two version of R2R are hereHEADmaster

1 files changed, 277 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/point.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/point.py
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+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/point.py
@@ -0,0 +1,277 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# pylint: disable=unused-import
+
+from dataclasses import asdict, dataclass, field
+from json import dumps, loads
+from typing import Optional, Sequence, Union
+
+# This kind of import is needed to avoid Sphinx errors.
+import opentelemetry.sdk.metrics._internal
+from opentelemetry.sdk.metrics._internal.exemplar import Exemplar
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.sdk.util.instrumentation import InstrumentationScope
+from opentelemetry.util.types import Attributes
+
+
+@dataclass(frozen=True)
+class NumberDataPoint:
+    """Single data point in a timeseries that describes the time-varying scalar
+    value of a metric.
+    """
+
+    attributes: Attributes
+    start_time_unix_nano: int
+    time_unix_nano: int
+    value: Union[int, float]
+    exemplars: Sequence[Exemplar] = field(default_factory=list)
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(asdict(self), indent=indent)
+
+
+@dataclass(frozen=True)
+class HistogramDataPoint:
+    """Single data point in a timeseries that describes the time-varying scalar
+    value of a metric.
+    """
+
+    attributes: Attributes
+    start_time_unix_nano: int
+    time_unix_nano: int
+    count: int
+    sum: Union[int, float]
+    bucket_counts: Sequence[int]
+    explicit_bounds: Sequence[float]
+    min: float
+    max: float
+    exemplars: Sequence[Exemplar] = field(default_factory=list)
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(asdict(self), indent=indent)
+
+
+@dataclass(frozen=True)
+class Buckets:
+    offset: int
+    bucket_counts: Sequence[int]
+
+
+@dataclass(frozen=True)
+class ExponentialHistogramDataPoint:
+    """Single data point in a timeseries whose boundaries are defined by an
+    exponential function. This timeseries describes the time-varying scalar
+    value of a metric.
+    """
+
+    attributes: Attributes
+    start_time_unix_nano: int
+    time_unix_nano: int
+    count: int
+    sum: Union[int, float]
+    scale: int
+    zero_count: int
+    positive: Buckets
+    negative: Buckets
+    flags: int
+    min: float
+    max: float
+    exemplars: Sequence[Exemplar] = field(default_factory=list)
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(asdict(self), indent=indent)
+
+
+@dataclass(frozen=True)
+class ExponentialHistogram:
+    """Represents the type of a metric that is calculated by aggregating as an
+    ExponentialHistogram of all reported measurements over a time interval.
+    """
+
+    data_points: Sequence[ExponentialHistogramDataPoint]
+    aggregation_temporality: (
+        "opentelemetry.sdk.metrics.export.AggregationTemporality"
+    )
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "data_points": [
+                    loads(data_point.to_json(indent=indent))
+                    for data_point in self.data_points
+                ],
+                "aggregation_temporality": self.aggregation_temporality,
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class Sum:
+    """Represents the type of a scalar metric that is calculated as a sum of
+    all reported measurements over a time interval."""
+
+    data_points: Sequence[NumberDataPoint]
+    aggregation_temporality: (
+        "opentelemetry.sdk.metrics.export.AggregationTemporality"
+    )
+    is_monotonic: bool
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "data_points": [
+                    loads(data_point.to_json(indent=indent))
+                    for data_point in self.data_points
+                ],
+                "aggregation_temporality": self.aggregation_temporality,
+                "is_monotonic": self.is_monotonic,
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class Gauge:
+    """Represents the type of a scalar metric that always exports the current
+    value for every data point. It should be used for an unknown
+    aggregation."""
+
+    data_points: Sequence[NumberDataPoint]
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "data_points": [
+                    loads(data_point.to_json(indent=indent))
+                    for data_point in self.data_points
+                ],
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class Histogram:
+    """Represents the type of a metric that is calculated by aggregating as a
+    histogram of all reported measurements over a time interval."""
+
+    data_points: Sequence[HistogramDataPoint]
+    aggregation_temporality: (
+        "opentelemetry.sdk.metrics.export.AggregationTemporality"
+    )
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "data_points": [
+                    loads(data_point.to_json(indent=indent))
+                    for data_point in self.data_points
+                ],
+                "aggregation_temporality": self.aggregation_temporality,
+            },
+            indent=indent,
+        )
+
+
+# pylint: disable=invalid-name
+DataT = Union[Sum, Gauge, Histogram, ExponentialHistogram]
+DataPointT = Union[
+    NumberDataPoint, HistogramDataPoint, ExponentialHistogramDataPoint
+]
+
+
+@dataclass(frozen=True)
+class Metric:
+    """Represents a metric point in the OpenTelemetry data model to be
+    exported."""
+
+    name: str
+    description: Optional[str]
+    unit: Optional[str]
+    data: DataT
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "name": self.name,
+                "description": self.description or "",
+                "unit": self.unit or "",
+                "data": loads(self.data.to_json(indent=indent)),
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class ScopeMetrics:
+    """A collection of Metrics produced by a scope"""
+
+    scope: InstrumentationScope
+    metrics: Sequence[Metric]
+    schema_url: str
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "scope": loads(self.scope.to_json(indent=indent)),
+                "metrics": [
+                    loads(metric.to_json(indent=indent))
+                    for metric in self.metrics
+                ],
+                "schema_url": self.schema_url,
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class ResourceMetrics:
+    """A collection of ScopeMetrics from a Resource"""
+
+    resource: Resource
+    scope_metrics: Sequence[ScopeMetrics]
+    schema_url: str
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "resource": loads(self.resource.to_json(indent=indent)),
+                "scope_metrics": [
+                    loads(scope_metrics.to_json(indent=indent))
+                    for scope_metrics in self.scope_metrics
+                ],
+                "schema_url": self.schema_url,
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class MetricsData:
+    """An array of ResourceMetrics"""
+
+    resource_metrics: Sequence[ResourceMetrics]
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "resource_metrics": [
+                    loads(resource_metrics.to_json(indent=indent))
+                    for resource_metrics in self.resource_metrics
+                ]
+            },
+            indent=indent,
+        )