semvec/pypi-packages huggingface dataset:
dev-python 0.1.1
mindmap dev
Requires Python: <4.0,>=3.12

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# dev

## <img src='https://i.imgur.com/5WuXRVs.png' style='height:40px;width:auto'> [Python](dev/python/index.md)

## <img src='https://i.imgur.com/c0S3aFz.png' style='height:40px;width:auto'> [JetBrains](dev/jetbrains/index.md)

## <img src='https://i.imgur.com/YeX1O1Q.png' style='height:40px;width:auto'> [Microsoft](dev/meta/index.md)

## <img src='https://i.imgur.com/VbuMhsl.png' style='height:40px;width:auto'> [Anaconda](dev/anaconda/index.md)

## <img src='https://i.imgur.com/nySomdb.png' style='height:40px;width:auto'> [Git](dev/git/index.md)
sdkdataservices 1.3.0
Common Module
Requires Python: >=3.12

# DataServices Module

The DataServices Module is a Python library that is common module for Financials and MarketData. It receives SDKDataInput from the two modules and takes care of the rest i.e. sending request to GDSAPI, receiving response in standard GDS response format. Finally, it unpacks the response from GDSAPI to dataframe and returns it as final response.

## Features

- invoke_data_service() is the entry point which will be invoked by Fin/Mkt data
- SDKDataRequest will have the mandatory arguments for GDSAPI i.e. functions, properties, mnamonics and identifiers
- invoke_data_service() will receive SDKDataInput which will contain token, proxy(if received from Fin/Mkt) and SDKDataRequest and forward this SDKDataInput to the rest of the functions, where sending of request to GDSAPI, receiving response, converting the response to dataframe will happen.
- Errors are well handled at every method.

## Installation

You can install the package using pip. Ensure you have Python 3.12+ installed.

```sh
pip install sdkdataservices==1.2.0
```

## Basic Usage
Here's a brief example of how to use this package:

```sh
from DataServices.model.sdk_data_input import SDKDataInput
from DataServices.model.sdk_data_request import SDKDataRequest
from DataServices.model.sdk_proxy import SDKProxy
from DataServices.services.impl.sdk_data_services_impl import SDKDataServicesImpl


# Create an instance of main class
sdk_data_services_impl = SDKDataServicesImpl()

# Define proxy settings
sdk_proxy = SDKProxy(proxy_username=None, proxy_password=None, proxy_host='proxy_host', proxy_port=8080)

# invoke the entry point method i.e. invoke_data_service by passing SDKDataInput like: 
sdk_data_request = SDKDataRequest(function="GDSP",properties={},identifiers=["aAPL:","IBM"],mnemonics=["IQ_FILINGDATE_IS","IQ_EBITDA"])
sdk_data_input = SDKDataInput(token, proxy, sdk_data_request)
final_output = sdk_data_services_impl.invoke_data_service(sdk_data_input)
print(final_output)


```


Configuration
By default, the config is set to Prod URL's. To give preference, supports the custom-properties file. For that, Should create a properties file with naming of 
```sh
config/sdk_application.properties
```
aily-core 0.1.0b10
Aily Core
Requires Python: <4.0.0,>=3.8.1

# Aily Code SDK Core

## Development

开发时，需要在根目录中创建 `.env` 文件，填写如下内容：

```bash
AILY_SDK_LOCAL_DEBUG=true
AILY_SDK_CLIENT_ID=c_xxx
AILY_SDK_CLIENT_SECRET=cxxx
AILY_SDK_DOMAIN=https://ae-openapi.feishu-boe.cn/
```

然后通过 `python -m unitest` 命令通过执行单元测试来调试代码。

### Build

```bash
python -m build
```

### Upload

```bash
python -m twine upload dist/* --verbose
```
cobaya-cosmo 0.2
Cosmological codes, wrappers and links to external packages for the Cobaya package
Requires Python: >=3.8.0
Keywords: cosmology, inference, monte carlo, mcmc



External packages
-----------------

This Python packages also lists a number of likelihoods, theory codes and wrappers written by other researchers and hosted somewhere else. Listing them here allows for the Cobaya installer to install them when mentioned just by name. You can see the current list at [LINK]

If you want your package to be featured here, please send a pull request [LINK] and include the following information:

- The name of your likelihood in the Cobaya input file. Please follow, where applicable, the following structure: `[observable_in_lower_case].[survey]_[release]_[dataset_or_likelihood]`, e.g. `cmb.planck_2018_highell_plikHM`, `sn.....`, etc. If it's not clear how to adapt this to your case, let us know in the PR/ISSUE and we can find a compromise. If you already have code containing a different class name, tell it to us (we'll add a "class: [name]" to the dict and a pip reference).

- A 1-sentence description of it.

- A link to one or more papers that describe it (if more than one is requested to be cited, this is not the place; see [LINK])

- How can people install your likelihood. If you follow [SOME RULES], we will make it so it's automatically installed by `cobaya-installed` when requested by name. 

- The current version number of the code

Though in principle all reasonable requests are accepted immediately, some level of documentation, tests, etc may be requested.


FAQ
---

I have updated my likelihood/wrapper/... and want Cobaya to reflect that. How do I do that?

--> Open a PR and mention the new version.