inspect timeline

Real rendered frames of the composite (all layers, text, captions), with the visible clip ids: one frame, a list, an even range, or a few around a point, up to 12 a call, in frames or seconds. Each carries its frame number, the picture’s size and a grid ruled every 0.1 in the timeline’s own coordinates, the values a clip’s frame box, crop insets and focus take; grid: false for the bare picture. span reads a stretch as one strip of frames, waveform and words instead. Words cut off, off the frame, overlapping, too small or too faint come back as notes on their frame, a motion graphic’s own words included once it has settled. Cloud processing.

POST

/

tools

/

inspect_timeline

inspect timeline

curl --request POST \
  --url https://eversince.ai/api/v1/tools/inspect_timeline \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "timeline_id": "<string>"
}
'

import requests

url = "https://eversince.ai/api/v1/tools/inspect_timeline"

payload = { "timeline_id": "<string>" }
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({timeline_id: '<string>'})
};

fetch('https://eversince.ai/api/v1/tools/inspect_timeline', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

{
  "ok": true,
  "data": {},
  "meta": {
    "request_id": "<string>",
    "cost": {},
    "cloud_processing": {
      "ran_on": "cloud",
      "minutes": 123,
      "queued_minutes": 123,
      "left_minutes": 123,
      "bought_minutes": 123,
      "note": "<string>"
    }
  },
  "media": [
    {}
  ]
}

{
  "ok": false,
  "error": {
    "code": "<string>",
    "message": "<string>",
    "retryable": true,
    "suggested_action": "<string>",
    "cloud_processing": {
      "ran_on": "cloud",
      "minutes": 123,
      "queued_minutes": 123,
      "left_minutes": 123,
      "bought_minutes": 123,
      "note": "<string>"
    }
  }
}

Authorizations

Authorization

string

header

required

An API key (es_live_…) from Settings, under API keys, or an OAuth access token.

Headers

Idempotency-Key

string

Retry a paid dispatch safely: the same key returns the job already dispatched instead of charging again.

x-workspace-id

string

The workspace this one call acts in, when it is not the key's own.

Body

application/json

timeline_id

string

required

The timeline id.

frames

integer[]

Exact frame numbers to render.

Maximum array length: 12

seconds

number[]

The same, as timestamps in seconds.

Maximum array length: 12

range

object

count frames spread evenly between start and end (frames).

Show child attributes

around

integer

A few frames around this frame number.

around_seconds

number

A few frames around this timestamp in seconds.

grid

boolean

False to leave the grid off and return the bare picture. Default true.

span

object

A stretch in seconds, up to 60: one strip of frames, waveform and words instead of separate frames, which is how a cut is judged against the sound. Its frames are drawn and charged like any others.

Show child attributes

Response

Done, or a job started for work that runs longer (follow it with get_jobs).

ok

any

required

data

object

required

meta

object

required

Show child attributes

media

object[]

inspect timeline

curl --request POST \
  --url https://eversince.ai/api/v1/tools/inspect_timeline \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "timeline_id": "<string>"
}
'

import requests

url = "https://eversince.ai/api/v1/tools/inspect_timeline"

payload = { "timeline_id": "<string>" }
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({timeline_id: '<string>'})
};

fetch('https://eversince.ai/api/v1/tools/inspect_timeline', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

{
  "ok": true,
  "data": {},
  "meta": {
    "request_id": "<string>",
    "cost": {},
    "cloud_processing": {
      "ran_on": "cloud",
      "minutes": 123,
      "queued_minutes": 123,
      "left_minutes": 123,
      "bought_minutes": 123,
      "note": "<string>"
    }
  },
  "media": [
    {}
  ]
}

{
  "ok": false,
  "error": {
    "code": "<string>",
    "message": "<string>",
    "retryable": true,
    "suggested_action": "<string>",
    "cloud_processing": {
      "ran_on": "cloud",
      "minutes": 123,
      "queued_minutes": 123,
      "left_minutes": 123,
      "bought_minutes": 123,
      "note": "<string>"
    }
  }
}