Get Origin Post-Quantum Data Over Time

radar.post_quantum.origin.timeseries_groups(, ) -> OriginTimeseriesGroupsResponse

GET/radar/post_quantum/origin/timeseries_groups/{dimension}

Returns a timeseries of origin post-quantum data grouped by the specified dimension.

Security

API Token

The preferred authorization scheme for interacting with the Cloudflare API. Create a token.

Example:Authorization: Bearer Sn3lZJTBX6kkg7OdcBUAxOO963GEIyGQqnFTOFYY

API Email + API Key

The previous authorization scheme for interacting with the Cloudflare API, used in conjunction with a Global API key.

Example:X-Auth-Email: user@example.com

The previous authorization scheme for interacting with the Cloudflare API. When possible, use API tokens instead of Global API keys.

Example:X-Auth-Key: 144c9defac04969c7bfad8efaa8ea194

Accepted Permissions (at least one required)

User Details WriteUser Details Read

ParametersExpand Collapse

dimension: Literal["KEY_AGREEMENT"]

Specifies the origin post-quantum data dimension by which to group the results.

date_end: Optional[SequenceNotStr[Union[str, datetime]]]

End of the date range (inclusive).

date_range: Optional[SequenceNotStr[str]]

Filters results by date range. For example, use 7d and 7dcontrol to compare this week with the previous week. Use this parameter or set specific start and end dates (dateStart and dateEnd parameters).

date_start: Optional[SequenceNotStr[Union[str, datetime]]]

Start of the date range.

format: Optional[Literal["JSON", "CSV"]]

Format in which results will be returned.

One of the following:

"JSON"

"CSV"

name: Optional[SequenceNotStr[str]]

Array of names used to label the series in the response.

ReturnsExpand Collapse

class OriginTimeseriesGroupsResponse: …

meta: Meta

Metadata for the results.

agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]

Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to Aggregation intervals.

One of the following:

"FIFTEEN_MINUTES"

"ONE_HOUR"

"ONE_DAY"

"ONE_WEEK"

"ONE_MONTH"

confidence_info: MetaConfidenceInfo

annotations: List[MetaConfidenceInfoAnnotation]

data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]

Data source for annotations.

One of the following:

"ALL"

"AI_BOTS"

"AI_GATEWAY"

"BGP"

"BOTS"

"CONNECTION_ANOMALY"

"CT"

"DNS"

"DNS_MAGNITUDE"

"DNS_AS112"

"DOS"

"EMAIL_ROUTING"

"EMAIL_SECURITY"

"FW"

"FW_PG"

"HTTP"

"HTTP_CONTROL"

"HTTP_CRAWLER_REFERER"

"HTTP_ORIGINS"

"IQI"

"LEAKED_CREDENTIALS"

"NET"

"ROBOTS_TXT"

"SPEED"

"WORKERS_AI"

description: str

end_date: datetime

formatdate-time

event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]

Event type for annotations.

One of the following:

"EVENT"

"GENERAL"

"OUTAGE"

"PARTIAL_PROJECTION"

"PIPELINE"

"TRAFFIC_ANOMALY"

is_instantaneous: bool

Whether event is a single point in time or a time range.

linked_url: str

formaturi

start_date: datetime

formatdate-time

level: int

Provides an indication of how much confidence Cloudflare has in the data.

date_range: List[MetaDateRange]

end_time: datetime

Adjusted end of date range.

formatdate-time

start_time: datetime

Adjusted start of date range.

formatdate-time

last_updated: datetime

Timestamp of the last dataset update.

formatdate-time

normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]

Normalization method applied to the results. Refer to Normalization methods.

One of the following:

"PERCENTAGE"

"MIN0_MAX"

"MIN_MAX"

"RAW_VALUES"

"PERCENTAGE_CHANGE"

"ROLLING_AVERAGE"

"OVERLAPPED_PERCENTAGE"

"RATIO"

units: List[MetaUnit]

Measurement units for the results.

name: str

value: str

serie_0: Serie0

timestamps: List[datetime]

Get Origin Post-Quantum Data Over Time

import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.post_quantum.origin.timeseries_groups(
    dimension="KEY_AGREEMENT",
)
print(response.meta)

{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z"
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "serie_0": {
      "timestamps": [
        "2023-08-08T10:15:00Z"
      ]
    }
  },
  "success": true
}

Returns Examples

{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z"
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "serie_0": {
      "timestamps": [
        "2023-08-08T10:15:00Z"
      ]
    }
  },
  "success": true
}