semvec/pypi-packages huggingface dataset:
ConsolidatedRMS 0.1
Python lib for Zerodha RMS consolidated scrips

# rms-consolidated-scrips-status
A utility that reads <a href="https://docs.google.com/spreadsheets/d/1ZTyh6GiHTwA1d-ApYdn5iCmRiBLZoAtwigS7VyLUk_Y/edit#gid=0">RMS Consolidated google spreadsheet</a> and returns a nested dictionary with allowed/non-allowed scrip for different product types

# Usage
Clone the repository and run
``` 
git clone https://github.com/zerodhatech/rms-consolidated-scrips-status.git
import mis_list
mis_list.mis_status()
```
# Sample Response
For MIS allowed Stocks
```
{'mis_allowed': {0: {'symbol': '3MINDIA:EQ', 'multiplier': '5X'}, 1: {'symbol': 'AARTIIND:EQ', 'multiplier': '5X'},
2: {'symbol': 'ABB:EQ', 'multiplier': '5X'}, 3: {'symbol': 'ABBOTINDIA:EQ', 'multiplier': '5X'}, 4:....}}
```
For Stocks not allowed for MIS 
```
{'mis_notallowed': {0: {'symbol': '21STCENMGM'}, 1: {'symbol': '3IINFOTECH'}, 2: {'symbol': '3PLAND'}, 
3: {'symbol': '5PAISA'}, 4: {'symbol': '63MOONS'}, 5: {'symbol': '7NR'}, 6: {'symbol': '8KMILES'}...}
```
opendt 1.2
Librairy to make a data augmmentation using tensorflow2.x and python3.x
Requires Python: >=3.6

# Make a data augmentation with tensorflow and python

Write your own data augmentation with tensorflow2 and python 3

# Librairies used:
 - [x] Python 3.6
 - [x] Tensorflow 2.1.0
 - [x] Matplotlib 3
 - [x] Tensorflow_docs
 - [x] PIL
 - [x] Brain :)


### How to have it
make sure that you have tensorflow installed on your machine(Laptop). For my case, I have it on ubuntu 18.04.
The best way for you to install dependencies, it is virtualenvwrapper. How to install it in 1 minute -> [VIDEO](https://youtu.be/Tl3q8Wm0jvI)
```
#for dependencies:
pip install -r requirements.txt

#clone it by typing:
git clone https://github.com/bm777/Data_augmentation.git
cd Data_augmentation

#run it by typing:
python3 Data_augment.py
```

### Result of augmentation
![Data augment](assets/result.png)


### View of original image
![Data augment](assets/original.png)


### Credict
[TF: tensorflow](https://www.tensorflow.org/tutorials/images/data_augmentation)