semvec/pypi-packages huggingface dataset:
tesserhocr2df 0.10
tesseract hocr to pandas DataFrame
License: MIT
Keywords: tesseract, hocr


# tesseract hocr to pandas DataFrame

## pip install tesserhocr2df

### Tested against Windows 10 / Python 3.11 / Anaconda 


```PY
from tesserhocr2df import text2df
from PrettyColorPrinter import add_printer

add_printer(1)
df = text2df(
    img=r"C:\Users\hansc\Desktop\2024-03-29 02_18_09-C__ProgramData_BlueStacks_nxt.png",
    add_after_tesseract_path="",
    add_at_the_end="-l eng+por --psm 3",
    tesseractpath=r"C:\Program Files\Tesseract-OCR\tesseract.exe",
    add_imgs=True,
)
print(df)
```
langchain-moonshot 0.0.7
An integration package connecting Moonshot AI and LangChain
License: MIT
Requires Python: <4.0,>=3.8.1

# langchain-moonshot

This package contains the LangChain integrations for MoonshotAI through the `openai` SDK.

## Installation and Setup

- Install the LangChain partner package
```bash
pip install langchain-moonshot
```
- Get a Moonshot api key and set it as an environment variable (`MOONSHOT_API_KEY`)
- Get a Moonshot base url and set it as an environment variable (`MOONSHOT_API_BASE`)


## LLM

See a [usage example](http://python.langchain.com/docs/integrations/llms/openai).

```python
from langchain_moonshot import OpenAI
```

Obsolete: If you are using a model hosted on `Azure`, you should use different wrapper for that:
```python
from langchain_openai import AzureOpenAI
```
For a more detailed walkthrough of the `Azure` wrapper, see [here](http://python.langchain.com/docs/integrations/llms/azure_openai)


## Chat model

See a [usage example](http://python.langchain.com/docs/integrations/chat/openai).

```python
from langchain_moonshot import ChatOpenAI
```

Obsolete: If you are using a model hosted on `Azure`, you should use different wrapper for that:
```python
from langchain_openai import AzureChatOpenAI
```
For a more detailed walkthrough of the `Azure` wrapper, see [here](http://python.langchain.com/docs/integrations/chat/azure_chat_openai)


## Text Embedding Model

See a [usage example](http://python.langchain.com/docs/integrations/text_embedding/openai)

```python
from langchain_moonshot import OpenAIEmbeddings
```

Obsolete: If you are using a model hosted on `Azure`, you should use different wrapper for that:
```python
from langchain_openai import AzureOpenAIEmbeddings
```
For a more detailed walkthrough of the `Azure` wrapper, see [here](https://python.langchain.com/docs/integrations/text_embedding/azureopenai)
lv-linkpy 1.0.3
a python interface to remote LabVIEW controls
License: MIT
Requires Python: >=3.6

lv-linkpy
===================================
a python interface to remote LabVIEW controls.
<br>
Python version above 3.6 is required.

![](banner.jpg)


This package depends on Pykit for LabVIEW, a LabVIEW addon.
For more information, please see
[Pykit for LabVIEW documentation](https://www.zdiao.xyz/pykit-doc/).

## Installation

* `pip install lv-linkpy`


## Register the control on LabVIEW

## Usage

### Create a session

```python
from lvlinkpy import Session
s = Session(1919)
```

### Check control references 

```python
s.print_control_info()
```
This code will print all control references outline which are registered on LabVIEW. 

### Get control value

User can get the value from the name of the control.
Suppose there is a control called "Boolean" in the LabVIEW.
```python
print(s.get_value("Boolean"))
```

If the control name is incorrectly given, it will raise an error.
```python
s.get_value("fdzggZWSdgs")
```


### Set control value

User can set the value by the name of the control.
Suppose there is a control called "Boolean" in the LabVIEW, 
this code will set the control value to "True".
```python
s.set_value(valueName="Boolean", value=True)
```

This package will perform type check before send to LabVIEW.
So, If user send a string to a bool control, it will raise an error.

However, `ignore_check=True` can avoid type check on python. 
It may lead an error on LabVIEW.
```python
s.set_value(valueName="Boolean", value="string")
s.set_value(valueName="Boolean", value="string", ignore_check=True)
```

`mechaction=True` argument can activate mechanical actions of the control.
(for example firing an event case)

```python
s.set_value(valueName="Boolean", value=True, mechaction=True)
```

Notice that, a button-like latch action is not supported. 
If you want to activate an event case of a button, 
you can add a hidden boolean to the target event case structure on LabVIEW.
robot-azure-sync 1.0.0
Synchronization functionalities for Azure-related tasks.
License: MIT
Keywords: SYNC, SYNCHRONIZATION, ROBOT FRAMEWORK, AZURE

# Robot Framework Azure Sync 

## Overview

The **Robot Framework Azure Sync** package provides synchronization capabilities for Azure-related tasks. It includes scripts to retrieve data from Azure Test Cases **(`robot_azure_sync_get.py`)**, to update Azure Test Cases **(`robot_azure_sync_patch.py`)**, and to run Robot Framework tests with specific tags.

## Installation

To install `robot_azure_sync`, you can use `pip`. Open a terminal and run:

```bash
pip install robot_azure_sync
```

## Using the project source code

Alternatively, you can download the project source code from the repository and use it directly. You will need to have Python installed on your system.

1. Clone the repository:

```bash
git clone https://github.com/fabiorisantosquispe/robot_azure_sync.git
```

2. Navigate to the project directory:

```bash
cd robot_azure_sync
```
3. Use the provided scripts

- sync_utils.py: Contains utility functions for synchronization.
- robot_azure_sync_get.py: Script to get data from Azure Test Cases.
- robot_azure_sync_patch.py: Script to patch data to Azure Test Cases.
- robot_azure_sync.py: Main script for synchronization and running Robot Framework tests.


# Usage
## Using the installed package
If you installed the package via pip, you can use the following commands:

```bash
#Run synchronize_get and synchronize_patch
robot_azure_sync

#Just run sync_get
robot_azure_sync get

#Run sync_patch only
robot_azure_sync patch
```

## Using the project source code
If you are using the project source code directly, you can execute the scripts using Python:

```bash
# Run synchronize_get and synchronize_patch
python robot_azure_sync.py

# Just run sync_get
python robot_azure_sync.py get

# Run sync_patch only
python robot_azure_sync.py patch
```

# Configuration
The package requires a configuration file **'sync_config.json'** with Azure-related settings. If the file is not found, it will be created interactively.

Example **'sync_config.json'**:
```JSON
{
     "path": "tests",
     "credentials": {
       "personal_access_token": "your_azure_personal_access_token",
       "organization_name": "your_organization_name",
       "project_name": "your_project_name"
     },
     "tag_config": {
       "test_case": "TC",
       "user_story": "US",
       "bug": "Bug",
       "title": "Title",
       "TestedBy-Reverse": "",
       "IterationPath": "",
       "AutomationStatus": "",
       "ignore_sync": "",
       "System.Tags": "",
       "Priority": ""
     },
     "constants": {
       "System.AreaPath": "",
       "System.TeamProject": "",
       "settings_section": "",
       "test_cases_section": ""
     }
}
```