semvec/pypi-packages huggingface dataset:
loadingpy 0.1.3
fancy progress bar
Requires Python: >=3.8

# loadingpy

In this repository, we provide a custom made progress bar for python iterables. This library can be used as is or modified for any purposes (see licence).

## for deep learning

There is now a new progress bar available for deep learning purposes (I guess it could be leveraged for other stuff as well...). Say, you want to train a model using a dataset $D$ over $e$ epochs. Using `TrainBar`you can get a double progress bar (first for the epochs and second for the steps in the current epoch) on a single line. you can check the [test](tests/test_loadingpy.py) or this simple example:

```python
from loadingpy import TrainBar

for data in TrainBar(
        trainloader,
        num_epochs=e,
        base_str="training",
    ):
        inputs, labels = data
```

## Example
You can install with pip `pip install loadingpy` and use as follows

```python
from loadingpy import PyBar

loss = 0.0
accuracy = 0.0
for inputs, labels in PyBar(dataset, monitoring=[loss, accuracy], naming=["loss", "accuracy"], base_str="training"):
    # do whatever you please
    loss += 0.0 # update monitoring variables in place
    accuracy += 0.0 # update monitoring variables in place
```

For a more detailed exampel (in torch) check this [tutorial](https://gitlab.com/ey_datakalab/loadingpy/-/blob/main/notebooks/unit_test.ipynb). You can use a global argument in order to disable the verbatim from the loading bars as follows:

```python
from loadingpy import BarConfig

BarConfig["disable loading bar"] = True
```

## Arguments

Here is a list of the arguments and their description
| argument | description | type |
| :---: | :---: | :---: |
| iterable | python object that can be iterated over | can be a list, tuple, range, np.ndarray, torch.Tensor, dataset,... |
| monitoring | a python object (or list of python objects) that will be printed after each iteration using the following format f'{monitoring}'. IF they are updated during the loop, make sure to update inplace, in order to see the changes | an be a tensor, float or list of these |
| naming | if you want to add a descritpion prefix to the monitoring variables | str or list of str |
| total_steps | number of iterations to perform (if you set it to a lower value than the length of the iterable, then the process will stop after the given total_steps) | int |
| base_str | prefix description of the loop we are iterating over | str |
| color | which color to use for the loading bar | str |
epdfpy 0.0.1
Python GUI module for pair distribution analysis on amorphous material
Requires Python: >=3.8

# ePDFpy
ePDFpy is a standalone tool for reduced electron pair distribution function (ePDF) analysis. Users can do ePDF analysis with the diffraction images obtained by transmission electron microscope (TEM), and use the outputs to deduce the atomic structures.

This software is Python-based software with an interactive graphical user interface (GUI) environment built with PyQt5. Currently, built with the python3.8 version but confirmed that still compatible with python3.10.

# Installation
For the convenience, it is recommended to use Anaconda (www.anaconda.com/download) or Miniconda ( https://docs.conda.io/en/latest/miniconda.html) to set up a virtual environment. All dependencies can easily installed by using 'environment.yaml' file in the conda terminal:

First, users must install pre-requiste libralies, which are listed in below:

- numpy >= 1.22.4
- scipy >= 1.9.1
- opencv (cv2) >= 4.6.0
- pandas >= 1.4.4
- pyqt5 >= 5.15.7
- pyqtgraph >= 0.12.4
- hyperspy >= 1.7.1
- mrcfile >= 1.4.2
- pyqtdarktheme == 2.1.0

Users can install all dependencies manually using pip install or conda install. 
```
pip install -r requirements.txt
```
or
```
conda env create --file environment.yaml
```

It is recommended to install ePDFpy via PyPI or Anaconda distribution. Users can establish the virtual environment (), then use following command line to install.

```
pip install epdfpy
```
or
```
conda install epdfpy
```

## Using source code

If users want to install ePDFpy directly from the source code, download the source code, and moved into the directory path. Using terminal type:
```
python setup.py install
```

## ePDFpy GUI package
For those who are not familiar with Python, users can simply download executable files for each OS (Windows, Mac, Linux), which are compiled with PyInstaller.
Simply open the executable file:
- Windows: ePDFpy.exe
- Mac: ePDFpy.app



# Running GUI

After installation, users can open ePDFpy simply typing command line in the terminal:
```
epdfpy
```
which will automatically opens the GUI script (run_ui.py) in source folder.

Those who are not accustomed to Python, just open executable files from distributed package.

# Example
![example](https://github.com/GWlab-SKKU/ePDFpy/assets/59153513/aa1f59c5-0daa-4276-81f4-d48a829b3b56)


# License

GNU GPLv3

**ePDFpy** is open-source software distributed under a GPLv3 license.
It is free to use, alter, or build on, provided that any work derived from **ePDFpy** is also kept free and open under a GPLv3 license.
ErrorType 0.1.3
Error type analysis
Requires Python: >=3.6

# Project Title

## Purpose
This project serves as a tool for error analysis, specifically designed for processing and analyzing data related to a certain system. It includes functions to load data, judge errors, and perform error analysis, with the capability to output results to CSV files.

## Usage
1. **Load Data:**
   - Use the `load_data` function to fetch data from a specified URL based on certain conditions.
   - The function supports different types of data such as program, copy, and trigger information.

2. **Error Analysis:**
   - Utilize the `error_analysis` function to analyze errors in the loaded data.
   - The analysis includes identifying issues in program, copy, and trigger data and categorizing problems based on specific conditions.

3. **CSV Output:**
   - The `error_analysis_to_csv` function allows you to save the error analysis results to CSV files.
   - You can specify a single day or a date range for the analysis.

## Dependencies
- Python 3.x
- pandas
- json
- requests
- datetime
- warnings
- os

## Author
[YUKAI LIAO]