wallaroo.wallaroo_ml_ops_api_client.models.assays_get_assay_results_response_200_item_window_summary
1import datetime 2from typing import Any, Dict, List, Type, TypeVar, Union, cast 3 4import attr 5from dateutil.parser import isoparse 6 7from ..models.assays_get_assay_results_response_200_item_window_summary_aggregation import \ 8 AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation 9from ..types import UNSET, Unset 10 11T = TypeVar("T", bound="AssaysGetAssayResultsResponse200ItemWindowSummary") 12 13@attr.s(auto_attribs=True) 14class AssaysGetAssayResultsResponse200ItemWindowSummary: 15 """ Result from summarizing one sample collection. 16 17 Attributes: 18 count (int): 19 min_ (float): 20 max_ (float): 21 mean (float): 22 median (float): 23 std (float): Standard deviation. 24 edges (List[float]): 25 edge_names (List[str]): 26 aggregated_values (List[float]): 27 aggregation (AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation): 28 start (Union[Unset, None, datetime.datetime]): 29 end (Union[Unset, None, datetime.datetime]): 30 """ 31 32 count: int 33 min_: float 34 max_: float 35 mean: float 36 median: float 37 std: float 38 edges: List[float] 39 edge_names: List[str] 40 aggregated_values: List[float] 41 aggregation: AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation 42 start: Union[Unset, None, datetime.datetime] = UNSET 43 end: Union[Unset, None, datetime.datetime] = UNSET 44 additional_properties: Dict[str, Any] = attr.ib(init=False, factory=dict) 45 46 47 def to_dict(self) -> Dict[str, Any]: 48 count = self.count 49 min_ = self.min_ 50 max_ = self.max_ 51 mean = self.mean 52 median = self.median 53 std = self.std 54 edges = self.edges 55 56 57 58 59 edge_names = self.edge_names 60 61 62 63 64 aggregated_values = self.aggregated_values 65 66 67 68 69 aggregation = self.aggregation.value 70 71 start: Union[Unset, None, str] = UNSET 72 if not isinstance(self.start, Unset): 73 start = self.start.isoformat() if self.start else None 74 75 end: Union[Unset, None, str] = UNSET 76 if not isinstance(self.end, Unset): 77 end = self.end.isoformat() if self.end else None 78 79 80 field_dict: Dict[str, Any] = {} 81 field_dict.update(self.additional_properties) 82 field_dict.update({ 83 "count": count, 84 "min": min_, 85 "max": max_, 86 "mean": mean, 87 "median": median, 88 "std": std, 89 "edges": edges, 90 "edge_names": edge_names, 91 "aggregated_values": aggregated_values, 92 "aggregation": aggregation, 93 }) 94 if start is not UNSET: 95 field_dict["start"] = start 96 if end is not UNSET: 97 field_dict["end"] = end 98 99 return field_dict 100 101 102 103 @classmethod 104 def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T: 105 d = src_dict.copy() 106 count = d.pop("count") 107 108 min_ = d.pop("min") 109 110 max_ = d.pop("max") 111 112 mean = d.pop("mean") 113 114 median = d.pop("median") 115 116 std = d.pop("std") 117 118 edges = cast(List[float], d.pop("edges")) 119 120 121 edge_names = cast(List[str], d.pop("edge_names")) 122 123 124 aggregated_values = cast(List[float], d.pop("aggregated_values")) 125 126 127 aggregation = AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation(d.pop("aggregation")) 128 129 130 131 132 _start = d.pop("start", UNSET) 133 start: Union[Unset, None, datetime.datetime] 134 if _start is None: 135 start = None 136 elif isinstance(_start, Unset): 137 start = UNSET 138 else: 139 start = isoparse(_start) 140 141 142 143 144 _end = d.pop("end", UNSET) 145 end: Union[Unset, None, datetime.datetime] 146 if _end is None: 147 end = None 148 elif isinstance(_end, Unset): 149 end = UNSET 150 else: 151 end = isoparse(_end) 152 153 154 155 156 assays_get_assay_results_response_200_item_window_summary = cls( 157 count=count, 158 min_=min_, 159 max_=max_, 160 mean=mean, 161 median=median, 162 std=std, 163 edges=edges, 164 edge_names=edge_names, 165 aggregated_values=aggregated_values, 166 aggregation=aggregation, 167 start=start, 168 end=end, 169 ) 170 171 assays_get_assay_results_response_200_item_window_summary.additional_properties = d 172 return assays_get_assay_results_response_200_item_window_summary 173 174 @property 175 def additional_keys(self) -> List[str]: 176 return list(self.additional_properties.keys()) 177 178 def __getitem__(self, key: str) -> Any: 179 return self.additional_properties[key] 180 181 def __setitem__(self, key: str, value: Any) -> None: 182 self.additional_properties[key] = value 183 184 def __delitem__(self, key: str) -> None: 185 del self.additional_properties[key] 186 187 def __contains__(self, key: str) -> bool: 188 return key in self.additional_properties
@attr.s(auto_attribs=True)
class
AssaysGetAssayResultsResponse200ItemWindowSummary:
14@attr.s(auto_attribs=True) 15class AssaysGetAssayResultsResponse200ItemWindowSummary: 16 """ Result from summarizing one sample collection. 17 18 Attributes: 19 count (int): 20 min_ (float): 21 max_ (float): 22 mean (float): 23 median (float): 24 std (float): Standard deviation. 25 edges (List[float]): 26 edge_names (List[str]): 27 aggregated_values (List[float]): 28 aggregation (AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation): 29 start (Union[Unset, None, datetime.datetime]): 30 end (Union[Unset, None, datetime.datetime]): 31 """ 32 33 count: int 34 min_: float 35 max_: float 36 mean: float 37 median: float 38 std: float 39 edges: List[float] 40 edge_names: List[str] 41 aggregated_values: List[float] 42 aggregation: AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation 43 start: Union[Unset, None, datetime.datetime] = UNSET 44 end: Union[Unset, None, datetime.datetime] = UNSET 45 additional_properties: Dict[str, Any] = attr.ib(init=False, factory=dict) 46 47 48 def to_dict(self) -> Dict[str, Any]: 49 count = self.count 50 min_ = self.min_ 51 max_ = self.max_ 52 mean = self.mean 53 median = self.median 54 std = self.std 55 edges = self.edges 56 57 58 59 60 edge_names = self.edge_names 61 62 63 64 65 aggregated_values = self.aggregated_values 66 67 68 69 70 aggregation = self.aggregation.value 71 72 start: Union[Unset, None, str] = UNSET 73 if not isinstance(self.start, Unset): 74 start = self.start.isoformat() if self.start else None 75 76 end: Union[Unset, None, str] = UNSET 77 if not isinstance(self.end, Unset): 78 end = self.end.isoformat() if self.end else None 79 80 81 field_dict: Dict[str, Any] = {} 82 field_dict.update(self.additional_properties) 83 field_dict.update({ 84 "count": count, 85 "min": min_, 86 "max": max_, 87 "mean": mean, 88 "median": median, 89 "std": std, 90 "edges": edges, 91 "edge_names": edge_names, 92 "aggregated_values": aggregated_values, 93 "aggregation": aggregation, 94 }) 95 if start is not UNSET: 96 field_dict["start"] = start 97 if end is not UNSET: 98 field_dict["end"] = end 99 100 return field_dict 101 102 103 104 @classmethod 105 def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T: 106 d = src_dict.copy() 107 count = d.pop("count") 108 109 min_ = d.pop("min") 110 111 max_ = d.pop("max") 112 113 mean = d.pop("mean") 114 115 median = d.pop("median") 116 117 std = d.pop("std") 118 119 edges = cast(List[float], d.pop("edges")) 120 121 122 edge_names = cast(List[str], d.pop("edge_names")) 123 124 125 aggregated_values = cast(List[float], d.pop("aggregated_values")) 126 127 128 aggregation = AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation(d.pop("aggregation")) 129 130 131 132 133 _start = d.pop("start", UNSET) 134 start: Union[Unset, None, datetime.datetime] 135 if _start is None: 136 start = None 137 elif isinstance(_start, Unset): 138 start = UNSET 139 else: 140 start = isoparse(_start) 141 142 143 144 145 _end = d.pop("end", UNSET) 146 end: Union[Unset, None, datetime.datetime] 147 if _end is None: 148 end = None 149 elif isinstance(_end, Unset): 150 end = UNSET 151 else: 152 end = isoparse(_end) 153 154 155 156 157 assays_get_assay_results_response_200_item_window_summary = cls( 158 count=count, 159 min_=min_, 160 max_=max_, 161 mean=mean, 162 median=median, 163 std=std, 164 edges=edges, 165 edge_names=edge_names, 166 aggregated_values=aggregated_values, 167 aggregation=aggregation, 168 start=start, 169 end=end, 170 ) 171 172 assays_get_assay_results_response_200_item_window_summary.additional_properties = d 173 return assays_get_assay_results_response_200_item_window_summary 174 175 @property 176 def additional_keys(self) -> List[str]: 177 return list(self.additional_properties.keys()) 178 179 def __getitem__(self, key: str) -> Any: 180 return self.additional_properties[key] 181 182 def __setitem__(self, key: str, value: Any) -> None: 183 self.additional_properties[key] = value 184 185 def __delitem__(self, key: str) -> None: 186 del self.additional_properties[key] 187 188 def __contains__(self, key: str) -> bool: 189 return key in self.additional_properties
Result from summarizing one sample collection.
Attributes: count (int): min_ (float): max_ (float): mean (float): median (float): std (float): Standard deviation. edges (List[float]): edge_names (List[str]): aggregated_values (List[float]): aggregation (AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation): start (Union[Unset, None, datetime.datetime]): end (Union[Unset, None, datetime.datetime]):
AssaysGetAssayResultsResponse200ItemWindowSummary( count: int, min_: float, max_: float, mean: float, median: float, std: float, edges: List[float], edge_names: List[str], aggregated_values: List[float], aggregation: wallaroo.wallaroo_ml_ops_api_client.models.assays_get_assay_results_response_200_item_window_summary_aggregation.AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation, start: Union[wallaroo.wallaroo_ml_ops_api_client.types.Unset, NoneType, datetime.datetime] = <wallaroo.wallaroo_ml_ops_api_client.types.Unset object>, end: Union[wallaroo.wallaroo_ml_ops_api_client.types.Unset, NoneType, datetime.datetime] = <wallaroo.wallaroo_ml_ops_api_client.types.Unset object>)
2def __init__(self, count, min_, max_, mean, median, std, edges, edge_names, aggregated_values, aggregation, start=attr_dict['start'].default, end=attr_dict['end'].default): 3 self.count = count 4 self.min_ = min_ 5 self.max_ = max_ 6 self.mean = mean 7 self.median = median 8 self.std = std 9 self.edges = edges 10 self.edge_names = edge_names 11 self.aggregated_values = aggregated_values 12 self.aggregation = aggregation 13 self.start = start 14 self.end = end 15 self.additional_properties = __attr_factory_additional_properties()
Method generated by attrs for class AssaysGetAssayResultsResponse200ItemWindowSummary.
def
to_dict(self) -> Dict[str, Any]:
48 def to_dict(self) -> Dict[str, Any]: 49 count = self.count 50 min_ = self.min_ 51 max_ = self.max_ 52 mean = self.mean 53 median = self.median 54 std = self.std 55 edges = self.edges 56 57 58 59 60 edge_names = self.edge_names 61 62 63 64 65 aggregated_values = self.aggregated_values 66 67 68 69 70 aggregation = self.aggregation.value 71 72 start: Union[Unset, None, str] = UNSET 73 if not isinstance(self.start, Unset): 74 start = self.start.isoformat() if self.start else None 75 76 end: Union[Unset, None, str] = UNSET 77 if not isinstance(self.end, Unset): 78 end = self.end.isoformat() if self.end else None 79 80 81 field_dict: Dict[str, Any] = {} 82 field_dict.update(self.additional_properties) 83 field_dict.update({ 84 "count": count, 85 "min": min_, 86 "max": max_, 87 "mean": mean, 88 "median": median, 89 "std": std, 90 "edges": edges, 91 "edge_names": edge_names, 92 "aggregated_values": aggregated_values, 93 "aggregation": aggregation, 94 }) 95 if start is not UNSET: 96 field_dict["start"] = start 97 if end is not UNSET: 98 field_dict["end"] = end 99 100 return field_dict
@classmethod
def
from_dict(cls: Type[~T], src_dict: Dict[str, Any]) -> ~T:
104 @classmethod 105 def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T: 106 d = src_dict.copy() 107 count = d.pop("count") 108 109 min_ = d.pop("min") 110 111 max_ = d.pop("max") 112 113 mean = d.pop("mean") 114 115 median = d.pop("median") 116 117 std = d.pop("std") 118 119 edges = cast(List[float], d.pop("edges")) 120 121 122 edge_names = cast(List[str], d.pop("edge_names")) 123 124 125 aggregated_values = cast(List[float], d.pop("aggregated_values")) 126 127 128 aggregation = AssaysGetAssayResultsResponse200ItemWindowSummaryAggregation(d.pop("aggregation")) 129 130 131 132 133 _start = d.pop("start", UNSET) 134 start: Union[Unset, None, datetime.datetime] 135 if _start is None: 136 start = None 137 elif isinstance(_start, Unset): 138 start = UNSET 139 else: 140 start = isoparse(_start) 141 142 143 144 145 _end = d.pop("end", UNSET) 146 end: Union[Unset, None, datetime.datetime] 147 if _end is None: 148 end = None 149 elif isinstance(_end, Unset): 150 end = UNSET 151 else: 152 end = isoparse(_end) 153 154 155 156 157 assays_get_assay_results_response_200_item_window_summary = cls( 158 count=count, 159 min_=min_, 160 max_=max_, 161 mean=mean, 162 median=median, 163 std=std, 164 edges=edges, 165 edge_names=edge_names, 166 aggregated_values=aggregated_values, 167 aggregation=aggregation, 168 start=start, 169 end=end, 170 ) 171 172 assays_get_assay_results_response_200_item_window_summary.additional_properties = d 173 return assays_get_assay_results_response_200_item_window_summary