Series.\n\n Parameters\n ----------\n axis : {0 or 'index', 1 or 'columns', None}, default None\n A specific axis to squeeze. By default, all length-1 axes are\n squeezed.\n\n Returns\n -------\n DataFrame, Series, or scalar\n The projection after squeezing `axis` or all the axes.\n\n See Also\n --------\n Series.iloc : Integer-location based indexing for selecting scalars.\n DataFrame.iloc : Integer-location based indexing for selecting Series.\n Series.to_frame : Inverse of DataFrame.squeeze for a\n single-column DataFrame.\n\n Examples\n --------\n >>> primes = ps.Series([2, 3, 5, 7])\n\n Slicing might produce a Series with a single value:\n\n >>> even_primes = primes[primes % 2 == 0]\n >>> even_primes\n 0 2\n dtype: int64\n\n >>> even_primes.squeeze()\n 2\n\n Squeezing objects with more than one value in every axis does nothing:\n\n >>> odd_primes = primes[primes % 2 == 1]\n >>> odd_primes\n 1 3\n 2 5\n 3 7\n dtype: int64\n\n >>> odd_primes.squeeze()\n 1 3\n 2 5\n 3 7\n dtype: int64\n\n Squeezing is even more effective when used with DataFrames.\n\n >>> df = ps.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])\n >>> df\n a b\n 0 1 2\n 1 3 4\n\n Slicing a single column will produce a DataFrame with the columns\n having only one value:\n\n >>> df_a = df[['a']]\n >>> df_a\n a\n 0 1\n 1 3\n\n So the columns can be squeezed down, resulting in a Series:\n\n >>> df_a.squeeze('columns')\n 0 1\n 1 3\n Name: a, dtype: int64\n\n Slicing a single row from a single column will produce a single\n scalar DataFrame:\n\n >>> df_1a = df.loc[[1], ['a']]\n >>> df_1a\n a\n 1 3\n\n Squeezing the rows produces a single scalar Series:\n\n >>> df_1a.squeeze('rows')\n a 3\n Name: 1, dtype: int64\n\n Squeezing all axes will project directly into a scalar:\n\n >>> df_1a.squeeze()\n 3\n "
if (axis is not None):
axis = ('index' if (axis == 'rows') else axis)
axis = validate_axis(axis)
if isinstance(self, ps.DataFrame):
from pyspark.pandas.series import first_series
is_squeezable = (len(self.columns[:2]) == 1)
if (not is_squeezable):
return self
series_from_column = first_series(self)
has_single_value = (len(series_from_column.head(2)) == 1)
if has_single_value:
result = self._to_internal_pandas().squeeze(axis)
return (ps.Series(result) if isinstance(result, pd.Series) else result)
elif (axis == 0):
return self
else:
return series_from_column
else:
self_top_two = cast('Series', self).head(2)
has_single_value = (len(self_top_two) == 1)
return cast(Union[(Scalar, ps.Series)], (self_top_two[0] if has_single_value else self)) |
def truncate(self, before: Optional[Any]=None, after: Optional[Any]=None, axis: Optional[Axis]=None, copy: bool_type=True) -> DataFrameOrSeries:
'\n Truncate a Series or DataFrame before and after some index value.\n\n This is a useful shorthand for boolean indexing based on index\n values above or below certain thresholds.\n\n .. note:: This API is dependent on :meth:`Index.is_monotonic_increasing`\n which can be expensive.\n\n Parameters\n ----------\n before : date, str, int\n Truncate all rows before this index value.\n after : date, str, int\n Truncate all rows after this index value.\n axis : {0 or \'index\', 1 or \'columns\'}, optional\n Axis to truncate. Truncates the index (rows) by default.\n copy : bool, default is True,\n Return a copy of the truncated section.\n\n Returns\n -------\n type of caller\n The truncated Series or DataFrame.\n\n See Also\n --------\n DataFrame.loc : Select a subset of a DataFrame by label.\n DataFrame.iloc : Select a subset of a DataFrame by position.\n\n Examples\n --------\n >>> df = ps.DataFrame({\'A\': [\'a\', \'b\', \'c\', \'d\', \'e\'],\n ... \'B\': [\'f\', \'g\', \'h\', \'i\', \'j\'],\n ... \'C\': [\'k\', \'l\', \'m\', \'n\', \'o\']},\n ... index=[1, 2, 3, 4, 5])\n >>> df\n A B C\n 1 a f k\n 2 b g l\n 3 c h m\n 4 d i n\n 5 e j o\n\n >>> df.truncate(before=2, after=4)\n A B C\n 2 b g l\n 3 c h m\n 4 d i n\n\n The columns of a DataFrame can be truncated.\n\n >>> df.truncate(before="A", after="B", axis="columns")\n A B\n 1 a f\n 2 b g\n 3 c h\n 4 d i\n 5 e j\n\n For Series, only rows can be truncated.\n\n >>> df[\'A\'].truncate(before=2, after=4)\n 2 b\n 3 c\n 4 d\n Name: A, dtype: object\n\n A Series has index that sorted integers.\n\n >>> s = ps.Series([10, 20, 30, 40, 50, 60, 70],\n ... index=[1, 2, 3, 4, 5, 6, 7])\n >>> s\n 1 10\n 2 20\n 3 30\n 4 40\n 5 50\n 6 60\n 7 70\n dtype: int64\n\n >>> s.truncate(2, 5)\n 2 20\n 3 30\n 4 40\n 5 50\n dtype: int64\n\n A Series has index that sorted strings.\n\n >>> s = ps.Series([10, 20, 30, 40, 50, 60, 70],\n ... index=[\'a\', \'b\', \'c\', \'d\', \'e\', \'f\', \'g\'])\n >>> s\n a 10\n b 20\n c 30\n d 40\n e 50\n f 60\n g 70\n dtype: int64\n\n >>> s.truncate(\'b\', \'e\')\n b 20\n c 30\n d 40\n e 50\n dtype: int64\n '
from pyspark.pandas.series import first_series
axis = validate_axis(axis)
indexes = self.index
indexes_increasing = indexes.is_monotonic_increasing
if ((not indexes_increasing) and (not indexes.is_monotonic_decreasing)):
raise ValueError('truncate requires a sorted index')
if ((before is None) and (after is None)):
return cast(Union[(ps.DataFrame, ps.Series)], (self.copy() if copy else self))
if (((before is not None) and (after is not None)) and (before > after)):
raise ValueError(('Truncate: %s must be after %s' % (after, before)))
if isinstance(self, ps.Series):
if indexes_increasing:
result = first_series(self.to_frame().loc[before:after]).rename(self.name)
else:
result = first_series(self.to_frame().loc[after:before]).rename(self.name)
elif isinstance(self, ps.DataFrame):
if (axis == 0):
if indexes_increasing:
result = self.loc[before:after]
else:
result = self.loc[after:before]
elif (axis == 1):
result = self.loc[:, before:after]
return cast(DataFrameOrSeries, (result.copy() if copy else result)) | 6,132,906,944,478,476,000 | Truncate a Series or DataFrame before and after some index value.
This is a useful shorthand for boolean indexing based on index
values above or below certain thresholds.
.. note:: This API is dependent on :meth:`Index.is_monotonic_increasing`
which can be expensive.
Parameters
----------
before : date, str, int
Truncate all rows before this index value.
after : date, str, int
Truncate all rows after this index value.
axis : {0 or 'index', 1 or 'columns'}, optional
Axis to truncate. Truncates the index (rows) by default.
copy : bool, default is True,
Return a copy of the truncated section.
Returns
-------
type of caller
The truncated Series or DataFrame.
See Also
--------
DataFrame.loc : Select a subset of a DataFrame by label.
DataFrame.iloc : Select a subset of a DataFrame by position.
Examples
--------
>>> df = ps.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
... 'B': ['f', 'g', 'h', 'i', 'j'],
... 'C': ['k', 'l', 'm', 'n', 'o']},
... index=[1, 2, 3, 4, 5])
>>> df
A B C
1 a f k
2 b g l
3 c h m
4 d i n
5 e j o
>>> df.truncate(before=2, after=4)
A B C
2 b g l
3 c h m
4 d i n
The columns of a DataFrame can be truncated.
>>> df.truncate(before="A", after="B", axis="columns")
A B
1 a f
2 b g
3 c h
4 d i
5 e j
For Series, only rows can be truncated.
>>> df['A'].truncate(before=2, after=4)
2 b
3 c
4 d
Name: A, dtype: object
A Series has index that sorted integers.
>>> s = ps.Series([10, 20, 30, 40, 50, 60, 70],
... index=[1, 2, 3, 4, 5, 6, 7])
>>> s
1 10
2 20
3 30
4 40
5 50
6 60
7 70
dtype: int64
>>> s.truncate(2, 5)
2 20
3 30
4 40
5 50
dtype: int64
A Series has index that sorted strings.
>>> s = ps.Series([10, 20, 30, 40, 50, 60, 70],
... index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])
>>> s
a 10
b 20
c 30
d 40
e 50
f 60
g 70
dtype: int64
>>> s.truncate('b', 'e')
b 20
c 30
d 40
e 50
dtype: int64 | python/pyspark/pandas/generic.py | truncate | XpressAI/spark | python | def truncate(self, before: Optional[Any]=None, after: Optional[Any]=None, axis: Optional[Axis]=None, copy: bool_type=True) -> DataFrameOrSeries:
'\n Truncate a Series or DataFrame before and after some index value.\n\n This is a useful shorthand for boolean indexing based on index\n values above or below certain thresholds.\n\n .. note:: This API is dependent on :meth:`Index.is_monotonic_increasing`\n which can be expensive.\n\n Parameters\n ----------\n before : date, str, int\n Truncate all rows before this index value.\n after : date, str, int\n Truncate all rows after this index value.\n axis : {0 or \'index\', 1 or \'columns\'}, optional\n Axis to truncate. Truncates the index (rows) by default.\n copy : bool, default is True,\n Return a copy of the truncated section.\n\n Returns\n -------\n type of caller\n The truncated Series or DataFrame.\n\n See Also\n --------\n DataFrame.loc : Select a subset of a DataFrame by label.\n DataFrame.iloc : Select a subset of a DataFrame by position.\n\n Examples\n --------\n >>> df = ps.DataFrame({\'A\': [\'a\', \'b\', \'c\', \'d\', \'e\'],\n ... \'B\': [\'f\', \'g\', \'h\', \'i\', \'j\'],\n ... \'C\': [\'k\', \'l\', \'m\', \'n\', \'o\']},\n ... index=[1, 2, 3, 4, 5])\n >>> df\n A B C\n 1 a f k\n 2 b g l\n 3 c h m\n 4 d i n\n 5 e j o\n\n >>> df.truncate(before=2, after=4)\n A B C\n 2 b g l\n 3 c h m\n 4 d i n\n\n The columns of a DataFrame can be truncated.\n\n >>> df.truncate(before="A", after="B", axis="columns")\n A B\n 1 a f\n 2 b g\n 3 c h\n 4 d i\n 5 e j\n\n For Series, only rows can be truncated.\n\n >>> df[\'A\'].truncate(before=2, after=4)\n 2 b\n 3 c\n 4 d\n Name: A, dtype: object\n\n A Series has index that sorted integers.\n\n >>> s = ps.Series([10, 20, 30, 40, 50, 60, 70],\n ... index=[1, 2, 3, 4, 5, 6, 7])\n >>> s\n 1 10\n 2 20\n 3 30\n 4 40\n 5 50\n 6 60\n 7 70\n dtype: int64\n\n >>> s.truncate(2, 5)\n 2 20\n 3 30\n 4 40\n 5 50\n dtype: int64\n\n A Series has index that sorted strings.\n\n >>> s = ps.Series([10, 20, 30, 40, 50, 60, 70],\n ... index=[\'a\', \'b\', \'c\', \'d\', \'e\', \'f\', \'g\'])\n >>> s\n a 10\n b 20\n c 30\n d 40\n e 50\n f 60\n g 70\n dtype: int64\n\n >>> s.truncate(\'b\', \'e\')\n b 20\n c 30\n d 40\n e 50\n dtype: int64\n '
from pyspark.pandas.series import first_series
axis = validate_axis(axis)
indexes = self.index
indexes_increasing = indexes.is_monotonic_increasing
if ((not indexes_increasing) and (not indexes.is_monotonic_decreasing)):
raise ValueError('truncate requires a sorted index')
if ((before is None) and (after is None)):
return cast(Union[(ps.DataFrame, ps.Series)], (self.copy() if copy else self))
if (((before is not None) and (after is not None)) and (before > after)):
raise ValueError(('Truncate: %s must be after %s' % (after, before)))
if isinstance(self, ps.Series):
if indexes_increasing:
result = first_series(self.to_frame().loc[before:after]).rename(self.name)
else:
result = first_series(self.to_frame().loc[after:before]).rename(self.name)
elif isinstance(self, ps.DataFrame):
if (axis == 0):
if indexes_increasing:
result = self.loc[before:after]
else:
result = self.loc[after:before]
elif (axis == 1):
result = self.loc[:, before:after]
return cast(DataFrameOrSeries, (result.copy() if copy else result)) |
def to_markdown(self, buf: Optional[Union[(IO[str], str)]]=None, mode: Optional[str]=None) -> str:
'\n Print Series or DataFrame in Markdown-friendly format.\n\n .. note:: This method should only be used if the resulting pandas object is expected\n to be small, as all the data is loaded into the driver\'s memory.\n\n Parameters\n ----------\n buf : writable buffer, defaults to sys.stdout\n Where to send the output. By default, the output is printed to\n sys.stdout. Pass a writable buffer if you need to further process\n the output.\n mode : str, optional\n Mode in which file is opened.\n **kwargs\n These parameters will be passed to `tabulate`.\n\n Returns\n -------\n str\n Series or DataFrame in Markdown-friendly format.\n\n Notes\n -----\n Requires the `tabulate <https://pypi.org/project/tabulate>`_ package.\n\n Examples\n --------\n >>> psser = ps.Series(["elk", "pig", "dog", "quetzal"], name="animal")\n >>> print(psser.to_markdown()) # doctest: +SKIP\n | | animal |\n |---:|:---------|\n | 0 | elk |\n | 1 | pig |\n | 2 | dog |\n | 3 | quetzal |\n\n >>> psdf = ps.DataFrame(\n ... data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}\n ... )\n >>> print(psdf.to_markdown()) # doctest: +SKIP\n | | animal_1 | animal_2 |\n |---:|:-----------|:-----------|\n | 0 | elk | dog |\n | 1 | pig | quetzal |\n '
if (LooseVersion(pd.__version__) < LooseVersion('1.0.0')):
raise NotImplementedError('`to_markdown()` only supported in pandas-on-Spark with pandas >= 1.0.0')
args = locals()
psser_or_psdf = self
internal_pandas = psser_or_psdf._to_internal_pandas()
return validate_arguments_and_invoke_function(internal_pandas, self.to_markdown, type(internal_pandas).to_markdown, args) | -2,431,315,716,865,093,000 | Print Series or DataFrame in Markdown-friendly format.
.. note:: This method should only be used if the resulting pandas object is expected
to be small, as all the data is loaded into the driver's memory.
Parameters
----------
buf : writable buffer, defaults to sys.stdout
Where to send the output. By default, the output is printed to
sys.stdout. Pass a writable buffer if you need to further process
the output.
mode : str, optional
Mode in which file is opened.
**kwargs
These parameters will be passed to `tabulate`.
Returns
-------
str
Series or DataFrame in Markdown-friendly format.
Notes
-----
Requires the `tabulate <https://pypi.org/project/tabulate>`_ package.
Examples
--------
>>> psser = ps.Series(["elk", "pig", "dog", "quetzal"], name="animal")
>>> print(psser.to_markdown()) # doctest: +SKIP
| | animal |
|---:|:---------|
| 0 | elk |
| 1 | pig |
| 2 | dog |
| 3 | quetzal |
>>> psdf = ps.DataFrame(
... data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
... )
>>> print(psdf.to_markdown()) # doctest: +SKIP
| | animal_1 | animal_2 |
|---:|:-----------|:-----------|
| 0 | elk | dog |
| 1 | pig | quetzal | | python/pyspark/pandas/generic.py | to_markdown | XpressAI/spark | python | def to_markdown(self, buf: Optional[Union[(IO[str], str)]]=None, mode: Optional[str]=None) -> str:
'\n Print Series or DataFrame in Markdown-friendly format.\n\n .. note:: This method should only be used if the resulting pandas object is expected\n to be small, as all the data is loaded into the driver\'s memory.\n\n Parameters\n ----------\n buf : writable buffer, defaults to sys.stdout\n Where to send the output. By default, the output is printed to\n sys.stdout. Pass a writable buffer if you need to further process\n the output.\n mode : str, optional\n Mode in which file is opened.\n **kwargs\n These parameters will be passed to `tabulate`.\n\n Returns\n -------\n str\n Series or DataFrame in Markdown-friendly format.\n\n Notes\n -----\n Requires the `tabulate <https://pypi.org/project/tabulate>`_ package.\n\n Examples\n --------\n >>> psser = ps.Series(["elk", "pig", "dog", "quetzal"], name="animal")\n >>> print(psser.to_markdown()) # doctest: +SKIP\n | | animal |\n |---:|:---------|\n | 0 | elk |\n | 1 | pig |\n | 2 | dog |\n | 3 | quetzal |\n\n >>> psdf = ps.DataFrame(\n ... data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}\n ... )\n >>> print(psdf.to_markdown()) # doctest: +SKIP\n | | animal_1 | animal_2 |\n |---:|:-----------|:-----------|\n | 0 | elk | dog |\n | 1 | pig | quetzal |\n '
if (LooseVersion(pd.__version__) < LooseVersion('1.0.0')):
raise NotImplementedError('`to_markdown()` only supported in pandas-on-Spark with pandas >= 1.0.0')
args = locals()
psser_or_psdf = self
internal_pandas = psser_or_psdf._to_internal_pandas()
return validate_arguments_and_invoke_function(internal_pandas, self.to_markdown, type(internal_pandas).to_markdown, args) |
def bfill(self: FrameLike, axis: Optional[Axis]=None, inplace: bool_type=False, limit: Optional[int]=None) -> FrameLike:
"\n Synonym for `DataFrame.fillna()` or `Series.fillna()` with ``method=`bfill```.\n\n .. note:: the current implementation of 'bfill' uses Spark's Window\n without specifying partition specification. This leads to move all data into\n single partition in single machine and could cause serious\n performance degradation. Avoid this method against very large dataset.\n\n Parameters\n ----------\n axis : {0 or `index`}\n 1 and `columns` are not supported.\n inplace : boolean, default False\n Fill in place (do not create a new object)\n limit : int, default None\n If method is specified, this is the maximum number of consecutive NaN values to\n forward/backward fill. In other words, if there is a gap with more than this number of\n consecutive NaNs, it will only be partially filled. If method is not specified,\n this is the maximum number of entries along the entire axis where NaNs will be filled.\n Must be greater than 0 if not None\n\n Returns\n -------\n DataFrame or Series\n DataFrame or Series with NA entries filled.\n\n Examples\n --------\n >>> psdf = ps.DataFrame({\n ... 'A': [None, 3, None, None],\n ... 'B': [2, 4, None, 3],\n ... 'C': [None, None, None, 1],\n ... 'D': [0, 1, 5, 4]\n ... },\n ... columns=['A', 'B', 'C', 'D'])\n >>> psdf\n A B C D\n 0 NaN 2.0 NaN 0\n 1 3.0 4.0 NaN 1\n 2 NaN NaN NaN 5\n 3 NaN 3.0 1.0 4\n\n Propagate non-null values backward.\n\n >>> psdf.bfill()\n A B C D\n 0 3.0 2.0 1.0 0\n 1 3.0 4.0 1.0 1\n 2 NaN 3.0 1.0 5\n 3 NaN 3.0 1.0 4\n\n For Series\n\n >>> psser = ps.Series([None, None, None, 1])\n >>> psser\n 0 NaN\n 1 NaN\n 2 NaN\n 3 1.0\n dtype: float64\n\n >>> psser.bfill()\n 0 1.0\n 1 1.0\n 2 1.0\n 3 1.0\n dtype: float64\n "
return self.fillna(method='bfill', axis=axis, inplace=inplace, limit=limit) | -4,754,868,684,147,332,000 | Synonym for `DataFrame.fillna()` or `Series.fillna()` with ``method=`bfill```.
.. note:: the current implementation of 'bfill' uses Spark's Window
without specifying partition specification. This leads to move all data into
single partition in single machine and could cause serious
performance degradation. Avoid this method against very large dataset.
Parameters
----------
axis : {0 or `index`}
1 and `columns` are not supported.
inplace : boolean, default False
Fill in place (do not create a new object)
limit : int, default None
If method is specified, this is the maximum number of consecutive NaN values to
forward/backward fill. In other words, if there is a gap with more than this number of
consecutive NaNs, it will only be partially filled. If method is not specified,
this is the maximum number of entries along the entire axis where NaNs will be filled.
Must be greater than 0 if not None
Returns
-------
DataFrame or Series
DataFrame or Series with NA entries filled.
Examples
--------
>>> psdf = ps.DataFrame({
... 'A': [None, 3, None, None],
... 'B': [2, 4, None, 3],
... 'C': [None, None, None, 1],
... 'D': [0, 1, 5, 4]
... },
... columns=['A', 'B', 'C', 'D'])
>>> psdf
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 1.0 4
Propagate non-null values backward.
>>> psdf.bfill()
A B C D
0 3.0 2.0 1.0 0
1 3.0 4.0 1.0 1
2 NaN 3.0 1.0 5
3 NaN 3.0 1.0 4
For Series
>>> psser = ps.Series([None, None, None, 1])
>>> psser
0 NaN
1 NaN
2 NaN
3 1.0
dtype: float64
>>> psser.bfill()
0 1.0
1 1.0
2 1.0
3 1.0
dtype: float64 | python/pyspark/pandas/generic.py | bfill | XpressAI/spark | python | def bfill(self: FrameLike, axis: Optional[Axis]=None, inplace: bool_type=False, limit: Optional[int]=None) -> FrameLike:
"\n Synonym for `DataFrame.fillna()` or `Series.fillna()` with ``method=`bfill```.\n\n .. note:: the current implementation of 'bfill' uses Spark's Window\n without specifying partition specification. This leads to move all data into\n single partition in single machine and could cause serious\n performance degradation. Avoid this method against very large dataset.\n\n Parameters\n ----------\n axis : {0 or `index`}\n 1 and `columns` are not supported.\n inplace : boolean, default False\n Fill in place (do not create a new object)\n limit : int, default None\n If method is specified, this is the maximum number of consecutive NaN values to\n forward/backward fill. In other words, if there is a gap with more than this number of\n consecutive NaNs, it will only be partially filled. If method is not specified,\n this is the maximum number of entries along the entire axis where NaNs will be filled.\n Must be greater than 0 if not None\n\n Returns\n -------\n DataFrame or Series\n DataFrame or Series with NA entries filled.\n\n Examples\n --------\n >>> psdf = ps.DataFrame({\n ... 'A': [None, 3, None, None],\n ... 'B': [2, 4, None, 3],\n ... 'C': [None, None, None, 1],\n ... 'D': [0, 1, 5, 4]\n ... },\n ... columns=['A', 'B', 'C', 'D'])\n >>> psdf\n A B C D\n 0 NaN 2.0 NaN 0\n 1 3.0 4.0 NaN 1\n 2 NaN NaN NaN 5\n 3 NaN 3.0 1.0 4\n\n Propagate non-null values backward.\n\n >>> psdf.bfill()\n A B C D\n 0 3.0 2.0 1.0 0\n 1 3.0 4.0 1.0 1\n 2 NaN 3.0 1.0 5\n 3 NaN 3.0 1.0 4\n\n For Series\n\n >>> psser = ps.Series([None, None, None, 1])\n >>> psser\n 0 NaN\n 1 NaN\n 2 NaN\n 3 1.0\n dtype: float64\n\n >>> psser.bfill()\n 0 1.0\n 1 1.0\n 2 1.0\n 3 1.0\n dtype: float64\n "
return self.fillna(method='bfill', axis=axis, inplace=inplace, limit=limit) |
def ffill(self: FrameLike, axis: Optional[Axis]=None, inplace: bool_type=False, limit: Optional[int]=None) -> FrameLike:
"\n Synonym for `DataFrame.fillna()` or `Series.fillna()` with ``method=`ffill```.\n\n .. note:: the current implementation of 'ffill' uses Spark's Window\n without specifying partition specification. This leads to move all data into\n single partition in single machine and could cause serious\n performance degradation. Avoid this method against very large dataset.\n\n Parameters\n ----------\n axis : {0 or `index`}\n 1 and `columns` are not supported.\n inplace : boolean, default False\n Fill in place (do not create a new object)\n limit : int, default None\n If method is specified, this is the maximum number of consecutive NaN values to\n forward/backward fill. In other words, if there is a gap with more than this number of\n consecutive NaNs, it will only be partially filled. If method is not specified,\n this is the maximum number of entries along the entire axis where NaNs will be filled.\n Must be greater than 0 if not None\n\n Returns\n -------\n DataFrame or Series\n DataFrame or Series with NA entries filled.\n\n Examples\n --------\n >>> psdf = ps.DataFrame({\n ... 'A': [None, 3, None, None],\n ... 'B': [2, 4, None, 3],\n ... 'C': [None, None, None, 1],\n ... 'D': [0, 1, 5, 4]\n ... },\n ... columns=['A', 'B', 'C', 'D'])\n >>> psdf\n A B C D\n 0 NaN 2.0 NaN 0\n 1 3.0 4.0 NaN 1\n 2 NaN NaN NaN 5\n 3 NaN 3.0 1.0 4\n\n Propagate non-null values forward.\n\n >>> psdf.ffill()\n A B C D\n 0 NaN 2.0 NaN 0\n 1 3.0 4.0 NaN 1\n 2 3.0 4.0 NaN 5\n 3 3.0 3.0 1.0 4\n\n For Series\n\n >>> psser = ps.Series([2, 4, None, 3])\n >>> psser\n 0 2.0\n 1 4.0\n 2 NaN\n 3 3.0\n dtype: float64\n\n >>> psser.ffill()\n 0 2.0\n 1 4.0\n 2 4.0\n 3 3.0\n dtype: float64\n "
return self.fillna(method='ffill', axis=axis, inplace=inplace, limit=limit) | 6,601,667,604,905,121,000 | Synonym for `DataFrame.fillna()` or `Series.fillna()` with ``method=`ffill```.
.. note:: the current implementation of 'ffill' uses Spark's Window
without specifying partition specification. This leads to move all data into
single partition in single machine and could cause serious
performance degradation. Avoid this method against very large dataset.
Parameters
----------
axis : {0 or `index`}
1 and `columns` are not supported.
inplace : boolean, default False
Fill in place (do not create a new object)
limit : int, default None
If method is specified, this is the maximum number of consecutive NaN values to
forward/backward fill. In other words, if there is a gap with more than this number of
consecutive NaNs, it will only be partially filled. If method is not specified,
this is the maximum number of entries along the entire axis where NaNs will be filled.
Must be greater than 0 if not None
Returns
-------
DataFrame or Series
DataFrame or Series with NA entries filled.
Examples
--------
>>> psdf = ps.DataFrame({
... 'A': [None, 3, None, None],
... 'B': [2, 4, None, 3],
... 'C': [None, None, None, 1],
... 'D': [0, 1, 5, 4]
... },
... columns=['A', 'B', 'C', 'D'])
>>> psdf
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 1.0 4
Propagate non-null values forward.
>>> psdf.ffill()
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 3.0 4.0 NaN 5
3 3.0 3.0 1.0 4
For Series
>>> psser = ps.Series([2, 4, None, 3])
>>> psser
0 2.0
1 4.0
2 NaN
3 3.0
dtype: float64
>>> psser.ffill()
0 2.0
1 4.0
2 4.0
3 3.0
dtype: float64 | python/pyspark/pandas/generic.py | ffill | XpressAI/spark | python | def ffill(self: FrameLike, axis: Optional[Axis]=None, inplace: bool_type=False, limit: Optional[int]=None) -> FrameLike:
"\n Synonym for `DataFrame.fillna()` or `Series.fillna()` with ``method=`ffill```.\n\n .. note:: the current implementation of 'ffill' uses Spark's Window\n without specifying partition specification. This leads to move all data into\n single partition in single machine and could cause serious\n performance degradation. Avoid this method against very large dataset.\n\n Parameters\n ----------\n axis : {0 or `index`}\n 1 and `columns` are not supported.\n inplace : boolean, default False\n Fill in place (do not create a new object)\n limit : int, default None\n If method is specified, this is the maximum number of consecutive NaN values to\n forward/backward fill. In other words, if there is a gap with more than this number of\n consecutive NaNs, it will only be partially filled. If method is not specified,\n this is the maximum number of entries along the entire axis where NaNs will be filled.\n Must be greater than 0 if not None\n\n Returns\n -------\n DataFrame or Series\n DataFrame or Series with NA entries filled.\n\n Examples\n --------\n >>> psdf = ps.DataFrame({\n ... 'A': [None, 3, None, None],\n ... 'B': [2, 4, None, 3],\n ... 'C': [None, None, None, 1],\n ... 'D': [0, 1, 5, 4]\n ... },\n ... columns=['A', 'B', 'C', 'D'])\n >>> psdf\n A B C D\n 0 NaN 2.0 NaN 0\n 1 3.0 4.0 NaN 1\n 2 NaN NaN NaN 5\n 3 NaN 3.0 1.0 4\n\n Propagate non-null values forward.\n\n >>> psdf.ffill()\n A B C D\n 0 NaN 2.0 NaN 0\n 1 3.0 4.0 NaN 1\n 2 3.0 4.0 NaN 5\n 3 3.0 3.0 1.0 4\n\n For Series\n\n >>> psser = ps.Series([2, 4, None, 3])\n >>> psser\n 0 2.0\n 1 4.0\n 2 NaN\n 3 3.0\n dtype: float64\n\n >>> psser.ffill()\n 0 2.0\n 1 4.0\n 2 4.0\n 3 3.0\n dtype: float64\n "
return self.fillna(method='ffill', axis=axis, inplace=inplace, limit=limit) |
def clean_slug(slug):
'Clean a possible Slug string to remove dashes and lower case.'
return slug.replace('-', '').lower() | 8,895,740,661,734,946,000 | Clean a possible Slug string to remove dashes and lower case. | src/coolbeans/plugins/sheetsaccount.py | clean_slug | runarp/coolbeans | python | def clean_slug(slug):
return slug.replace('-', ).lower() |
def coolbean_sheets(entries, context):
'Given a set of entries, pull out any slugs and add them to the context'
settings = context.setdefault('coolbean-accounts', {})
for entry in entries:
if isinstance(entry, data.Open):
document = entry.meta.get('document_name', None)
tab = entry.meta.get('document_tab', None)
slug = entry.meta.get('slug', '')
if (document and tab and slug):
settings[slug] = {'account': entry.account, 'document': document, 'tab': tab, 'currencies': entry.currencies}
elif (document or tab):
print(f'Skipping {entry.account}: {document}/{tab}/{slug}')
return (entries, []) | 5,413,436,553,337,824,000 | Given a set of entries, pull out any slugs and add them to the context | src/coolbeans/plugins/sheetsaccount.py | coolbean_sheets | runarp/coolbeans | python | def coolbean_sheets(entries, context):
settings = context.setdefault('coolbean-accounts', {})
for entry in entries:
if isinstance(entry, data.Open):
document = entry.meta.get('document_name', None)
tab = entry.meta.get('document_tab', None)
slug = entry.meta.get('slug', )
if (document and tab and slug):
settings[slug] = {'account': entry.account, 'document': document, 'tab': tab, 'currencies': entry.currencies}
elif (document or tab):
print(f'Skipping {entry.account}: {document}/{tab}/{slug}')
return (entries, []) |
def remote_entries(entries, options_map):
'\n\n @param entries:\n @param options_map:\n @return:\n '
errors = []
settings = options_map['coolbeans']
secrets_file = get_setting('google-apis', settings)
connection = google_connect(secrets_file)
new_entries_path = None
new_entries_file = get_setting('new-entries-bean', settings)
if new_entries_file:
new_entries_path = pathlib.Path(new_entries_file)
remote_accounts = {}
for entry in entries:
if (not isinstance(entry, data.Open)):
continue
document_name = entry.meta.get('document_name', None)
default_currency = (entry.currencies[0] if entry.currencies else DEFAULT_CURRENCY)
if document_name:
options = dict(document_name=document_name, document_tab=entry.meta.get('document_tab', None), reverse_amount=entry.meta.get('reverse', False), default_currency=default_currency, entry=entry, entry_file=new_entries_path)
remote_accounts[entry.account] = options
new_entries = []
for (account, options) in remote_accounts.items():
try:
new_entries += load_remote_account(connection=connection, errors=errors, account=account, options=options)
except Exception as exc:
logger.error(f'while processing {account}', exc_info=exc)
if (new_entries and new_entries_path):
from beancount.parser import printer
with new_entries_path.open('w') as stream:
printer.print_entries(new_entries, file=stream)
logger.info(f'Wrote {len(new_entries)} new account(s) to {new_entries_path}.')
return ((entries + new_entries), errors) | -2,312,854,070,579,368,400 | @param entries:
@param options_map:
@return: | src/coolbeans/plugins/sheetsaccount.py | remote_entries | runarp/coolbeans | python | def remote_entries(entries, options_map):
'\n\n @param entries:\n @param options_map:\n @return:\n '
errors = []
settings = options_map['coolbeans']
secrets_file = get_setting('google-apis', settings)
connection = google_connect(secrets_file)
new_entries_path = None
new_entries_file = get_setting('new-entries-bean', settings)
if new_entries_file:
new_entries_path = pathlib.Path(new_entries_file)
remote_accounts = {}
for entry in entries:
if (not isinstance(entry, data.Open)):
continue
document_name = entry.meta.get('document_name', None)
default_currency = (entry.currencies[0] if entry.currencies else DEFAULT_CURRENCY)
if document_name:
options = dict(document_name=document_name, document_tab=entry.meta.get('document_tab', None), reverse_amount=entry.meta.get('reverse', False), default_currency=default_currency, entry=entry, entry_file=new_entries_path)
remote_accounts[entry.account] = options
new_entries = []
for (account, options) in remote_accounts.items():
try:
new_entries += load_remote_account(connection=connection, errors=errors, account=account, options=options)
except Exception as exc:
logger.error(f'while processing {account}', exc_info=exc)
if (new_entries and new_entries_path):
from beancount.parser import printer
with new_entries_path.open('w') as stream:
printer.print_entries(new_entries, file=stream)
logger.info(f'Wrote {len(new_entries)} new account(s) to {new_entries_path}.')
return ((entries + new_entries), errors) |
def clean_record(record: typing.Dict[(str, str)]):
"This is a bit of a hack. But using get_all_records doesn't leave many\n options"
new_record = {}
for (k, v) in record.items():
k = slugify.slugify(k.lower().strip())
v = str(v)
for (field, names) in ALIASES.items():
new_record.setdefault(field, '')
if (k in names):
new_record[field] += (('. ' if new_record[field] else '') + v)
k = None
break
if (k == 'amount'):
v = v.replace(',', '')
for s in STRIP_SYMOLS:
v = v.replace(s, '')
if (v and (not v[0].isdecimal()) and (not (v[0] == '-'))):
v = v[1:]
try:
v = decimal.Decimal(v)
except decimal.InvalidOperation:
v = 0
if k:
new_record[k] = v
return new_record | -8,839,127,115,597,063,000 | This is a bit of a hack. But using get_all_records doesn't leave many
options | src/coolbeans/plugins/sheetsaccount.py | clean_record | runarp/coolbeans | python | def clean_record(record: typing.Dict[(str, str)]):
"This is a bit of a hack. But using get_all_records doesn't leave many\n options"
new_record = {}
for (k, v) in record.items():
k = slugify.slugify(k.lower().strip())
v = str(v)
for (field, names) in ALIASES.items():
new_record.setdefault(field, )
if (k in names):
new_record[field] += (('. ' if new_record[field] else ) + v)
k = None
break
if (k == 'amount'):
v = v.replace(',', )
for s in STRIP_SYMOLS:
v = v.replace(s, )
if (v and (not v[0].isdecimal()) and (not (v[0] == '-'))):
v = v[1:]
try:
v = decimal.Decimal(v)
except decimal.InvalidOperation:
v = 0
if k:
new_record[k] = v
return new_record |
def load_remote_account(connection: gspread.Client, errors: list, account: str, options: typing.Dict[(str, str)]):
'Try to Load Entries from URL into Account.\n\n options include:\n - document_name -- the Actual Google Doc name\n - document_tab -- the Tab name on the Doc\n - default_currency - the entry currency if None is provided\n - reverse_amount - if true, assume positive entries are credits\n\n '
entries = []
document_name = options['document_name']
document_tab = (options.get('document_tab', 0) or 0)
default_currency = options['default_currency']
reverse_amount = options.get('reverse_amount', False)
if (not document_name):
return
m = ((- 1) if reverse_amount else 1)
logger.info(f'Attempting to download entries for {account} from {document_name}.{document_tab}')
workbook = connection.open(document_name)
sheet = None
try:
document_tab = int(document_tab)
sheet = workbook.get_worksheet(document_tab)
except ValueError:
pass
if (sheet is None):
sheet = workbook.worksheet(document_tab)
records = sheet.get_all_records()
import re
row = 0
for record in records:
row += 1
record = clean_record(record)
if (('date' not in record) or (not record['date'])):
continue
if (('amount' not in record) or (not record['amount'])):
continue
narration = record.pop('narration', None)
payee = record.pop('payee', None)
tagstr = record.pop('tags', '')
tags = (set(re.split('\\W+', tagstr)) if tagstr else set())
date = dateparser.parse(record.pop('date'))
if date:
date = datetime.date(year=date.year, month=date.month, day=date.day)
linkstr = record.pop('links', '')
links = (set(re.split('\\W+', linkstr)) if linkstr else set())
meta = {'filename': str(options['entry_file']), 'lineno': 0, 'document-sheet-row': f'{document_name}/{document_tab}/{(row + 1)}'}
amount = (decimal.Decimal(record.pop('amount')) * m)
currency = record.pop('currency', default_currency)
entry_account = record.pop('account')
for (k, v) in record.items():
if v:
meta[k] = v
try:
if (not entry_account):
errors.append(f"Skipping Record with Blank Account: {meta['document-sheet-row']}")
logger.warning(f"Skipping Record with Blank Account: {meta['document-sheet-row']}")
continue
entry = data.Transaction(date=date, narration=narration, payee=payee, tags=tags, meta=meta, links=links, flag='*', postings=[data.Posting(account=account, units=data.Amount(amount, currency), cost=None, price=None, flag='*', meta={}), data.Posting(account=entry_account, units=data.Amount((- amount), currency), cost=None, price=None, flag='*', meta={})])
entries.append(entry)
except Exception as exc:
logger.error(f'Error while parsing {record}', exc_info=exc)
errors.append(str(exc))
logger.info(f'Loaded {len(entries)} entries for {account} from {document_name}.{document_tab}')
return entries | -5,359,603,005,264,168,000 | Try to Load Entries from URL into Account.
options include:
- document_name -- the Actual Google Doc name
- document_tab -- the Tab name on the Doc
- default_currency - the entry currency if None is provided
- reverse_amount - if true, assume positive entries are credits | src/coolbeans/plugins/sheetsaccount.py | load_remote_account | runarp/coolbeans | python | def load_remote_account(connection: gspread.Client, errors: list, account: str, options: typing.Dict[(str, str)]):
'Try to Load Entries from URL into Account.\n\n options include:\n - document_name -- the Actual Google Doc name\n - document_tab -- the Tab name on the Doc\n - default_currency - the entry currency if None is provided\n - reverse_amount - if true, assume positive entries are credits\n\n '
entries = []
document_name = options['document_name']
document_tab = (options.get('document_tab', 0) or 0)
default_currency = options['default_currency']
reverse_amount = options.get('reverse_amount', False)
if (not document_name):
return
m = ((- 1) if reverse_amount else 1)
logger.info(f'Attempting to download entries for {account} from {document_name}.{document_tab}')
workbook = connection.open(document_name)
sheet = None
try:
document_tab = int(document_tab)
sheet = workbook.get_worksheet(document_tab)
except ValueError:
pass
if (sheet is None):
sheet = workbook.worksheet(document_tab)
records = sheet.get_all_records()
import re
row = 0
for record in records:
row += 1
record = clean_record(record)
if (('date' not in record) or (not record['date'])):
continue
if (('amount' not in record) or (not record['amount'])):
continue
narration = record.pop('narration', None)
payee = record.pop('payee', None)
tagstr = record.pop('tags', )
tags = (set(re.split('\\W+', tagstr)) if tagstr else set())
date = dateparser.parse(record.pop('date'))
if date:
date = datetime.date(year=date.year, month=date.month, day=date.day)
linkstr = record.pop('links', )
links = (set(re.split('\\W+', linkstr)) if linkstr else set())
meta = {'filename': str(options['entry_file']), 'lineno': 0, 'document-sheet-row': f'{document_name}/{document_tab}/{(row + 1)}'}
amount = (decimal.Decimal(record.pop('amount')) * m)
currency = record.pop('currency', default_currency)
entry_account = record.pop('account')
for (k, v) in record.items():
if v:
meta[k] = v
try:
if (not entry_account):
errors.append(f"Skipping Record with Blank Account: {meta['document-sheet-row']}")
logger.warning(f"Skipping Record with Blank Account: {meta['document-sheet-row']}")
continue
entry = data.Transaction(date=date, narration=narration, payee=payee, tags=tags, meta=meta, links=links, flag='*', postings=[data.Posting(account=account, units=data.Amount(amount, currency), cost=None, price=None, flag='*', meta={}), data.Posting(account=entry_account, units=data.Amount((- amount), currency), cost=None, price=None, flag='*', meta={})])
entries.append(entry)
except Exception as exc:
logger.error(f'Error while parsing {record}', exc_info=exc)
errors.append(str(exc))
logger.info(f'Loaded {len(entries)} entries for {account} from {document_name}.{document_tab}')
return entries |
def setUp(self):
'Setup'
super(TestPropertyOnlyOne, self).setUp()
self.collection.register(OnlyOne()) | -8,261,507,296,794,374,000 | Setup | test/unit/rules/resources/properties/test_onlyone.py | setUp | awkspace/cfn-python-lint | python | def setUp(self):
super(TestPropertyOnlyOne, self).setUp()
self.collection.register(OnlyOne()) |
def test_file_positive(self):
'Test Positive'
self.helper_file_positive() | -1,556,978,985,838,885,400 | Test Positive | test/unit/rules/resources/properties/test_onlyone.py | test_file_positive | awkspace/cfn-python-lint | python | def test_file_positive(self):
self.helper_file_positive() |
def test_file_negative(self):
'Test failure'
self.helper_file_negative('test/fixtures/templates/bad/resources/properties/onlyone.yaml', 5) | 4,632,762,795,947,568,000 | Test failure | test/unit/rules/resources/properties/test_onlyone.py | test_file_negative | awkspace/cfn-python-lint | python | def test_file_negative(self):
self.helper_file_negative('test/fixtures/templates/bad/resources/properties/onlyone.yaml', 5) |
def testWhileTypeErrors(self):
'Test typing error messages for while.'
tuple_treedef = tree_util.tree_structure((1.0, 1.0))
leaf_treedef = tree_util.tree_structure(0.0)
with self.assertRaisesRegex(TypeError, re.escape(f'cond_fun must return a boolean scalar, but got pytree {tuple_treedef}.')):
lax.while_loop((lambda c: (1.0, 1.0)), (lambda c: c), 0.0)
with self.assertRaisesRegex(TypeError, re.escape('cond_fun must return a boolean scalar, but got output type(s) [ShapedArray(float32[])].')):
lax.while_loop((lambda c: np.float32(1.0)), (lambda c: c), np.float32(0.0))
with self.assertRaisesRegex(TypeError, re.escape(f'body_fun output and input must have same type structure, got {tuple_treedef} and {leaf_treedef}.')):
lax.while_loop((lambda c: True), (lambda c: (1.0, 1.0)), 0.0)
with self.assertRaisesWithLiteralMatch(TypeError, 'body_fun output and input must have identical types, got\nShapedArray(bool[], weak_type=True)\nand\nShapedArray(float32[]).'):
lax.while_loop((lambda c: True), (lambda c: True), np.float32(0.0)) | -5,920,863,230,374,971,000 | Test typing error messages for while. | tests/lax_control_flow_test.py | testWhileTypeErrors | cdfreeman-google/jax | python | def testWhileTypeErrors(self):
tuple_treedef = tree_util.tree_structure((1.0, 1.0))
leaf_treedef = tree_util.tree_structure(0.0)
with self.assertRaisesRegex(TypeError, re.escape(f'cond_fun must return a boolean scalar, but got pytree {tuple_treedef}.')):
lax.while_loop((lambda c: (1.0, 1.0)), (lambda c: c), 0.0)
with self.assertRaisesRegex(TypeError, re.escape('cond_fun must return a boolean scalar, but got output type(s) [ShapedArray(float32[])].')):
lax.while_loop((lambda c: np.float32(1.0)), (lambda c: c), np.float32(0.0))
with self.assertRaisesRegex(TypeError, re.escape(f'body_fun output and input must have same type structure, got {tuple_treedef} and {leaf_treedef}.')):
lax.while_loop((lambda c: True), (lambda c: (1.0, 1.0)), 0.0)
with self.assertRaisesWithLiteralMatch(TypeError, 'body_fun output and input must have identical types, got\nShapedArray(bool[], weak_type=True)\nand\nShapedArray(float32[]).'):
lax.while_loop((lambda c: True), (lambda c: True), np.float32(0.0)) |
def testForiLoopErrors(self):
'Test typing error messages for while.'
with self.assertRaisesRegex(TypeError, 'arguments to fori_loop must have equal types'):
lax.fori_loop(np.int16(0), jnp.int32(10), (lambda i, c: c), jnp.float32(7)) | -5,943,806,257,004,347,000 | Test typing error messages for while. | tests/lax_control_flow_test.py | testForiLoopErrors | cdfreeman-google/jax | python | def testForiLoopErrors(self):
with self.assertRaisesRegex(TypeError, 'arguments to fori_loop must have equal types'):
lax.fori_loop(np.int16(0), jnp.int32(10), (lambda i, c: c), jnp.float32(7)) |
def testCondTypeErrors(self):
'Test typing error messages for cond.'
with self.assertRaisesRegex(TypeError, re.escape('Pred type must be either boolean or number, got <function')):
lax.cond((lambda x: True), (lambda top: 2.0), (lambda fop: 3.0), 1.0)
with self.assertRaisesRegex(TypeError, re.escape("Pred must be a scalar, got foo of type <class 'str'>")):
lax.cond('foo', (lambda top: 2.0), (lambda fop: 3.0), 1.0)
with self.assertRaisesRegex(TypeError, re.escape("Pred must be a scalar, got (1.0, 1.0) of type <class 'tuple'>")):
lax.cond((1.0, 1.0), (lambda top: 2.0), (lambda fop: 3.0), 1.0)
with self.assertRaisesRegex(TypeError, re.escape(f'true_fun and false_fun output must have same type structure, got {tree_util.tree_structure(2.0)} and {tree_util.tree_structure((3.0, 3.0))}.')):
lax.cond(True, (lambda top: 2.0), (lambda fop: (3.0, 3.0)), 1.0)
with self.assertRaisesRegex(TypeError, textwrap.dedent('\n true_fun and false_fun output must have identical types, got\n ShapedArray\\(float32\\[1\\]\\)\n and\n ShapedArray\\(float32\\[\\].*\\).').strip()):
lax.cond(True, (lambda top: jnp.array([1.0], jnp.float32)), (lambda fop: jnp.float32(1.0)), 1.0) | -5,686,292,944,912,370,000 | Test typing error messages for cond. | tests/lax_control_flow_test.py | testCondTypeErrors | cdfreeman-google/jax | python | def testCondTypeErrors(self):
with self.assertRaisesRegex(TypeError, re.escape('Pred type must be either boolean or number, got <function')):
lax.cond((lambda x: True), (lambda top: 2.0), (lambda fop: 3.0), 1.0)
with self.assertRaisesRegex(TypeError, re.escape("Pred must be a scalar, got foo of type <class 'str'>")):
lax.cond('foo', (lambda top: 2.0), (lambda fop: 3.0), 1.0)
with self.assertRaisesRegex(TypeError, re.escape("Pred must be a scalar, got (1.0, 1.0) of type <class 'tuple'>")):
lax.cond((1.0, 1.0), (lambda top: 2.0), (lambda fop: 3.0), 1.0)
with self.assertRaisesRegex(TypeError, re.escape(f'true_fun and false_fun output must have same type structure, got {tree_util.tree_structure(2.0)} and {tree_util.tree_structure((3.0, 3.0))}.')):
lax.cond(True, (lambda top: 2.0), (lambda fop: (3.0, 3.0)), 1.0)
with self.assertRaisesRegex(TypeError, textwrap.dedent('\n true_fun and false_fun output must have identical types, got\n ShapedArray\\(float32\\[1\\]\\)\n and\n ShapedArray\\(float32\\[\\].*\\).').strip()):
lax.cond(True, (lambda top: jnp.array([1.0], jnp.float32)), (lambda fop: jnp.float32(1.0)), 1.0) |
def testSwitchErrors(self):
'Test typing error messages for switch.'
with self.assertRaisesRegex(TypeError, re.escape('Index type must be an integer, got <function')):
lax.switch((lambda x: True), [(lambda _: 2.0), (lambda _: 3.0)], 1.0)
with self.assertRaisesRegex(TypeError, re.escape('Index type must be an integer, got foo.')):
lax.switch('foo', [(lambda _: 2.0), (lambda _: 3.0)], 1.0)
with self.assertRaisesRegex(TypeError, re.escape('Branch index must be scalar, got (1.0, 1.0) of shape (2,).')):
lax.switch((1.0, 1.0), [(lambda _: 2.0), (lambda _: 3.0)], 1.0)
with self.assertRaisesRegex(ValueError, re.escape('Empty branch sequence')):
lax.switch(0, [], 1.0)
with self.assertRaisesRegex(TypeError, re.escape(f'branch 0 and 1 outputs must have same type structure, got {tree_util.tree_structure(2.0)} and {tree_util.tree_structure((3.0, 3.0))}.')):
lax.switch(1, [(lambda _: 2.0), (lambda _: (3.0, 3.0))], 1.0)
with self.assertRaisesRegex(TypeError, textwrap.dedent('\n branch 0 and 1 outputs must have identical types, got\n ShapedArray\\(float32\\[1\\]\\)\n and\n ShapedArray\\(float32\\[\\].*\\).').strip()):
lax.switch(1, [(lambda _: jnp.array([1.0], jnp.float32)), (lambda _: jnp.float32(1.0))], 1.0) | -1,112,016,817,928,494,600 | Test typing error messages for switch. | tests/lax_control_flow_test.py | testSwitchErrors | cdfreeman-google/jax | python | def testSwitchErrors(self):
with self.assertRaisesRegex(TypeError, re.escape('Index type must be an integer, got <function')):
lax.switch((lambda x: True), [(lambda _: 2.0), (lambda _: 3.0)], 1.0)
with self.assertRaisesRegex(TypeError, re.escape('Index type must be an integer, got foo.')):
lax.switch('foo', [(lambda _: 2.0), (lambda _: 3.0)], 1.0)
with self.assertRaisesRegex(TypeError, re.escape('Branch index must be scalar, got (1.0, 1.0) of shape (2,).')):
lax.switch((1.0, 1.0), [(lambda _: 2.0), (lambda _: 3.0)], 1.0)
with self.assertRaisesRegex(ValueError, re.escape('Empty branch sequence')):
lax.switch(0, [], 1.0)
with self.assertRaisesRegex(TypeError, re.escape(f'branch 0 and 1 outputs must have same type structure, got {tree_util.tree_structure(2.0)} and {tree_util.tree_structure((3.0, 3.0))}.')):
lax.switch(1, [(lambda _: 2.0), (lambda _: (3.0, 3.0))], 1.0)
with self.assertRaisesRegex(TypeError, textwrap.dedent('\n branch 0 and 1 outputs must have identical types, got\n ShapedArray\\(float32\\[1\\]\\)\n and\n ShapedArray\\(float32\\[\\].*\\).').strip()):
lax.switch(1, [(lambda _: jnp.array([1.0], jnp.float32)), (lambda _: jnp.float32(1.0))], 1.0) |
def testScanTypeErrors(self):
'Test typing error messages for scan.'
a = jnp.arange(5)
with self.assertRaisesRegex(TypeError, re.escape('scan body output must be a pair, got ShapedArray(float32[]).')):
lax.scan((lambda c, x: np.float32(0.0)), 0, a)
with self.assertRaisesRegex(TypeError, re.escape(f'scan carry output and input must have same type structure, got {tree_util.tree_structure((0, 0, 0))} and {tree_util.tree_structure((1, (2, 3)))}')):
lax.scan((lambda c, x: ((0, 0, 0), x)), (1, (2, 3)), a)
with self.assertRaisesRegex(TypeError, re.escape(f'scan carry output and input must have same type structure, got {tree_util.tree_structure(a)} and {tree_util.tree_structure(None)}.')):
lax.scan((lambda c, x: (0, x)), None, a)
with self.assertRaisesWithLiteralMatch(TypeError, 'scan carry output and input must have identical types, got\nShapedArray(int32[])\nand\nShapedArray(float32[]).'):
lax.scan((lambda c, x: (np.int32(0), x)), np.float32(1.0), a)
with self.assertRaisesRegex(TypeError, re.escape(f'scan carry output and input must have same type structure, got {tree_util.tree_structure(a)} and {tree_util.tree_structure((1, 2))}.')):
lax.scan((lambda c, x: (0, x)), (1, 2), a) | -7,095,610,930,942,497,000 | Test typing error messages for scan. | tests/lax_control_flow_test.py | testScanTypeErrors | cdfreeman-google/jax | python | def testScanTypeErrors(self):
a = jnp.arange(5)
with self.assertRaisesRegex(TypeError, re.escape('scan body output must be a pair, got ShapedArray(float32[]).')):
lax.scan((lambda c, x: np.float32(0.0)), 0, a)
with self.assertRaisesRegex(TypeError, re.escape(f'scan carry output and input must have same type structure, got {tree_util.tree_structure((0, 0, 0))} and {tree_util.tree_structure((1, (2, 3)))}')):
lax.scan((lambda c, x: ((0, 0, 0), x)), (1, (2, 3)), a)
with self.assertRaisesRegex(TypeError, re.escape(f'scan carry output and input must have same type structure, got {tree_util.tree_structure(a)} and {tree_util.tree_structure(None)}.')):
lax.scan((lambda c, x: (0, x)), None, a)
with self.assertRaisesWithLiteralMatch(TypeError, 'scan carry output and input must have identical types, got\nShapedArray(int32[])\nand\nShapedArray(float32[]).'):
lax.scan((lambda c, x: (np.int32(0), x)), np.float32(1.0), a)
with self.assertRaisesRegex(TypeError, re.escape(f'scan carry output and input must have same type structure, got {tree_util.tree_structure(a)} and {tree_util.tree_structure((1, 2))}.')):
lax.scan((lambda c, x: (0, x)), (1, 2), a) |
@jtu.skip_on_flag('jax_skip_slow_tests', True)
def test_custom_linear_solve_pytree(self):
'Test custom linear solve with inputs and outputs that are pytrees.'
def unrolled_matvec(mat, x):
'Apply a Python list of lists of scalars to a list of scalars.'
result = []
for i in range(len(mat)):
v = 0
for j in range(len(x)):
if (mat[i][j] is not None):
v += (mat[i][j] * x[j])
result.append(v)
return result
def unrolled_substitution_solve(matvec, b, lower_tri):
'Solve a triangular unrolled system with fwd/back substitution.'
zero = jnp.zeros(())
one = jnp.ones(())
x = [zero for _ in b]
ordering = (range(len(b)) if lower_tri else range((len(b) - 1), (- 1), (- 1)))
for i in ordering:
residual = (b[i] - matvec(x)[i])
diagonal = matvec([(one if (i == j) else zero) for j in range(len(b))])[i]
x[i] = (residual / diagonal)
return x
def custom_unrolled_lower_tri_solve(mat, b):
return lax.custom_linear_solve(partial(unrolled_matvec, mat), b, partial(unrolled_substitution_solve, lower_tri=True), partial(unrolled_substitution_solve, lower_tri=False))
mat = [[1.0, None, None, None, None, None, None], [1.0, 1.0, None, None, None, None, None], [None, 1.0, 1.0, None, None, None, None], [None, None, 1.0, 1.0, None, None, None], [None, None, None, 1.0, 1.0, None, None], [None, None, None, None, None, 2.0, None], [None, None, None, None, None, 4.0, 3.0]]
rng = np.random.RandomState(0)
b = list(rng.randn(7))
jtu.check_grads(custom_unrolled_lower_tri_solve, (mat, b), order=2, rtol={jnp.float32: 0.02})
b_bat = list(b)
b_bat[3] = rng.randn(3)
jtu.check_grads(api.vmap(custom_unrolled_lower_tri_solve, in_axes=(None, [None, None, None, 0, None, None, None]), out_axes=[0, 0, 0, 0, 0, None, None]), (mat, b_bat), order=2, rtol={jnp.float32: 0.01})
mat[2][1] = rng.randn(3)
mat_axis_tree = [[(0 if ((i == 2) and (j == 1)) else None) for j in range(7)] for i in range(7)]
jtu.check_grads(api.vmap(custom_unrolled_lower_tri_solve, in_axes=(mat_axis_tree, None), out_axes=[0, 0, 0, 0, 0, None, None]), (mat, b), order=2) | -7,709,107,645,537,896,000 | Test custom linear solve with inputs and outputs that are pytrees. | tests/lax_control_flow_test.py | test_custom_linear_solve_pytree | cdfreeman-google/jax | python | @jtu.skip_on_flag('jax_skip_slow_tests', True)
def test_custom_linear_solve_pytree(self):
def unrolled_matvec(mat, x):
'Apply a Python list of lists of scalars to a list of scalars.'
result = []
for i in range(len(mat)):
v = 0
for j in range(len(x)):
if (mat[i][j] is not None):
v += (mat[i][j] * x[j])
result.append(v)
return result
def unrolled_substitution_solve(matvec, b, lower_tri):
'Solve a triangular unrolled system with fwd/back substitution.'
zero = jnp.zeros(())
one = jnp.ones(())
x = [zero for _ in b]
ordering = (range(len(b)) if lower_tri else range((len(b) - 1), (- 1), (- 1)))
for i in ordering:
residual = (b[i] - matvec(x)[i])
diagonal = matvec([(one if (i == j) else zero) for j in range(len(b))])[i]
x[i] = (residual / diagonal)
return x
def custom_unrolled_lower_tri_solve(mat, b):
return lax.custom_linear_solve(partial(unrolled_matvec, mat), b, partial(unrolled_substitution_solve, lower_tri=True), partial(unrolled_substitution_solve, lower_tri=False))
mat = [[1.0, None, None, None, None, None, None], [1.0, 1.0, None, None, None, None, None], [None, 1.0, 1.0, None, None, None, None], [None, None, 1.0, 1.0, None, None, None], [None, None, None, 1.0, 1.0, None, None], [None, None, None, None, None, 2.0, None], [None, None, None, None, None, 4.0, 3.0]]
rng = np.random.RandomState(0)
b = list(rng.randn(7))
jtu.check_grads(custom_unrolled_lower_tri_solve, (mat, b), order=2, rtol={jnp.float32: 0.02})
b_bat = list(b)
b_bat[3] = rng.randn(3)
jtu.check_grads(api.vmap(custom_unrolled_lower_tri_solve, in_axes=(None, [None, None, None, 0, None, None, None]), out_axes=[0, 0, 0, 0, 0, None, None]), (mat, b_bat), order=2, rtol={jnp.float32: 0.01})
mat[2][1] = rng.randn(3)
mat_axis_tree = [[(0 if ((i == 2) and (j == 1)) else None) for j in range(7)] for i in range(7)]
jtu.check_grads(api.vmap(custom_unrolled_lower_tri_solve, in_axes=(mat_axis_tree, None), out_axes=[0, 0, 0, 0, 0, None, None]), (mat, b), order=2) |
def unrolled_matvec(mat, x):
'Apply a Python list of lists of scalars to a list of scalars.'
result = []
for i in range(len(mat)):
v = 0
for j in range(len(x)):
if (mat[i][j] is not None):
v += (mat[i][j] * x[j])
result.append(v)
return result | -1,598,892,281,679,112,400 | Apply a Python list of lists of scalars to a list of scalars. | tests/lax_control_flow_test.py | unrolled_matvec | cdfreeman-google/jax | python | def unrolled_matvec(mat, x):
result = []
for i in range(len(mat)):
v = 0
for j in range(len(x)):
if (mat[i][j] is not None):
v += (mat[i][j] * x[j])
result.append(v)
return result |
def unrolled_substitution_solve(matvec, b, lower_tri):
'Solve a triangular unrolled system with fwd/back substitution.'
zero = jnp.zeros(())
one = jnp.ones(())
x = [zero for _ in b]
ordering = (range(len(b)) if lower_tri else range((len(b) - 1), (- 1), (- 1)))
for i in ordering:
residual = (b[i] - matvec(x)[i])
diagonal = matvec([(one if (i == j) else zero) for j in range(len(b))])[i]
x[i] = (residual / diagonal)
return x | 146,545,813,991,085,980 | Solve a triangular unrolled system with fwd/back substitution. | tests/lax_control_flow_test.py | unrolled_substitution_solve | cdfreeman-google/jax | python | def unrolled_substitution_solve(matvec, b, lower_tri):
zero = jnp.zeros(())
one = jnp.ones(())
x = [zero for _ in b]
ordering = (range(len(b)) if lower_tri else range((len(b) - 1), (- 1), (- 1)))
for i in ordering:
residual = (b[i] - matvec(x)[i])
diagonal = matvec([(one if (i == j) else zero) for j in range(len(b))])[i]
x[i] = (residual / diagonal)
return x |
def nodeset(self):
'set of all node idxs'
raise NotImplementedError() | -6,226,151,121,687,879,000 | set of all node idxs | LocalMercurial/mercurial/dagutil.py | nodeset | l2dy/machg | python | def nodeset(self):
raise NotImplementedError() |
def heads(self):
'list of head ixs'
raise NotImplementedError() | -2,098,944,108,082,554,600 | list of head ixs | LocalMercurial/mercurial/dagutil.py | heads | l2dy/machg | python | def heads(self):
raise NotImplementedError() |
def parents(self, ix):
'list of parents ixs of ix'
raise NotImplementedError() | 2,204,374,004,237,189,400 | list of parents ixs of ix | LocalMercurial/mercurial/dagutil.py | parents | l2dy/machg | python | def parents(self, ix):
raise NotImplementedError() |
def inverse(self):
'inverse DAG, where parents becomes children, etc.'
raise NotImplementedError() | 1,920,263,474,415,640,000 | inverse DAG, where parents becomes children, etc. | LocalMercurial/mercurial/dagutil.py | inverse | l2dy/machg | python | def inverse(self):
raise NotImplementedError() |
def ancestorset(self, starts, stops=None):
'\n set of all ancestors of starts (incl), but stop walk at stops (excl)\n '
raise NotImplementedError() | 5,984,893,086,663,339,000 | set of all ancestors of starts (incl), but stop walk at stops (excl) | LocalMercurial/mercurial/dagutil.py | ancestorset | l2dy/machg | python | def ancestorset(self, starts, stops=None):
'\n \n '
raise NotImplementedError() |
def descendantset(self, starts, stops=None):
'\n set of all descendants of starts (incl), but stop walk at stops (excl)\n '
return self.inverse().ancestorset(starts, stops) | 8,106,918,935,212,782,000 | set of all descendants of starts (incl), but stop walk at stops (excl) | LocalMercurial/mercurial/dagutil.py | descendantset | l2dy/machg | python | def descendantset(self, starts, stops=None):
'\n \n '
return self.inverse().ancestorset(starts, stops) |
def headsetofconnecteds(self, ixs):
'\n subset of connected list of ixs so that no node has a descendant in it\n\n By "connected list" we mean that if an ancestor and a descendant are in\n the list, then so is at least one path connecting them.\n '
raise NotImplementedError() | 3,914,432,140,549,609,500 | subset of connected list of ixs so that no node has a descendant in it
By "connected list" we mean that if an ancestor and a descendant are in
the list, then so is at least one path connecting them. | LocalMercurial/mercurial/dagutil.py | headsetofconnecteds | l2dy/machg | python | def headsetofconnecteds(self, ixs):
'\n subset of connected list of ixs so that no node has a descendant in it\n\n By "connected list" we mean that if an ancestor and a descendant are in\n the list, then so is at least one path connecting them.\n '
raise NotImplementedError() |
def externalize(self, ix):
'return a list of (or set if given a set) of node ids'
return self._externalize(ix) | -5,374,513,761,426,537,000 | return a list of (or set if given a set) of node ids | LocalMercurial/mercurial/dagutil.py | externalize | l2dy/machg | python | def externalize(self, ix):
return self._externalize(ix) |
def externalizeall(self, ixs):
'return a list of (or set if given a set) of node ids'
ids = self._externalizeall(ixs)
if isinstance(ixs, set):
return set(ids)
return list(ids) | -807,789,611,213,240,000 | return a list of (or set if given a set) of node ids | LocalMercurial/mercurial/dagutil.py | externalizeall | l2dy/machg | python | def externalizeall(self, ixs):
ids = self._externalizeall(ixs)
if isinstance(ixs, set):
return set(ids)
return list(ids) |
def internalize(self, id):
'return a list of (or set if given a set) of node ixs'
return self._internalize(id) | 2,039,032,217,749,656,000 | return a list of (or set if given a set) of node ixs | LocalMercurial/mercurial/dagutil.py | internalize | l2dy/machg | python | def internalize(self, id):
return self._internalize(id) |
def internalizeall(self, ids, filterunknown=False):
'return a list of (or set if given a set) of node ids'
ixs = self._internalizeall(ids, filterunknown)
if isinstance(ids, set):
return set(ixs)
return list(ixs) | -8,972,105,589,571,481,000 | return a list of (or set if given a set) of node ids | LocalMercurial/mercurial/dagutil.py | internalizeall | l2dy/machg | python | def internalizeall(self, ids, filterunknown=False):
ixs = self._internalizeall(ids, filterunknown)
if isinstance(ids, set):
return set(ixs)
return list(ixs) |
def linearize(self, ixs):
'linearize and topologically sort a list of revisions\n\n The linearization process tries to create long runs of revs where\n a child rev comes immediately after its first parent. This is done by\n visiting the heads of the given revs in inverse topological order,\n and for each visited rev, visiting its second parent, then its first\n parent, then adding the rev itself to the output list.\n '
sorted = []
visit = list(self.headsetofconnecteds(ixs))
visit.sort(reverse=True)
finished = set()
while visit:
cur = visit.pop()
if (cur < 0):
cur = ((- cur) - 1)
if (cur not in finished):
sorted.append(cur)
finished.add(cur)
else:
visit.append(((- cur) - 1))
visit += [p for p in self.parents(cur) if ((p in ixs) and (p not in finished))]
assert (len(sorted) == len(ixs))
return sorted | -5,283,142,161,144,543,000 | linearize and topologically sort a list of revisions
The linearization process tries to create long runs of revs where
a child rev comes immediately after its first parent. This is done by
visiting the heads of the given revs in inverse topological order,
and for each visited rev, visiting its second parent, then its first
parent, then adding the rev itself to the output list. | LocalMercurial/mercurial/dagutil.py | linearize | l2dy/machg | python | def linearize(self, ixs):
'linearize and topologically sort a list of revisions\n\n The linearization process tries to create long runs of revs where\n a child rev comes immediately after its first parent. This is done by\n visiting the heads of the given revs in inverse topological order,\n and for each visited rev, visiting its second parent, then its first\n parent, then adding the rev itself to the output list.\n '
sorted = []
visit = list(self.headsetofconnecteds(ixs))
visit.sort(reverse=True)
finished = set()
while visit:
cur = visit.pop()
if (cur < 0):
cur = ((- cur) - 1)
if (cur not in finished):
sorted.append(cur)
finished.add(cur)
else:
visit.append(((- cur) - 1))
visit += [p for p in self.parents(cur) if ((p in ixs) and (p not in finished))]
assert (len(sorted) == len(ixs))
return sorted |
def test_login_required_to_view_ingredients(self):
'Test that authentication is needed to view the ingredients.'
res = self.client.get(INGREDIENTS_URL)
self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) | 1,475,239,166,932,663,800 | Test that authentication is needed to view the ingredients. | app/recipe_app/tests/test_ingredients_api.py | test_login_required_to_view_ingredients | oyekanmiayo/recipe-app-api | python | def test_login_required_to_view_ingredients(self):
res = self.client.get(INGREDIENTS_URL)
self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) |
def test_retrieve_ingredients_is_successful(self):
'Test retrieve ingredients'
Ingredient.objects.create(user=self.user, name='Carrot')
Ingredient.objects.create(user=self.user, name='Lemon')
res = self.client.get(INGREDIENTS_URL)
ingredients = Ingredient.objects.all().order_by('-name')
serializer = IngredientSerializer(ingredients, many=True)
self.assertEqual(res.status_code, status.HTTP_200_OK)
self.assertEqual(res.data, serializer.data) | 6,252,634,421,779,660,000 | Test retrieve ingredients | app/recipe_app/tests/test_ingredients_api.py | test_retrieve_ingredients_is_successful | oyekanmiayo/recipe-app-api | python | def test_retrieve_ingredients_is_successful(self):
Ingredient.objects.create(user=self.user, name='Carrot')
Ingredient.objects.create(user=self.user, name='Lemon')
res = self.client.get(INGREDIENTS_URL)
ingredients = Ingredient.objects.all().order_by('-name')
serializer = IngredientSerializer(ingredients, many=True)
self.assertEqual(res.status_code, status.HTTP_200_OK)
self.assertEqual(res.data, serializer.data) |
def test_retrieved_ingredients_limited_to_user(self):
"Tests that only the user's ingredients are retrieved"
user2 = create_user(fname='Test2', lname='User2', email='[email protected]', password='test2pass')
Ingredient.objects.create(user=user2, name='Carrot')
ingredient = Ingredient.objects.create(user=self.user, name='Lemon')
res = self.client.get(INGREDIENTS_URL)
self.assertEqual(res.status_code, status.HTTP_200_OK)
self.assertEqual(len(res.data), 1)
self.assertEqual(res.data[0]['name'], ingredient.name) | 6,069,663,110,617,207,000 | Tests that only the user's ingredients are retrieved | app/recipe_app/tests/test_ingredients_api.py | test_retrieved_ingredients_limited_to_user | oyekanmiayo/recipe-app-api | python | def test_retrieved_ingredients_limited_to_user(self):
user2 = create_user(fname='Test2', lname='User2', email='[email protected]', password='test2pass')
Ingredient.objects.create(user=user2, name='Carrot')
ingredient = Ingredient.objects.create(user=self.user, name='Lemon')
res = self.client.get(INGREDIENTS_URL)
self.assertEqual(res.status_code, status.HTTP_200_OK)
self.assertEqual(len(res.data), 1)
self.assertEqual(res.data[0]['name'], ingredient.name) |
def test_create_ingredient_is_successful(self):
'Test that creating a new ingredient is successful.'
payload = {'name': 'Lemon'}
self.client.post(INGREDIENTS_URL, payload)
exists = Ingredient.objects.filter(user=self.user, name=payload['name']).exists()
self.assertTrue(exists) | -6,702,414,564,749,636,000 | Test that creating a new ingredient is successful. | app/recipe_app/tests/test_ingredients_api.py | test_create_ingredient_is_successful | oyekanmiayo/recipe-app-api | python | def test_create_ingredient_is_successful(self):
payload = {'name': 'Lemon'}
self.client.post(INGREDIENTS_URL, payload)
exists = Ingredient.objects.filter(user=self.user, name=payload['name']).exists()
self.assertTrue(exists) |
def test_create_ingredient_with_invalid_details_invalid(self):
'Test that ingredients is not created with invalid details'
payload = {'name': ''}
res = self.client.post(INGREDIENTS_URL, payload)
self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) | -1,150,530,564,363,053,600 | Test that ingredients is not created with invalid details | app/recipe_app/tests/test_ingredients_api.py | test_create_ingredient_with_invalid_details_invalid | oyekanmiayo/recipe-app-api | python | def test_create_ingredient_with_invalid_details_invalid(self):
payload = {'name': }
res = self.client.post(INGREDIENTS_URL, payload)
self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) |
@property
def priority(self):
'Priority.'
try:
return self._priority
except Exception as e:
raise e | 4,155,562,068,715,867,000 | Priority. | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | priority | HanseMerkur/nitro-python | python | @property
def priority(self):
try:
return self._priority
except Exception as e:
raise e |
@priority.setter
def priority(self, priority):
'Priority.\n\n :param priority: \n\n '
try:
self._priority = priority
except Exception as e:
raise e | 4,503,601,667,460,624,400 | Priority.
:param priority: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | priority | HanseMerkur/nitro-python | python | @priority.setter
def priority(self, priority):
'Priority.\n\n :param priority: \n\n '
try:
self._priority = priority
except Exception as e:
raise e |
@property
def gotopriorityexpression(self):
'Expression specifying the priority of the next policy which will get evaluated if the current policy rule evaluates to TRUE.'
try:
return self._gotopriorityexpression
except Exception as e:
raise e | 8,059,722,376,498,583,000 | Expression specifying the priority of the next policy which will get evaluated if the current policy rule evaluates to TRUE. | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | gotopriorityexpression | HanseMerkur/nitro-python | python | @property
def gotopriorityexpression(self):
try:
return self._gotopriorityexpression
except Exception as e:
raise e |
@gotopriorityexpression.setter
def gotopriorityexpression(self, gotopriorityexpression):
'Expression specifying the priority of the next policy which will get evaluated if the current policy rule evaluates to TRUE.\n\n :param gotopriorityexpression: \n\n '
try:
self._gotopriorityexpression = gotopriorityexpression
except Exception as e:
raise e | -6,898,664,913,910,547,000 | Expression specifying the priority of the next policy which will get evaluated if the current policy rule evaluates to TRUE.
:param gotopriorityexpression: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | gotopriorityexpression | HanseMerkur/nitro-python | python | @gotopriorityexpression.setter
def gotopriorityexpression(self, gotopriorityexpression):
'Expression specifying the priority of the next policy which will get evaluated if the current policy rule evaluates to TRUE.\n\n :param gotopriorityexpression: \n\n '
try:
self._gotopriorityexpression = gotopriorityexpression
except Exception as e:
raise e |
@property
def policyname(self):
'Name of the policy bound to the LB vserver.'
try:
return self._policyname
except Exception as e:
raise e | -5,365,522,352,492,252,000 | Name of the policy bound to the LB vserver. | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | policyname | HanseMerkur/nitro-python | python | @property
def policyname(self):
try:
return self._policyname
except Exception as e:
raise e |
@policyname.setter
def policyname(self, policyname):
'Name of the policy bound to the LB vserver.\n\n :param policyname: \n\n '
try:
self._policyname = policyname
except Exception as e:
raise e | -1,122,037,315,034,263,200 | Name of the policy bound to the LB vserver.
:param policyname: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | policyname | HanseMerkur/nitro-python | python | @policyname.setter
def policyname(self, policyname):
'Name of the policy bound to the LB vserver.\n\n :param policyname: \n\n '
try:
self._policyname = policyname
except Exception as e:
raise e |
@property
def name(self):
'Name for the virtual server. Must begin with an ASCII alphanumeric or underscore (_) character, and must contain only ASCII alphanumeric, underscore, hash (#), period (.), space, colon (:), at sign (@), equal sign (=), and hyphen (-) characters. Can be changed after the virtual server is created.\n CLI Users: If the name includes one or more spaces, enclose the name in double or single quotation marks (for example, "my vserver" or \'my vserver\'). .<br/>Minimum length = 1.\n\n\n '
try:
return self._name
except Exception as e:
raise e | 6,946,818,036,726,950,000 | Name for the virtual server. Must begin with an ASCII alphanumeric or underscore (_) character, and must contain only ASCII alphanumeric, underscore, hash (#), period (.), space, colon (:), at sign (@), equal sign (=), and hyphen (-) characters. Can be changed after the virtual server is created.
CLI Users: If the name includes one or more spaces, enclose the name in double or single quotation marks (for example, "my vserver" or 'my vserver'). .<br/>Minimum length = 1. | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | name | HanseMerkur/nitro-python | python | @property
def name(self):
'Name for the virtual server. Must begin with an ASCII alphanumeric or underscore (_) character, and must contain only ASCII alphanumeric, underscore, hash (#), period (.), space, colon (:), at sign (@), equal sign (=), and hyphen (-) characters. Can be changed after the virtual server is created.\n CLI Users: If the name includes one or more spaces, enclose the name in double or single quotation marks (for example, "my vserver" or \'my vserver\'). .<br/>Minimum length = 1.\n\n\n '
try:
return self._name
except Exception as e:
raise e |
@name.setter
def name(self, name):
'Name for the virtual server. Must begin with an ASCII alphanumeric or underscore (_) character, and must contain only ASCII alphanumeric, underscore, hash (#), period (.), space, colon (:), at sign (@), equal sign (=), and hyphen (-) characters. Can be changed after the virtual server is created.\n CLI Users: If the name includes one or more spaces, enclose the name in double or single quotation marks (for example, "my vserver" or \'my vserver\'). .<br/>Minimum length = 1\n\n :param name: \n\n '
try:
self._name = name
except Exception as e:
raise e | 4,977,305,799,432,753,000 | Name for the virtual server. Must begin with an ASCII alphanumeric or underscore (_) character, and must contain only ASCII alphanumeric, underscore, hash (#), period (.), space, colon (:), at sign (@), equal sign (=), and hyphen (-) characters. Can be changed after the virtual server is created.
CLI Users: If the name includes one or more spaces, enclose the name in double or single quotation marks (for example, "my vserver" or 'my vserver'). .<br/>Minimum length = 1
:param name: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | name | HanseMerkur/nitro-python | python | @name.setter
def name(self, name):
'Name for the virtual server. Must begin with an ASCII alphanumeric or underscore (_) character, and must contain only ASCII alphanumeric, underscore, hash (#), period (.), space, colon (:), at sign (@), equal sign (=), and hyphen (-) characters. Can be changed after the virtual server is created.\n CLI Users: If the name includes one or more spaces, enclose the name in double or single quotation marks (for example, "my vserver" or \'my vserver\'). .<br/>Minimum length = 1\n\n :param name: \n\n '
try:
self._name = name
except Exception as e:
raise e |
@property
def bindpoint(self):
'The bindpoint to which the policy is bound.<br/>Possible values = REQUEST, RESPONSE.'
try:
return self._bindpoint
except Exception as e:
raise e | -4,716,883,532,547,503,000 | The bindpoint to which the policy is bound.<br/>Possible values = REQUEST, RESPONSE. | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | bindpoint | HanseMerkur/nitro-python | python | @property
def bindpoint(self):
try:
return self._bindpoint
except Exception as e:
raise e |
@bindpoint.setter
def bindpoint(self, bindpoint):
'The bindpoint to which the policy is bound.<br/>Possible values = REQUEST, RESPONSE\n\n :param bindpoint: \n\n '
try:
self._bindpoint = bindpoint
except Exception as e:
raise e | -1,073,388,628,172,473,000 | The bindpoint to which the policy is bound.<br/>Possible values = REQUEST, RESPONSE
:param bindpoint: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | bindpoint | HanseMerkur/nitro-python | python | @bindpoint.setter
def bindpoint(self, bindpoint):
'The bindpoint to which the policy is bound.<br/>Possible values = REQUEST, RESPONSE\n\n :param bindpoint: \n\n '
try:
self._bindpoint = bindpoint
except Exception as e:
raise e |
@property
def labeltype(self):
'The invocation type.<br/>Possible values = reqvserver, resvserver, policylabel.'
try:
return self._labeltype
except Exception as e:
raise e | 3,900,809,274,961,790,000 | The invocation type.<br/>Possible values = reqvserver, resvserver, policylabel. | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | labeltype | HanseMerkur/nitro-python | python | @property
def labeltype(self):
try:
return self._labeltype
except Exception as e:
raise e |
@labeltype.setter
def labeltype(self, labeltype):
'The invocation type.<br/>Possible values = reqvserver, resvserver, policylabel\n\n :param labeltype: \n\n '
try:
self._labeltype = labeltype
except Exception as e:
raise e | 4,802,778,948,420,752,000 | The invocation type.<br/>Possible values = reqvserver, resvserver, policylabel
:param labeltype: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | labeltype | HanseMerkur/nitro-python | python | @labeltype.setter
def labeltype(self, labeltype):
'The invocation type.<br/>Possible values = reqvserver, resvserver, policylabel\n\n :param labeltype: \n\n '
try:
self._labeltype = labeltype
except Exception as e:
raise e |
@property
def labelname(self):
'Name of the label invoked.'
try:
return self._labelname
except Exception as e:
raise e | -1,357,503,408,469,624,600 | Name of the label invoked. | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | labelname | HanseMerkur/nitro-python | python | @property
def labelname(self):
try:
return self._labelname
except Exception as e:
raise e |
@labelname.setter
def labelname(self, labelname):
'Name of the label invoked.\n\n :param labelname: \n\n '
try:
self._labelname = labelname
except Exception as e:
raise e | 8,556,602,361,713,529,000 | Name of the label invoked.
:param labelname: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | labelname | HanseMerkur/nitro-python | python | @labelname.setter
def labelname(self, labelname):
'Name of the label invoked.\n\n :param labelname: \n\n '
try:
self._labelname = labelname
except Exception as e:
raise e |
@property
def invoke(self):
'Invoke policies bound to a virtual server or policy label.'
try:
return self._invoke
except Exception as e:
raise e | -2,666,846,950,919,318,000 | Invoke policies bound to a virtual server or policy label. | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | invoke | HanseMerkur/nitro-python | python | @property
def invoke(self):
try:
return self._invoke
except Exception as e:
raise e |
@invoke.setter
def invoke(self, invoke):
'Invoke policies bound to a virtual server or policy label.\n\n :param invoke: \n\n '
try:
self._invoke = invoke
except Exception as e:
raise e | 1,051,209,353,183,409,900 | Invoke policies bound to a virtual server or policy label.
:param invoke: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | invoke | HanseMerkur/nitro-python | python | @invoke.setter
def invoke(self, invoke):
'Invoke policies bound to a virtual server or policy label.\n\n :param invoke: \n\n '
try:
self._invoke = invoke
except Exception as e:
raise e |
@property
def sc(self):
'Use SureConnect on the virtual server.<br/>Default value: OFF<br/>Possible values = ON, OFF.'
try:
return self._sc
except Exception as e:
raise e | -6,716,229,637,475,839,000 | Use SureConnect on the virtual server.<br/>Default value: OFF<br/>Possible values = ON, OFF. | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | sc | HanseMerkur/nitro-python | python | @property
def sc(self):
try:
return self._sc
except Exception as e:
raise e |
def _get_nitro_response(self, service, response):
'converts nitro response into object and returns the object array in case of get request.\n\n :param service: \n :param response: \n\n '
try:
result = service.payload_formatter.string_to_resource(lbvserver_appfwpolicy_binding_response, response, self.__class__.__name__)
if (result.errorcode != 0):
if (result.errorcode == 444):
service.clear_session(self)
if result.severity:
if (result.severity == 'ERROR'):
raise nitro_exception(result.errorcode, str(result.message), str(result.severity))
else:
raise nitro_exception(result.errorcode, str(result.message), str(result.severity))
return result.lbvserver_appfwpolicy_binding
except Exception as e:
raise e | -8,598,073,818,403,330,000 | converts nitro response into object and returns the object array in case of get request.
:param service:
:param response: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | _get_nitro_response | HanseMerkur/nitro-python | python | def _get_nitro_response(self, service, response):
'converts nitro response into object and returns the object array in case of get request.\n\n :param service: \n :param response: \n\n '
try:
result = service.payload_formatter.string_to_resource(lbvserver_appfwpolicy_binding_response, response, self.__class__.__name__)
if (result.errorcode != 0):
if (result.errorcode == 444):
service.clear_session(self)
if result.severity:
if (result.severity == 'ERROR'):
raise nitro_exception(result.errorcode, str(result.message), str(result.severity))
else:
raise nitro_exception(result.errorcode, str(result.message), str(result.severity))
return result.lbvserver_appfwpolicy_binding
except Exception as e:
raise e |
def _get_object_name(self):
'Returns the value of object identifier argument'
try:
if (self.name is not None):
return str(self.name)
return None
except Exception as e:
raise e | 2,555,744,638,475,687,000 | Returns the value of object identifier argument | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | _get_object_name | HanseMerkur/nitro-python | python | def _get_object_name(self):
try:
if (self.name is not None):
return str(self.name)
return None
except Exception as e:
raise e |
@classmethod
def add(cls, client, resource):
'\n\n :param client: \n :param resource: \n\n '
try:
if (resource and (type(resource) is not list)):
updateresource = lbvserver_appfwpolicy_binding()
updateresource.name = resource.name
updateresource.policyname = resource.policyname
updateresource.priority = resource.priority
updateresource.gotopriorityexpression = resource.gotopriorityexpression
updateresource.bindpoint = resource.bindpoint
updateresource.invoke = resource.invoke
updateresource.labeltype = resource.labeltype
updateresource.labelname = resource.labelname
return updateresource.update_resource(client)
else:
if (resource and (len(resource) > 0)):
updateresources = [lbvserver_appfwpolicy_binding() for _ in range(len(resource))]
for i in range(len(resource)):
updateresources[i].name = resource[i].name
updateresources[i].policyname = resource[i].policyname
updateresources[i].priority = resource[i].priority
updateresources[i].gotopriorityexpression = resource[i].gotopriorityexpression
updateresources[i].bindpoint = resource[i].bindpoint
updateresources[i].invoke = resource[i].invoke
updateresources[i].labeltype = resource[i].labeltype
updateresources[i].labelname = resource[i].labelname
return cls.update_bulk_request(client, updateresources)
except Exception as e:
raise e | -1,235,520,822,178,636,500 | :param client:
:param resource: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | add | HanseMerkur/nitro-python | python | @classmethod
def add(cls, client, resource):
'\n\n :param client: \n :param resource: \n\n '
try:
if (resource and (type(resource) is not list)):
updateresource = lbvserver_appfwpolicy_binding()
updateresource.name = resource.name
updateresource.policyname = resource.policyname
updateresource.priority = resource.priority
updateresource.gotopriorityexpression = resource.gotopriorityexpression
updateresource.bindpoint = resource.bindpoint
updateresource.invoke = resource.invoke
updateresource.labeltype = resource.labeltype
updateresource.labelname = resource.labelname
return updateresource.update_resource(client)
else:
if (resource and (len(resource) > 0)):
updateresources = [lbvserver_appfwpolicy_binding() for _ in range(len(resource))]
for i in range(len(resource)):
updateresources[i].name = resource[i].name
updateresources[i].policyname = resource[i].policyname
updateresources[i].priority = resource[i].priority
updateresources[i].gotopriorityexpression = resource[i].gotopriorityexpression
updateresources[i].bindpoint = resource[i].bindpoint
updateresources[i].invoke = resource[i].invoke
updateresources[i].labeltype = resource[i].labeltype
updateresources[i].labelname = resource[i].labelname
return cls.update_bulk_request(client, updateresources)
except Exception as e:
raise e |
@classmethod
def delete(cls, client, resource):
'\n\n :param client: \n :param resource: \n\n '
try:
if (resource and (type(resource) is not list)):
deleteresource = lbvserver_appfwpolicy_binding()
deleteresource.name = resource.name
deleteresource.policyname = resource.policyname
deleteresource.bindpoint = resource.bindpoint
deleteresource.priority = resource.priority
return deleteresource.delete_resource(client)
else:
if (resource and (len(resource) > 0)):
deleteresources = [lbvserver_appfwpolicy_binding() for _ in range(len(resource))]
for i in range(len(resource)):
deleteresources[i].name = resource[i].name
deleteresources[i].policyname = resource[i].policyname
deleteresources[i].bindpoint = resource[i].bindpoint
deleteresources[i].priority = resource[i].priority
return cls.delete_bulk_request(client, deleteresources)
except Exception as e:
raise e | 5,475,910,030,271,163,000 | :param client:
:param resource: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | delete | HanseMerkur/nitro-python | python | @classmethod
def delete(cls, client, resource):
'\n\n :param client: \n :param resource: \n\n '
try:
if (resource and (type(resource) is not list)):
deleteresource = lbvserver_appfwpolicy_binding()
deleteresource.name = resource.name
deleteresource.policyname = resource.policyname
deleteresource.bindpoint = resource.bindpoint
deleteresource.priority = resource.priority
return deleteresource.delete_resource(client)
else:
if (resource and (len(resource) > 0)):
deleteresources = [lbvserver_appfwpolicy_binding() for _ in range(len(resource))]
for i in range(len(resource)):
deleteresources[i].name = resource[i].name
deleteresources[i].policyname = resource[i].policyname
deleteresources[i].bindpoint = resource[i].bindpoint
deleteresources[i].priority = resource[i].priority
return cls.delete_bulk_request(client, deleteresources)
except Exception as e:
raise e |
@classmethod
def get(cls, service, name):
'Use this API to fetch lbvserver_appfwpolicy_binding resources.\n\n :param service: \n :param name: \n\n '
try:
obj = lbvserver_appfwpolicy_binding()
obj.name = name
response = obj.get_resources(service)
return response
except Exception as e:
raise e | 3,176,071,130,034,916,400 | Use this API to fetch lbvserver_appfwpolicy_binding resources.
:param service:
:param name: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | get | HanseMerkur/nitro-python | python | @classmethod
def get(cls, service, name):
'Use this API to fetch lbvserver_appfwpolicy_binding resources.\n\n :param service: \n :param name: \n\n '
try:
obj = lbvserver_appfwpolicy_binding()
obj.name = name
response = obj.get_resources(service)
return response
except Exception as e:
raise e |
@classmethod
def get_filtered(cls, service, name, filter_):
'Use this API to fetch filtered set of lbvserver_appfwpolicy_binding resources.\n Filter string should be in JSON format.eg: "port:80,servicetype:HTTP".\n\n :param service: \n :param name: \n :param filter_: \n\n '
try:
obj = lbvserver_appfwpolicy_binding()
obj.name = name
option_ = options()
option_.filter = filter_
response = obj.getfiltered(service, option_)
return response
except Exception as e:
raise e | -2,542,339,016,633,254,000 | Use this API to fetch filtered set of lbvserver_appfwpolicy_binding resources.
Filter string should be in JSON format.eg: "port:80,servicetype:HTTP".
:param service:
:param name:
:param filter_: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | get_filtered | HanseMerkur/nitro-python | python | @classmethod
def get_filtered(cls, service, name, filter_):
'Use this API to fetch filtered set of lbvserver_appfwpolicy_binding resources.\n Filter string should be in JSON format.eg: "port:80,servicetype:HTTP".\n\n :param service: \n :param name: \n :param filter_: \n\n '
try:
obj = lbvserver_appfwpolicy_binding()
obj.name = name
option_ = options()
option_.filter = filter_
response = obj.getfiltered(service, option_)
return response
except Exception as e:
raise e |
@classmethod
def count(cls, service, name):
'Use this API to count lbvserver_appfwpolicy_binding resources configued on NetScaler.\n\n :param service: \n :param name: \n\n '
try:
obj = lbvserver_appfwpolicy_binding()
obj.name = name
option_ = options()
option_.count = True
response = obj.get_resources(service, option_)
if response:
return response[0].__dict__['___count']
return 0
except Exception as e:
raise e | 7,837,158,330,813,566,000 | Use this API to count lbvserver_appfwpolicy_binding resources configued on NetScaler.
:param service:
:param name: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | count | HanseMerkur/nitro-python | python | @classmethod
def count(cls, service, name):
'Use this API to count lbvserver_appfwpolicy_binding resources configued on NetScaler.\n\n :param service: \n :param name: \n\n '
try:
obj = lbvserver_appfwpolicy_binding()
obj.name = name
option_ = options()
option_.count = True
response = obj.get_resources(service, option_)
if response:
return response[0].__dict__['___count']
return 0
except Exception as e:
raise e |
@classmethod
def count_filtered(cls, service, name, filter_):
'Use this API to count the filtered set of lbvserver_appfwpolicy_binding resources.\n Filter string should be in JSON format.eg: "port:80,servicetype:HTTP".\n\n :param service: \n :param name: \n :param filter_: \n\n '
try:
obj = lbvserver_appfwpolicy_binding()
obj.name = name
option_ = options()
option_.count = True
option_.filter = filter_
response = obj.getfiltered(service, option_)
if response:
return response[0].__dict__['___count']
return 0
except Exception as e:
raise e | 7,667,054,189,601,754,000 | Use this API to count the filtered set of lbvserver_appfwpolicy_binding resources.
Filter string should be in JSON format.eg: "port:80,servicetype:HTTP".
:param service:
:param name:
:param filter_: | nitro/resource/config/lb/lbvserver_appfwpolicy_binding.py | count_filtered | HanseMerkur/nitro-python | python | @classmethod
def count_filtered(cls, service, name, filter_):
'Use this API to count the filtered set of lbvserver_appfwpolicy_binding resources.\n Filter string should be in JSON format.eg: "port:80,servicetype:HTTP".\n\n :param service: \n :param name: \n :param filter_: \n\n '
try:
obj = lbvserver_appfwpolicy_binding()
obj.name = name
option_ = options()
option_.count = True
option_.filter = filter_
response = obj.getfiltered(service, option_)
if response:
return response[0].__dict__['___count']
return 0
except Exception as e:
raise e |
async def ping(self, ctx):
'\n Args:\n ctx: FContext\n '
pass | 2,692,111,709,897,499,000 | Args:
ctx: FContext | test/expected/python.asyncio/service_extension_same_file/f_Pinger.py | ping | trevorackerman-wk/frugal | python | async def ping(self, ctx):
'\n Args:\n ctx: FContext\n '
pass |
def __init__(self, provider, middleware=None):
'\n Create a new Client with an FServiceProvider containing a transport\n and protocol factory.\n\n Args:\n provider: FServiceProvider\n middleware: ServiceMiddleware or list of ServiceMiddleware\n '
middleware = (middleware or [])
if (middleware and (not isinstance(middleware, list))):
middleware = [middleware]
super(Client, self).__init__(provider, middleware=middleware)
middleware += provider.get_middleware()
self._methods.update({'ping': Method(self._ping, middleware)}) | 6,234,326,604,996,853,000 | Create a new Client with an FServiceProvider containing a transport
and protocol factory.
Args:
provider: FServiceProvider
middleware: ServiceMiddleware or list of ServiceMiddleware | test/expected/python.asyncio/service_extension_same_file/f_Pinger.py | __init__ | trevorackerman-wk/frugal | python | def __init__(self, provider, middleware=None):
'\n Create a new Client with an FServiceProvider containing a transport\n and protocol factory.\n\n Args:\n provider: FServiceProvider\n middleware: ServiceMiddleware or list of ServiceMiddleware\n '
middleware = (middleware or [])
if (middleware and (not isinstance(middleware, list))):
middleware = [middleware]
super(Client, self).__init__(provider, middleware=middleware)
middleware += provider.get_middleware()
self._methods.update({'ping': Method(self._ping, middleware)}) |
async def ping(self, ctx):
'\n Args:\n ctx: FContext\n '
return (await self._methods['ping']([ctx])) | -7,926,762,067,287,352,000 | Args:
ctx: FContext | test/expected/python.asyncio/service_extension_same_file/f_Pinger.py | ping | trevorackerman-wk/frugal | python | async def ping(self, ctx):
'\n Args:\n ctx: FContext\n '
return (await self._methods['ping']([ctx])) |
def __init__(self, handler, middleware=None):
'\n Create a new Processor.\n\n Args:\n handler: Iface\n '
if (middleware and (not isinstance(middleware, list))):
middleware = [middleware]
super(Processor, self).__init__(handler, middleware=middleware)
self.add_to_processor_map('ping', _ping(Method(handler.ping, middleware), self.get_write_lock())) | -6,148,350,140,761,615,000 | Create a new Processor.
Args:
handler: Iface | test/expected/python.asyncio/service_extension_same_file/f_Pinger.py | __init__ | trevorackerman-wk/frugal | python | def __init__(self, handler, middleware=None):
'\n Create a new Processor.\n\n Args:\n handler: Iface\n '
if (middleware and (not isinstance(middleware, list))):
middleware = [middleware]
super(Processor, self).__init__(handler, middleware=middleware)
self.add_to_processor_map('ping', _ping(Method(handler.ping, middleware), self.get_write_lock())) |
def final_eos_is_already_included(header_block: Union[(UnfinishedHeaderBlock, UnfinishedBlock, HeaderBlock, FullBlock)], blocks: BlockchainInterface, sub_slot_iters: uint64) -> bool:
'\n Args:\n header_block: An overflow block, with potentially missing information about the new sub slot\n blocks: all blocks that have been included before header_block\n sub_slot_iters: sub_slot_iters at the header_block\n\n Returns: True iff the missing sub slot was already included in a previous block. Returns False if the sub\n slot was not included yet, and therefore it is the responsibility of this block to include it\n\n '
if (len(header_block.finished_sub_slots) > 0):
return False
curr: BlockRecord = blocks.block_record(header_block.prev_header_hash)
seen_overflow_block = (curr.overflow and ((header_block.total_iters - curr.total_iters) < (sub_slot_iters // 2)))
while ((not curr.first_in_sub_slot) and (not (curr.height == 0))):
if (curr.overflow and ((header_block.total_iters - curr.total_iters) < (sub_slot_iters // 2))):
seen_overflow_block = True
curr = blocks.block_record(curr.prev_hash)
if (curr.first_in_sub_slot and seen_overflow_block):
return True
return False | 1,981,469,205,373,613,600 | Args:
header_block: An overflow block, with potentially missing information about the new sub slot
blocks: all blocks that have been included before header_block
sub_slot_iters: sub_slot_iters at the header_block
Returns: True iff the missing sub slot was already included in a previous block. Returns False if the sub
slot was not included yet, and therefore it is the responsibility of this block to include it | cactus/consensus/get_block_challenge.py | final_eos_is_already_included | Cactus-Network/cactus-blockchain | python | def final_eos_is_already_included(header_block: Union[(UnfinishedHeaderBlock, UnfinishedBlock, HeaderBlock, FullBlock)], blocks: BlockchainInterface, sub_slot_iters: uint64) -> bool:
'\n Args:\n header_block: An overflow block, with potentially missing information about the new sub slot\n blocks: all blocks that have been included before header_block\n sub_slot_iters: sub_slot_iters at the header_block\n\n Returns: True iff the missing sub slot was already included in a previous block. Returns False if the sub\n slot was not included yet, and therefore it is the responsibility of this block to include it\n\n '
if (len(header_block.finished_sub_slots) > 0):
return False
curr: BlockRecord = blocks.block_record(header_block.prev_header_hash)
seen_overflow_block = (curr.overflow and ((header_block.total_iters - curr.total_iters) < (sub_slot_iters // 2)))
while ((not curr.first_in_sub_slot) and (not (curr.height == 0))):
if (curr.overflow and ((header_block.total_iters - curr.total_iters) < (sub_slot_iters // 2))):
seen_overflow_block = True
curr = blocks.block_record(curr.prev_hash)
if (curr.first_in_sub_slot and seen_overflow_block):
return True
return False |
@property
def color(self):
"\n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, rebeccapurple, saddlebrown, salmon,\n sandybrown, seagreen, seashell, sienna, silver,\n skyblue, slateblue, slategray, slategrey, snow,\n springgreen, steelblue, tan, teal, thistle, tomato,\n turquoise, violet, wheat, white, whitesmoke,\n yellow, yellowgreen\n\n Returns\n -------\n str\n "
return self['color'] | -6,704,044,870,786,772,000 | The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str | packages/python/plotly/plotly/graph_objs/scatterternary/marker/colorbar/_tickfont.py | color | 1abner1/plotly.py | python | @property
def color(self):
"\n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, rebeccapurple, saddlebrown, salmon,\n sandybrown, seagreen, seashell, sienna, silver,\n skyblue, slateblue, slategray, slategrey, snow,\n springgreen, steelblue, tan, teal, thistle, tomato,\n turquoise, violet, wheat, white, whitesmoke,\n yellow, yellowgreen\n\n Returns\n -------\n str\n "
return self['color'] |
@property
def family(self):
'\n HTML font family - the typeface that will be applied by the web\n browser. The web browser will only be able to apply a font if\n it is available on the system which it operates. Provide\n multiple font families, separated by commas, to indicate the\n preference in which to apply fonts if they aren\'t available on\n the system. The Chart Studio Cloud (at https://chart-\n studio.plotly.com or on-premise) generates images on a server,\n where only a select number of fonts are installed and\n supported. These include "Arial", "Balto", "Courier New",\n "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas\n One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans\n Narrow", "Raleway", "Times New Roman".\n \n The \'family\' property is a string and must be specified as:\n - A non-empty string\n\n Returns\n -------\n str\n '
return self['family'] | 3,791,649,582,837,001,000 | HTML font family - the typeface that will be applied by the web
browser. The web browser will only be able to apply a font if
it is available on the system which it operates. Provide
multiple font families, separated by commas, to indicate the
preference in which to apply fonts if they aren't available on
the system. The Chart Studio Cloud (at https://chart-
studio.plotly.com or on-premise) generates images on a server,
where only a select number of fonts are installed and
supported. These include "Arial", "Balto", "Courier New",
"Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
The 'family' property is a string and must be specified as:
- A non-empty string
Returns
-------
str | packages/python/plotly/plotly/graph_objs/scatterternary/marker/colorbar/_tickfont.py | family | 1abner1/plotly.py | python | @property
def family(self):
'\n HTML font family - the typeface that will be applied by the web\n browser. The web browser will only be able to apply a font if\n it is available on the system which it operates. Provide\n multiple font families, separated by commas, to indicate the\n preference in which to apply fonts if they aren\'t available on\n the system. The Chart Studio Cloud (at https://chart-\n studio.plotly.com or on-premise) generates images on a server,\n where only a select number of fonts are installed and\n supported. These include "Arial", "Balto", "Courier New",\n "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas\n One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans\n Narrow", "Raleway", "Times New Roman".\n \n The \'family\' property is a string and must be specified as:\n - A non-empty string\n\n Returns\n -------\n str\n '
return self['family'] |
@property
def size(self):
"\n The 'size' property is a number and may be specified as:\n - An int or float in the interval [1, inf]\n\n Returns\n -------\n int|float\n "
return self['size'] | 4,214,108,177,685,330,000 | The 'size' property is a number and may be specified as:
- An int or float in the interval [1, inf]
Returns
-------
int|float | packages/python/plotly/plotly/graph_objs/scatterternary/marker/colorbar/_tickfont.py | size | 1abner1/plotly.py | python | @property
def size(self):
"\n The 'size' property is a number and may be specified as:\n - An int or float in the interval [1, inf]\n\n Returns\n -------\n int|float\n "
return self['size'] |
def __init__(self, arg=None, color=None, family=None, size=None, **kwargs):
'\n Construct a new Tickfont object\n \n Sets the color bar\'s tick label font\n\n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of :class:`plotly.graph_objs.scatterternary\n .marker.colorbar.Tickfont`\n color\n\n family\n HTML font family - the typeface that will be applied by\n the web browser. The web browser will only be able to\n apply a font if it is available on the system which it\n operates. Provide multiple font families, separated by\n commas, to indicate the preference in which to apply\n fonts if they aren\'t available on the system. The Chart\n Studio Cloud (at https://chart-studio.plotly.com or on-\n premise) generates images on a server, where only a\n select number of fonts are installed and supported.\n These include "Arial", "Balto", "Courier New", "Droid\n Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas\n One", "Old Standard TT", "Open Sans", "Overpass", "PT\n Sans Narrow", "Raleway", "Times New Roman".\n size\n\n\n Returns\n -------\n Tickfont\n '
super(Tickfont, self).__init__('tickfont')
if ('_parent' in kwargs):
self._parent = kwargs['_parent']
return
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.scatterternary.marker.colorbar.Tickfont \nconstructor must be a dict or \nan instance of :class:`plotly.graph_objs.scatterternary.marker.colorbar.Tickfont`')
self._skip_invalid = kwargs.pop('skip_invalid', False)
self._validate = kwargs.pop('_validate', True)
_v = arg.pop('color', None)
_v = (color if (color is not None) else _v)
if (_v is not None):
self['color'] = _v
_v = arg.pop('family', None)
_v = (family if (family is not None) else _v)
if (_v is not None):
self['family'] = _v
_v = arg.pop('size', None)
_v = (size if (size is not None) else _v)
if (_v is not None):
self['size'] = _v
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | 7,054,196,890,296,316,000 | Construct a new Tickfont object
Sets the color bar's tick label font
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of :class:`plotly.graph_objs.scatterternary
.marker.colorbar.Tickfont`
color
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
size
Returns
-------
Tickfont | packages/python/plotly/plotly/graph_objs/scatterternary/marker/colorbar/_tickfont.py | __init__ | 1abner1/plotly.py | python | def __init__(self, arg=None, color=None, family=None, size=None, **kwargs):
'\n Construct a new Tickfont object\n \n Sets the color bar\'s tick label font\n\n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of :class:`plotly.graph_objs.scatterternary\n .marker.colorbar.Tickfont`\n color\n\n family\n HTML font family - the typeface that will be applied by\n the web browser. The web browser will only be able to\n apply a font if it is available on the system which it\n operates. Provide multiple font families, separated by\n commas, to indicate the preference in which to apply\n fonts if they aren\'t available on the system. The Chart\n Studio Cloud (at https://chart-studio.plotly.com or on-\n premise) generates images on a server, where only a\n select number of fonts are installed and supported.\n These include "Arial", "Balto", "Courier New", "Droid\n Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas\n One", "Old Standard TT", "Open Sans", "Overpass", "PT\n Sans Narrow", "Raleway", "Times New Roman".\n size\n\n\n Returns\n -------\n Tickfont\n '
super(Tickfont, self).__init__('tickfont')
if ('_parent' in kwargs):
self._parent = kwargs['_parent']
return
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.scatterternary.marker.colorbar.Tickfont \nconstructor must be a dict or \nan instance of :class:`plotly.graph_objs.scatterternary.marker.colorbar.Tickfont`')
self._skip_invalid = kwargs.pop('skip_invalid', False)
self._validate = kwargs.pop('_validate', True)
_v = arg.pop('color', None)
_v = (color if (color is not None) else _v)
if (_v is not None):
self['color'] = _v
_v = arg.pop('family', None)
_v = (family if (family is not None) else _v)
if (_v is not None):
self['family'] = _v
_v = arg.pop('size', None)
_v = (size if (size is not None) else _v)
if (_v is not None):
self['size'] = _v
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False |
def print_localconfvalue(name):
'Syntax: [storm localconfvalue conf-name]\n\n Prints out the value for conf-name in the local Storm configs.\n The local Storm configs are the ones in ~/.storm/storm.yaml merged\n in with the configs in defaults.yaml.\n '
print(((name + ': ') + confvalue(name, [USER_CONF_DIR]))) | 4,783,880,202,942,562,000 | Syntax: [storm localconfvalue conf-name]
Prints out the value for conf-name in the local Storm configs.
The local Storm configs are the ones in ~/.storm/storm.yaml merged
in with the configs in defaults.yaml. | bin/storm.py | print_localconfvalue | JamiesZhang/Storm | python | def print_localconfvalue(name):
'Syntax: [storm localconfvalue conf-name]\n\n Prints out the value for conf-name in the local Storm configs.\n The local Storm configs are the ones in ~/.storm/storm.yaml merged\n in with the configs in defaults.yaml.\n '
print(((name + ': ') + confvalue(name, [USER_CONF_DIR]))) |
def print_remoteconfvalue(name):
"Syntax: [storm remoteconfvalue conf-name]\n\n Prints out the value for conf-name in the cluster's Storm configs.\n The cluster's Storm configs are the ones in $STORM-PATH/conf/storm.yaml\n merged in with the configs in defaults.yaml.\n\n This command must be run on a cluster machine.\n "
print(((name + ': ') + confvalue(name, [CLUSTER_CONF_DIR]))) | 5,267,290,543,717,283,000 | Syntax: [storm remoteconfvalue conf-name]
Prints out the value for conf-name in the cluster's Storm configs.
The cluster's Storm configs are the ones in $STORM-PATH/conf/storm.yaml
merged in with the configs in defaults.yaml.
This command must be run on a cluster machine. | bin/storm.py | print_remoteconfvalue | JamiesZhang/Storm | python | def print_remoteconfvalue(name):
"Syntax: [storm remoteconfvalue conf-name]\n\n Prints out the value for conf-name in the cluster's Storm configs.\n The cluster's Storm configs are the ones in $STORM-PATH/conf/storm.yaml\n merged in with the configs in defaults.yaml.\n\n This command must be run on a cluster machine.\n "
print(((name + ': ') + confvalue(name, [CLUSTER_CONF_DIR]))) |
def parse_args(string):
'Takes a string of whitespace-separated tokens and parses it into a list.\n Whitespace inside tokens may be quoted with single quotes, double quotes or\n backslash (similar to command-line arguments in bash).\n\n >>> parse_args(r\'\'\'"a a" \'b b\' c\\ c "d\'d" \'e"e\' \'f\'f\' "g"g" "i""i" \'j\'\'j\' k" "k l\' l\' mm n\\n\'\'\')\n [\'a a\', \'b b\', \'c c\', "d\'d", \'e"e\', "f\'f", \'g"g\', \'ii\', \'jj\', \'k k\', \'l l\', \'mm\', r\'n\n\']\n '
re_split = re.compile('((?:\n [^\\s"\'\\\\] |\n "(?: [^"\\\\] | \\\\.)*" |\n \'(?: [^\'\\\\] | \\\\.)*\' |\n \\\\.\n )+)', re.VERBOSE)
args = re_split.split(string)[1::2]
args = [re.compile('"((?:[^"\\\\]|\\\\.)*)"').sub('\\1', x) for x in args]
args = [re.compile("'((?:[^'\\\\]|\\\\.)*)'").sub('\\1', x) for x in args]
return [re.compile('\\\\(.)').sub('\\1', x) for x in args] | -1,373,043,317,218,771,500 | Takes a string of whitespace-separated tokens and parses it into a list.
Whitespace inside tokens may be quoted with single quotes, double quotes or
backslash (similar to command-line arguments in bash).
>>> parse_args(r'''"a a" 'b b' c\ c "d'd" 'e"e' 'f'f' "g"g" "i""i" 'j''j' k" "k l' l' mm n\n''')
['a a', 'b b', 'c c', "d'd", 'e"e', "f'f", 'g"g', 'ii', 'jj', 'k k', 'l l', 'mm', r'n
'] | bin/storm.py | parse_args | JamiesZhang/Storm | python | def parse_args(string):
'Takes a string of whitespace-separated tokens and parses it into a list.\n Whitespace inside tokens may be quoted with single quotes, double quotes or\n backslash (similar to command-line arguments in bash).\n\n >>> parse_args(r\'\'\'"a a" \'b b\' c\\ c "d\'d" \'e"e\' \'f\'f\' "g"g" "ii" \'j\'\'j\' k" "k l\' l\' mm n\\n\'\'\')\n [\'a a\', \'b b\', \'c c\', "d\'d", \'e"e\', "f\'f", \'g"g\', \'ii\', \'jj\', \'k k\', \'l l\', \'mm\', r\'n\n\']\n '
re_split = re.compile('((?:\n [^\\s"\'\\\\] |\n "(?: [^"\\\\] | \\\\.)*" |\n \'(?: [^\'\\\\] | \\\\.)*\' |\n \\\\.\n )+)', re.VERBOSE)
args = re_split.split(string)[1::2]
args = [re.compile('"((?:[^"\\\\]|\\\\.)*)"').sub('\\1', x) for x in args]
args = [re.compile("'((?:[^'\\\\]|\\\\.)*)'").sub('\\1', x) for x in args]
return [re.compile('\\\\(.)').sub('\\1', x) for x in args] |
def local(jarfile, klass, *args):
'Syntax: [storm local topology-jar-path class ...]\n\n Runs the main method of class with the specified arguments but pointing to a local cluster\n The storm jars and configs in ~/.storm are put on the classpath.\n The process is configured so that StormSubmitter\n (http://storm.apache.org/releases/current/javadocs/org/apache/storm/StormSubmitter.html)\n and others will interact with a local cluster instead of the one configured by default.\n\n Most options should work just like with the storm jar command.\n\n local also adds in the option --local-ttl which sets the number of seconds the\n local cluster will run for before it shuts down.\n\n --java-debug lets you turn on java debugging and set the parameters passed to -agentlib:jdwp on the JDK\n --java-debug transport=dt_socket,address=localhost:8000\n will open up a debugging server on port 8000.\n '
[ttl, debug_args, args] = parse_local_opts(args)
extrajvmopts = [('-Dstorm.local.sleeptime=' + ttl)]
if (debug_args != None):
extrajvmopts = (extrajvmopts + [('-agentlib:jdwp=' + debug_args)])
run_client_jar(jarfile, 'org.apache.storm.LocalCluster', ([klass] + list(args)), client=False, daemon=False, extrajvmopts=extrajvmopts) | -86,530,798,587,278,830 | Syntax: [storm local topology-jar-path class ...]
Runs the main method of class with the specified arguments but pointing to a local cluster
The storm jars and configs in ~/.storm are put on the classpath.
The process is configured so that StormSubmitter
(http://storm.apache.org/releases/current/javadocs/org/apache/storm/StormSubmitter.html)
and others will interact with a local cluster instead of the one configured by default.
Most options should work just like with the storm jar command.
local also adds in the option --local-ttl which sets the number of seconds the
local cluster will run for before it shuts down.
--java-debug lets you turn on java debugging and set the parameters passed to -agentlib:jdwp on the JDK
--java-debug transport=dt_socket,address=localhost:8000
will open up a debugging server on port 8000. | bin/storm.py | local | JamiesZhang/Storm | python | def local(jarfile, klass, *args):
'Syntax: [storm local topology-jar-path class ...]\n\n Runs the main method of class with the specified arguments but pointing to a local cluster\n The storm jars and configs in ~/.storm are put on the classpath.\n The process is configured so that StormSubmitter\n (http://storm.apache.org/releases/current/javadocs/org/apache/storm/StormSubmitter.html)\n and others will interact with a local cluster instead of the one configured by default.\n\n Most options should work just like with the storm jar command.\n\n local also adds in the option --local-ttl which sets the number of seconds the\n local cluster will run for before it shuts down.\n\n --java-debug lets you turn on java debugging and set the parameters passed to -agentlib:jdwp on the JDK\n --java-debug transport=dt_socket,address=localhost:8000\n will open up a debugging server on port 8000.\n '
[ttl, debug_args, args] = parse_local_opts(args)
extrajvmopts = [('-Dstorm.local.sleeptime=' + ttl)]
if (debug_args != None):
extrajvmopts = (extrajvmopts + [('-agentlib:jdwp=' + debug_args)])
run_client_jar(jarfile, 'org.apache.storm.LocalCluster', ([klass] + list(args)), client=False, daemon=False, extrajvmopts=extrajvmopts) |
def jar(jarfile, klass, *args):
'Syntax: [storm jar topology-jar-path class ...]\n\n Runs the main method of class with the specified arguments.\n The storm worker dependencies and configs in ~/.storm are put on the classpath.\n The process is configured so that StormSubmitter\n (http://storm.apache.org/releases/current/javadocs/org/apache/storm/StormSubmitter.html)\n will upload the jar at topology-jar-path when the topology is submitted.\n\n When you want to ship other jars which is not included to application jar, you can pass them to --jars option with comma-separated string.\n For example, --jars "your-local-jar.jar,your-local-jar2.jar" will load your-local-jar.jar and your-local-jar2.jar.\n And when you want to ship maven artifacts and its transitive dependencies, you can pass them to --artifacts with comma-separated string.\n You can also exclude some dependencies like what you\'re doing in maven pom.\n Please add exclusion artifacts with \'^\' separated string after the artifact.\n For example, -artifacts "redis.clients:jedis:2.9.0,org.apache.kafka:kafka-clients:1.0.0^org.slf4j:slf4j-api" will load jedis and kafka-clients artifact and all of transitive dependencies but exclude slf4j-api from kafka.\n\n When you need to pull the artifacts from other than Maven Central, you can pass remote repositories to --artifactRepositories option with comma-separated string.\n Repository format is "<name>^<url>". \'^\' is taken as separator because URL allows various characters.\n For example, --artifactRepositories "jboss-repository^http://repository.jboss.com/maven2,HDPRepo^http://repo.hortonworks.com/content/groups/public/" will add JBoss and HDP repositories for dependency resolver.\n You can provide local maven repository directory via --mavenLocalRepositoryDirectory if you would like to use specific directory. It might help when you don\'t have \'.m2/repository\' directory in home directory, because CWD is sometimes non-deterministic (fragile).\n\n You can also provide proxy information to let dependency resolver utilizing proxy if needed. There\'re three parameters for proxy:\n --proxyUrl: URL representation of proxy (\'http://host:port\')\n --proxyUsername: username of proxy if it requires basic auth\n --proxyPassword: password of proxy if it requires basic auth\n\n Complete example of options is here: `./bin/storm jar example/storm-starter/storm-starter-topologies-*.jar org.apache.storm.starter.RollingTopWords blobstore-remote2 remote --jars "./external/storm-redis/storm-redis-1.1.0.jar,./external/storm-kafka-client/storm-kafka-client-1.1.0.jar" --artifacts "redis.clients:jedis:2.9.0,org.apache.kafka:kafka-clients:1.0.0^org.slf4j:slf4j-api" --artifactRepositories "jboss-repository^http://repository.jboss.com/maven2,HDPRepo^http://repo.hortonworks.com/content/groups/public/"`\n\n When you pass jars and/or artifacts options, StormSubmitter will upload them when the topology is submitted, and they will be included to classpath of both the process which runs the class, and also workers for that topology.\n\n If for some reason you need to have the full storm classpath, not just the one for the worker you may include the command line option `--storm-server-classpath`. Please be careful because this will add things to the classpath that will not be on the worker classpath and could result in the worker not running.\n '
[server_class_path, args] = parse_jar_opts(args)
run_client_jar(jarfile, klass, list(args), client=(not server_class_path), daemon=False) | -5,195,932,568,094,356,000 | Syntax: [storm jar topology-jar-path class ...]
Runs the main method of class with the specified arguments.
The storm worker dependencies and configs in ~/.storm are put on the classpath.
The process is configured so that StormSubmitter
(http://storm.apache.org/releases/current/javadocs/org/apache/storm/StormSubmitter.html)
will upload the jar at topology-jar-path when the topology is submitted.
When you want to ship other jars which is not included to application jar, you can pass them to --jars option with comma-separated string.
For example, --jars "your-local-jar.jar,your-local-jar2.jar" will load your-local-jar.jar and your-local-jar2.jar.
And when you want to ship maven artifacts and its transitive dependencies, you can pass them to --artifacts with comma-separated string.
You can also exclude some dependencies like what you're doing in maven pom.
Please add exclusion artifacts with '^' separated string after the artifact.
For example, -artifacts "redis.clients:jedis:2.9.0,org.apache.kafka:kafka-clients:1.0.0^org.slf4j:slf4j-api" will load jedis and kafka-clients artifact and all of transitive dependencies but exclude slf4j-api from kafka.
When you need to pull the artifacts from other than Maven Central, you can pass remote repositories to --artifactRepositories option with comma-separated string.
Repository format is "<name>^<url>". '^' is taken as separator because URL allows various characters.
For example, --artifactRepositories "jboss-repository^http://repository.jboss.com/maven2,HDPRepo^http://repo.hortonworks.com/content/groups/public/" will add JBoss and HDP repositories for dependency resolver.
You can provide local maven repository directory via --mavenLocalRepositoryDirectory if you would like to use specific directory. It might help when you don't have '.m2/repository' directory in home directory, because CWD is sometimes non-deterministic (fragile).
You can also provide proxy information to let dependency resolver utilizing proxy if needed. There're three parameters for proxy:
--proxyUrl: URL representation of proxy ('http://host:port')
--proxyUsername: username of proxy if it requires basic auth
--proxyPassword: password of proxy if it requires basic auth
Complete example of options is here: `./bin/storm jar example/storm-starter/storm-starter-topologies-*.jar org.apache.storm.starter.RollingTopWords blobstore-remote2 remote --jars "./external/storm-redis/storm-redis-1.1.0.jar,./external/storm-kafka-client/storm-kafka-client-1.1.0.jar" --artifacts "redis.clients:jedis:2.9.0,org.apache.kafka:kafka-clients:1.0.0^org.slf4j:slf4j-api" --artifactRepositories "jboss-repository^http://repository.jboss.com/maven2,HDPRepo^http://repo.hortonworks.com/content/groups/public/"`
When you pass jars and/or artifacts options, StormSubmitter will upload them when the topology is submitted, and they will be included to classpath of both the process which runs the class, and also workers for that topology.
If for some reason you need to have the full storm classpath, not just the one for the worker you may include the command line option `--storm-server-classpath`. Please be careful because this will add things to the classpath that will not be on the worker classpath and could result in the worker not running. | bin/storm.py | jar | JamiesZhang/Storm | python | def jar(jarfile, klass, *args):
'Syntax: [storm jar topology-jar-path class ...]\n\n Runs the main method of class with the specified arguments.\n The storm worker dependencies and configs in ~/.storm are put on the classpath.\n The process is configured so that StormSubmitter\n (http://storm.apache.org/releases/current/javadocs/org/apache/storm/StormSubmitter.html)\n will upload the jar at topology-jar-path when the topology is submitted.\n\n When you want to ship other jars which is not included to application jar, you can pass them to --jars option with comma-separated string.\n For example, --jars "your-local-jar.jar,your-local-jar2.jar" will load your-local-jar.jar and your-local-jar2.jar.\n And when you want to ship maven artifacts and its transitive dependencies, you can pass them to --artifacts with comma-separated string.\n You can also exclude some dependencies like what you\'re doing in maven pom.\n Please add exclusion artifacts with \'^\' separated string after the artifact.\n For example, -artifacts "redis.clients:jedis:2.9.0,org.apache.kafka:kafka-clients:1.0.0^org.slf4j:slf4j-api" will load jedis and kafka-clients artifact and all of transitive dependencies but exclude slf4j-api from kafka.\n\n When you need to pull the artifacts from other than Maven Central, you can pass remote repositories to --artifactRepositories option with comma-separated string.\n Repository format is "<name>^<url>". \'^\' is taken as separator because URL allows various characters.\n For example, --artifactRepositories "jboss-repository^http://repository.jboss.com/maven2,HDPRepo^http://repo.hortonworks.com/content/groups/public/" will add JBoss and HDP repositories for dependency resolver.\n You can provide local maven repository directory via --mavenLocalRepositoryDirectory if you would like to use specific directory. It might help when you don\'t have \'.m2/repository\' directory in home directory, because CWD is sometimes non-deterministic (fragile).\n\n You can also provide proxy information to let dependency resolver utilizing proxy if needed. There\'re three parameters for proxy:\n --proxyUrl: URL representation of proxy (\'http://host:port\')\n --proxyUsername: username of proxy if it requires basic auth\n --proxyPassword: password of proxy if it requires basic auth\n\n Complete example of options is here: `./bin/storm jar example/storm-starter/storm-starter-topologies-*.jar org.apache.storm.starter.RollingTopWords blobstore-remote2 remote --jars "./external/storm-redis/storm-redis-1.1.0.jar,./external/storm-kafka-client/storm-kafka-client-1.1.0.jar" --artifacts "redis.clients:jedis:2.9.0,org.apache.kafka:kafka-clients:1.0.0^org.slf4j:slf4j-api" --artifactRepositories "jboss-repository^http://repository.jboss.com/maven2,HDPRepo^http://repo.hortonworks.com/content/groups/public/"`\n\n When you pass jars and/or artifacts options, StormSubmitter will upload them when the topology is submitted, and they will be included to classpath of both the process which runs the class, and also workers for that topology.\n\n If for some reason you need to have the full storm classpath, not just the one for the worker you may include the command line option `--storm-server-classpath`. Please be careful because this will add things to the classpath that will not be on the worker classpath and could result in the worker not running.\n '
[server_class_path, args] = parse_jar_opts(args)
run_client_jar(jarfile, klass, list(args), client=(not server_class_path), daemon=False) |
def sql(sql_file, topology_name):
'Syntax: [storm sql sql-file topology-name], or [storm sql sql-file --explain] when activating explain mode\n\n Compiles the SQL statements into a Trident topology and submits it to Storm.\n If user activates explain mode, SQL Runner analyzes each query statement and shows query plan instead of submitting topology.\n\n --jars and --artifacts, and --artifactRepositories, --mavenLocalRepositoryDirectory, --proxyUrl, --proxyUsername, --proxyPassword options available for jar are also applied to sql command.\n Please refer "help jar" to see how to use --jars and --artifacts, and --artifactRepositories, --proxyUrl, --proxyUsername, --proxyPassword options.\n You normally want to pass these options since you need to set data source to your sql which is an external storage in many cases.\n '
global DEP_JARS_OPTS, DEP_ARTIFACTS_OPTS, DEP_ARTIFACTS_REPOSITORIES_OPTS, DEP_MAVEN_LOCAL_REPOSITORY_DIRECTORY, DEP_PROXY_URL, DEP_PROXY_USERNAME, DEP_PROXY_PASSWORD
local_jars = DEP_JARS_OPTS
artifact_to_file_jars = resolve_dependencies(DEP_ARTIFACTS_OPTS, DEP_ARTIFACTS_REPOSITORIES_OPTS, DEP_MAVEN_LOCAL_REPOSITORY_DIRECTORY, DEP_PROXY_URL, DEP_PROXY_USERNAME, DEP_PROXY_PASSWORD)
sql_runtime_jars = get_jars_full(os.path.join(STORM_TOOLS_LIB_DIR, 'sql', 'runtime'))
local_jars.extend(sql_runtime_jars)
extrajars = [USER_CONF_DIR, STORM_BIN_DIR]
extrajars.extend(local_jars)
extrajars.extend(artifact_to_file_jars.values())
sql_core_jars = get_wildcard_dir(os.path.join(STORM_TOOLS_LIB_DIR, 'sql', 'core'))
extrajars.extend(sql_core_jars)
if (topology_name == '--explain'):
args = ['--file', sql_file, '--explain']
else:
args = ['--file', sql_file, '--topology', topology_name]
exec_storm_class('org.apache.storm.sql.StormSqlRunner', jvmtype='-client', extrajars=extrajars, args=args, daemon=False, jvmopts=([('-Dstorm.dependency.jars=' + ','.join(local_jars))] + [('-Dstorm.dependency.artifacts=' + json.dumps(artifact_to_file_jars))])) | 4,051,998,150,311,554,000 | Syntax: [storm sql sql-file topology-name], or [storm sql sql-file --explain] when activating explain mode
Compiles the SQL statements into a Trident topology and submits it to Storm.
If user activates explain mode, SQL Runner analyzes each query statement and shows query plan instead of submitting topology.
--jars and --artifacts, and --artifactRepositories, --mavenLocalRepositoryDirectory, --proxyUrl, --proxyUsername, --proxyPassword options available for jar are also applied to sql command.
Please refer "help jar" to see how to use --jars and --artifacts, and --artifactRepositories, --proxyUrl, --proxyUsername, --proxyPassword options.
You normally want to pass these options since you need to set data source to your sql which is an external storage in many cases. | bin/storm.py | sql | JamiesZhang/Storm | python | def sql(sql_file, topology_name):
'Syntax: [storm sql sql-file topology-name], or [storm sql sql-file --explain] when activating explain mode\n\n Compiles the SQL statements into a Trident topology and submits it to Storm.\n If user activates explain mode, SQL Runner analyzes each query statement and shows query plan instead of submitting topology.\n\n --jars and --artifacts, and --artifactRepositories, --mavenLocalRepositoryDirectory, --proxyUrl, --proxyUsername, --proxyPassword options available for jar are also applied to sql command.\n Please refer "help jar" to see how to use --jars and --artifacts, and --artifactRepositories, --proxyUrl, --proxyUsername, --proxyPassword options.\n You normally want to pass these options since you need to set data source to your sql which is an external storage in many cases.\n '
global DEP_JARS_OPTS, DEP_ARTIFACTS_OPTS, DEP_ARTIFACTS_REPOSITORIES_OPTS, DEP_MAVEN_LOCAL_REPOSITORY_DIRECTORY, DEP_PROXY_URL, DEP_PROXY_USERNAME, DEP_PROXY_PASSWORD
local_jars = DEP_JARS_OPTS
artifact_to_file_jars = resolve_dependencies(DEP_ARTIFACTS_OPTS, DEP_ARTIFACTS_REPOSITORIES_OPTS, DEP_MAVEN_LOCAL_REPOSITORY_DIRECTORY, DEP_PROXY_URL, DEP_PROXY_USERNAME, DEP_PROXY_PASSWORD)
sql_runtime_jars = get_jars_full(os.path.join(STORM_TOOLS_LIB_DIR, 'sql', 'runtime'))
local_jars.extend(sql_runtime_jars)
extrajars = [USER_CONF_DIR, STORM_BIN_DIR]
extrajars.extend(local_jars)
extrajars.extend(artifact_to_file_jars.values())
sql_core_jars = get_wildcard_dir(os.path.join(STORM_TOOLS_LIB_DIR, 'sql', 'core'))
extrajars.extend(sql_core_jars)
if (topology_name == '--explain'):
args = ['--file', sql_file, '--explain']
else:
args = ['--file', sql_file, '--topology', topology_name]
exec_storm_class('org.apache.storm.sql.StormSqlRunner', jvmtype='-client', extrajars=extrajars, args=args, daemon=False, jvmopts=([('-Dstorm.dependency.jars=' + ','.join(local_jars))] + [('-Dstorm.dependency.artifacts=' + json.dumps(artifact_to_file_jars))])) |
def kill(*args):
"Syntax: [storm kill topology-name [-w wait-time-secs]]\n\n Kills the topology with the name topology-name. Storm will\n first deactivate the topology's spouts for the duration of\n the topology's message timeout to allow all messages currently\n being processed to finish processing. Storm will then shutdown\n the workers and clean up their state. You can override the length\n of time Storm waits between deactivation and shutdown with the -w flag.\n "
if (not args):
print_usage(command='kill')
sys.exit(2)
exec_storm_class('org.apache.storm.command.KillTopology', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) | 7,946,931,335,110,760,000 | Syntax: [storm kill topology-name [-w wait-time-secs]]
Kills the topology with the name topology-name. Storm will
first deactivate the topology's spouts for the duration of
the topology's message timeout to allow all messages currently
being processed to finish processing. Storm will then shutdown
the workers and clean up their state. You can override the length
of time Storm waits between deactivation and shutdown with the -w flag. | bin/storm.py | kill | JamiesZhang/Storm | python | def kill(*args):
"Syntax: [storm kill topology-name [-w wait-time-secs]]\n\n Kills the topology with the name topology-name. Storm will\n first deactivate the topology's spouts for the duration of\n the topology's message timeout to allow all messages currently\n being processed to finish processing. Storm will then shutdown\n the workers and clean up their state. You can override the length\n of time Storm waits between deactivation and shutdown with the -w flag.\n "
if (not args):
print_usage(command='kill')
sys.exit(2)
exec_storm_class('org.apache.storm.command.KillTopology', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) |
def upload_credentials(*args):
'Syntax: [storm upload-credentials topology-name [credkey credvalue]*]\n\n Uploads a new set of credentials to a running topology\n '
if (not args):
print_usage(command='upload-credentials')
sys.exit(2)
exec_storm_class('org.apache.storm.command.UploadCredentials', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) | 3,213,165,739,736,285,000 | Syntax: [storm upload-credentials topology-name [credkey credvalue]*]
Uploads a new set of credentials to a running topology | bin/storm.py | upload_credentials | JamiesZhang/Storm | python | def upload_credentials(*args):
'Syntax: [storm upload-credentials topology-name [credkey credvalue]*]\n\n Uploads a new set of credentials to a running topology\n '
if (not args):
print_usage(command='upload-credentials')
sys.exit(2)
exec_storm_class('org.apache.storm.command.UploadCredentials', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) |
def blobstore(*args):
'Syntax: [storm blobstore cmd]\n\n list [KEY...] - lists blobs currently in the blob store\n cat [-f FILE] KEY - read a blob and then either write it to a file, or STDOUT (requires read access).\n create [-f FILE] [-a ACL ...] [--replication-factor NUMBER] KEY - create a new blob. Contents comes from a FILE\n or STDIN. ACL is in the form [uo]:[username]:[r-][w-][a-] can be comma separated list.\n update [-f FILE] KEY - update the contents of a blob. Contents comes from\n a FILE or STDIN (requires write access).\n delete KEY - delete an entry from the blob store (requires write access).\n set-acl [-s ACL] KEY - ACL is in the form [uo]:[username]:[r-][w-][a-] can be comma\n separated list (requires admin access).\n replication --read KEY - Used to read the replication factor of the blob.\n replication --update --replication-factor NUMBER KEY where NUMBER > 0. It is used to update the\n replication factor of a blob.\n For example, the following would create a mytopo:data.tgz key using the data\n stored in data.tgz. User alice would have full access, bob would have\n read/write access and everyone else would have read access.\n storm blobstore create mytopo:data.tgz -f data.tgz -a u:alice:rwa,u:bob:rw,o::r\n '
exec_storm_class('org.apache.storm.command.Blobstore', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) | -7,622,729,085,902,317,000 | Syntax: [storm blobstore cmd]
list [KEY...] - lists blobs currently in the blob store
cat [-f FILE] KEY - read a blob and then either write it to a file, or STDOUT (requires read access).
create [-f FILE] [-a ACL ...] [--replication-factor NUMBER] KEY - create a new blob. Contents comes from a FILE
or STDIN. ACL is in the form [uo]:[username]:[r-][w-][a-] can be comma separated list.
update [-f FILE] KEY - update the contents of a blob. Contents comes from
a FILE or STDIN (requires write access).
delete KEY - delete an entry from the blob store (requires write access).
set-acl [-s ACL] KEY - ACL is in the form [uo]:[username]:[r-][w-][a-] can be comma
separated list (requires admin access).
replication --read KEY - Used to read the replication factor of the blob.
replication --update --replication-factor NUMBER KEY where NUMBER > 0. It is used to update the
replication factor of a blob.
For example, the following would create a mytopo:data.tgz key using the data
stored in data.tgz. User alice would have full access, bob would have
read/write access and everyone else would have read access.
storm blobstore create mytopo:data.tgz -f data.tgz -a u:alice:rwa,u:bob:rw,o::r | bin/storm.py | blobstore | JamiesZhang/Storm | python | def blobstore(*args):
'Syntax: [storm blobstore cmd]\n\n list [KEY...] - lists blobs currently in the blob store\n cat [-f FILE] KEY - read a blob and then either write it to a file, or STDOUT (requires read access).\n create [-f FILE] [-a ACL ...] [--replication-factor NUMBER] KEY - create a new blob. Contents comes from a FILE\n or STDIN. ACL is in the form [uo]:[username]:[r-][w-][a-] can be comma separated list.\n update [-f FILE] KEY - update the contents of a blob. Contents comes from\n a FILE or STDIN (requires write access).\n delete KEY - delete an entry from the blob store (requires write access).\n set-acl [-s ACL] KEY - ACL is in the form [uo]:[username]:[r-][w-][a-] can be comma\n separated list (requires admin access).\n replication --read KEY - Used to read the replication factor of the blob.\n replication --update --replication-factor NUMBER KEY where NUMBER > 0. It is used to update the\n replication factor of a blob.\n For example, the following would create a mytopo:data.tgz key using the data\n stored in data.tgz. User alice would have full access, bob would have\n read/write access and everyone else would have read access.\n storm blobstore create mytopo:data.tgz -f data.tgz -a u:alice:rwa,u:bob:rw,o::r\n '
exec_storm_class('org.apache.storm.command.Blobstore', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) |
def heartbeats(*args):
'Syntax: [storm heartbeats [cmd]]\n\n list PATH - lists heartbeats nodes under PATH currently in the ClusterState.\n get PATH - Get the heartbeat data at PATH\n '
exec_storm_class('org.apache.storm.command.Heartbeats', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) | 5,319,599,684,336,833,000 | Syntax: [storm heartbeats [cmd]]
list PATH - lists heartbeats nodes under PATH currently in the ClusterState.
get PATH - Get the heartbeat data at PATH | bin/storm.py | heartbeats | JamiesZhang/Storm | python | def heartbeats(*args):
'Syntax: [storm heartbeats [cmd]]\n\n list PATH - lists heartbeats nodes under PATH currently in the ClusterState.\n get PATH - Get the heartbeat data at PATH\n '
exec_storm_class('org.apache.storm.command.Heartbeats', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) |
def activate(*args):
"Syntax: [storm activate topology-name]\n\n Activates the specified topology's spouts.\n "
if (not args):
print_usage(command='activate')
sys.exit(2)
exec_storm_class('org.apache.storm.command.Activate', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) | -5,705,921,422,986,034,000 | Syntax: [storm activate topology-name]
Activates the specified topology's spouts. | bin/storm.py | activate | JamiesZhang/Storm | python | def activate(*args):
"Syntax: [storm activate topology-name]\n\n Activates the specified topology's spouts.\n "
if (not args):
print_usage(command='activate')
sys.exit(2)
exec_storm_class('org.apache.storm.command.Activate', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) |
def set_log_level(*args):
"\n Dynamically change topology log levels\n\n Syntax: [storm set_log_level -l [logger name]=[log level][:optional timeout] -r [logger name] topology-name]\n where log level is one of:\n ALL, TRACE, DEBUG, INFO, WARN, ERROR, FATAL, OFF\n and timeout is integer seconds.\n\n e.g.\n ./bin/storm set_log_level -l ROOT=DEBUG:30 topology-name\n\n Set the root logger's level to DEBUG for 30 seconds\n\n ./bin/storm set_log_level -l com.myapp=WARN topology-name\n\n Set the com.myapp logger's level to WARN for 30 seconds\n\n ./bin/storm set_log_level -l com.myapp=WARN -l com.myOtherLogger=ERROR:123 topology-name\n\n Set the com.myapp logger's level to WARN indifinitely, and com.myOtherLogger\n to ERROR for 123 seconds\n\n ./bin/storm set_log_level -r com.myOtherLogger topology-name\n\n Clears settings, resetting back to the original level\n "
exec_storm_class('org.apache.storm.command.SetLogLevel', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) | 1,301,626,724,388,236,500 | Dynamically change topology log levels
Syntax: [storm set_log_level -l [logger name]=[log level][:optional timeout] -r [logger name] topology-name]
where log level is one of:
ALL, TRACE, DEBUG, INFO, WARN, ERROR, FATAL, OFF
and timeout is integer seconds.
e.g.
./bin/storm set_log_level -l ROOT=DEBUG:30 topology-name
Set the root logger's level to DEBUG for 30 seconds
./bin/storm set_log_level -l com.myapp=WARN topology-name
Set the com.myapp logger's level to WARN for 30 seconds
./bin/storm set_log_level -l com.myapp=WARN -l com.myOtherLogger=ERROR:123 topology-name
Set the com.myapp logger's level to WARN indifinitely, and com.myOtherLogger
to ERROR for 123 seconds
./bin/storm set_log_level -r com.myOtherLogger topology-name
Clears settings, resetting back to the original level | bin/storm.py | set_log_level | JamiesZhang/Storm | python | def set_log_level(*args):
"\n Dynamically change topology log levels\n\n Syntax: [storm set_log_level -l [logger name]=[log level][:optional timeout] -r [logger name] topology-name]\n where log level is one of:\n ALL, TRACE, DEBUG, INFO, WARN, ERROR, FATAL, OFF\n and timeout is integer seconds.\n\n e.g.\n ./bin/storm set_log_level -l ROOT=DEBUG:30 topology-name\n\n Set the root logger's level to DEBUG for 30 seconds\n\n ./bin/storm set_log_level -l com.myapp=WARN topology-name\n\n Set the com.myapp logger's level to WARN for 30 seconds\n\n ./bin/storm set_log_level -l com.myapp=WARN -l com.myOtherLogger=ERROR:123 topology-name\n\n Set the com.myapp logger's level to WARN indifinitely, and com.myOtherLogger\n to ERROR for 123 seconds\n\n ./bin/storm set_log_level -r com.myOtherLogger topology-name\n\n Clears settings, resetting back to the original level\n "
exec_storm_class('org.apache.storm.command.SetLogLevel', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) |
def listtopos(*args):
'Syntax: [storm list]\n\n List the running topologies and their statuses.\n '
exec_storm_class('org.apache.storm.command.ListTopologies', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) | 715,366,473,802,686,500 | Syntax: [storm list]
List the running topologies and their statuses. | bin/storm.py | listtopos | JamiesZhang/Storm | python | def listtopos(*args):
'Syntax: [storm list]\n\n List the running topologies and their statuses.\n '
exec_storm_class('org.apache.storm.command.ListTopologies', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) |
def deactivate(*args):
"Syntax: [storm deactivate topology-name]\n\n Deactivates the specified topology's spouts.\n "
if (not args):
print_usage(command='deactivate')
sys.exit(2)
exec_storm_class('org.apache.storm.command.Deactivate', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) | 7,762,466,626,678,783,000 | Syntax: [storm deactivate topology-name]
Deactivates the specified topology's spouts. | bin/storm.py | deactivate | JamiesZhang/Storm | python | def deactivate(*args):
"Syntax: [storm deactivate topology-name]\n\n Deactivates the specified topology's spouts.\n "
if (not args):
print_usage(command='deactivate')
sys.exit(2)
exec_storm_class('org.apache.storm.command.Deactivate', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) |
def rebalance(*args):
'Syntax: [storm rebalance topology-name [-w wait-time-secs] [-n new-num-workers] [-e component=parallelism]* [-r \'{"component1": {"resource1": new_amount, "resource2": new_amount, ... }*}\'] [-t \'{"conf1": newValue, *}\']]\n\n Sometimes you may wish to spread out the workers for a running topology.\n For example, let\'s say you have a 10 node cluster running\n 4 workers per node, and then let\'s say you add another 10 nodes to\n the cluster. You may wish to have Storm spread out the workers for the\n running topology so that each node runs 2 workers. One way to do this\n is to kill the topology and resubmit it, but Storm provides a "rebalance"\n command that provides an easier way to do this.\n\n Rebalance will first deactivate the topology for the duration of the\n message timeout (overridable with the -w flag) make requested adjustments to the topology\n and let the scheduler try to find a better scheduling based off of the\n new situation. The topology will then return to its previous state of activation\n (so a deactivated topology will still be deactivated and an activated\n topology will go back to being activated).\n\n Some of what you can change about a topology includes the number of requested workers (-n flag)\n The number of executors for a given component (-e flag) the resources each component is\n requesting as used by the resource aware scheduler (-r flag) and configs (-t flag).\n '
if (not args):
print_usage(command='rebalance')
sys.exit(2)
exec_storm_class('org.apache.storm.command.Rebalance', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) | -8,924,590,559,328,843,000 | Syntax: [storm rebalance topology-name [-w wait-time-secs] [-n new-num-workers] [-e component=parallelism]* [-r '{"component1": {"resource1": new_amount, "resource2": new_amount, ... }*}'] [-t '{"conf1": newValue, *}']]
Sometimes you may wish to spread out the workers for a running topology.
For example, let's say you have a 10 node cluster running
4 workers per node, and then let's say you add another 10 nodes to
the cluster. You may wish to have Storm spread out the workers for the
running topology so that each node runs 2 workers. One way to do this
is to kill the topology and resubmit it, but Storm provides a "rebalance"
command that provides an easier way to do this.
Rebalance will first deactivate the topology for the duration of the
message timeout (overridable with the -w flag) make requested adjustments to the topology
and let the scheduler try to find a better scheduling based off of the
new situation. The topology will then return to its previous state of activation
(so a deactivated topology will still be deactivated and an activated
topology will go back to being activated).
Some of what you can change about a topology includes the number of requested workers (-n flag)
The number of executors for a given component (-e flag) the resources each component is
requesting as used by the resource aware scheduler (-r flag) and configs (-t flag). | bin/storm.py | rebalance | JamiesZhang/Storm | python | def rebalance(*args):
'Syntax: [storm rebalance topology-name [-w wait-time-secs] [-n new-num-workers] [-e component=parallelism]* [-r \'{"component1": {"resource1": new_amount, "resource2": new_amount, ... }*}\'] [-t \'{"conf1": newValue, *}\']]\n\n Sometimes you may wish to spread out the workers for a running topology.\n For example, let\'s say you have a 10 node cluster running\n 4 workers per node, and then let\'s say you add another 10 nodes to\n the cluster. You may wish to have Storm spread out the workers for the\n running topology so that each node runs 2 workers. One way to do this\n is to kill the topology and resubmit it, but Storm provides a "rebalance"\n command that provides an easier way to do this.\n\n Rebalance will first deactivate the topology for the duration of the\n message timeout (overridable with the -w flag) make requested adjustments to the topology\n and let the scheduler try to find a better scheduling based off of the\n new situation. The topology will then return to its previous state of activation\n (so a deactivated topology will still be deactivated and an activated\n topology will go back to being activated).\n\n Some of what you can change about a topology includes the number of requested workers (-n flag)\n The number of executors for a given component (-e flag) the resources each component is\n requesting as used by the resource aware scheduler (-r flag) and configs (-t flag).\n '
if (not args):
print_usage(command='rebalance')
sys.exit(2)
exec_storm_class('org.apache.storm.command.Rebalance', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, STORM_BIN_DIR]) |
def get_errors(*args):
'Syntax: [storm get-errors topology-name]\n\n Get the latest error from the running topology. The returned result contains\n the key value pairs for component-name and component-error for the components in error.\n The result is returned in json format.\n '
if (not args):
print_usage(command='get-errors')
sys.exit(2)
exec_storm_class('org.apache.storm.command.GetErrors', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, os.path.join(STORM_DIR, 'bin')]) | -7,079,342,827,459,128,000 | Syntax: [storm get-errors topology-name]
Get the latest error from the running topology. The returned result contains
the key value pairs for component-name and component-error for the components in error.
The result is returned in json format. | bin/storm.py | get_errors | JamiesZhang/Storm | python | def get_errors(*args):
'Syntax: [storm get-errors topology-name]\n\n Get the latest error from the running topology. The returned result contains\n the key value pairs for component-name and component-error for the components in error.\n The result is returned in json format.\n '
if (not args):
print_usage(command='get-errors')
sys.exit(2)
exec_storm_class('org.apache.storm.command.GetErrors', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, os.path.join(STORM_DIR, 'bin')]) |
def healthcheck(*args):
'Syntax: [storm node-health-check]\n\n Run health checks on the local supervisor.\n '
exec_storm_class('org.apache.storm.command.HealthCheck', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, os.path.join(STORM_DIR, 'bin')]) | -5,369,723,348,416,531,000 | Syntax: [storm node-health-check]
Run health checks on the local supervisor. | bin/storm.py | healthcheck | JamiesZhang/Storm | python | def healthcheck(*args):
'Syntax: [storm node-health-check]\n\n Run health checks on the local supervisor.\n '
exec_storm_class('org.apache.storm.command.HealthCheck', args=args, jvmtype='-client', extrajars=[USER_CONF_DIR, os.path.join(STORM_DIR, 'bin')]) |
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