semvec/pypi-packages huggingface dataset:
carp-compression 0.1.3
use CARP to compress image
License: MIT
Requires Python: >=3.9


The module pybind11 need compiler clang 13 to compile cpp files, so we need to quit the base environment built by anaconda(if you use anaconda cronically). You can enter "conda deactivate" in terminal. Python version required here is Python 3.9.13.


You also need to install the c++ libraray armadillo in order to compile tree_class.cpp.
dbt2looker 0.11.0
Generate lookml view files from dbt models
License: MIT
Requires Python: >=3.7

# dbt2looker

Use `dbt2looker` to generate Looker view files automatically from dbt models.

Want a deeper integration between dbt and your BI tool? You should also checkout [Lightdash - the open source alternative to Looker](https://github.com/lightdash/lightdash)

**Features**

* **Column descriptions** synced to looker
* **Dimension** for each column in dbt model
* **Dimension groups** for datetime/timestamp/date columns
* **Measures** defined through dbt column `metadata` [see below](#defining-measures)
* Looker types
* Warehouses: BigQuery, Snowflake, Redshift (postgres to come)

[![demo](https://raw.githubusercontent.com/hubble-data/dbt2looker/main/docs/demo.gif)](https://asciinema.org/a/407407)

## Quickstart

Run `dbt2looker` in the root of your dbt project *after compiling looker docs*.

**Generate Looker view files for all models:**
```shell
dbt docs generate
dbt2looker
```

**Generate Looker view files for all models tagged `prod`**
```shell
dbt2looker --tag prod
```

## Install

**Install from PyPi repository**

Install from pypi into a fresh virtual environment.

```
# Create virtual env
python3.7 -m venv dbt2looker-venv
source dbt2looker-venv/bin/activate

# Install
pip install dbt2looker

# Run
dbt2looker
```

**Build from source**

Requires [poetry](https://python-poetry.org/docs/) and python >=3.7

For development, it is recommended to use python 3.7:

```
# Ensure you're using 3.7
poetry env use 3.7  
# alternative: poetry env use /usr/local/opt/python@3.7/bin/python3

# Install dependencies and main package
poetry install

# Run dbtlooker in poetry environment
poetry run dbt2looker
```

## Defining measures

You can define looker measures in your dbt `schema.yml` files. For example:

```yaml
models:
  - name: pages
    columns:
      - name: url
        description: "Page url"
      - name: event_id
        description: unique event id for page view
        meta:
           measures:
             page_views:
               type: count
```