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How to use describe_limits method in localstack
Best Python code snippet using localstack_python
scan_generator.py
Source:scan_generator.py 
...16 def points_for_level(self, level: int, rng=None) -> List[Any]:17 """18 """19 raise NotImplementedError20 def describe_limits(self, target: Dict[str, Any]) -> None:21 """22 """23 raise NotImplementedError24class RefiningGenerator(ScanGenerator):25 """Generates progressively finer grid by halving distance between points each level.26 """27 def __init__(self, lower, upper, randomise_order):28 self.lower = float(min(lower, upper))29 self.upper = float(max(lower, upper))30 self.randomise_order = randomise_order31 def has_level(self, level: int) -> bool:32 ""33 # For floating-point parameters, a refining scan, in practical terms, never runs34 # out of points. Will need to be amended for integer parameters.35 return True36 def points_for_level(self, level: int, rng=None) -> List[Any]:37 ""38 if level == 0:39 return [self.lower, self.upper]40 d = self.upper - self.lower41 num = 2**(level - 1)42 points = np.arange(num) * d / num + d / (2 * num) + self.lower43 if self.randomise_order:44 rng.shuffle(points)45 return points46 def describe_limits(self, target: Dict[str, Any]) -> None:47 ""48 target["min"] = self.lower49 target["max"] = self.upper50class ExpandingGenerator(ScanGenerator):51 """Generates points with given, constant spacing in progressively growing range52 around a given centre.53 """54 def __init__(self,55 centre,56 spacing,57 randomise_order: bool,58 limit_lower=None,59 limit_upper=None):60 """61 :param limit_lower: Optional lower limit (inclusive) to the range of generated62 points. Useful for representing scans on parameters the range of which is63 limited (e.g. to be non-negative).64 :param limit_upper: See `limit_lower`.65 """66 self.centre = centre67 self.spacing = abs(spacing)68 self.randomise_order = randomise_order69 self.limit_lower = limit_lower if limit_lower is not None else float("-inf")70 if centre < self.limit_lower:71 raise ValueError("Given scan centre exceeds lower limit")72 self.limit_upper = limit_upper if limit_upper is not None else float("inf")73 if centre > self.limit_upper:74 raise ValueError("Given scan centre exceeds upper limit")75 def has_level(self, level: int) -> bool:76 ""77 def num_points(limit):78 return np.floor(abs(self.centre - limit) / self.spacing)79 return level <= max(num_points(self.limit_lower), num_points(self.limit_upper))80 def points_for_level(self, level: int, rng=None) -> List[Any]:81 ""82 if level == 0:83 return [self.centre]84 points = []85 lower = self.centre - level * self.spacing86 if lower >= self.limit_lower:87 points.append(lower)88 upper = self.centre + level * self.spacing89 if upper <= self.limit_upper:90 points.append(upper)91 if self.randomise_order:92 rng.shuffle(points)93 return points94 def describe_limits(self, target: Dict[str, Any]) -> None:95 ""96 if self.limit_lower > float("-inf"):97 target["min"] = self.limit_lower98 if self.limit_upper < float("inf"):99 target["max"] = self.limit_upper100 target["increment"] = self.spacing101class LinearGenerator(ScanGenerator):102 """Generates equally spaced points between two endpoints."""103 def __init__(self, start, stop, num_points, randomise_order):104 if num_points < 2:105 raise ValueError("Need at least 2 points in linear scan")106 self.start = start107 self.stop = stop108 self.num_points = num_points109 self.randomise_order = randomise_order110 def has_level(self, level: int) -> bool:111 ""112 return level == 0113 def points_for_level(self, level: int, rng=None) -> List[Any]:114 ""115 assert level == 0116 points = np.linspace(start=self.start,117 stop=self.stop,118 num=self.num_points,119 endpoint=True)120 if self.randomise_order:121 rng.shuffle(points)122 return points.tolist()123 def describe_limits(self, target: Dict[str, Any]) -> None:124 ""125 target["min"] = min(self.start, self.stop)126 target["max"] = max(self.start, self.stop)127 target["increment"] = abs(self.stop - self.start) / (self.num_points - 1)128class CentreSpanGenerator(LinearGenerator):129 """Generates equally spaced points in ``centre``±``half_span``."""130 def __init__(self,131 centre,132 half_span,133 num_points: int,134 randomise_order: bool,135 limit_lower=None,136 limit_upper=None):137 """138 :param limit_lower: Optional lower limit (inclusive) to the range of generated139 points. Useful for representing scans on parameters the range of which is140 limited (e.g. to be non-negative).141 :param limit_upper: See `limit_lower`.142 """143 if num_points < 1:144 raise ValueError("Need at least one point in centre/span scan")145 self.num_points = num_points146 self.randomise_order = randomise_order147 self.start = centre - half_span148 if limit_lower is not None:149 self.start = max(self.start, limit_lower)150 self.stop = centre + half_span151 if limit_upper is not None:152 self.stop = min(self.stop, limit_upper)153 if self.start > self.stop:154 raise ValueError("Empty centre/span scan (lower limit larger than upper)")155 if num_points == 1:156 self.start = self.stop = centre157class ListGenerator(ScanGenerator):158 """Generates points by reading from an explicitly specified list."""159 def __init__(self, values, randomise_order):160 self.values = values161 self.randomise_order = randomise_order162 def has_level(self, level: int) -> bool:163 ""164 return level == 0165 def points_for_level(self, level: int, rng=None) -> List[Any]:166 ""167 assert level == 0168 values = self.values169 if self.randomise_order:170 values = np.array(self.values)171 rng.shuffle(values)172 return values173 def describe_limits(self, target: Dict[str, Any]) -> None:174 ""175 values = np.array(self.values)176 if np.issubdtype(values.dtype, np.number):177 target["min"] = np.min(values)178 target["max"] = np.max(values)179GENERATORS = {180 "refining": RefiningGenerator,181 "expanding": ExpandingGenerator,182 "linear": LinearGenerator,183 "centre_span": CentreSpanGenerator,184 "list": ListGenerator185}186class ScanOptions:187 """...
dynamo_limit_handler.py
Source:dynamo_limit_handler.py
1class DynamoLimitHandler:2 def __init__(self, attribute):3 self.attribute = attribute4 def handle(self, client, stream_name):5 response = client.describe_limits()6 return attributes.get(self.attribute)7 def handle_response(self, name, value):...
db.describe_limits.py
Source:db.describe_limits.py
1#!/usr/bin/env python2import boto33ddb = boto3.client('dynamodb')...
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