semvec/pypi-packages huggingface dataset:
superset-client 1.0.0
A simple REST API Client for Apache-Superset
Requires Python: >=3.6

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# Superset Client

A Python Client for Apache Superset REST API.

This is a Alpha version. Stability is not guaranteed.

## Usage

Setup a superset client:
```python3
from supersetapiclient.client import SupersetClient

client = SupersetClient(
    host="http://localhost:8080",
    username="admin",
    password="admin",
)
```

When developping in local (only), you may need to accept insecure transport (i.e. http).
This is NOT recommanded outside of local development environement, that is requesting `localhost`.

```python3
import os

os.environ['OAUTHLIB_INSECURE_TRANSPORT'] = '1'
```

## Quickstart

Get all dashboards or find one by name:
```python3
# Get all dashboards
dashboards = client.dashboards.find()

# Get a dashboard by name
dashboard = client.dashboards.find(dashboard_title="Example")[0]
```

Update dashboard colors, some properties and save changes to server:
```python3
# Update label_colors mapping
print(dashboard.colors)
dashboard.update_colors({
    "label": "#fcba03"
})
print(dashboard.colors)

# Change dashboard title
dashboard.dashboard_title = "New title"

# Save all changes
dashboard.save()
```

## Documentation

- [Service of Assets Management](https://confluence.idgcapital.com/pages/viewpage.action?pageId=50725725)
- [Service of Data Building](https://confluence.idgcapital.com/pages/viewpage.action?pageId=50725719)
- [Directory of Data Platform](https://confluence.idgcapital.com/pages/viewpage.action?pageId=46401007)

## Contributing

Before committing to this repository, you must have [pre-commit](https://pre-commit.com) installed, and install
the following pre-commit hooks:

```sh
pre-commit install --install-hooks -t pre-commit -t pre-push
```

## License

This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details
MoReSQUE 1.0
Uncertainty quantification module for mosaik

Modular, Recognizing System for Quantification of Uncertainty with Ensembles (MoReSQUE)
=======================================================================================

The provided packages are supplementary material for the PhD thesis
"A Non-Intrusive Uncertainty Quantification System for Modular Smart Grid Co-Simulation"
by Cornelius Steinbrink.

Repository Structure
--------------------

The code of the uncertainty quantification system itself is given in the folder
"moresque". Furthermore, two example studies are provided to illustrate the
utilization of the moresque software.
The folder "example_pv" contains a simple example with only two interconnected
simulators (solar irradiation and pv panel) and purely interval-based uncertainty representation.
The folder "example_scenario", on the other hand, provides a more complex example
with more (and partly composite) simulators and different forms of uncertainty
representation.

The code of all simulators used for the examples is given in the folder
"simulators_for_example".
Code for further testing is provided in the folder "testing_utility_components".
It contains the module "sim_testbench" that includes a Monte Carlo Simulation module as an alternative to MoReSQUE 
for UQ in mosaik. The other modules provide
basic input and output capabilities for MoReSQUE as well as MCS in order to
enable UQ studies with single simulators.

Installation
------------

The MoReSQUE software package has been developed and tested solely under Linux.

Installation of MoReSQUE may be conducted via::

    $ pip install .

in the "moresque" folder (the folder with the setup.py script).
This automatically installs all dependencies:
mosaik-api,
numpy,
scipy,
pyDOE,
arrow,
statsmodels

In order to run the example, mosaik has to be installed via:
$ pip install mosaik
Furthermore, the packages in the folder "simulators_for_example" have to be
installed. The all include setup.py scripts so that the are installed the same
way as MoReSQUE. In order to install the database module "mosaik-hdf5" it
typically is necessary to first run::

    $ pip install Cython
    $ pip install h5py

All other dependencies should be correctly installed with the particular
installation scripts.
citybrain-official 0.15
retrieve data from citybrain.org
Requires Python: >=3

# simplify data retrieving on citybrain.org

### retrieve **raw data** from **odps data source** on citybrain.org 
```python
from citybrain_official import client

data = client.retrieve_df(dataAddress='', sql='')
print(data)
```

### retrieve data as **pandas dataframe** from **odps data source** on citybrain.org 
```python
from citybrain_official import client

data = client.retrieve_df(dataAddress='', sql='')
print(data)
```

### retrieve **raw data** from **file data source** on citybrain.org 
```python
from citybrain_official import client

data = client.retrieve_df(dataAddress='')
print(data)
```

### retrieve data as **pandas dataframe** from **csv data source** on citybrain.org 
```python
from citybrain_official import client

data = client.retrieve_df(dataAddress='')
print(data)
```