semvec/pypi-packages huggingface dataset:
Harvard-Lib 1.0.0
A Python library that contains multiple other libraries created by Ulif Systems
Requires Python: >=3.6

# Harvard by Ulif
Harvard is a python package that contains all of Ulif Systems python APIS and applications.

# Libraries
* TwoFALib - `import twofalib`
* UlifUtils - Hosts config file for ulif apis   
* DiscordRPC = `import discordrpc`

# Applications
* UManage - Project Manager and Live code collab
official-thor-love-and-thunder-2022-online-streaming-free 1.2
Python project for testing
Requires Python: >=2.7

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pyDIMM-package 2.1
A Python Dirichlet Multinomial Mixture Model

# pyDIMM
A Dirichlet Multinomial Mixture Model in Python.

## Ready

Please first compile the files in `clibs`. You can use cmake.
```console
cd clibs && make
```

Also, you can compile it by yourself. Compile `./clibs/pyDIMM_libs.c` by the instructions in the head of that file, and then you will get a `./clibs/pyDIMM_libs.so` file.  
```console
cd clibs && gcc -lm -shared -fPIC -o pyDIMM_libs.so pyDIMM_libs.c
```

Check the files now.
```
└───pyDIMM
    |
    ├───clibs
    |   ├───makefile
    |   ├───pyDIMM_libs.c
    |   └───pyDIMM_libs.so
    |
    └───pyDIMM_class.py
```

## How to use

All the methods are packaged in the class `DIMM`. So you should first import the `DIMM` class in `pyDIMM_class.py` in your python code.

```python
from pyDIMM_class import DIMM
```

### Train (by EM algorithm)

When you want to train a DIMM, you should first create an instance with the dataset and some parameters (For the detail of parameters, refer to the doc in `pyDIMM_class.py`).

- Example 1
    ```python
    dimm_0 = DIMM(observe_data=your_data, n_components=3, alpha_init='kmeans')
    ```
- Example 2
    ```python
    dimm_1 = DIMM(observe_data=your_data, n_components=5, alpha_init='manual', prior_label=your_label, print_log=True)
    ```

Then, use EM algorithm to train the model. The EM algorithm is written in C (yes, it's the code in `pyDIMM.c`, we use `ctypes` to implement that.).

- Example
    ```python
    dimm_0.EM(max_round=100, max_loglik_tol=1e-3, max_pie_tol=1e-5)
    ```

OK, the DIMM is already trained now. We need to get the result back. All the result information is in one dictionary.

- Example
    ```python
    result_0 = dimm_0.get_model()
    print(result_0)
    ```
    you're supposed to see
    ```
    {
        'alpha': array([...]),
    'pie': array([...]),
    'delta': array([...]),
    'loglik': ...,
    'AIC': ...,
    'BIC': ...
    }
    ```

That's all! You get it.  

>Note:  
Once you get the trained DIMM model, the result parameters are stored in the DIMM instance. So every time you want to retrieve the result back, just call the `get_model()` method. (Only if you don't change the instance before, such as call `EM()` again. That will of course change the result stored.)

### Predict

Sometimes you are not only want to fit a DIMM, but also want to use this model to predict some other data (If you don't want, forget it). Fortunately, we have the method `predict()`.

- Example
    ```python
    predict_res = dimm_0.predict(another_data)
    ```

Then you'll get the predict result `label` and `delta` in the `predict_res` dictionary. Find the detail explanation in the doc in codes.

### Save & Load

All information of your DIMM instance can be saved to `.npy` file and then can be loaded anytime and anywhere.
- Example
    ```python
    dimm_0.save('./dimm_0_file')
    ```
    After this, a new file called `dimm_0_file.npy`(the postfix .npy is automatically added) will appear in your current folder. You can read from the file later.
    ```python
    dimm_load = DIMM.load('./dimm_0_file.npy')
    ```
    

## Contact

Ziqi Rong <ziqirong01@gmail.com>
weather4py 0.1.4
a library for your weathery needs.
Requires Python: >=3.10,<4.0

# Weather4Py
a weather library for python to get the weather of a location or set of coordinates, with the ability to save to a json. By default it's C, and it can be changed to F temperature measuring system.

[Github repository for Weather4Py](https://github.com/MintTeaNeko/Weather4Py)

# Here's some examples to how to use the library.
```py
from WeatherPy import WeatherMan

# takes an input of metric(C)/imperial(F)
WeatherMan("owo").set_measurement("metric")

# to get the region of a location
print(WeatherMan("38.8,37.7").region)
# result: Kuluncak/Malatya, Turkey

# to get the weather of a location
print(WeatherMan("israel").weather)
# result: Sunny

# to get the temperature of a location
print(WeatherMan("texas").temperature)
# result: 30

# to get the humidity of a location
print(WeatherMan("newyork").humidity)
# result: 64%

# to reobtain infromation about a location.
WeatherMan("owo").obtain_information()

# to save the results to a folder in json format
WeatherMan("38.8,37.7").save_to_file("output.json")
```