semvec/pypi-packages huggingface dataset:
psql2bigquery 0.0.3
Export PostgreSQL databases to Google Cloud Platform BigQuery
Requires Python: >=3.7,<3.10
Keywords: postgresql,bigquery,sink

# PostgreSQL to BigQuery

Install with: `pip install psql2bigquery`

Get usage instructions with: `psql2bigquery run --help`

## Sample usage

```
poetry run psql2bigquery run \
--db-host localhost \
--db-port 5432 \
--db-user username \
--db-password secret-password \
--db-name my_api \
--gcp-project my-project \
--gcp-dataset my_api \
--include table_name_a \
--include table_name_b \
--gcp-credential-path /path/to/credential.json
```

## Logging

There's a possibility to use Sentry.io for error logging.

Just set the environment variable `SENTRY_DSN` and psql2bigquery will automatically configure the logger.

Additionally, the environment variable `ENV` can be used as Sentry environment.


## Contributing

- Fork this project
- Install dependencies with `make dependencies`
  - Make sure you have Python 3 installed. (pyenv)[https://github.com/pyenv/pyenv#installation] is highly recommended
- You can test the client locally (without installing the package) with `poetry run psql2bigquery <command>`
- Make a PR with as much details as possible
MRN 0.1.1
Library used to Normalize numerical time-series data
Keywords: python,normalization,standardization,long term memory,LTM,LTM Normalization

# Memory Retention Normalization - MRN

This Library is used for normalizing data array with respect to long term memory retention

Timeseries Data's have influence of long term memory retention on their current data

This phenomenon happens in the field of finance sports and other live streaming timeseries datas

Developed by Philip Pankaj (c) 2021

## Memory Retention Rate
#### 1 - Retain Max. Memory
#### 0 - Retain Least Memory
![](https://raw.githubusercontent.com/philip-hash/MRN/main/memory%20retention%20rate.png)

## Example with Nasdaq 100 data
![](https://raw.githubusercontent.com/philip-hash/MTN/main/MRN_Nasdaq_Graph.png)

```python
#importing our MRN
from MRN.scale import Normalization

#importing all required library
from yahoo_fin.stock_info import get_data
from datetime import date

#getting nifty data and converyting to numpy array
now=date.today().strftime("%d/%m/%Y")  
from_date="5/5/2006" # m/d/year
data= get_data("^IXIC", start_date=from_date, end_date=now, index_as_date = True, interval="1d")
data=data['close'].dropna()
data=data.to_numpy()

#initializing MRN and transforming data
mrn=Normalization(data,0.5)
n_data=mrn.transform()

#importing matplotlib and plotting
import matplotlib.pyplot as plt
plt.figure(figsize=(40, 40))

fig, ax1 = plt.subplots()
ax1.set_ylabel('Nasdaq-100')
ax1.plot(data[-1000:], color = 'tab:blue')

ax2=ax1.twinx()
ax2.set_ylabel('MRN-Normalized')
ax2.plot(n_data[-1000:],color='tab:green')
plt.show()

```
keqing-sword-LaoshuBaby 0.1.0
A small example package
Requires Python: >=3.6

# OSMChina-Keqing_sword

基于Python的对`.osm`文件的访问库

## 技术细节和文件

### kqs_class.py

OSM数据对象库

### kqs_crud.py

模拟数据库操作行为，不过只有查是可以批量的，增删只能单id，改是增删组合 ~~省事~~

## 命名来源

项目Leader @Jyunhou 钦点

[![](https://avatars.githubusercontent.com/u/45530478?v=4)](https://zh.wikipedia.org/wiki/%E5%8E%9F%E7%A5%9E%E8%A7%92%E8%89%B2%E5%88%97%E8%A1%A8#%E7%92%83%E6%9C%88%E4%B8%83%E6%98%9F)