semvec/pypi-packages huggingface dataset:
aq-dbmigration 1.0.2
a database migration tool
Requires Python: >=3.8

# AQ-DBMigration
a db migration tool in python   

## home
[github: nanshens/AQ-DBMigration](https://github.com/nanshens/AQ-DBMigration)

## install
`pip install aq-dbmigration`   

## how to use   

```python
    config = MigrationConfig("db_name", "db_user", "db_password", "db_host", "db_port", DBConstant.Type.PostgreSQL, "db file path")
    db = DBMigration(config)
    db.migration()
```

if you want to skip migration, you can set skip is True in db.migration(), like this:

```python
    db.migration(True)
```



## sql file
file format: V{version}__{description}.sql   
example: V1__test.sql, V2__issue1.sql, V2.1__issue11.sql, V2.12__issue12.sql   

version:
version = main sequence + . + sub sequence, so order by main first, then sub.
example: V1.2 run first and V1.13 second, because 2 < 13.   

please start from V1

## MigrationConfig

### file path
please use absolute path.   
if you use flask, you can set db_path in flask config,
like this `app.config['DB_MIGRATION_PATH'] = os.path.join(app.root_path, 'resource', 'db_migration')`
and use `MigrationConfig("db_name", "db_user", "db_password", "db_host", "db_port", DBConstant.Type.PostgreSQL, app.config['DB_MIGRATION_PATH'])` to init   


### baseline
When used for the first time, it will set the baseline, and subsequent changes will not take effect.   
Only read sql files larger than baseline.   


## database support
1. postgresql(tested pass)
2. mysql(in testing)
3. maria(in testing)
4. sqlite(in testing)


## License
This project is licensed under the terms of the [GNU General Public License v3.0](LICENSE).
bigquery-helper 0.1.0
A helper package for Google BigQuery operations
Requires Python: >=3.7

# BigQuery Helper

BigQuery Helper is a Python package that simplifies working with Google BigQuery. It provides an easy-to-use interface for common BigQuery operations such as executing queries, inserting rows, and working with DataFrames.

## Installation

You can install BigQuery Helper using pip:

```
pip install bigquery-helper
```

## Usage

To use BigQuery Helper, you'll need a Google Cloud project and credentials. Here's how to get started:

1. Set up a Google Cloud project and enable the BigQuery API.
2. Create a service account and download the JSON key file.
3. Set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of your JSON key file:

   ```
   export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/keyfile.json"
   ```

   Alternatively, you can pass the credentials directly when initializing the BigQueryHelper.

Here's a quick example of how to use BigQuery Helper:

```python
from bigquery_helper import BigQueryHelper

# Initialize BigQueryHelper
bq_helper = BigQueryHelper(project_id="your-project-id", credentials="/path/to/your/keyfile.json")

# Execute a query and get results as a DataFrame
query = "SELECT * FROM `your_dataset.your_table` LIMIT 10"
df = bq_helper.query_to_dataframe(query)
print(df)

# Insert rows into a table
rows_to_insert = [
    {"column1": "value1", "column2": 123},
    {"column1": "value2", "column2": 456}
]
bq_helper.insert_rows("your_dataset.your_table", rows_to_insert)

# Upload a DataFrame to BigQuery
import pandas as pd
df_to_upload = pd.DataFrame({"column1": ["a", "b"], "column2": [1, 2]})
bq_helper.dataframe_to_table(df_to_upload, "your_dataset.new_table", if_exists="replace")

# Update a column in a table
bq_helper.update_column("your_dataset.your_table", "id_column", "status_column", [1, 2, 3])

# Get schema of a table
schema = bq_helper.get_schema("your_dataset.your_table")
print(schema)
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License.
eve-SMLM 0.2.1
Eve-SMLM: A Python package for single molecule localization microscopy from event-based sensors
Requires Python: <3.10,>=3.9
Keywords: SMLM, molecule, EBS, neuromorphic, event, microscopy

## EVE - General-purpose software for eveSMLM localization

## About EVE
EVE is a user-interfaced software package that provides several methods to localize emitters from single molecule localization microscopy (SMLM) experiments performed on event-based sensors (eveSMLM).

For full information about EVE, please look at the corresponding scientific manuscript: "EVE is an open modular data analysis software for event-based localization microscopy" by Laura M. Weber$^‡$, Koen J.A. Martens$^‡$, Clément Cabriel, Joel J. Gates, Manon Albecq, Fredrik Vermeulen, Katharina Hein, Ignacio Izeddin, and Ulrike Endesfelder.

Event-based data differs fundamentally from conventional camera images. Unlike traditional sensors, event-based sensors only capture intensity changes, registering them as either positive (when intensity surpasses a predefined threshold) or negative events (when intensity drops below a predefined threshold). As a result, only a list of x and y pixel coordinate pairs is stored together with the detected event polarities and timestamps.

EVE is designed to quickly and directly process and analyse event-based single molecule data. The event-based data analysis is divided into two main parts:<br>  
1. **Candidate Finding:** The complete event-list is searched for characteristic event clusters that are generated by blinking fluorophores. Potential candidate clusters are then extracted and returned for further processing.<br>  
2. **Candidate Fitting:** The x,y,(z),t-localization is determined for each candidate cluster.<br>  
3. **Postprocessing and Evaluation:** This module includes various analytical routines to process and interpret the data.<br>