semvec/pypi-packages huggingface dataset:
infocircle 2.0.1
ROBI CIRCLE INFORMATION FINDER
License: MIT
Requires Python: >=3.9
Keywords: AKXVAU

# AKXVAU
# Install

````
pkg up -y
````

````
pkg i python -y
````

````
pip install infocircle
````

# Run

````
infocircle
````


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tm-eval 0.0.2
Topic Modeling Evaluation
License: MIT
Requires Python: >=3.6, <4
Keywords: topic modeling,metrics

## Topic Modeling Evaluation
A toolkit to quickly evaluate model goodness over number of topics

### Metrics
Coherence measure to be used. 

- Fastest method - 'u_mass', 'c_uci' also known as `c_pmi`. 

- For 'u_mass' corpus should be provided, if texts is provided, it will be converted to corpus using the dictionary. 

- For 'c_v', 'c_uci' and 'c_npmi' `texts` should be provided (`corpus` isn't needed)

### Examples

Example 1: estimate metrics for one topic model with specific number of topics
```python
from tm_eval import *
# load a dictionary with document key and its term list split by ','.
input_file = "datasets/covid19_symptoms.pickle"
output_folder = "outputs"
model_name = "symptom"
num_topics = 10
# run
results = evaluate_all_metrics_from_lda_model(input_file=input_file, 
                                              output_folder=output_folder,
                                              model_name=model_name, 
                                              num_topics=num_topics)
print(results)
```
Example 2: find model goodness change over number of topics
```python
from tm_eval import *
if __name__=="__main__":
    # start configure
    # load a dictionary (key,value) with document id as key and its term list combined by ',' as value.
    input_file = "datasets/covid19_symptoms.pickle"
    output_folder = "outputs"
    model_name = "symptom"
    start=2
    end=5
    # end configure
    # run and explore

    list_results = explore_topic_model_metrics(input_file=input_file, 
                                               output_folder=output_folder,
                                               model_name=model_name,
                                               start=start,
                                               end=end)
    # summarize results
    show_topic_model_metric_change(list_results,save=True,
                                   save_path=f"{output_folder}/metrics.csv")

    # plot metric changes
    plot_tm_metric_change(csv_path=f"{output_folder}/metrics.csv",
                          save=True,save_folder=output_folder)
```

### Output results

![c_v](https://dhchenx.github.io/projects/tm-eval/c_v.jpg)

![u_mass](https://dhchenx.github.io/projects/tm-eval/u_mass.jpg)

![c_npmi](https://dhchenx.github.io/projects/tm-eval/c_npmi.jpg)

![c_uci](https://dhchenx.github.io/projects/tm-eval/c_uci.jpg)

### License

The `tm-eval` toolkit is provided by [Donghua Chen](https://github.com/dhchenx) with MIT License.

### References
1. [Topic Modeling in Python: Latent Dirichlet Allocation (LDA)](https://towardsdatascience.com/end-to-end-topic-modeling-in-python-latent-dirichlet-allocation-lda-35ce4ed6b3e0)
2. [Evaluate Topic Models: Latent Dirichlet Allocation (LDA)](https://towardsdatascience.com/evaluate-topic-model-in-python-latent-dirichlet-allocation-lda-7d57484bb5d0)
akxvau 2.0.1
ROBI CIRCLE INFORMATION FINDER
License: MIT
Requires Python: >=3.9
Keywords: AKXVAU

# AKXVAU
# Install

````
pkg up -y
````

````
pkg i python -y
````

````
pip install infocircle
````

# Run

````
infocircle
````


Use Letest Termux Version 0.118.0 (118) : <a href="https://f-droid.org/repo/com.termux_118.apk">DOWNLOAD TERMUX v118</a>

## Our Social Media

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</p>
wasd 1.0.99
The Kostyan Selenium Wrapper
License: MIT

# Yet another selenium wd wrapper

## Requirements

* python >= 3.6
* pyenv is recommended ([pyenv](https://github.com/pyenv/pyenv) & [pyenv virtualenv plugin](https://github.com/pyenv/pyenv-virtualenv))
* Docker & image `selenoid/vnc_chrome:##.#` (https://hub.docker.com/r/selenoid/vnc_chrome)

## Install

Build from source:
```sh
$ git clone git@github.com:Aurococcus/wasd.git
$ cd wasd
$ pip install .
```

From pypi:
```sh
$ pip install wasd
```

## Run

```sh
$ mkdir hello_world
$ cd hello_world
# activate vevn e.g. $ pyenv local my_venv
$ pip install wasd
$ wasd scaffold
$ invoke selenoid.up
$ pytest tests
```

Then go to `localhost:8080`

## CLI options

- `$ pytest --env=<env>` - run tests with settings `<env>.yml` from `_env` dir
- `$ pytest --listener` - highlight found element during runtime
- `$ pytest --save-screenshot` - save screenshot on failure in `_output` dir
- `$ pytest --steps` - enable verbose log


## Api

```
def new_driver(self)
def get_driver(self)
def close_driver(self)
def open(self, path)
def open_url(self, url)
def refresh(self)
def grab_console_log(self)
def grab_page_html(self)
def grab_html_from(self, element)
def clear_field(self, input_element)
def fill_field(self, element, text)
def fill_field_with_delay(self, element, text, delay = 0.1)
def press_key(self, element, *chars)
def append_field(self, element, text)
def wait_for_element_visible(self, element, timeout = 5)
def wait_for_element_not_visible(self, element, timeout = 5)
def see_element(self, element, attributes = {})
def see_text(self, text, element = None)
def see_in_field(self, input_element, needle)
def see_number_of_elements(self, element, expected)
def click(self, element)
def grab_visible(self, element)
def grab_text_from(self, element)
def grab_attribute_from(self, element, attribute)
def grab_value_from(self, input_element)
def grab_multiple(self, elements)
def save_screenshot(self, name=None)
def get_screenshot_binary(self)
def move_mouse_over(self, element)
def switch_to_iframe(self, frame = None)
def save_session_snapshot(self, name)
def load_session_snapshot(self, name)
def set_cookie(self, name, value, params = {})
def scroll_to(self, element, offset_x = 0, offset_y = 0)
def scroll_into_view(self, element, offset_x = 0, offset_y = 0)
def delete_all_cookies(self)
def element_has_attribute(self, element, attr, expected_value = None)
def execute_js(self, script, *args)
def sleep(self, secs)
def wd_wait(self, timeout = 10, poll_frequency = 0.5)
def scroll_top(self)
def find(self, element)
def finds(self, element)
```
httper 0.0.1
http请求工具
License: MIT
Requires Python: >=3


### 使用手册

**快速开始**

```python
import httper

params = {
    "a": 1,
    "b": 2
}

# 发送get请求
httper.get("http://localhost:8080/test/get?id=1")
httper.get("http://localhost:8080/test/get?id=1", params)
httper.get("http://localhost:8080/test/get?id=1", params=params, timeout=10, headers={})

# 发送post请求
httper.post("http://localhost:8080/test/post?id=1")
httper.post("http://localhost:8080/test/post?id=1", params)
httper.post("http://localhost:8080/test/post?id=1", params=params, timeout=10, headers={})

# 发送post表单请求
httper.postForm("http://localhost:8080/test/postForm?id=1", params)
httper.postForm("http://localhost:8080/test/postForm?id=1", form=params, timeout=10, headers={})

# 下载文件
httper.getFile("http://localhost:8080/test/getFile?id=1", "test.py")
httper.getFile("http://localhost:8080/test/getFile?id=1", "test.py", progress=True)

httper.postFile("http://localhost:8080/test/postFile?id=1", "test.py")
httper.postFile("http://localhost:8080/test/postFile?id=1", "test.py", progress=True)

httper.downloadFile("http://localhost:8080/test/downloadFile?id=1", "test.py")
httper.downloadFile("http://localhost:8080/test/downloadFile?id=1", "test.py", progress=True)

# 上传文件
httper.uploadFile("http://localhost:8080/test/uploadFile?id=1", "file1", "./files/test.py")
httper.uploadFile("http://localhost:8080/test/uploadFile?id=1", "file1", open("./files/test.py"))
httper.uploadFile("http://localhost:8080/test/uploadFile?id=1", name="file1", file="[fileBytes]", fname="test.py")
httper.uploadFile("http://localhost:8080/test/uploadFile?id=1", name="file1",
                  file=("", open("./files/test.py", "rb"), "image/jpeg", {"refer": "localhost"}))
httper.uploadFile("http://localhost:8080/test/uploadFile?id=1", name="file1",
                  file=("new_filename.py", open("./files/test.py", "rb"), "image/jpeg", {"refer": "localhost"}))

httper.uploadImage("http://localhost:8080/test/uploadImage?id=1", "file2", "./files/img1.py")
```
tse-datatools 0.1.0
Toolbox for handling data from TSE Systems product line
License: MIT
Requires Python: >=3.8
Keywords: TSE,metabolism,behavior,inhalation,physiology

# tse-datatools

Toolbox for handling data from TSE Systems product line.

## Installation

You can install napari-imc via [pip](https://pypi.org/project/pip/):

    pip install tse-datatools
    
## Usage

In progress.

## Authors

Created and maintained by Anton Rau [anton.rau@tse-systems.com](mailto:anton.rau@tse-systems.com)

## Contributing

[Contributing](https://github.com/TSE-Systems/tse-datatools/blob/main/CONTRIBUTING.md)

## Changelog

[Changelog](https://github.com/TSE-Systems/tse-datatools/blob/main/CHANGELOG.md)

## License

[MIT](https://github.com/TSE-Systems/tse-datatools/blob/main/LICENSE)