Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Rename: Analyze and log -> Analyze and send #7784

Merged
merged 1 commit into from
Oct 16, 2024
Merged

Rename: Analyze and log -> Analyze and send #7784

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions docs/content/getting-started/data-out.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,14 +3,14 @@ title: Get data out of Rerun
order: 450
---

At its core, Rerun is a database. The viewer includes the [dataframe view](../reference/types/views/dataframe_view) to explore data in tabular form, and the SDK includes an API to export the data as dataframes from the recording. These features can be used, for example, to perform analysis on the data and log back the results to the original recording.
At its core, Rerun is a database. The viewer includes the [dataframe view](../reference/types/views/dataframe_view) to explore data in tabular form, and the SDK includes an API to export the data as dataframes from the recording. These features can be used, for example, to perform analysis on the data and send back the results to the original recording.

In this three-part guide, we explore such a workflow by implementing an "open jaw detector" on top of our [face tracking example](https://rerun.io/examples/video-image/face_tracking). This process is split into three steps:

1. [Explore a recording with the dataframe view](data-out/explore-as-dataframe)
2. [Export the dataframe](data-out/export-dataframe)
3. [Analyze the data and log the results](data-out/analyze-and-log)
3. [Analyze the data and send back the results](data-out/analyze-and-send)

Note: this guide uses the popular [Pandas](https://pandas.pydata.org) dataframe package. The same concept however applies in the same way for alternative dataframe packages such as [Polars](https://pola.rs).

If you just want to see the final result, jump to the [complete script](data-out/analyze-and-log.md#complete-script) at the end of the third section.
If you just want to see the final result, jump to the [complete script](data-out/analyze-and-send.md#complete-script) at the end of the third section.
16 changes: 8 additions & 8 deletions ...tting-started/data-out/analyze-and-log.md → ...ting-started/data-out/analyze-and-send.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,11 +1,11 @@
---
title: Analyze the data and log the results
title: Analyze the data and send back the results
order: 3
---



In the previous sections, we explored our data and exported it to a Pandas dataframe. In this section, we will analyze the data to extract a "jaw open state" signal and log it back to the viewer.
In the previous sections, we explored our data and exported it to a Pandas dataframe. In this section, we will analyze the data to extract a "jaw open state" signal and send it back to the viewer.



Expand All @@ -20,7 +20,7 @@ df["jawOpenState"] = df["jawOpen"] > 0.15
```


## Log the result back to the viewer
## Send the result back to the viewer

The first step is to initialize the logging SDK targeting the same recording we just analyzed.
This requires matching both the application ID and recording ID precisely.
Expand All @@ -37,11 +37,11 @@ rr.connect()

_Note_: When automating data analysis, it is typically preferable to log the results to an distinct RRD file next to the source RRD (using `rr.save()`). In such a situation, it is also valid to use the same app ID and recording ID. This allows opening both the source and result RRDs in the viewer, which will display data from both files under the same recording.

We will log our jaw open state data in two forms:
We will send our jaw open state data in two forms:
1. As a standalone `Scalar` component, to hold the raw data.
2. As a `Text` component on the existing bounding box entity, such that we obtain a textual representation of the state in the visualization.

Here is how to log the data as a scalar:
Here is how to send the data as a scalar:

```python
rr.send_columns(
Expand All @@ -53,9 +53,9 @@ rr.send_columns(
)
```

With use the [`rr.send_column()`](../../howto/send_columns.md) API to efficiently send the entire column of data in a single batch.
We use the [`rr.send_column()`](../../howto/send_columns.md) API to efficiently send the entire column of data in a single batch.

Next, let's log the same data as `Text` component:
Next, let's send the same data as `Text` component:

```python
target_entity = "/video/detector/faces/0/bbox"
Expand Down Expand Up @@ -84,6 +84,6 @@ The OPEN/CLOSE label is displayed along the bounding box on the 2D view, and the

### Complete script

Here is the complete script used by this guide to load data, analyze it, and log the result back:
Here is the complete script used by this guide to load data, analyze it, and send the result back:

snippet: tutorials/data_out
Loading