semvec/pypi-packages huggingface dataset:
aurelian 0.4.2
aurelian
License: MIT
Requires Python: <4.0,>=3.11

[![DOI](https://zenodo.org/badge/932483388.svg)](https://doi.org/10.5281/zenodo.15299996)

# Aurelian: Agentic Universal Research Engine for Literature, Integration, Annotation, and Navigation

| [Documentation](https://monarch-initiative.github.io/aurelian) |

```
aurelian --help
```

Most commands will start up a different AI agent.

## Examples of use

### TALISMAN

gene set enrichment

### Aria

checking papers against checklists

### GO-CAM agent

This agent is for exploring, chatting with, and reviewing GO-CAMs

Docs: [gocam_agent](https://monarch-initiative.github.io/aurelian/agents/gocam_agent/)

It can be used to generate reviews according to guidelines for GO-CAMs:

* [GO-CAM Reviews](https://cmungall.github.io/go-cam-reviews/)

It can also generate SVGs, demonstrating innate knowledge of both the visual grammar of pathway diagrams and the semantics of the underlying biology.


<img alt="img" src="https://cmungall.github.io/go-cam-reviews/figures/FIG-646ff70100005137-IL33_signaling_pathway__Human_.svg" />

### GO-Ann agent

This agent is for exploring, chatting with, and reviewing standard annotations


Docs: [go_ann_agent](https://monarch-initiative.github.io/aurelian/agents/go_ann_agent/)

Example review using TF guidelines:

https://github.com/geneontology/go-annotation/issues/5743

## Troubleshooting

### Installing linkml-store

Some agents require linkml-store pre-indexed. E.g. a mongodb with gocams for cam agent.
Consult the [linkml-store documentation](https://linkml.io/linkml-store/) for more information.

### Semantic search over ontologies

If an agent requires ontology search it will use the semsql/OAK sqlite database.
The first time querying it will use linkml-store to create an LLM index. Requires OAI key.
This may be slow first iteration. Will be cached until your pystow cache regenerates.
rocoda 0.1.1
RoCoDA Alpha 0.1
License: MIT
Requires Python: >=3.10

# RoCoDA Alpha 0.1
## Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations

Project Description for RoCoDA [here](https://rocoda.github.io/)

To install via `uv` (recommend)

```bash
uv venv
source .venv/bin/activate
uv pip install rocoda
```

# Examples

Instantiate DataPrep Object:


```python
#NOTE: all data preparation is done in-place on disk
from rocoda.data.prep import DataPrep
from task import StackThree_D0 #registers the task

data = DataPrep(
    hdf5_path="stack_three.hdf5", 
    env_type="robosuite")
```

Render a Frame:

```python
from PIL import Image

img = data.render_demo_state(0, 10, camera_name="agentview")
img = Image.fromarray(img)
img = img.transpose(Image.FLIP_TOP_BOTTOM)
display(img)
```

Define Subtask Boundaries via Heuristic:

```python
from rocoda.environments.robosuite import RobosuiteEnvironment
from typing import Mapping

# Define subtask heuristic for StackThree
def heuristic(rocoda_env:RobosuiteEnvironment)->Mapping[str,bool]:

    env = rocoda_env.get_underlying_env() #gets the robosuite env

    signals = {
        "grasp_1": False,
        "stack_1": False,
        "grasp_2": False,
        "stack_2": False
    }

    signals["grasp_1"] = env._check_grasp(gripper=env.robots[0].gripper, object_geoms=env.cubeA)

    signals["stack_1"] = env._check_cubeA_stacked()

    signals["grasp_2"] = env._check_grasp(gripper=env.robots[0].gripper, object_geoms=env.cubeC)

    return signals
            
# Apply subtask termination heuristic
data.apply_subtask_term_heuristic( ["grasp_1","stack_1","grasp_2","stack_2"], heuristic )
```

Define the Causal Groups:

```python
# Expects [{subtask_name: {object_name: [object_name, ...]}}] * num_robots

causality = {
    "grasp_1": [("robot0", "cubeA")],
    "stack_1": [("robot0", "cubeA", "cubeB")],
    "grasp_2": [("robot0", "cubeC"),("cubeA","cubeB")],
    "stack_2": [("robot0", "cubeA", "cubeB", "cubeC")]
}

# Set the causality groups for the dataset
data.set_causal_groups(causality)
```

Run Causal Augmentation:

```python
from rocoda.augments import causal

causal.augment(
    filepath="stack_three.hdf5",
    num_new_episodes=10,
    across_demos=True)
```