semvec/pypi-packages huggingface dataset:
cdef-pil-080126-2200 0.1.0
Parental income loss analysis pipeline.
Requires Python: >=3.9

# Parental Income Loss

## Stacked Event-Study Outputs
The stacked example writes results to:
- `output/stacked_event_study/stacked_results.csv`
- `output/stacked_event_study/stacked_results.parquet`

Each row contains:
- `sample`, `chapter`, `component` (event_time or horizon_*),
- `reference_year`, `target_year`, `event_time`,
- `att`, `se`, `conf_low`, `conf_high`,
- `n_pairs`, `n_treated`, `n_control`, `convergence`,
- `bucket_periods`, `horizon_start`, `horizon_end`, `horizon_label`, `bucket_weight`.

Bucket weighting defaults to treated-weighted (`n_treated`). Override with:
- `STACKED_BUCKET_WEIGHT=pairs`

## Quickstart: Custom Components
- Implement one or more of: `PopulationAssembler`, `CohortSelector`, `ExposureDetector`, `MatchingEngine`.
- Wire them into the pipeline with `CohortWorkflow::with_components`.
- Use `docs/component_contracts.md` to ensure required columns are present.

Example wiring:
```rust
let workflow = CohortWorkflow::with_components(builder, selector, matcher);
let outputs = workflow.run(sources, &detector, &registries, &plan, None)?;
```

## Stacked Event Study CLI
- Run the standalone stacked event-study on a parquet file:
  `cargo run -- --stacked-input path/to/stacked_inputs.parquet --did-outcome perindkialt13`
- Optional flags: `--stacked-samples`, `--stacked-chapter-col`, `--stacked-icd-col`,
  `--stacked-output-dir`, plus the existing `--stacked-*` config flags.

## Docs
- Component contracts: `docs/component_contracts.md`
- Custom component example: `crates/pil/examples/custom_components.rs`
- Custom selector example: `crates/pil/examples/custom_selector.rs`
- Custom matcher example: `crates/pil/examples/custom_matcher.rs`
- Custom detector example: `crates/pil/examples/custom_detector.rs`
agilent-format 0.4.7
File reader for Agilent Resolutions Pro FT-IR images
Requires Python: >=3.10

# python-agilent-file-formats
Python library for reading FT-IR imaging datasets created by Resolutions Pro on
Agilent Cary instruments with FPA area detectors.

Port of https://bitbucket.org/AlexHenderson/agilent-file-formats/ to Python

Developed for use in the [orange-spectroscopy](https://github.com/Quasars/orange-spectroscopy) add-in, which is part 
of the [Quasar](https://quasar.codes/) data analysis program.

## Installation

The package can be installed from [PyPI](https://pypi.org) as:

`pip install agilent-format`

## Usage

There are four primary classes for loading data, depending on the type:

| Class              | Data Type                  | Extension   |
|--------------------|----------------------------|-------------|
| `agilentImage`     | Single-tile FPA image      | .dat        |
| `agilentMosaic`    | Mosaic FPA image           | .dmt        |
| `agilentImageIFG`  | Single-tile interferograms | .seq        |
| `agilentMosaicIFG` | Mosaic interferograms      | .dmt        |

To use, load the corresponding data loader class with the appropriate filename:

```python
from agilent_format import agilentImage

ai = agilentImage("agilent_format/datasets/4_noimage_agg256.dat")

ai.data         # 3-dimensional numpy array (height x width x wavenumbers)
ai.wavenumbers  # list of wavenumbers in order of .data array
# Pixel size can be calculated by:
px_size = ai.info['FPA Pixel Size'] * ai.info['PixelAggregationSize']
```
tom-eso 0.2.2
TOM Toolkit Facility module for the European Southern Observatory telescopes
Requires Python: <3.13,>=3.10

# tom_eso
European Southern Obervatory Facility module for TOM Toolkit

NOTE: This TOM Toolkit facility module is in the prototype stage. Any type of feedback is greatly appreciated. Please feel free to create an Issue.

This module is designed mainly to facilitate getting Target and Observation data
from your TOM to the ESO P2 Tool (without having to re-enter it). Submitting
observations is still expected to be done through the P2 Tool itself.

This facility is still in prototype stage and feature requests are welcome.
Please let us know your use cases.

## Installation

1. Install the module into your TOM environment:

    ```shell
    pip install tom-eso
    ```

You'll want to update your `pyproject.toml` or `requirements.txt` file as well.

2. In your project `settings.py`, add `tom_eso` to your `INSTALLED_APPS` setting:

    ```python
    INSTALLED_APPS = [
        ...
        'tom_eso',
    ]
    ```

3. Add `tom_eso.eso.ESOFacility` to the `TOM_FACILITY_CLASSES` in your TOM's
`settings.py`:
   ```python
    TOM_FACILITY_CLASSES = [
        'tom_observations.facilities.lco.LCOFacility',
        ...
        'tom_eso.eso.ESOFacility',
    ]
   ```   

4. Create the ESOProfile tables in your database:

    ```bash
    $ ./manage.py migrate
    ```


## Configuration Options

After installation, each user will have an `ESOProfile` card in their TOM user profile where they can
enter their ESO P2 Tool `username` and `password` and set the ESO environment to `Demo`, `Production`,
or `Production La Silla`.

If a TOM admin wants to have a single default set of ESO credentials accessible by all of their TOM's users, they can 
include the following to thier `settings.py`:

```python
FACILITIES = {
        ...
        # defaults set from ESO p2 API Tutorial
        # https://www.eso.org/sci/observing/phase2/p2intro/Phase2API/api--python-programming-tutorial.html
        # You should have your own credentials.
        'ESO': {
            'environment': os.getenv('ESO_ENVIRONMENT', 'demo'),
            'username': os.getenv('ESO_USERNAME', '52052'),
            'password': os.getenv('ESO_PASSWORD', 'tutorial'),
        },
    }
```
Note: The user specific credentials will always take precendence over these TOM-wide defaults.
procbench 0.1.1
Process Benchmarking Tool
Requires Python: >=3.12

# ProcBench

Process Benchmarking Tool.

- [ProcBench](#procbench)
  - [Setup](#setup)
    - [As a Tool](#as-a-tool)
    - [For Development](#for-development)
  - [Usage](#usage)
    - [Prepare your Test Cases](#prepare-your-test-cases)
    - [Run the Test Cases](#run-the-test-cases)
    - [Export HTML Report](#export-html-report)

## Setup

### As a Tool

Get ready:

- Python 3.12+

then:

```sh
pip install procbench
```

### For Development

Get ready:

- Python 3.12+
- `uv`, the Python package manager.

then:

```sh
cd <project_root>
uv venv
uv sync
```

**NOTE:** For the Usage below, just replace
`procbench ...` with `uv run -m procbench ...`.

## Usage

### Prepare your Test Cases

Make a copy of `sample-test-case.json`, e.g.
`tc01.json`. Change the parameters as needed.

You can make several test cases, e.g. `tc02.json`, `tc03.json`.
Suppose we have those three test cases.

### Run the Test Cases

Suppose you want to dump the output to `result.json`, run:

```sh
procbench run -o result.json tc01.json tc02.json tc03.json
```

For verbose debug output, add `-v` flag:

```sh
procbench run -v -o result.json tc01.json tc02.json tc03.json
```

The results JSON file will contain detailed benchmarking results
and is machine-parseable. See [sample-results.json](./sample-results.json)
for a grasp of the structure.

### Export HTML Report

```sh
procbench export html result.json report.html
```

![(HTML report 1 image)](docs/images/html-report-1.png)

![(HTML report 2 image)](docs/images/html-report-2.png)