semvec/pypi-packages huggingface dataset:
dailylog1 0.2.3
Logging utility to help reduce email traffic
Requires Python: <4.0,>=3.9
Keywords: logging

# dailylog

[![Build Status](https://github.com/wtfo-guru/Dailyloglinux/workflows/Dailylog/badge.svg?branch=main&event=push)](https://github.com/wtfo-guru/dailylog/actions?query=workflow%3ADailyloglinux)
[![codecov](https://codecov.io/gh/wtfo-guru/dailylog/branch/main/graph/badge.svg)](https://codecov.io/gh/wtfo-guru/dailylog)
[![Python Version](https://img.shields.io/pypi/pyversions/dailylog1.svg)](https://pypi.org/project/dailylog1/)
[![wemake-python-styleguide](https://img.shields.io/badge/style-wemake-000000.svg)](https://github.com/wemake-services/wemake-python-styleguide)

## Overview

I have many scripts run by cron and report errors via cron's email. This is great until
one of the scripts that runs frequently starts failing. Rather than waking to a full
mailbox, using this tool will throttled specific errors to a log and only show the error
every 24 hours by default. The time can be adjusted by passing a parameter.

## Installation

Check out this repo and cd into the project directory and run:

```bash

pip install dailylog1

```

## Usage

```bash
dailylog --help
Usage: dailylog [OPTIONS] COMMAND [ARGS]...

  Entry point for click script.

Options:
  -C, --cache TEXT        specify alternate cache file (default ~/.cache/dailylog.json)
  -c, --config TEXT       specify alternate config file (default ~/.config/dailylog.yaml)
  -d, --debug             increment debug level
  -t, --test / --no-test  specify test mode
  -v, --verbose           increment verbosity level
  -V, --version           show version and exit
  -h, --help              Show this message and exit.

Commands:
  log              Log an error.
  set-default-log  Set a new default log.
```

```bash
dailylog log --help
Usage: dailylog log [OPTIONS]

  Log a message.

Options:
  -k TEXT     Specify key  [required]
  -m TEXT     Specify message  [required]
  -s INTEGER  Specify seconds to suppress (default 86400 [one day])
  -l INTEGER  Specify one of logging levels (default: logging.ERROR)
  -f TEXT     Specify alternate log file
  -h, --help  Show this message and exit.
```

```bash
dailylog set-default-log --help
Usage: dailylog set-default-log [OPTIONS] LOG_FN

  Set a new default log.

Options:
  -h, --help  Show this message and exit.
```

## Documentation

- [Stable](https://dailylog1.readthedocs.io/en/stable)

- [Latest](https://dailylog1.readthedocs.io/en/latest)

## License

[MIT](https://github.com/wtfo-guru/dailylog/blob/main/LICENSE)
xero-mcp 0.1.10
Xero MCP Server
Requires Python: >=3.10

# Xero MCP Integration

A powerful integration that connects Claude with your Xero accounting data, enabling intelligent financial analysis and insights.

## Features

### 1. Financial Dashboard
- Quick access to financial overview
- Real-time bank balances
- Key performance metrics
- Executive summaries

### 2. Financial Reports
- Balance Sheet analysis
- Profit & Loss statements
- Cash flow tracking
- Budget variance analysis

### 3. Account Management
- Chart of accounts access
- Receivables monitoring
- Payables tracking
- Account reconciliation

### 4. Transaction Analysis
- Recent transaction review
- Bank transaction history
- Payment tracking
- Invoice management

## Getting Started

1. Set up your Xero credentials:
   - Create a `.env` file in the root directory
   - Add your Xero API credentials:
     ```
     XERO_CLIENT_ID=your_client_id
     XERO_CLIENT_SECRET=your_client_secret
     ```

2. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

3. Run the integration:
   ```bash
   python -m xero_mcp.app
   ```

## Using the Integration

### Available Resources

Access financial data through organized resource paths:

- `xero://dashboard/overview` - Comprehensive financial overview
- `xero://reports/balance_sheet` - Balance sheet reports
- `xero://reports/profit_and_loss` - P&L statements
- `xero://accounts/chart` - Chart of accounts
- `xero://accounts/receivables` - Receivables summary
- `xero://accounts/payables` - Payables summary
- `xero://transactions/recent` - Recent transactions

### Financial Analysis Prompts

Use these prompts for intelligent financial analysis:

1. **Financial Health Analysis**
   - Comprehensive financial health assessment
   - Key metrics and ratios
   - Industry benchmarks
   - Actionable recommendations

2. **Cash Flow Analysis**
   - Cash flow patterns
   - Working capital management
   - Forecasting and planning
   - Optimization strategies

3. **Receivables Management**
   - Collection health metrics
   - Risk analysis
   - Action plans
   - Performance tracking

4. **Budget Analysis**
   - Variance analysis
   - Performance tracking
   - Trend identification
   - Strategic planning

## Example Usage

Ask Claude questions like:

- "How is our overall financial health looking?"
- "Analyze our cash flow for the last 3 months"
- "Review our aged receivables and suggest collection strategies"
- "Compare our budget performance against actuals"
- "What are our key financial metrics trending towards?"

## Security

This integration:
- Uses secure OAuth2 authentication
- Stores credentials securely
- Implements token refresh
- Maintains secure connections

## Support

For issues or questions:
1. Check the documentation
2. Review the troubleshooting guide
3. Submit an issue on GitHub

## Future Enhancements

Planned features:
- Additional financial analysis tools
- Custom report generation
- Advanced forecasting capabilities
- Integration with other financial services
hydra-random-sweeper 1.0.1
Random Hydra Sweeper plugin

# List Sweeper plugin for Hydra

Sweeper plugin for Hydra which creates a list option additionally to the cartesian product ("grid"), 
which allows to sweep over the zipped list of parameters. 
This allows to test only a subset of the cartesian product and it is useful for small hyperparameter searches.

## Installation
```bash
pip install hydra-list-sweeper
```
This will install the plugin in your current environment.

You can check if the plugin is installed by adding `--info plugins` to your command line.
The plugin should be listed in the output as `hydra_plugins.list_sweeper_plugin.list_sweeper`.

In order to enable this plugin, you need to override the default sweeper in your configuration file:
```yaml
defaults:
  - _self_
  # override this to use the new list sweeper:
  - override hydra/sweeper: list
```

## Usage

`List sweeper`  uses the a similar syntax as the standard sweeper, but instead of a `params` key, it uses `grid_params` and `list_params`:
```yaml
hydra:
  mode: MULTIRUN
  sweeper:
    # standard grid search
    grid_params:
      env: 5_clubs_juggling, balancing_stick
    # additional list sweeper
    list_params:
      algorithm.lr: 0.001, 0.0001
      algorithm.beta_1: [0.9, 0.99]  # both notations work
```
This configuration will create 4 jobs:
```text
env=5_clubs_juggling, algorithm.lr=0.001, algorithm.beta_1=0.9
env=5_clubs_juggling, algorithm.lr=0.0001, algorithm.beta_1=0.99
env=balancing_stick, algorithm.lr=0.001, algorithm.beta_1=0.9
env=balancing_stick, algorithm.lr=0.0001, algorithm.beta_1=0.99
``` 

Basically, it grids over all grid params, creating the standard cartesian product, 
and then for each of these combinations, it creates a job for each of the list params.
You can additionally overwrite single values with command line arguments, and even define your grid_params in the command line:
```yaml
hydra:
  mode: MULTIRUN
  sweeper:
    # additional list sweeper
    list_params:
      algorithm.lr: 0.001, 0.0001
      algorithm.beta_1: [0.9, 0.99]  # both notations work
```
Combined with this command
```bash
python my_app.py env=5_clubs_juggling,balancing_stick
```
 will produce the same results as the first example. Also, you can override configs with the command line and the grid_params:

```yaml
hydra:
  mode: MULTIRUN
  sweeper:
    # standard grid search
    grid_params:
      env: 5_clubs_juggling, balancing_stick
    # additional list sweeper
    list_params:
      algorithm.lr: 0.001, 0.0001
      algorithm.beta_1: [0.9, 0.99]  # both notations work
```
 Combined with this command:
```bash
python my_app.py algorithm.epsilon=1.0e-4
```

will produce the same results as the first example, but epsilon will be set to 1.0e-4 for all jobs.

If you remove the `list_params` section, it will behave exactly as the standard grid sweeper (at least it should do, if you find a bug, please report it).
deepread 0.0.3
A PDF reading and analysis application
Requires Python: >=3.10

# DeepRead

A desktop application for reading and analyzing PDFs with LLM support.

![Deep Read screenshot](./statics/demo.png)

## Overview

DeepRead helps you get more from your PDF documents by combining a clean reading experience with AI-powered analysis. With DeepRead, you can:
- Read PDF documents in a distraction-free interface
- Chat with AI about document content to gain deeper insights
- Extract and process text from complex PDFs

## Installation

### Option 1: Install via pip

```bash
pip install deepread
```

After installation, launch the application with:

```bash
deepread serve
```

The server will start at http://127.0.0.1:8000 by default.

### Option 2: Download desktop application

Pre-built desktop applications are available for:
- Windows
- macOS
- Linux

Download the latest version from our [Releases](https://github.com/yourusername/DeepRead/releases) page.

## Development

For local development, you can run:

```bash
# Start the frontend and backend for web development
npm run dev:web

# Start with Electron support
npm run dev:electron
```
harmony-to-organoid 0.5.0
Utility for extracting individual organoid max projections from the export of Opera Phenix Harmony software.
Requires Python: >=3.10

# Harmony to Organoid

## Example output

Check out [this page](example_output/README.md) for exemplary output files.

## How to Prepare

You can install the tool via pip:

```
pip install harmony-to-organoid
```

## How to Run

### How to run from commandline

Process your data exported from Harmony by executing the command `harmony-to-organoid process`.

**Parameters:**

- `--index-file`: **(Required)** Path to the Harmony export index XML file (`Index.idx.xml`) or (`Index.xml`) 
  located next to the corresponding TIFF files.
- `--output-dir`: **(Required)** Path to your desired output directory.
- `--lut-min`: **(Optional)** Comma-separated minimum LUT (Lookup Table) values for each channel.
- `--lut-max`: **(Optional)** Comma-separated maximum LUT values for each channel.
- `--colors`: **(Optional)** Comma-separated colors for each channel.
- `--align-fields`: **(Optional)** Align fields using ashlar. True by default.
- `--exclude-fields`: **(Optional)** Comma separated list of field IDs which should not be processed.

**Example:**

```bash
harmony-to-organoid process \
    --index-file MY/INPUT/DATA/Index.xml \
    --output-dir MY/OUTPUT/DATA \
    --lut-min 100,100,100,100 \
    --lut-max 2000,1000,1500,2000 \
    --colors white,orange,blue,#ff0000 \
    --exclude-fields 2,3,7
```

**Output:**

After successfully running the processing command, your specified output directory (`MY/OUTPUT/DATA`) will contain the following:

- **Projections:**  
  Maximum intensity projection images (`projections/`) generated for each data group, providing 2D summaries of your 3D data.

- **Plots:**  
  Visual representations of the data are stored in `plots`, including group bounding boxes and projections placed within 
  their  respective areas, ensuring spatial consistency and aiding in data interpretation.

- **README Files:**  
  Automatically generated documentation (`README.md` and `README.html`) that includes embedded plots for easy reference and sharing.

### How to run from Jupyter notebook

Check out our example notebook `notebook.ipynb`.